Miracle-dz/UniFAR-FTUs huggingface dataset:
Improved Lower Bounds for the Restricted Isometry Property of Subsampled Fourier Matrices

Let $A$ be an $N \times N$ Fourier matrix over $\mathbb{F}_p^{\log{N}/\log{p}}$ for some prime $p$. We improve upon known lower bounds for the number of rows of $A$ that must be sampled so that the resulting matrix $M$ satisfies the restricted isometry property for $k$-sparse vectors. This property states that $\|Mv\|_2^2$ is approximately $\|v\|_2^2$ for all $k$-sparse vectors $v$. In particular, if $k = \Omega( \log^2{N})$, we show that $\Omega(k\log{k}\log{N}/\log{p})$ rows must be sampled to satisfy the restricted isometry property with constant probability.
Time series models for realized covariance matrices based on the matrix-F distribution

We propose a new Conditional BEKK matrix-F (CBF) model for the time-varying realized covariance (RCOV) matrices. This CBF model is capable of capturing heavy-tailed RCOV, which is an important stylized fact but could not be handled adequately by the Wishart-based models. To further mimic the long memory feature of the RCOV, a special CBF model with the conditional heterogeneous autoregressive (HAR) structure is introduced. Moreover, we give a systematical study on the probabilistic properties and statistical inferences of the CBF model, including exploring its stationarity, establishing the asymptotics of its maximum likelihood estimator, and giving some new inner-product-based tests for its model checking. In order to handle a large dimensional RCOV matrix, we construct two reduced CBF models -- the variance-target CBF model (for moderate but fixed dimensional RCOV matrix) and the factor CBF model (for high dimensional RCOV matrix). For both reduced models, the asymptotic theory of the estimated parameters is derived. The importance of our entire methodology is illustrated by simulation results and two real examples.
Quantum Neural Network States: A Brief Review of Methods and Applications

One of the main challenges of quantum many‐body physics is the exponential growth in the dimensionality of the Hilbert space with system size. This growth makes solving the Schrödinger equation of the system extremely difficult. Nonetheless, many physical systems have a simplified internal structure that typically makes the parameters needed to characterize their ground states exponentially smaller. Many numerical methods then become available to capture the physics of the system. Among modern numerical techniques, neural networks, which show great power in approximating functions and extracting features of big data, are now attracting much interest. In this work, the progress in using artificial neural networks to build quantum many‐body states is reviewed. The Boltzmann machine representation is taken as a prototypical example to illustrate various aspects of the neural network states. The classical neural networks are also briefly reviewed, and it is illustrated how to use neural networks to represent quantum states and density operators. Some physical properties of the neural network states are discussed. For applications, the progress in many‐body calculations based on neural network states, the neural network state approach to tomography, and the classical simulation of quantum computing based on Boltzmann machine states are briefly reviewed.
A Novel Algorithm for Controlling Active and Reactive Power Flows of Electric Vehicles in Buildings and Its Impact on the Distribution Network

In the literature, many optimization algorithms were developed to control electrical loads, especially Electric Vehicles (EVs) in buildings. Despite the success of the existing algorithms in improving the power profile of charging EVs and reducing the total electricity bill of the end-users, these algorithms didn’t show significant contribution in improving the voltage profile on the network, especially with the existence of highly inductive loads. The control of the active power may not be sufficient to regulate the voltage, even if sophisticated optimization algorithms and control strategies are used. To fill the gap in the literature, we propose a new algorithm that is able to control both the active and reactive power flows using electric vehicles in buildings and homes. The algorithm is composed of two parts; the first part uses optimization to control the active power and minimize the electricity bill, while the second part controls the reactive power using the bidirectional converter in the EV in a way that the voltage profile on the distribution transformer respects its limits. The new approach is validated through a comparative study of four different scenarios, (i) without EV, (ii) with EV using uncoordinated charging, (iii) with EV using coordinated charging, (iv) with EV using our proposed algorithm. Results show that our algorithm has maintained the voltage within the recommended limits, and it has minimized the peak load, the electricity cost, and the techno-economic losses on the network.
Partial ablation of Ti/Al nano-layer thin film by single femtosecond laser pulse

The effects of ultra-short laser pulses on reactive Ti/Al nano-layered thin film were investigated. The thin film composed of alternated titanium and aluminium nano-layers, was deposited by ion-sputtering. Single pulse irradiation was conducted in the air with focused and linearly polarized femtosecond laser beam - of 1026 nm wavelength and pulse duration of 170 fs. Laser induced composition and morphological changes, using different microscopy techniques and energy dispersive X-ray spectroscopy, were investigated. Following results were obtained: (i) one step partial/selective ablation of upper Ti layer from nano-layer Ti/Al at low laser fluence and (ii) two step ablation or entire ablation of nano-layer Ti/Al at higher laser fluence. Single pulse selective ablation of the upper Ti layer was confirmed based on profiling (AFM) along the ablation steps and reduction of Ti concentration (EDX) in the ablated areas. Ablation threshold was estimated using well known procedure for ultra-short laser pulses - spot diameter square versus logarithm of pulse energy. To interpret the experimental observations, simulations have been performed to explore the thermal response of the multiple layered structure (Ti(5x(Al/Ti))) after irradiation with a characteristic value of single laser pulse of fluence F = 320 mJ/cm2. The results are in agreement with the calculations.
Improving Cross-Corpus Speech Emotion Recognition with Adversarial Discriminative Domain Generalization (ADDoG)

Automatic speech emotion recognition provides computers with critical context to enable user understanding. While methods trained and tested within the same dataset have been shown successful, they often fail when applied to unseen datasets. To address this, recent work has focused on adversarial methods to find more generalized representations of emotional speech. However, many of these methods have issues converging, and only involve datasets collected in laboratory conditions. In this paper, we introduce Adversarial Discriminative Domain Generalization (ADDoG), which follows an easier to train “meet in the middle” approach. The model iteratively moves representations learned for each dataset closer to one another, improving cross-dataset generalization. We also introduce Multiclass ADDoG, or MADDoG, which is able to extend the proposed method to more than two datasets, simultaneously. Our results show consistent convergence for the introduced methods, with significantly improved results when not using labels from the target dataset. We also show how, in most cases, ADDoG and MADDoG can be used to improve upon baseline state-of-the-art methods when target dataset labels are added and in-the-wild data are considered. Even though our experiments focus on cross-corpus speech emotion, these methods could be used to remove unwanted factors of variation in other settings.
Sentiment Analysis on IMDB Movie Comments and Twitter Data by Machine Learning and Vector Space Techniques

This study's goal is to create a model of sentiment analysis on a 2000 rows IMDB movie comments and 3200 Twitter data by using machine learning and vector space techniques; positive or negative preliminary information about the text is to provide. In the study, a vector space was created in the KNIME Analytics platform, and a classification study was performed on this vector space by Decision Trees, Naive Bayes and Support Vector Machines classification algorithms. The conclusions obtained were compared in terms of each algorithms. The classification results for IMDB movie comments are obtained as 94,00%, 73,20%, and 85,50% by Decision Tree, Naive Bayes and SVM algorithms. The classification results for Twitter data set are presented as 82,76%, 75,44% and 72,50% by Decision Tree, Naive Bayes SVM algorithms as well. It is seen that the best classification results presented in both data sets are which calculated by SVM algorithm.
Gibbs and Boltzmann Entropy in Classical and Quantum Mechanics

The Gibbs entropy of a macroscopic classical system is a function of a probability distribution over phase space, i.e., of an ensemble. In contrast, the Boltzmann entropy is a function on phase space, and is thus defined for an individual system. Our aim is to discuss and compare these two notions of entropy, along with the associated ensemblist and individualist views of thermal equilibrium. Using the Gibbsian ensembles for the computation of the Gibbs entropy, the two notions yield the same (leading order) values for the entropy of a macroscopic system in thermal equilibrium. The two approaches do not, however, necessarily agree for non-equilibrium systems. For those, we argue that the Boltzmann entropy is the one that corresponds to thermodynamic entropy, in particular in connection with the second law of thermodynamics. Moreover, we describe the quantum analog of the Boltzmann entropy, and we argue that the individualist (Boltzmannian) concept of equilibrium is supported by the recent works on thermalization of closed quantum systems.
Autonomy as license to operate: Establishing the internal and external conditions of informed choice in marketing

The assumption that consumers voluntarily accept or decline marketing offerings provides the ethical justification that gives marketing as a social system its license to operate. Consumer autonomy is, therefore, the key ethical principle of marketing in capitalistic economies. However, even in domains with extensive regulatory frameworks and advanced market conditions, consumers are often ill-informed or underinformed. The resultant lack of epistemic confidence diminishes consumers’ ability to make informed choices. At the same time, consumers are by default exposed to promotional content designed to persuade them to accept marketing offerings. This threatens personal autonomy. We develop a concept of consumer autonomy which marketing regulations should protect and promote to enhance informed decision-making. We design autonomy to be robust in situations where individuals are exposed to persuasive attempts to influence them to choose a specific course of action. As such, our concept of autonomy is applicable to a range of contexts beyond marketing where it is necessary to balance external influences and individual autonomy.
Painting with baryons: augmenting N-body simulations with gas using deep generative models

Running hydrodynamical simulations to produce mock data of large-scale structure and baryonic probes, such as the thermal Sunyaev-Zeldovich (tSZ) effect, at cosmological scales is computationally challenging. We propose to leverage the expressive power of deep generative models to find an effective description of the large-scale gas distribution and temperature. We train two deep generative models, a variational auto-encoder and a generative adversarial network, on pairs of matter density and pressure slices from the BAHAMAS hydrodynamical simulation. The trained models are able to successfully map matter density to the corresponding gas pressure. We then apply the trained models on 100 lines-of-sight from SLICS, a suite of N-body simulations optimised for weak lensing covariance estimation, to generate maps of the tSZ effect. The generated tSZ maps are found to be statistically consistent with those from BAHAMAS. We conclude by considering a specific observable, the angular cross-power spectrum between the weak lensing convergence and the tSZ effect and its variance, where we find excellent agreement between the predictions from BAHAMAS and SLICS, thus enabling the use of SLICS for tSZ covariance estimation.
Existence of Kirillov-Reshetikhin crystals of type $E_6^{(1)}$

We prove that every Kirillov-Reshetikhin module associated with the trivalent node in type $E_6^{(1)}$ has a crystal pseudobase, by applying the criterion introduced by Kang et.al. In order to apply the criterion, we need to prove some statements concerning values of a bilinear form. We achieve this by using the canonical bases of extremal weight modules.
On the Stability and Generalization of Learning with Kernel Activation Functions

In this brief we investigate the generalization properties of a recently-proposed class of non-parametric activation functions, the kernel activation functions (KAFs). KAFs introduce additional parameters in the learning process in order to adapt nonlinearities individually on a per-neuron basis, exploiting a cheap kernel expansion of every activation value. While this increase in flexibility has been shown to provide significant improvements in practice, a theoretical proof for its generalization capability has not been addressed yet in the literature. Here, we leverage recent literature on the stability properties of non-convex models trained via stochastic gradient descent (SGD). By indirectly proving two key smoothness properties of the models under consideration, we prove that neural networks endowed with KAFs generalize well when trained with SGD for a finite number of steps. Interestingly, our analysis provides a guideline for selecting one of the hyper-parameters of the model, the bandwidth of the scalar Gaussian kernel. A short experimental evaluation validates the proof.
Caring helps: Trait empathy is related to better coping strategies and differs in the poor versus the rich

Coping has been extensively studied in health psychology; however, factors influencing the usage of different coping strategies have received limited attention. In five studies (N = 3702), we explored the relationship between trait empathy and coping strategies, and how subjective socioeconomic status (SES) moderates this relationship. In Studies 1–4, we found that people with higher level of empathic concern use more adaptive coping strategies, seek more social support, and use fewer maladaptive coping strategies. Moreover, higher trait empathy related to more adaptive coping strategies among the poor, and fewer maladaptive coping strategies among the rich. In Study 5, we tested the potential biological basis of the relationship between trait empathy and coping by examining the effect of the oxytocin receptor gene (OXTR) rs53576 polymorphism on coping. We found that individuals with the GG phenotype—who in previous research have been found to be more empathic—were more likely to seek social support than AG or AA individuals. Furthermore, in line with findings in Studies 1–4, amongst people with low SES, individuals with GG genotype used more adaptive coping strategies than AG or AA individuals. Our results highlight the selective role trait empathy plays in influencing coping strategy deployment, depending on the SES of individuals.
SFT COMPUTATIONS AND INTERSECTION THEORY IN HIGHER-DIMENSIONAL CONTACT MANIFOLDS

Abstract I construct infinitely many nondiffeomorphic examples of $5$ -dimensional contact manifolds which are tight, admit no strong fillings and do not have Giroux torsion. I obtain obstruction results for symplectic cobordisms, for which I give a proof not relying on the polyfold abstract perturbation scheme for Symplectic Field Theory (SFT). These results are part of my PhD thesis [23], and are the first applications of higher-dimensional Siefring intersection theory for holomorphic curves and hypersurfaces, as outlined in [23, 24], as a prequel to [30].
Road User Detection in Videos

Successive frames of a video are highly redundant, and the most popular object detection methods do not take advantage of this fact. Using multiple consecutive frames can improve detection of small objects or difficult examples and can improve speed and detection consistency in a video sequence, for instance by interpolating features between frames. In this work, a novel approach is introduced to perform online video object detection using two consecutive frames of video sequences involving road users. Two new models, RetinaNet-Double and RetinaNet-Flow, are proposed, based respectively on the concatenation of a target frame with a preceding frame, and the concatenation of the optical flow with the target frame. The models are trained and evaluated on three public datasets. Experiments show that using a preceding frame improves performance over single frame detectors, but using explicit optical flow usually does not.
Less is More: Born's Rule from Quantum Frequentism

Accounting for Born's rule from first principles remains an open problem among the foundational issues in quantum mechanics. Proponents of the many-worlds interpretation have argued that Born's rule is observed simply because those histories that violate the rule have vanishing norms, and so must be unphysical. This argument has only been made explicit for contrived situations involving measurements on infinitely many identically-prepared systems. We prove a more general result, namely that for systems containing infinitely many degrees of freedom in arbitrarily-prepared states the universal wavefunction contains no histories that violate Born's rule.
Weighing the evidence for using vascular conductance, not resistance, in comparative cardiovascular physiology

ABSTRACT Vascular resistance and conductance are reciprocal indices of vascular tone that are often assumed to be interchangeable. However, in most animals in vivo, blood flow (i.e. cardiac output) typically varies much more than arterial blood pressure. When blood flow changes at a constant pressure, the relationship between conductance and blood flow is linear, whereas the relationship between resistance and blood flow is non-linear. Thus, for a given change in blood flow, the change in resistance depends on the starting point, whereas the attendant change in conductance is proportional to the change in blood flow regardless of the starting conditions. By comparing the effects of physical activity at different temperatures or between species – concepts at the heart of comparative cardiovascular physiology – we demonstrate that the difference between choosing resistance or conductance can be marked. We also explain here how the ratio of conductance in the pulmonary and systemic circulations provides a more intuitive description of cardiac shunt patterns in the reptilian cardiovascular system than the more commonly used ratio of resistance. Finally, we posit that, although the decision to use conductance or resistance should be made on a case-by-case basis, in most circumstances, conductance is a more faithful portrayal of cardiovascular regulation in vertebrates. Summary: Vascular conductance and resistance are reciprocal variables, but are not interchangeable terms. We argue that when blood flow changes by a greater relative magnitude than blood pressure, conductance yields a more faithful representation of cardiovascular status.
Deforming black holes with even multipolar differential rotation boundary

A bstractMotivated by the novel asymptotically global AdS4 solutions with deforming horizon in [21], we analyze the boundary metric with even multipolar differential rotation and numerically construct a family of deforming solutions with quadrupolar differential rotation boundary, including two classes of solutions: solitons and black holes. In contrast o solutions with dipolar differential rotation boundary, we find that even though the norm of Killing vector ∂t becomes spacelike for certain regions of polar angle θ when ε > 2, solitons and black holes with quadrupolar differential rotation still exist and do not develop hair due to superradiance. Moreover, at the same temperature, the horizonal deformation of quadrupolar rotation is smaller than that of dipolar rotation. Furthermore, we also study the entropy and quasinormal modes of the solutions, which have the analogous properties to that of dipolar rotation.
Nanofabrication of normally-off GaN vertical nanowire MESFETs

Gallium nitride (GaN) all-around (wrap) gate vertical nanowire (V-NW) field-effect transistors (FETs) are favorable for enhanced electrostatic control of the gate and selectivity for normally on/off operation. In this work, GaN V-NW FETs with a Schottky barrier gate (V-NW MESFETs), were fabricated for the first time. A nanofabrication process with comprehensive description of all processing steps is reported. It was validated with the demonstration of GaN V-NW MESFETs consisting of an array of 900 (30 × 30) GaN NWs with the narrowest until now reported diameter of 100 nm and all-around gate length of 250 nm. The GaN NWs were formed by a top-down approach, which combines conventional nanopatterning techniques and anisotropic wet etching of an initial GaN epilayer, grown by plasma assisted molecular beam epitaxy on a sapphire (0001) substrate. DC I–V characteristics exhibited normally-off operation and threshold voltage of +0.4 V, due to electron depletion region from the all-around Schottky barrier. A maximum drain-source current density (Jds) of 330 A cm−2 and maximum transconductance (gm) of 285 S cm−2 were obtained from I–V measurements. The results and directions for further optimization were discussed.
Beer Aroma and Quality Traits Assessment Using Artificial Intelligence

Increasing beer quality demands from consumers have put pressure on brewers to target specific steps within the beer-making process to modify beer styles and quality traits. However, this demands more robust methodologies to assess the final aroma profiles and physicochemical characteristics of beers. This research shows the construction of artificial intelligence (AI) models based on aroma profiles, chemometrics, and chemical fingerprinting using near-infrared spectroscopy (NIR) obtained from 20 commercial beers used as targets. Results showed that machine learning models obtained using NIR from beers as inputs were accurate and robust in the prediction of six important aromas for beer (Model 1; R = 0.91; b = 0.87) and chemometrics (Model 2; R = 0.93; b = 0.90). Additionally, two more accurate models were obtained from robotics (RoboBEER) to obtain the same aroma profiles (Model 3; R = 0.99; b = 1.00) and chemometrics (Model 4; R = 0.98; b = 1.00). Low-cost robotics and sensors coupled with computer vision and machine learning modeling could help brewers in the decision-making process to target specific consumer preferences and to secure higher consumer demands.
Skip prediction using boosting trees based on acoustic features of tracks in sessions

The Spotify Sequential Skip Prediction Challenge focuses on predicting if a track in a session will be skipped by the user or not. In this paper, we describe our approach to this problem and the final system that was submitted to the challenge by our team from the Music Technology Group (MTG) under the name "aferraro". This system consists in combining the predictions of multiple boosting trees models trained with features extracted from the sessions and the tracks. The proposed approach achieves good overall performance (MAA of 0.554), with our model ranked 14th out of more than 600 submissions in the final leaderboard.