Miracle-dz/UniFAR-FTUs huggingface dataset:
Neural envelope tracking predicts speech intelligibility and hearing aid benefit in children with hearing loss

Early assessment of hearing aid benefit is crucial, as the extent to which hearing aids provide audible speech information predicts speech and language outcomes. A growing body of research has proposed neural envelope tracking as an objective measure of speech, particularly for individuals unable to provide reliable behavioral feedback. However, its potential for evaluating speech intelligibility and hearing aid benefit in hearing-impaired children remains unexplored. This study examined neural envelope tracking in hearing-impaired children through two separate experiments. EEG data was recorded while children listened to age-appropriate stories (experiment 1) or an animated movie (experiment 2) under aided and unaided conditions (using personal hearing aids) at multiple stimulus intensities. Results in the delta band demonstrated that neural tracking increased with increasing stimulus intensity, but only in the unaided condition. In the aided condition, neural tracking remained stable across a wide range of intensities, as long as speech intelligibility was maintained. This suggests that acoustic degradation of the speech signal does not necessarily impact neural tracking. Additionally, the use of personal hearing aids significantly enhanced neural envelope tracking, particularly in challenging speech conditions (which would be inaudible when unaided). Furthermore, neural envelope tracking strongly correlated with behaviorally measured speech intelligibility. Altogether, these findings indicate that neural envelope tracking could be a valuable tool for predicting speech intelligibility benefits derived from personal hearing aids in hearing-impaired children. Incorporating narrated stories or engaging movies expands the accessibility of these methods even in clinical settings, offering new avenues for using objective speech measures to guide pediatric audiology decision-making.
Stimulus-dependent functional network topology in mouse visual cortex

Information is processed by networks of neurons in the brain. On the timescale of sensory processing, those neuronal networks have relatively fixed anatomical connectivity, while functional connectivity, which defines the interactions between neurons, can vary depending on the ongoing activity of the neurons within the network. We thus hypothesized that different types of stimuli, which drive different neuronal activities in the network, could lead those networks to display stimulus-dependent functional connectivity patterns. To test this hypothesis, we analyzed electrophysiological data from the Allen Brain Observatory, which utilized Neuropixels probes to simultaneously record stimulus-evoked activity from hundreds of neurons across 6 different regions of mouse visual cortex. The recordings had single-cell resolution and high temporal fidelity, enabling us to determine fine-scale functional connectivity. Comparing the functional connectivity patterns observed when different stimuli were presented to the mice, we made several nontrivial observations. First, while the frequencies of different connectivity motifs (i.e., the patterns of connectivity between triplets of neurons) were preserved across stimuli, the identities of the neurons within those motifs changed. This means that functional connectivity dynamically changes along with the input stimulus, but does so in a way that preserves the motif frequencies. Secondly, we found that the degree to which functional modules are contained within a single brain region (as opposed to being distributed between regions) increases with increasing stimulus complexity. This suggests a mechanism for how the brain could dynamically alter its computations based on its inputs. Altogether, our work reveals unexpected stimulus-dependence to the way groups of neurons interact to process incoming sensory information.
Wing Kinematics and Flexibility for Optimal Manoeuvring and Escape

Understanding how animals control the dynamic stall vortices in their wake is critical to developing micro-aerial vehicles and autonomous underwater vehicles, not to mention wind turbines, delta wings, and rotor craft that undergo similar dynamic stall processes. Applying this knowledge to biomimetic engineering problems requires progress in three areas: (i) understanding the flow physics of natural swimmers and flyers; (ii) developing flow measurement techniques to resolve this physics; and (iii) deriving low-cost models suitable for studying the vast parameter space observed in nature. This body of work, which consists of five research chapters, focuses on the leading-edge vortex (LEV) that forms on profiles undergoing rapid manoeuvres, delta wings, and similar devices. Lagrangian particle tracking is used throughout this thesis to track the mass and circulation transport in the LEV on manoeuvring profiles. The growth and development of the LEV is studied in relation to: flapping and plunging profile kinematics; spanwise flow from profile sweep and spanwise profile bending; and varying the angle-of-attack gradient along the profile span. Finally, scaling relationships derived from the observations above are used to develop a low-cost model for LEV growth, that is validated on a flat-plate delta wing. Together these results contribute to each of the three topics identified above, as a step towards developing robust, agile biomimetic swimmers and flyers.
Intrinsic correlations of galaxy sizes and luminosities in weak lensing

Correlations of the observed sizes and luminosities of galaxies can be used to estimate the magnification that arises through weak gravitational lensing. However, the intrinsic properties of galaxies can be similarly correlated through local physical effects, and these present a possible contamination to the weak lensing estimation. In this thesis we model these intrinsic correlations using the halo model, assuming that both the sizes and luminosities of galaxies reflect the mass in the associated halo, assuming the observed galaxy properties correlate closely with the mass of the haloes and sub-haloes. Larger and more luminous galaxies live in more massive haloes, and even if the sub-halo population is largely independent of the halo mass, the sizes and luminosities of the largest sub-haloes will still be limited by the total halo mass. We use this simple model to predict what would be observed for a magnification estimator based on galaxy sizes and magnitudes, and how the intrinsic signal correlates with the true lensing convergence. Additionally, we include in our analysis the effects of cuts in the sample and we model the size-magnitude distribution and study how the cuts in the survey affect the intrinsic mean size and magnitude as well as the inferred convergence power spectrum when using galaxy sizes and magnitudes. Studying these correlations is important both to improve our understanding of galaxy properties and because they are a potential systematic for weak lensing size magnification measurements. Our model assumes that the density field drives these intrinsic correlations and we also model the distribution of satellite galaxies. We calculate the possible contamination to measurements of lensing convergence power spectrum from galaxy sizes and luminosities, and show that the cross-correlation of intrinsic properties with convergence is potentially an important systematic. We also explore how these intrinsic correlations may affect surveys with different redshift depth. We find that, in this simple approach, intrinsic size and luminosities correlations cannot be neglected in order to estimate lensing convergence power spectrum for constraining cosmological parameters.
Time-inconsistent optimal control problems with regime-switching

In this paper, a time-inconsistent optimal control problem is studied for diffusion processes modulated by a continuous-time Markov chain. In the performance functional, the running cost and terminal cost depend on not only the initial time, but also the initial state of the Markov chain. By modifying the method of multi-person game, we obtain an equilibrium Hamilton-Jacobi-Bellman equation under proper conditions. The well-posedness of this equilibrium HJB Equation is studied in the case where the diffusion term is independent of the control variable. Furthermore, a time-inconsistent linear-quadratic control problem is considered as a special case.
Efficient Nested Simulation for Conditional Tail Expectation of Variable Annuities

Monte Carlo simulations—in particular, nested Monte Carlo simulations—are commonly used in variable annuity (VA) risk modeling. However, the computational burden associated with nested simulations is substantial. We propose an Importance-Allocated Nested Simulation (IANS) method to reduce the computational burden, using a two-stage process. The first stage uses a low-cost analytic proxy to identify the tail scenarios most likely to contribute to the Conditional Tail Expectation risk measure. In the second stage we allocate the entire inner simulation computational budget to the scenarios identified in the first stage. Our numerical experiments show that, in the VA context, IANS can be up to 30 times more efficient than a standard Monte Carlo experiment, measured by relative mean squared errors, when both are given the same computational budget.
Semi-supervised Domain Adaptation for EEG-based Epileptic Seizure Classification

—Epilepsy is a neurological disorder, which is usually detected by electroencephalogram (EEG) signals. Convolutional neural networks are the most widely used deep learning approaches in seizure classification. Automatic EEG-based epilepsy classification faces the challenges of inadequate labeled data, class imbalance, and individual differences. To address these issues, this paper proposes APE-cosine, a semi-supervised domain adaptation approach for cross-patient seizure classification. We adopted three networks as feature extraction layers and added data augmentation strategies to alleviate the influence of class imbalance. Experiments on three different convolutional neural networks demonstrated that APE-cosine outperformed state-of-the-art algorithms, including the original APE algorithm.
Circuits in the motor cortex explain oscillatory responses to transcranial magnetic stimulation

Transcranial Magnetic Stimulation (TMS) is a popular method used to investigate brain function. Stimulation over the motor cortex evokes muscle contractions known as motor evoked potentials (MEPs) and also high frequency volleys of electrical activity measured in the cervical spinal cord. The physiological mechanisms of these experimentally derived responses remain unclear, but it is thought that the connections between circuits of excitatory and inhibitory neurons play a vital role. Using a spiking neural network model of the motor cortex, we explained the generation of waves of activity, so called ‘I-waves’, following cortical stimulation. The model reproduces a number of experimentally known responses including direction of TMS, increased inhibition and changes in strength. Using populations of thousands of neurons in a model of cortical circuitry we showed that the cortex generated transient oscillatory responses without any tuning, and that neuron parameters such as refractory period and delays influenced the pattern and timing of those oscillations. By comparing our network with simpler, previously proposed circuits, we explored the contributions of specific connections and found that recurrent inhibitory connections are vital in producing later waves which significantly impact the production of motor evoked potentials in downstream muscles (Thickbroom, 2011). This model builds on previous work to increase our understanding of how complex circuitry of the cortex is involved in the generation of I-waves.
Drivers of Land Degradation in Trays Ecosystem as Modulated under a Changing Climate: Case Study of Côte d'Ivoire

 Abstract— Land degradation is a serious problem in developing countries including Cote d’Ivoire, which has its economy focused on agriculture. It occurs in all kinds of ecosystems over the world. However, the drivers of land degradation vary from one region to another, and from one ecosystem to another. Thus, identifying these drivers is an essential prerequisite to develop and implement appropriate policies to reverse the trend of land degradation in the country, especially in the trays ecosystem. Using the binary logistic model with primary data obtained through 780 farmers surveyed, we analyze and identify the drivers of land degradation in the trays ecosystem. The descriptive statistics show that 52% of farmers interviewed have stated facing land degradation in their farmland. This high rate shows the extent of land degradation in this ecosystem. Also, the results obtained from the binary logit regression reveal that land degradation is significantly influenced by a set of variables such as sex, education, slope, erosion, pesticide, agricultural activity, deforestation, and temperature. The drivers identified are mostly local, as a result, the government must implement some policies and strategies that facilitate and incentive the adoption of sustainable land management practices by farmers to reverse the negative trend of land degradation.
Robust Bayesian Estimation and Inference for Dynamic Stochastic General Equilibrium Models

This paper introduces a new algorithm to conduct robust Bayesian estimation and inference in dynamic stochastic general equilibrium models. The algorithm combines standard Bayesian methods with an equivalence characterization of model solutions. This algorithm allows researchers to perform the following analysis: First, find the complete range of posterior means of both the deep parameters and any parameters of interest robust to the choice of priors in a sense I make precise. Second, derive the robust Bayesian credible region for these parameters. I prove the validity of this algorithm and apply this method to the models in Cochrane (2011) and An and Schorfheide (2007) to achieve robust estimations for structural parameters and impulse responses. In addition, I conduct a sensitivity analysis of optimal monetary policy rules with respect to the choice of priors and provide bounds to the optimal Taylor rule parameters.
Neonate Atlantic Sharpnose Shark (Rhizoprionodon terraenovae) Relative Abundance and Body Condition in Two South Carolina Estuaries Varying in Urbanization

iv List of Tables . viii List of Figures . ix Introduction . 1 Methods . 8 Survey Site Description . 8 Survey Methods . 9 Shark Capture, Handling, and Processing . 10 Data Analysis . 11 Results . 14 Elasmobranch Catch . 14 Body Condition and Growth . 16 Bycatch . 16 Noise Pollution . 17 Discussion . 19 Abundance of R. Terraenovae . 19 Body Condition and Growth of R. terraenovae . 22 Diversity . 25 Anthropogenic Factors . 27 North Inlet as a Nursery . 29
MIT Open Access Articles Re-measuring gentrification

We develop an expectations-based measure of gentriﬁcation. Property values today incorporate market participants’ expectations of the neighborhood’s future. We contrast this with present-oriented variables like demographics. To operationalize the signal implicit in property values, we contrast the percentile rank of a neighborhood’s average house price to that of its average income, relative to its metropolitan area. When a neighborhood’s house value percentile begins to rise above its income percentile, that is a signal of gentriﬁcation. We show that a gap between the house value and income percentiles predicts future income growth. We further validate our metric against existing approaches to identify gentriﬁcation, ﬁnding that it aligns meaningfully with qualitative analyses built on local insight. Compared to existing quantitative approaches, we obtain similar results but usually observe them in earlier years and with more parsimonious data. Our approach has several advantages: conceptual simplicity, communicative ﬂexibility with graphical and map forms, and availability for small geographies on an annual basis with minimal lag.
The Inﬂuencing Mechanism of Psychological Empowerment on Learning Satisfaction and Learning Performance in Blended Learning Environment – An Empirical Analysis

GenZstudentsareverydifferentcomparedtothepreviousgenerations’ students. The traditional teaching methods can no longer meet their learning needs instead by learning through the Internet. Therefore, teachers must use the Internet to create an environment suitable for their learning. The blended teaching method isanewmodelthatcanmeetthelearningneedsofGenZ.Thisstudyaimstoexplore howthefourdimensionsofpsychologicalempowermentcanbeusedinablended learningenvironmenttoimprovestudents’creativity,learningsatisfaction,and learningperformance.Thestudycollected356validquestionnaires,theproposed conceptualmodelwasvalidatedusingthestructuralequationmodel.Thereliability ofthisstudyisgreaterthan0.7,andthevalidityistestedbyCRandAVE,which meettherequirementsofscholars.Theresultsshowthatinthefourdimensionsof psychologicalempowerment.Firstly,enhancingstudents’learningcompetence, andunderstandingthemeaningandimpactoflearningcanhelpimprovestudents’ satisfaction.Secondly,enhancingstudents’learningcompetence,learningself-determination,andlearningimpacthasapositiveimpactonstudents’creativity. Thirdly,whenstudentshavehigherlearningcompetenceandimpact,ithelpsto improvestudentslearningperformance.Fourthly,whenstudentsaremorecreative and have learning satisfaction with the course, they can improve their learning performance.
Differences in Word Usage Patterns between “Well-Recovered” Aphasic Patients and Control Subjects on a Picture Description Task

In tenth grade, I discovered that a friend, Alan Du, had also taken NACLO for years. Together, we decided to start a Linguistics Club at our school. At first, we focused solely on NACLO practice. I worked on setting up our school’s test site, which soon became one of the most successful in the nation. Gradually, we also introduced student lectures about the science of language. We started learning haphazard bits of syntax and phonology and pragmatics, but we got stuck pretty soon. There’s no textbook for teenagers curious about the Chomsky–Schützenberger Hierarchy, and we had trouble putting together any sort of coherent curriculum.
Secondary User Access Control (SUAC) via Quadratic Programming in Massive MIMO Cognitive Radio Networks

In cognitive radio (CR) networks, a secondary user access control (SUAC) technique has been designed to enhance spectrum efficiency, in which a jamming signal is deliberately injected to maintain reliable sensing performance of authorized secondary users (A-SUs) and degrades unauthorized secondary users (UA-SUs) spectrum sensing results. We consider the problem of jamming signal design in massive multiple-input multiple-output (MIMO) CR networks wherein each primary user has a larger number of antennas that coexists with multiple secondary users. In this paper, we propose a jamming signal design framework that combines maximizing the jammer’s influences on UA-SUs and minimizing on A-SUs. The resulting problem is a non-convex quadratically constrained quadratic programming (QCQP) problem, and a semidefinite relaxation (SDR) method can be one of the approximate solutions but cannot meet our stringent constraints and lacks jamming efficiency. We propose a novel optimization algorithm based on the <inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula>-best methodology to design the jamming signal. Simulation results show the effectiveness of the proposed <inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula>-best based SUAC method in improving the spectrum sensing performance of A-SUs.
Probing many-body localization with ultracold atoms in optical lattices

This thesis reports on first experiments on the observation of ergodicity breaking in a two-component, interacting fermionic gas of Potassium-40 subject to quasi-periodic optical lattices in one and two dimensions, demonstrating the breakdown of thermalization in highly excited states of isolated quantum systems. Our observations can be explained by the phenomenon of many-body localization, a non-ergodic phase of matter, whose properties can be detected with non-equilibrium probes. To probe such a phase, we first design a far-from-equilibrium initial state and then monitor how much the system remembers of this design during time evolution. This allows us to identify regimes and timescales where disorder can protect local quantum information and distinguish such cases from regimes where interactions amalgamate local information into highly nonlocal quantum correlations. We then describe how this treatment systematically breaks down in all real-world systems as the system is (necessarily) coupled to some bath-like structure. Following very recent developments, we are further able to show that order can exist even in driven quantum systems, which were earlier thought to exhibit no interesting features, corresponding to an infinite temperature state. We also extend the experimental realm to explore two dimensions and show that the dynamics emerge as a distinct interplay of interactions, disorder, and dimensions. Upon probing a putative MBL transition, we further show signatures of a new kind of slow relaxation, potentially arising due to randomness in the initial state. We close with some theoretical results on probing quantum dynamics ensuing in the absence of transport and a discussion of exciting areas for future experiments.
Temporal Structural Probability Network for Weather Prediction

A large number of people and assets are destroyed each year as a result of severe weather, such as cyclones, lightning, storm, or rain. Scientists are developing machine learning approaches that can help forecasters better anticipate these catastrophes by better understanding the underlying mechanisms and generating more accurate empirical models. Temporal structural probability networks have been significantly improved in this research, allowing for autonomous discovery of spatiotemporal correlations and the capacity to learn with any form. Prediction of thunderstorms in Texas and aero plane instability in the United States are the focus of the evaluations of suggested method. As part of the work discussion on how to assess the effectiveness of a machine learning algorithm in the context of severe weather, the system give a collection of instructions cultured for given machine learning techniques and talk about how to implement the proposed strategy.