Miracle-dz/UniFAR-FTUs huggingface dataset:
Rogue waves on the periodic wave background in the focusing nonlinear Schrodinger equation.

We present exact solutions for rogue waves arising on the background of periodic waves in the focusing nonlinear Schrodinger equation. The exact solutions are obtained by characterizing the Lax spectrum related to the periodic waves and by using the one-fold Darboux transformation. The magnification factor of the rogue waves is computed in the closed analytical form. We relate the rogue wave solutions to the modulation instability of the periodic wave background.
Invertible generative models for inverse problems: mitigating representation error and dataset bias

Trained generative models have shown remarkable performance as priors for inverse problems in imaging -- for example, Generative Adversarial Network priors permit recovery of test images from 5-10x fewer measurements than sparsity priors. Unfortunately, these models may be unable to represent any particular image because of architectural choices, mode collapse, and bias in the training dataset. In this paper, we demonstrate that invertible neural networks, which have zero representation error by design, can be effective natural signal priors at inverse problems such as denoising, compressive sensing, and inpainting. Given a trained generative model, we study the empirical risk formulation of the desired inverse problem under a regularization that promotes high likelihood images, either directly by penalization or algorithmically by initialization. For compressive sensing, invertible priors can yield higher accuracy than sparsity priors across almost all undersampling ratios, and due to their lack of representation error, invertible priors can yield better reconstructions than GAN priors for images that have rare features of variation within the biased training set, including out-of-distribution natural images. We additionally compare performance for compressive sensing to unlearned methods, such as the deep decoder, and we establish theoretical bounds on expected recovery error in the case of a linear invertible model.
Finite-Time Analysis of Q-Learning with Linear Function Approximation

In this paper, we consider the model-free reinforcement learning problem and study the popular Q-learning algorithm with linear function approximation for finding the optimal policy. Despite its popularity, it is known that Q-learning with linear function approximation may diverge in general due to off-policy sampling. Our main contribution is to provide a finite-time bound for the performance of Q-learning with linear function approximation with constant step size under an assumption on the sampling policy. Unlike some prior work in the literature, we do not need to make the unnatural assumption that the samples are i.i. d. (since they are Markovian), and do not require an additional projection step in the algorithm. To show this result, we first consider a more general nonlinear stochastic approximation algorithm with Markovian noise, and derive a finite-time bound on the mean-square error, which we believe is of independent interest. Our proof is based on Lyapunov drift arguments and exploits the geometric mixing of the underlying Markov chain. We also provide numerical simulations to illustrate the effectiveness of our assumption on the sampling policy, and demonstrate the rate of convergence of Q-learning.
On Measuring Gender Bias in Translation of Gender-neutral Pronouns

Ethics regarding social bias has recently thrown striking issues in natural language processing. Especially for gender-related topics, the need for a system that reduces the model bias has grown in areas such as image captioning, content recommendation, and automated employment. However, detection and evaluation of gender bias in the machine translation systems are not yet thoroughly investigated, for the task being cross-lingual and challenging to define. In this paper, we propose a scheme for making up a test set that evaluates the gender bias in a machine translation system, with Korean, a language with gender-neutral pronouns. Three word/phrase sets are primarily constructed, each incorporating positive/negative expressions or occupations; all the terms are gender-independent or at least not biased to one side severely. Then, additional sentence lists are constructed concerning formality of the pronouns and politeness of the sentences. With the generated sentence set of size 4,236 in total, we evaluate gender bias in conventional machine translation systems utilizing the proposed measure, which is termed here as translation gender bias index (TGBI). The corpus and the code for evaluation is available on-line.
Candidates for RR Lyrae in binary systems from the OGLE Galactic bulge survey

We present 20 newly discovered candidates for binary systems with an RR~Lyrae companion. Using the photometric data from the Optical Gravitational Lensing Experiment (OGLE) and Korea Microlensing Telescope Network (KMTNet) for the Galactic bulge, we searched for binary candidates among non-modulated fundamental-mode RR~Lyrae variables. We constructed and investigated over 9000 $O-C$ diagrams for individual pulsators, in order to find signs of the light-travel time effect. We found 20 variables exhibiting cyclic variation in the $O-C$ (time of the observed minus calculated brightness maximum) diagram, which can be associated with a second component in the system, but for confirmation of binarity, a long-term radial velocity study is necessary. The modeling of the $O-C$ diagrams yields orbital parameters, estimates of the semi-amplitude of the radial velocity curve, and the mass function. The orbital periods of our candidates range from 3 to 15 years. Two of the binary candidates display a minimum mass higher than the assumed mass of the RR~Lyrae component, which points towards an evolved companion that can under some circumstances contribute significantly to the total light of the system.
Ultrasound detection of nonpalpable mammographically occult malignancy.

OBJECTIVE To evaluate the prevalence of occult malignancy with screening breast ultrasound. METHODS All ultrasound-guided core needle breast biopsies performed between January 1, 1999, and June 30, 2001, were retrospectively reviewed. Lesions were identified during screening breast ultrasound in high-risk women with no mammographic or palpable abnormality in either breast, a unilateral mammographic or palpable abnormality in the contralateral breast, or a unilateral mammographic or palpable abnormality in a different quadrant of the same breast. All ultrasound-detected lesions were histologically verified. RESULTS Six hundred and fifty-two women with a mean age of 49 years underwent 698 biopsies during the study period. Three hundred and forty-nine of these lesions were detected at screening breast ultrasound. Out of 349, 11 (3.2%) had a mammographically and clinically occult malignancy. Nine cancers were found in women with no mammographic or palpable abnormality. Two cancers were found in the same breast as the mammographic or palpable abnormality. None were found in the breast contralateral to a palpable or mammographic abnormality. CONCLUSION Screening breast ultrasound of high-risk women has a similar detection rate for occult carcinoma as screening mammography, but has a low positive predictive value in cases where biopsy is performed.
Chasing Convex Bodies with Linear Competitive Ratio

We study the problem of chasing convex bodies online: given a sequence of convex bodies the algorithm must respond with points in an online fashion (i.e., is chosen before is revealed). The objective is to minimize the sum of distances between successive points in this sequence. Bubeck et al. (STOC 2019) gave a -competitive algorithm for this problem. We give an algorithm that is -competitive for any sequence of length .
Adaptive Influence Maximization with Myopic Feedback

We study the adaptive influence maximization problem with myopic feedback under the independent cascade model: one sequentially selects k nodes as seeds one by one from a social network, and each selected seed returns the immediate neighbors it activates as the feedback available for later selections, and the goal is to maximize the expected number of total activated nodes, referred as the influence spread. We show that the adaptivity gap, the ratio between the optimal adaptive influence spread and the optimal non-adaptive influence spread, is at most 4 and at least e/(e-1), and the approximation ratios with respect to the optimal adaptive influence spread of both the non-adaptive greedy and adaptive greedy algorithms are at least \frac{1}{4}(1 - \frac{1}{e}) and at most \frac{e^2 + 1}{(e + 1)^2} < 1 - \frac{1}{e}. Moreover, the approximation ratio of the non-adaptive greedy algorithm is no worse than that of the adaptive greedy algorithm, when considering all graphs. Our result confirms a long-standing open conjecture of Golovin and Krause (2011) on the constant approximation ratio of adaptive greedy with myopic feedback, and it also suggests that adaptive greedy may not bring much benefit under myopic feedback.
The thermal-radiative wind in low-mass X-ray binary H1743−322: radiation hydrodynamic simulations

Blueshifted absorption lines are seen in high inclination black hole binary systems in their disc-dominated states, showing these power an equatorial disc wind. While some contribution from magnetic winds remain a possibility, thermal and thermal-radiative winds are expected to be present. We show results from radiation hydrodynamic simulations that show that the additional radiation force from atomic features (bound–free and lines) is important along with electron scattering. Together, these increase the wind velocity at high inclinations, so that they quantitatively match the observations in H1743−322, unlike purely thermal winds that are too slow. We highlight the role played by shadowing of the outer disc from the (subgrid) inner disc Compton heated layer, and show that the increase in shadow from the higher Compton temperature after the spectral transition to the hard state leads to strong suppression of the wind. Thermal-radiative winds explain all of the spectral features (and their disappearance) in this simplest wind system and magnetic winds play only a minor role. We speculate that thermal-radiative winds can explain all the spectral features seen in the more complex (larger disc size) binaries, GRO J1655−40 and GRS 1915+105, without requiring magnetic winds.
Exact Solutions in Poincaré Gauge Gravity Theory

In the framework of the gauge theory based on the Poincaré symmetry group, the gravitational field is described in terms of the coframe and the local Lorentz connection. Considered as gauge field potentials, they give rise to the corresponding field strength which are naturally identified with the torsion and the curvature on the Riemann–Cartan spacetime. We study the class of quadratic Poincaré gauge gravity models with the most general Yang–Mills type Lagrangian which contains all possible parity-even and parity-odd invariants built from the torsion and the curvature. Exact vacuum solutions of the gravitational field equations are constructed as a certain deformation of de Sitter geometry. They are black holes with nontrivial torsion.
Sparse estimation of historical functional linear models with a nested group bridge approach

The conventional historical functional linear model relates the current value of the functional response at time t to all past values of the functional covariate up to time t . Motivated by situations where it is more reasonable to assume that only recent, instead of all, past values of the functional covariate have an impact on the functional response, in this work we investigate the historical functional linear model with an unknown forward time lag into the history. In addition to estimating the bivariate regression coefficient function, we also aim to identify the historical time lag from the data, which is important in many applications. To this end, we propose an estimation procedure that uses the finite element method to conform naturally to the trapezoidal domain of the bivariate coefficient function. We use a nested group bridge penalty to facilitate simultaneous estimation of the bivariate coefficient function and the historical lag, and show that our proposed estimators are consistent. We demonstrate this method of estimation in a real data example investigating the effect of muscle activation recorded via the noninvasive electromyography (EMG) method on lip acceleration during speech production. In addition, we examine the finite sample performance of our proposed method in comparison with the conventional approach to estimation via simulation studies.
Augmented State Observer for Simultaneous Estimation of Charge State and Crossover in Self-Discharging Disproportionation Redox Flow Batteries

This paper presents an observer design for the simultaneous estimation of charge state and crossover flux in disproportionation redox flow batteries, which exhibits exponential estimation error convergence to a bounded residual set. The crossover flux of vanadium through the porous separator is considered as an unknown function of the battery states, model-approximated as the output of a persistently excited linear system. This parametric model and the simple isothermal lumped parameter model of the battery are combined to form an augmented space state representation suitable for the observer design, which is carried out via Lyapunov stability theory including the error-uncertainty involved in the approximation of the crossover flux. The observer gain is calculated by solving a polytopic linear matrix inequality problem via convex optimization. The performance of this design is evaluated with a laboratory flow battery prototype undergoing self-discharge.
Heavy neutrino production via 
Z′
 at the lifetime frontier

We investigate the pair production of right-handed neutrinos from the decay of an additional neutral $Z^{\prime}$ boson in the gauged $B-L$ model. Taking into account current constraints on the $Z^{\prime}$ mass and the associated gauge coupling $g_{1}^{\prime}$, we analyse the sensitivity of proposed experiments at the lifetime frontier, FASER 2, CODEX-b, MATHUSLA as well as a hypothetical version of the MAPP detector to a long lived heavy neutrino $N$ originating in the decays of the $Z^{\prime}$. We further complement this study with determining the reach of LHCb and a CMS-type detector for the high-luminosity LHC run. We demonstrate that in a background free scenario with $g_1^\prime = 10^{-3}$ near the current limit, FASER 2 is sensitive to the active-sterile neutrino mixing down to $V_{\mu N} \approx 10^{-4}$, while a reach of $V_{\mu N} \approx 10^{-5}$ can be obtained for CODEX-b and LHCb, in a mass regime of $m_N \approx 5-20$ GeV and $m_{Z^{\prime}} \approx 20-70$ GeV. Finally, MATHUSLA can probe $V_{\mu N} \approx 10^{-7}$ and cover the mixing regime expected in a canonical seesaw scenario of light neutrino mass generation.
L2-Cohomology, Derivations, and Quantum Markov Semi-Groups on q-Gaussian Algebras

We study (quasi-)cohomological properties through an analysis of quantum Markov semi-groups. We construct higher order Hochschild cocycles using gradient forms associated with a quantum Markov semi-group. By using Schatten-${\mathcal{S}}_p$ estimates we analyze when these cocycles take values in the coarse bimodule. For the 1-cocycles (the derivations) we show that under natural conditions we obtain the Akemann–Ostrand property. We apply this to $q$-Gaussian algebras $\Gamma _q(H_{{\mathbb{R}}})$. As a result $q$-Gaussians satisfy AO$^+$ for $\vert q \vert \leqslant \dim (H_{{\mathbb{R}}})^{-1/2}$. This includes a new range of $q$ in low dimensions compared to Shlyakhtenko [ 34].
On the Gelfand property for complex symmetric pairs

We first prove, for pairs consisting of a simply connected complex reductive group together with a connected subgroup, the equivalence between two different notions of Gelfand pairs. This partially answers a question posed by Gross, and allows us to use a criterion due to Aizenbud and Gourevitch, and based on Gelfand-Kazhdan's theorem, to study the Gelfand property for complex symmetric pairs. This criterion relies on the regularity of the pair and its descendants. We introduce the concept of a pleasant pair, as a means to prove regularity, and study, by recalling the classification theorem, the pleasantness of all complex symmetric pairs. On the other hand, we prove a method to compute all the descendants of a complex symmetric pair by using the extended Satake diagram, which we apply to all pairs. Finally, as an application, we prove that eight out of the twelve exceptional complex symmetric pairs, together with the infinite family $(\textrm{Spin}_{4q+2}, \textrm{Spin}_{4q+1})$, satisfy the Gelfand property, and state, in terms of the regularity of certain symmetric pairs, a sufficient condition for a conjecture by van Dijk and a reduction of a conjecture by Aizenbud and Gourevitch.
Ambivalence Predicts Symptomatology in Cognitive-Behavioral and Narrative Therapies: An Exploratory Study

Background: The identification of poor outcome predictors is essential if we are to prevent therapeutic failure. Ambivalence – defined as a conflictual relationship between two positions of the self: one favoring change and another one favoring problematic stability – has been consistently associated with poor outcomes. However, the precise relationship between ambivalence and clients’ symptomatology remains unclear. Objective: This study aims at assessing ambivalence’s power to predict symptomatology, using a longitudinal design. Methods: The complete 305 sessions of 16 narrative and cognitive-behavioral cases have been analyzed with the Ambivalence Coding System and outcome measures have been used for each session. Results: Ambivalence emerged as a significant predictor of subsequent symptomatology suggesting that ambivalence is not only related to treatment outcomes, but that it represents a strong predictor of subsequent symptomatology. Discussion: The implications of ambivalence’s power to predict outcomes for research and clinical practice are discussed.
Towards Practical Indoor Positioning Based on Massive MIMO Systems

We showcase the practicability of an indoor positioning system (IPS) solely based on neural networks (NNs) and the channel state information (CSI) of a (Massive) multiple-input multiple-output (MIMO) communication system, i.e., only build on the basis of data that is already existent in today's systems. As such our IPS system promises both, a good accuracy without the need of any additional protocol/signaling overhead for the user localization task. In particular, we propose a tailored NN structure with an additional phase branch as feature extractor and (compared to previous results) a significantly reduced amount of trainable parameters, leading to a minimization of the amount of required training data. We provide actual measurements for indoor scenarios with up to 64 antennas covering a large area of 80m^2. In the second part, several robustness investigations for real-measurements are conducted, i.e., once trained, we analyze the recall accuracy over a period of several days. Further, we analyze the impact of pedestrians walking in-between the measurements and show that finetuning and pre-training of the NN helps to mitigate effects of hardware drifts and alterations in the propagation environment over time. This reduces the amount of required training samples at equal precision and, thereby, decreases the effort of the costly training data acquisition.
Towards Interpretable Sparse Graph Representation Learning with Laplacian Pooling

Recent work in graph neural networks (GNNs) has led to improvements in molecular activity and property prediction tasks. Unfortunately, GNNs often fail to capture the relative importance of interactions between molecular substructures, in part due to the absence of efficient intermediate pooling steps. To address these issues, we propose LaPool (Laplacian Pooling), a novel, data-driven, and interpretable hierarchical graph pooling method that takes into account both node features and graph structure to improve molecular representation. We benchmark LaPool on molecular graph prediction and understanding tasks and show that it outperforms recent GNNs. Interestingly, LaPool also remains competitive on non-molecular tasks. Both quantitative and qualitative assessments are done to demonstrate LaPool's improved interpretability and highlight its potential benefits in drug design. Finally, we demonstrate LaPool's utility for the generation of valid and novel molecules by incorporating it into an adversarial autoencoder.
Robustness Quantification for Classification with Gaussian Processes

We consider Bayesian classification with Gaussian processes (GPs) and define robustness of a classifier in terms of the worst-case difference in the classification probabilities with respect to input perturbations. For a subset of the input space $T\subseteq \mathbb{R}^m$ such properties reduce to computing the infimum and supremum of the classification probabilities for all points in $T$. Unfortunately, computing the above values is very challenging, as the classification probabilities cannot be expressed analytically. Nevertheless, using the theory of Gaussian processes, we develop a framework that, for a given dataset $\mathcal{D}$, a compact set of input points $T\subseteq \mathbb{R}^m$ and an error threshold $\epsilon>0$, computes lower and upper bounds of the classification probabilities by over-approximating the exact range with an error bounded by $\epsilon$. We provide experimental comparison of several approximate inference methods for classification on tasks associated to MNIST and SPAM datasets showing that our results enable quantification of uncertainty in adversarial classification settings.
Relational Representation Learning for Dynamic (Knowledge) Graphs: A Survey

Graphs arise naturally in many real-world applications including social networks, recommender systems, ontologies, biology, and computational finance. Traditionally, machine learning models for graphs have been mostly designed for static graphs. However, many applications involve evolving graphs. This introduces important challenges for learning and inference since nodes, attributes, and edges change over time. In this survey, we review the recent advances in representation learning for dynamic graphs, including dynamic knowledge graphs. We describe existing models from an encoder-decoder perspective, categorize these encoders and decoders based on the techniques they employ, and analyze the approaches in each category. We also review several prominent applications and widely used datasets, and highlight directions for future research.
BreizhCrops: A Satellite Time Series Dataset for Crop Type Identification

This dataset challenges the time series community with the task of satellite-based vegetation identification on large scale real-world dataset of satellite data acquired during one entire year. It consists of time series data with associated crop types from 580k field parcels in Brittany, France (Breizh in local language). Along with this dataset, we provide results and code of a Long Short-Term Memory network and Transformer network as baselines. We release dataset, along with preprocessing scripts and baseline models in this https URL and encourage methodical researchers to benchmark and develop novel methods applied to satellite-based crop monitoring.