Miracle-dz/UniFAR-FTUs huggingface dataset:
Bayesian nonparametric spectral analysis of locally stationary processes

Based on a novel dynamic Whittle likelihood approximation for locally stationary processes, a Bayesian nonparametric approach to estimating the time-varying spectral density is proposed. This dynamic frequency-domain based likelihood approximation is able to depict the time-frequency evolution of the process by utilizing the moving periodogram previously introduced in the bootstrap literature. The posterior distribution is obtained by updating a bivariate extension of the Bernstein-Dirichlet process prior with the dynamic Whittle likelihood. Asymptotic properties such as sup-norm posterior consistency and L2-norm posterior contraction rates are presented. Additionally, this methodology enables model selection between stationarity and non-stationarity based on the Bayes factor. The finite-sample performance of the method is investigated in simulation studies and applications to real-life data-sets are presented.
First moment of central values of quadratic Dirichlet $L$-functions

We evaluate the first moment of central values of the family of quadratic Dirichlet $L$-functions using the method of double Dirichlet series. Under the generalized Riemann hypothesis, we prove an asymptotic formula with an error term of size that is the fourth root of that of the primary main term.
Multi-agent Reinforcement Learning for Regional Signal control in Large-scale Grid Trafﬁc network

—Adaptive trafﬁc signal control with Multi-agent Reinforcement Learning(MARL) is a very popular topic nowadays. In most existing novel methods, one agent controls single intersections and these methods focus on the cooperation between intersections. However, the non-stationary property of MARL still limits the performance of the above methods as the size of trafﬁc networks grows. One compromised strategy is to assign one agent with a region of intersections to reduce the number of agents. There are two challenges in this strategy, one is how to partition a trafﬁc network into small regions and the other is how to search for the optimal joint actions for a region of intersections. In this paper, we propose a novel training framework RegionLight where our region partition rule is based on the adjacency between the intersection and extended Branching Dueling Q-Network(BDQ) to Dynamic Branching Dueling Q-Network(DBDQ) to bound the growth of the size of joint action space and alleviate the bias introduced by imaginary intersections outside of the boundary of the trafﬁc network. Our experiments on both real datasets and synthetic datasets demonstrate that our framework performs best among other novel frameworks and that our region partition rule is robust.
A Landau-Zener formula for the Adiabatic Gauge Potential

By the adiabatic theorem, the probability of non-adiabatic transitions in a time-dependent quantum system vanishes in the adiabatic limit. The Landau-Zener (LZ) formula gives the leading functional behavior of the probability close to this limit. On the other hand, in counterdiabatic dynamics, one achieves effectively adiabatic evolution at finite driving speed by adding an extra field which suppresses non-adiabatic transitions: the adiabatic gauge potential (AGP). We investigate the mechanism by which the AGP suppresses the transition probability, changing it from the LZ formula to exactly zero. Quantitatively, we find that adding the AGP to the Hamiltonian modifies the LZ formula by a universal prefactor, independent of the adiabatic parameter, which vanishes in the counterdiabatic regime. Qualitatively, this prefactor can be understood as arising from the Aharonov-Bohm phases generated by the AGP between different paths in the complex time plane. Finally, we show that these results extend to a class of integrable time-dependent quantum Hamiltonians by proving that the AGP preserves their integrability condition.
Heart Murmur and Abnormal PCG Detection via Wavelet Scattering Transform & a 1D-CNN

This work leverages deep learning (DL) techniques in order to do automatic and accurate heart murmur detection from phonocardiogram (PCG) recordings. Two public PCG datasets (CirCor Digiscope 2022 dataset and PCG 2016 dataset) from Physionet online database are utilized to train and test three custom neural networks (NN): a 1D convolutional neural network (CNN), a long short-term memory (LSTM) recurrent neural network (RNN), and a convolutional RNN (C-RNN). Under our proposed method, we first do pre-processing on both datasets in order to prepare the data for the NNs. Key pre-processing steps include the following: denoising, segmentation, re-labeling of noise-only segments, data normalization, and time-frequency analysis of the PCG segments using wavelet scattering transform. To evaluate the performance of the three NNs we have implemented, we conduct four experiments, first three using PCG 2022 dataset, and fourth using PCG 2016 dataset. It turns out that our custom 1D-CNN outperforms other two NNs (LSTM- RNN and C-RNN) as well as the state-of-the-art. Specifically, for experiment E1 (murmur detection using original PCG 2022 dataset), our 1D-CNN model achieves an accuracy of 82.28%, weighted accuracy of 83.81%, F1-score of 65.79%, and and area under receive operating charactertic (AUROC) curve of 90.79%. For experiment E2 (mumur detection using PCG 2022 dataset with unknown class removed), our 1D-CNN model achieves an accuracy of 87.05%, F1-score of 87.72%, and AUROC of 94.4%. For experiment E3 (murmur detection using PCG 2022 dataset with re-labeling of segments), our 1D-CNN model achieves an accuracy of 82.86%, weighted accuracy of 86.30%, F1-score of 81.87%, and AUROC of 93.45%. For experiment E4 (abnormal PCG detection using PCG 2016 dataset), our 1D-CNN model achieves an accuracy of 96.30%, F1-score of 96.29% and AUROC of 98.17%.
Quantum Gas-Enabled Direct Mapping of Active Current Density in Percolating Networks of Nanowires

Electrically percolating nanowire networks are among the most promising candidates for next-generation transparent electrodes. Scientific interest in these materials stems from their intrinsic current distribution heterogeneity, leading to phenomena like percolating pathway rerouting and localized self-heating, which can cause irreversible damage. Without an experimental technique to resolve the current distribution and an underpinning nonlinear percolation model, one relies on empirical rules and safety factors to engineer materials. We introduce Bose–Einstein condensate microscopy to address the longstanding problem of imaging active current flow in 2D materials. We report on performance improvement of this technique whereby observation of dynamic redistribution of current pathways becomes feasible. We show how this, combined with existing thermal imaging methods, eliminates the need for assumptions between electrical and thermal properties. This will enable testing and modeling individual junction behavior and hot-spot formation. Investigating both reversible and irreversible mechanisms will contribute to improved performance and reliability of devices.
Targeted Analysis of High-Risk States Using an Oriented Variational Autoencoder

Variational autoencoder (VAE) neural networks can be trained to generate power system states that capture both marginal distribution and multivariate dependencies of historical data. The coordinates of the latent space codes of VAEs have been shown to correlate with conceptual features of the data, which can be leveraged to synthesize targeted data with desired features. However, the locations of the VAEs' latent space codes that correspond to specific properties are not constrained. Additionally, the generation of data with specific characteristics may require data with corresponding hard-to-get labels fed into the generative model for training. In this paper, to make data generation more controllable and efficient, an oriented variation autoencoder (OVAE) is proposed to constrain the link between latent space code and generated data in the form of a Spearman correlation, which provides increased control over the data synthesis process. On this basis, an importance sampling process is used to sample data in the latent space. Two cases are considered for testing the performance of the OVAE model: the data set is fully labeled with approximate information and the data set is incompletely labeled but with more accurate information. The experimental results show that, in both cases, the OVAE model correlates latent space codes with the generated data, and the efficiency of generating targeted samples is significantly improved.
Contextual Bandits with Packing and Covering Constraints: A Modular Lagrangian Approach via Regression

We consider contextual bandits with linear constraints (CBwLC), a variant of contextual bandits in which the algorithm consumes multiple resources subject to linear constraints on total consumption. This problem generalizes contextual bandits with knapsacks (CBwK), allowing for packing and covering constraints, as well as positive and negative resource consumption. We provide the first algorithm for CBwLC (or CBwK) that is based on regression oracles. The algorithm is simple, computationally efficient, and admits vanishing regret. It is statistically optimal for the variant of CBwK in which the algorithm must stop once some constraint is violated. Further, we provide the first vanishing-regret guarantees for CBwLC (or CBwK) that extend beyond the stochastic environment. We side-step strong impossibility results from prior work by identifying a weaker (and, arguably, fairer) benchmark to compare against. Our algorithm builds on LagrangeBwK (Immorlica et al., FOCS 2019), a Lagrangian-based technique for CBwK, and SquareCB (Foster and Rakhlin, ICML 2020), a regression-based technique for contextual bandits. Our analysis leverages the inherent modularity of both techniques.
Short-length SSVEP data extension by a novel generative adversarial networks based framework

Steady-state visual evoked potentials (SSVEPs) based brain-computer interface (BCI) has received considerable attention due to its high information transfer rate (ITR) and available quantity of targets. However, the performance of frequency identification methods heavily hinges on the amount of user calibration data and data length, which hinders the deployment in real-world applications. Recently, generative adversarial networks (GANs)-based data generation methods have been widely adopted to create synthetic electroencephalography (EEG) data, holds promise to address these issues. In this paper, we proposed a GAN-based end-to-end signal transformation network for Time-window length Extension, termed as TEGAN. TEGAN transforms short-length SSVEP signals into long-length artificial SSVEP signals. By incorporating a novel U-Net generator architecture and an auxiliary classifier into the network architecture, the TEGAN could produce conditioned features in the synthetic data. Additionally, we introduced a two-stage training strategy and the LeCam-divergence regularization term to regularize the training process of GAN during the network implementation. The proposed TEGAN was evaluated on two public SSVEP datasets (a 4-class dataset and a 12-class dataset). With the assistance of TEGAN, the performance of traditional frequency recognition methods and deep learning-based methods have been significantly improved under limited calibration data. And the classification performance gap of various frequency recognition methods has been narrowed. This study substantiates the feasibility of the proposed method to extend the data length for short-time SSVEP signals for developing a high-performance BCI system. The proposed GAN-based methods have the great potential of shortening the calibration time and cutting down the budget for various real-world BCI-based applications.
Frequency and factors associated with delay in breast cancer treatment in Brazil, according to data from the Oncology Panel, 2019-2020

Abstract Objective: to analyze treatment delay and the flow of care for women with breast cancer in Brazil in 2019 and 2020. Method: this was a follow-up study of breast cancer cases available from the Oncology Panel; a chi-square test and multilevel logistic regression were performed in order to analyze the explanatory variables associated with delay (greater than 60 days) in starting treatment. Results: 22,956 cases (54.5%) with delay in treatment were identified in 2019 and 17,722 (48.7%) in 2020; the Southeast region (54.6%) had the greatest proportion of delay; delay was greater when treatment was provided outside the municipality of residence and lower in 2020 compared to 2019; most outward flows were to the capital cities in the same Federative Units of residence. Conclusion: strategies to reduce cancer treatment delay and optimize health care networks in the Federative Units should be prioritized.
Reduced coupling between offline neural replay events and default mode network activation in schizophrenia

Abstract Schizophrenia is characterized by an abnormal resting state and default mode network brain activity. However, despite intense study, the mechanisms linking default mode network dynamics to neural computation remain elusive. During rest, sequential hippocampal reactivations, known as ‘replay’, are played out within default mode network activation windows, highlighting a potential role of replay-default mode network coupling in memory consolidation and model-based mental simulation. Here, we test a hypothesis of reduced replay-default mode network coupling in schizophrenia, using magnetoencephalography and a non-spatial sequence learning task designed to elicit off-task (i.e. resting state) neural replay. Participants with a diagnosis of schizophrenia (n = 28, mean age 28.2 years, range 20–40, 6 females, 13 not taking antipsychotic medication) and non-clinical control participants (n = 29, mean age 28.1 years, range 18–45, 6 females, matched at group level for age, intelligence quotient, gender, years in education and working memory) underwent a magnetoencephalography scan both during task completion and during a post-task resting state session. We used neural decoding to infer the time course of default mode network activation (time-delay embedding hidden Markov model) and spontaneous neural replay (temporally delayed linear modelling) in resting state magnetoencephalography data. Using multiple regression, we then quantified the extent to which default mode network activation was uniquely predicted by replay events that recapitulated the learned task sequences (i.e. ‘task-relevant’ replay-default mode network coupling). In control participants, replay-default mode network coupling was augmented following sequence learning, an augmentation that was specific for replay of task-relevant (i.e. learned) state transitions. This task-relevant replay-default mode network coupling effect was significantly reduced in schizophrenia (t(52) = 3.93, P = 0.018). Task-relevant replay-default mode network coupling predicted memory maintenance of learned sequences (ρ(52) = 0.31, P = 0.02). Importantly, reduced task-relevant replay-default mode network coupling in schizophrenia was not explained by differential replay or altered default mode network dynamics between groups nor by reference to antipsychotic exposure. Finally, task-relevant replay-default mode network coupling during rest correlated with stimulus-evoked default mode network modulation as measured in a separate task session. In the context of a proposed functional role of replay-default mode network coupling, our findings shed light on the functional significance of default mode network abnormalities in schizophrenia and provide for a consilience between task-based and resting state default mode network findings in this disorder.
Mechanical Positive Pressure Ventilation and Voice Training via the Blom® Tracheostomy Tube: A Case Study

Patients with a Blom® tracheostomy tube (containing a cuff) can vocalize while on mechanical ventilation, which can significantly improve the patient's quality of life. This is brought by the purpose-built structure of the tracheostomy tube that allows the expiration to be expelled through the glottis. However, this characteristic may complicate the measurement of the patient's tidal volume, as most of the expiration does not return to the ventilator. Owing to the necessity of insertion of the speech cannula, which acts as an inner cannula, to enable patient vocalization, the air passage likely becomes constricted, thus increasing airway resistance. Difficulty in applying appropriate positive end-expiratory pressure (PEEP) and ventilator auto-triggering may also be problematic. Therefore, alveolar ventilation is predicted to decrease without adjusting the ventilation settings. Our experience using the Blom® tracheostomy tube revealed some problems, and we provide suggestions for patient management. We herein report on the experience of a patient having inserted the Blom® tracheostomy tube receiving mechanical positive pressure ventilation during vocal training.
Riding out the storm: depleted fat stores and elevated hematocrit in a small bodied endotherm exposed to severe weather

Abstract In the mid-continental grasslands of North America, climate change is increasing the intensity and frequency of extreme weather events. Increasingly severe storms and prolonged periods of elevated temperatures can impose challenges that adversely affect an individual's condition and, ultimately, survival. However, despite mounting evidence that extreme weather events, such as heavy rain storms, can impose short-term physiological challenges, we know little regarding the putative costs of such weather events. To determine the consequences of extreme weather for small endotherms, we tested predictions of the relationships between both severe precipitation events and wet bulb temperatures (an index that combines temperature and humidity) prior to capture with body composition and hematocrit of grasshopper sparrows (Ammodramus savannarum) caught during the breeding season at the Konza Prairie Biological Station, Kansas, USA, between 2014 and 2016. We measured each individual's fat mass, lean mass and total body water using quantitative magnetic resonance in addition to their hematocrit. Individuals exposed to storms in the 24 hours prior to capture had less fat reserves, more lean mass, more water and higher hematocrit than those exposed to moderate weather conditions. Furthermore, individuals stored more fat if they experienced high wet bulb temperatures in the week prior to capture. Overall, the analysis of these data indicate that extreme weather events take a physiological toll on small endotherms, and individuals may be forced to deplete fat stores and increase erythropoiesis to meet the physiological demands associated with surviving a storm. Elucidating the potential strategies used to cope with severe weather may enable us to understand the energetic consequences of increasingly severe weather in a changing world.
Universal Abelian Covers of Rational Surface Singularities

The paper gives fundamental results on the universal abelian covers of rational surface singularities. Let (X,o) be a normal complex surface singularity germ with a rational homology sphere link. Then (X,o) has the universal abelian cover (Y,o) – (X,o). It is shown that if (X,o) is rational or minimally elliptic, and if it has a star‐shaped resolution graph, then (Y,o) is a complete intersection (a partial answer to the conjecture of Neumann and Wahl). A way is given to compute the multiplicity and the embedding dimension of (Y,o) from the resolution graph of (X,o) in the case when (X,o) is rational.
Extreme Function Theory for SHM: A Case Study for Wind Turbines

In many SHM applications, a statistical framework for the distinction between damaged and normal states is often desirable. Usually, conventional methods which employ such statistical tests aim at the identification of abnormal, or extreme, data points when compared to a model of normality. This comparison is mainly done piecewise on individual points in a univariate case, or on datasets, if multivariate features are available. The latter case commonly involves the fusion of the multivariate feature into a scalar variable which is then individually assessed, again in a statistical framework. Depending on the actual application, this approach may be limited, or prone to false identifications. A new theory, previously proposed by researchers at Oxford University and applied for novelty detection, attempts to identify functions instead of just datasets, which are then assessed in terms of their novelty. The extreme function theory develops a normal model of the structure based on functions which can be acquired from different sources of data, and thus account for a more robust damage detection. This paper presents the extreme function theory as an approach to SHM. A case study of monitoring wind turbines with the help of power curves i.e. models which relate the wind speed to the power produced, is presented. The data used in the study originate from a real wind farm. doi: 10.12783/SHM2015/342
An Improved Quantization Scheme for Lattice-Reduction Aided MIMO Detection Based on Gram-Schmidt Orthogonalization

Lattice reduction aided (LRA) has been introduced recently by Yao and Wornell as a new type of MIMO detector. However, the quantization step of LRA detector is only suboptimal. In this paper, we propose a list quantization scheme to reduce the effect of quantization error. The method is to generate a list of candidate symbols from the original LRA estimated symbol. The simulation shows that improved performance can be obtained while additional complexity is reasonable small. The performance of LRA-MMSE with list quantization is shown to be very close to ML performance in both case, 4-QAM and 16-QAM modulation.
Ni-Al diffusion barrier layer for integrating ferroelectric capacitors on Si

We report on the use of amorphous Ni–Al film (a-Ni–Al) as conductive diffusion barrier layer to integrate La0.5Sr0. 5CoO3∕PbZr0.4Ti0. 6O3∕La0.5Sr0. 5CoO3 capacitors on silicon. Cross-sectional observation by transmission electron microscope reveals clean and sharp interfaces without any discernible interdiffusion/reaction in the sample. The physical properties of the capacitors are vertically characterized as the parameters of memory elements. Excellent ferroelectric properties, e.g., large remnant polarization of ∼22μC∕cm2, small coercive voltage of ∼1.15V, being fatigue-free, good retention characteristic, imply that amorphous Ni–Al is an ideal candidate for diffusion barrier for the high-density ferroelectric random access memories integrated with silicon transistor technology.
Bistatic scattering and transmitting through a fractal rough surface with high permittivity using the physics-based two-grid method in conjunction with the forward-backward method and spectrum acceleration algorithm

To numerically calculate bistatic scattering from a fractal rough surface with high permittivity, a comprehensive approach of the forward-backward method (FBM) with spectral acceleration algorithm (SAA) combined with the physics-based two-grid (PBTG) method is developed. Bistatic scattering from and transmitting through one-dimensional (1-D) fractal rough surface with high permittivity, under TE and TM tapered wave incidence, are numerically simulated. Angular reflectivity and transmittivity demonstrate the energy conservation and the functional dependences upon the surface parameters.