Miracle-dz/UniFAR-FTUs huggingface dataset:
Testing goodness-of-fit and conditional independence with approximate co-sufficient sampling

Goodness-of-fit (GoF) testing is ubiquitous in statistics, with direct ties to model selection, confidence interval construction, conditional independence testing, and multiple testing, just to name a few applications. While testing the GoF of a simple (point) null hypothesis provides an analyst great flexibility in the choice of test statistic while still ensuring validity, most GoF tests for composite null hypotheses are far more constrained, as the test statistic must have a tractable distribution over the entire null model space. A notable exception is co-sufficient sampling (CSS): resampling the data conditional on a sufficient statistic for the null model guarantees valid GoF testing using any test statistic the analyst chooses. But CSS testing requires the null model to have a compact (in an information-theoretic sense) sufficient statistic, which only holds for a very limited class of models; even for a null model as simple as logistic regression, CSS testing is powerless. In this paper, we leverage the concept of approximate sufficiency to generalize CSS testing to essentially any parametric model with an asymptotically-efficient estimator; we call our extension "approximate CSS" (aCSS) testing. We quantify the finite-sample Type I error inflation of aCSS testing and show that it is vanishing under standard maximum likelihood asymptotics, for any choice of test statistic. We apply our proposed procedure both theoretically and in simulation to a number of models of interest to demonstrate its finite-sample Type I error and power.
Rainbow Odd Cycles

We prove that every family of (not necessarily distinct) odd cycles $O_1, \dots, O_{2\lceil n/2 \rceil-1}$ in the complete graph $K_n$ on $n$ vertices has a rainbow odd cycle (that is, a set of edges from distinct $O_i$'s, forming an odd cycle). As part of the proof, we characterize those families of $n$ odd cycles in $K_{n+1}$ that do not have any rainbow odd cycle. We also characterize those families of $n$ cycles in $K_{n+1}$, as well as those of $n$ edge-disjoint nonempty subgraphs of $K_{n+1}$, without any rainbow cycle.
Sign non-reversal property for totally positive matrices and testing total positivity on their interval hull

A matrix $A$ is totally positive (or non-negative) of order $k$, denoted $TP_k$ (or $TN_k$), if all minors of size $\leq k$ are positive (or non-negative). It is well-known that such matrices are characterized by the variation diminishing property together with the sign non-reversal property. We do away with the former, and show that $A$ is $TP_k$ if and only if every contiguous submatrix of size $\leq k$ has the sign non-reversal property. In fact this can be strengthened to only consider test vectors in $\mathbb{R}^k$ with alternating signs. We also show a similar characterization for all $TN_k$ matrices - more strongly, using at most one vector (with alternating signs) for each square submatrix. These characterizations are novel, and similar in spirit to the fundamental results characterizing $TP$ matrices by Gantmacher-Krein [Compos. Math. 1937] and $P$-matrices by Gale-Nikaido [Math. Ann. 1965]. As an application, we study the interval hull $\mathbb{I}(A,B)$ of two $m \times n$ matrices $A=(a_{ij})$ and $B = (b_{ij})$. This is the collection of $C \in \mathbb{R}^{m \times n}$ such that each $c_{ij}$ is between $a_{ij}$ and $b_{ij}$. Using the sign non-reversal property, we identify a two-element subset of $\mathbb{I}(A,B)$ that detects the $TP_k$ property for all of $\mathbb{I}(A,B)$ for arbitrary $k \geq 1$. In particular, this provides a test for total positivity (of any order), simultaneously for an entire class of rectangular matrices.
An Open-World Simulated Environment for Developmental Robotics

As the current trend of artificial intelligence is shifting towards self-supervised learning, conventional norms such as highly curated domain-specific data, application-specific learning models, extrinsic reward based learning policies etc. might not provide with the suitable ground for such developments. In this paper, we introduce SEDRo, a Simulated Environment for Developmental Robotics which allows a learning agent to have similar experiences that a human infant goes through from the fetus stage up to 12 months. A series of simulated tests based on developmental psychology will be used to evaluate the progress of a learning model.
Teenagers with type 1 diabetes mellitus: life experience report

Purpose: To understand the life experience of teenagers with type 1 diabetes mellitus and how they cope with the disease in daily life. Methods: The symbolic interactionism was the theoretical perspective guiding the study. Grounded theory served as the method for using the empirical data. Participants consisted of 10 teenagers with type 1 diabetes mellitus over a year. Results: Two phenomena explaining their life experience emerged: Regarding having diabetes as being normal and regarding having diabetes as being abnormal.  Conclusion: The two phenomena were not isolated or excluded from the life experience of the same teenager; there were phases in which the teenagers experienced one of the phenomena and vice-versa with greater or less intensity.
Visual Explanation for Identification of the Brain Bases for Dyslexia on fMRI Data

Brain imaging of mental health, neurodevelopmental and learning disorders has coupled with machine learning to identify patients based only on their brain activation, and ultimately identify features that generalize from smaller samples of data to larger ones. However, the success of machine learning classification algorithms on neurofunctional data has been limited to more homogeneous data sets of dozens of participants. More recently, larger brain imaging data sets have allowed for the application of deep learning techniques to classify brain states and clinical groups solely from neurofunctional features. Deep learning techniques provide helpful tools for classification in healthcare applications, including classification of structural 3D brain images. Recent approaches improved classification performance of larger functional brain imaging data sets, but they fail to provide diagnostic insights about the underlying conditions or provide an explanation from the neural features that informed the classification. We address this challenge by leveraging a number of network visualization techniques to show that, using such techniques in convolutional neural network layers responsible for learning high-level features, we are able to provide meaningful images for expert-backed insights into the condition being classified. Our results show not only accurate classification of developmental dyslexia from the brain imaging alone, but also provide automatic visualizations of the features involved that match contemporary neuroscientific knowledge, indicating that the visual explanations do help in unveiling the neurological bases of the disorder being classified.
Personalized Speech2Video with 3D Skeleton Regularization and Expressive Body Poses

In this paper, we propose a novel approach to convert given speech audio to a photo-realistic speaking video of a specific person, where the output video has synchronized, realistic, and expressive rich body dynamics. We achieve this by first generating 3D skeleton movements from the audio sequence using a recurrent neural network (RNN), and then synthesizing the output video via a conditional generative adversarial network (GAN). To make the skeleton movement realistic and expressive, we embed the knowledge of an articulated 3D human skeleton and a learned dictionary of personal speech iconic gestures into the generation process in both learning and testing pipelines. The former prevents the generation of unreasonable body distortion, while the later helps our model quickly learn meaningful body movement through a few recorded videos. To produce photo-realistic and high-resolution video with motion details, we propose to insert part attention mechanisms in the conditional GAN, where each detailed part, e.g. head and hand, is automatically zoomed in to have their own discriminators. To validate our approach, we collect a dataset with 20 high-quality videos from 1 male and 1 female model reading various documents under different topics. Compared with previous SoTA pipelines handling similar tasks, our approach achieves better results by a user study.
Can we Estimate Truck Accident Risk from Telemetric Data using Machine Learning?

Road accidents have a high societal cost that could be reduced through improved risk predictions using machine learning. This study investigates whether telemetric data collected on long-distance trucks can be used to predict the risk of accidents associated with a driver. We use a dataset provided by a truck transportation company containing the driving data of 1,141 drivers for 18 months. We evaluate two different machine learning approaches to perform this task. In the first approach, features are extracted from the time series data using the FRESH algorithm and then used to estimate the risk using Random Forests. In the second approach, we use a convolutional neural network to directly estimate the risk from the time series data. We find that neither approach is able to successfully estimate the risk of accidents on this dataset, in spite of many methodological attempts. We discuss the difficulties of using telemetric data for the estimation of the risk of accidents that could explain this negative result.
Automotive vehicle launch optimization based on differential evolution (DE) approach for increased driveability

An important target for automated transmission development is driveability during the launch process of a vehicle. For transmissions using launch clutches the friction energy has to be considered to avoid thermal breakdown. The launch process is controlled by electronic control units containing functions tunable by parameters. Optimizing those parameters can be time-consuming when testing on a real vehicle. Using a simulation model as input for evaluation of the objective function can lead to significant cost savings as well as shorter development time in automotive applications. In this work we contribute a lean mathematical model of a vehicle’s drivetrain tailored for optimization of the launch process. The model includes combustion engine, inertias, clutches, flexible shafts and control algorithms for engine and clutches. The two design parameters clutch closing time and scaling factor for clutch slip speed are chosen to optimize the objective function taking into account driveability and clutch friction energy. As the objective function is nonlinear and not even continuous, we propose using a DE approach, which does not require the knowledge of the objective function’s gradient. To further improve optimization results a surrogate objective function is built using a radial basis function approach. Found optimal parameters for clutch closing time and scaling factor for clutch slip speed shall be applied to a prototype vehicle as initial parametrization. Within this paper a realistic launch simulation with optimized parameters is shown. Furthermore the suitability of a DE approach for the given task is demonstrated.
Stochastic convective Brinkman-Forchheimer equations

The stochastic convective Brinkman-Forchheimer (SCBF) equations or the tamed Navier-Stokes equations in bounded or periodic domains are considered in this work. We show the existence of a pathwise unique strong solution (in the probabilistic sense) satisfying the energy equality (Ito formula) to the SCBF equations perturbed by multiplicative Gaussian noise. We exploited a monotonicity property of the linear and nonlinear operators as well as a stochastic generalization of the Minty-Browder technique in the proofs. The energy equality is obtained by approximating the solution using approximate functions constituting the elements of eigenspaces of the Stokes operator in such a way that the approximations are bounded and converge in both Sobolev and Lebesgue spaces simultaneously. We further discuss about the global in time regularity results of such strong solutions in periodic domains. The exponential stability results (in mean square and pathwise sense) for the stationary solutions is also established in this work for large effective viscosity. Moreover, a stabilization result of the stochastic convective Brinkman-Forchheimer equations by using a multiplicative noise is obtained. Finally, we prove the existence of a unique ergodic and strongly mixing invariant measure for the SCBF equations subject to multiplicative Gaussian noise, by making use of the exponential stability of strong solutions.
On the Feasibility of Deep Learning in Sensor Network Intrusion Detection

In this letter, we present a comprehensive analysis of the use of machine and deep learning (DL) solutions for IDS systems in wireless sensor networks (WSNs). To accomplish this, we introduce restricted Boltzmann machine-based clustered IDS (RBC-IDS), a potential DL-based IDS methodology for monitoring critical infrastructures by WSNs. We study the performance of RBC-IDS, and compare it to the previously proposed adaptive machine learning-based IDS: the adaptively supervised and clustered hybrid IDS (ASCH-IDS). Numerical results show that RBC-IDS and ASCH-IDS achieve the same detection and accuracy rates, though the detection time of RBC-IDS is approximately twice that of ASCH-IDS.
Evaluating a Simple Retraining Strategy as a Defense Against Adversarial Attacks

Though deep neural networks (DNNs) have shown superiority over other techniques in major fields like computer vision, natural language processing, robotics, recently, it has been proven that they are vulnerable to adversarial attacks. The addition of a simple, small and almost invisible perturbation to the original input image can be used to fool DNNs into making wrong decisions. With more attack algorithms being designed, a need for defending the neural networks from such attacks arises. Retraining the network with adversarial images is one of the simplest techniques. In this paper, we evaluate the effectiveness of such a retraining strategy in defending against adversarial attacks. We also show how simple algorithms like KNN can be used to determine the labels of the adversarial images needed for retraining. We present the results on two standard datasets namely, CIFAR-10 and TinyImageNet.
Seismic solar models from Ledoux discriminant inversions

Context. The Sun constitutes an excellent laboratory of fundamental physics. With the advent of helioseismology, we were able to probe its internal layers with unprecendented precision and thoroughness. However, the current state of solar modelling is still stained by tedious issues. One of these central problems is related to the disagreement between models computed with recent photospheric abundances and helioseismic constraints. The observed discrepancies raise questions on some fundamental ingredients entering the computation of solar and stellar evolution models. Aims. We used solar evolutionary models as initial conditions for reintegrating their structure using Ledoux discriminant inversions. The resulting models are defined as seismic solar models, satisfying the equations of hydrostatic equilibrium. These seismic models will allow us to better constrain the internal structure of the Sun and provide complementary information to that of calibrated standard and non-standard models. Methods. We used inversions of the Ledoux discriminant to reintegrate seismic solar models satisfying the equations of hydrostatic equilibrium. These seismic models were computed using various reference models with different equations of state, abundances, and opacity tables. We checked the robustness of our approach by confirming the good agreement of our seismic models in terms of sound speed, density, and entropy proxy inversions, as well as frequency-separation ratios of low-degree pressure modes. Results. Our method allows us to determine the Ledoux discriminant profile of the Sun with an excellent accuracy and compute full profiles of this quantity. Our seismic models show an agreement with seismic data of ≈0.1% in sound speed, density, and entropy proxy after seven iterations in addition to an excellent agreement with the observed frequency-separation ratios. They surpass all standard and non-standard evolutionary models including ad hoc modifications of their physical ingredients that aim to reproduce helioseismic constraints. Conclusions. The obtained seismic Ledoux discriminant profile, as well as the full consistent structure obtained from our reconstruction procedure paves the way for renewed attempts at constraining the solar modelling problem and the missing physical processes acting in the solar interior by breaking free from the hypotheses of evolutionary models.
A Learning-Based Approach to Performance Management: Drivers of LearningOriented Use of Performance Information

This article adds to the empirical knowledge of drivers of learning-oriented use of performance information, paying special interest to the creation of learning forums. On the basis of survey responses from public managers in Danish municipalities, we analyze learning forums together with already established key factors (support capacity, measurability of tasks, and innovative culture) impacting managers’ use performance information. Furthermore, we examine whether the relationship between learning forums and public managers’ learning-oriented use of performance information is moderated and/or mediated by support capacity, measurability, and innovative culture. The analyses show that both the existence of learning forums and having adequate support capacity are significant and positively related to managers’ learning-oriented use of performance information. It also shows that support capacity significantly and positively mediates the effect of learning forums on managers’ learning-oriented information use. The analyses show no significant moderations. The article contributes to the discussion of learning forums and managers’ information use by connecting learning forums directly to managers’ learning-oriented use of performance information by comparing the effect of learning forums relative to effects of other established important drivers of information use and by advancing the understanding of possible moderating and mediating mechanisms between learning forums and the managers’ learning oriented use of performance information.
On the eigenstructure of the $(\alpha,q)$-Bernstein operator

The well-known Weierstrass Approximation Theorem, proved by Karl Weierstrass in 1885, states that for any continuous function f defined in interval [a, b] and ǫ > 0, there exists a polynomial P such that | f (x) − P(x)| < ǫ. Since the proof of the theorem is lengthy and complicated, many researchers studied to find simple and effective proof. In 1912, using probability theory, S.N Bernstein developed Bernstein polynomials and published rather simple and effective proof for Weierstrass Approximation Theorem [2]. In 1987, Lupaş has defined q-analogue of the Bernstein polynomials for rational functions [12]. However, in 1997, Phillips has developed q-analogue of polynomial functions such that for q = 1, classical Bernstein operators are obtained [17]. After this date, many researchers studied q-Bernstein operators and with the help of this operator, q-analogues of many other operators are obtained [9, 11, 13, 14, 15, 19, 20]. Many researchers have studied the eigenstructure of the extensions of Bernstein and q-Bernstein operators. See, for example, [6, 7, 8, 10, 21]. The present work is based on the studies by S. Cooper and S. Waldron in 2000 related to the eigenstructure of the classical and multi-variable Bernstein operators [4, 5]. Eigenfunctions of the q-Bernstein operators and their asymptotic behavior are investigated by S. Ostrovska and M.Turan in 2013 [16]. In this study, eigenvalues and eigenfunctions of the
Geometric and control optimization of a two cross-flow turbine array

Cross-flow turbines, also known as vertical-axis turbines, convert the kinetic energy in moving fluid to mechanical energy using blades that rotate about an axis perpendicular to the incoming flow. In this work, the performance of a two-turbine array in a recirculating water channel was experimentally optimized across sixty-four unique array configurations. For each configuration, turbine performance was optimized using tip-speed ratio control, where the rotation rate for each turbine is optimized individually, and using coordinated control, where the turbines are optimized to operate at synchronous rotation rates, but with a phase difference. For each configuration and control strategy, the consequences of co- and counter-rotation were also evaluated. We hypothesize how array configurations and control cases influence interactions between turbines and impact array performance.
Clinical significance of pretreatment tumor growth rate for locally advanced non-small cell lung cancer.

Background Locally advanced non-small cell lung cancer (NSCLC) may exhibit significant tumor growth before the initiation of definitive chemoradiation therapy (CRT). We thus investigated the prognostic value of pretreatment tumor growth rate as measured by specific growth rate (SGR). Methods We conducted a retrospective review of 42 patients with locally advanced NSCLC treated with definitive concurrent CRT. For each patient, we contoured the primary gross tumor volume (GTV) on the pretreatment diagnostic chest computed tomography (CT) scan and the radiation therapy (RT) planning CT scan. We then calculated SGR based on the primary GTV from each scan and the time interval between scans. We used log-rank tests and univariate Cox regression models to quantify differences in progression-free survival (PFS), overall survival (OS) and recurrence based on SGR. Results We divided patients into two groups for analysis: those with an SGR greater than or equal to the upper tercile value of 0.94%/day (high SGR) and those with SGR less than 0.94%/day (low SGR). Patients with high SGRs versus low SGRs experienced inferior PFS (median, 5.6 vs. 13.6 months, P=0.016), without a significant difference in OS. The inferior PFS in the high SGR group persisted on multivariate analysis [adjusted hazard ratio (HR) 2.37, 95% confidence interval (CI): 1.07-5.25, P=0.034]. The risk of distant recurrence was higher in the high SGR group (HR 2.62, 95% CI: 1.08-6.38, P=0.033), but there was no difference in the risk of locoregional recurrence between groups. Conclusions Pretreatment SGR was associated with inferior PFS and distant control among patients with locally advanced NSCLC treated with concurrent CRT. Further studies in larger populations may aid in elucidating optimal SGR cut-off points for risk stratification.
Nanoscale nonlinear magnonic ring resonator

The field of magnonics, which aims at using spin waves as carriers in data processing devices, has attracted increasing interest in recent years. We present and study micromagnetically a nonlinear nanoscale magnonic ring resonator device for enabling implementations of magnonic logic gates and neuromorphic magnonic circuits. In the linear regime, the transmission curve of this device exhibits the behavior of a notch filter, which filters out the spin waves of resonant frequencies. By increasing the spin-wave input power, this resonance frequency is shifted leading to transmission curves reminiscent to the activation functions of neurons. An analytical theory is developed to describe the transmission curve of the magnonic ring resonators in the linear and nonlinear regimes and it is validated by a comprehensive micromagnetic study. The proposed magnonic ring resonator provides a multi-functional nonlinear building block for unconventional magnonic circuits.