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Item Capital income jumps and wealth distribution(The Econometric Society, 2024) Benhabib, Jess; Cui, Wei; Miao, JianjunCompared to the distributions of earnings, the distributions of wealth in the US and many other countries are strikingly concentrated on the top and skewed to the right. To explain the income and wealth inequality, we provide a tractable heterogeneous‐agent model with incomplete markets in continuous time. We separate illiquid capital assets from liquid bond assets and introduce jump risks to capital income, which are crucial for generating a thicker tail of the wealth distribution than that of the labor income distribution. Under recursive utility, we derive optimal consumption and wealth in closed form and show that the stationary wealth distribution has an exponential right tail that closely approximates a power‐law distribution. Our calibrated model can match the income and wealth distributions in the US data including the extreme right tail of the wealth distribution.Item Tracking the evolution of student interactions with an LLM-powered tutor(ACM, 2024-03-18) Gold, Kevin; Geng, ShuangItem Acoustic identification of the voicing boundary during intervocalic offsets and onsets based on vocal fold vibratory measures(MDPI AG, 2021-05) Vojtech, Jennifer M.; Cilento, Dante D.; Luong, Austin T.; Noordzij, Jacob P.; Diaz-Cadiz, Manuel; Groll, Matti D.; Buckley, Daniel P.; McKenna, Victoria S.; Noordzij, J Pieter; Stepp, Cara E.Methods for automating relative fundamental frequency (RFF)-an acoustic estimate of laryngeal tension-rely on manual identification of voiced/unvoiced boundaries from acoustic signals. This study determined the effect of incorporating features derived from vocal fold vibratory transitions for acoustic boundary detection. Simultaneous microphone and flexible nasendoscope recordings were collected from adults with typical voices (N=69) and with voices characterized by excessive laryngeal tension (N=53) producing voiced-unvoiced-voiced utterances. Acoustic features that coincided with vocal fold vibratory transitions were identified and incorporated into an automated RFF algorithm ("aRFF-APH"). Voiced/unvoiced boundary detection accuracy was compared between the aRFF-APH algorithm, a recently published version of the automated RFF algorithm ("aRFF-AP"), and gold-standard, manual RFF estimation. Chi-square tests were performed to characterize differences in boundary cycle identification accuracy among the three RFF estimation methods. Voiced/unvoiced boundary detection accuracy significantly differed by RFF estimation method for voicing offsets and onsets. Of 7721 productions, 76.0% of boundaries were accurately identified via the aRFF-APH algorithm, compared to 70.3% with the aRFF-AP algorithm and 20.4% with manual estimation. Incorporating acoustic features that corresponded with voiced/unvoiced boundaries led to improvements in boundary detection accuracy that surpassed the gold-standard method for calculating RFF.Item Neurovascular impulse response function (IRF) during spontaneous activity differentially reflects intrinsic neuromodulation across cortical regions(2024-09-15) Rauscher, Bradley C.; Fomin-Thunemann, Natalie; Kura, Sreekanth; Doran, Patrick R.; Perez, Pablo D.; Kılıç, Kıvılcım; Martin, Emily A.; Balog, Dora; Chai, Nathan X.; Froio, Francesca A.; Bloniasz, Patrick F.; Herrema, Kate E.; Tang, Rockwell; Knudstrup, Scott G.; Garcia, Andrew; Jiang, John X.; Gavornik, Jeffrey P.; Kleinfeld, David; Hasselmo, Michael E.; Lewis, Laura D.; Sakadzic, Sava; Tian, Lei; Mishne, Gal; Stephen, Emily P.; Thunemann, Martin; Boas, David A.; Devor, AnnaAscending neuromodulatory projections from deep brain nuclei generate internal brain states that differentially engage specific neuronal cell types. Because neurovascular coupling is cell-type specific and neuromodulatory transmitters have vasoactive properties, we hypothesized that the impulse response function (IRF) linking spontaneous neuronal activity with hemodynamics would depend on neuromodulation. To test this hypothesis, we used optical imaging to measure (1) release of neuromodulatory transmitters norepinephrine (NE) or acetylcholine (ACh), (2) Ca2+activity of local cortical neurons, and (3) changes in hemoglobin concentration and oxygenation across the dorsal surface of cerebral cortex during spontaneous neuronal activity in awake mice. A canonical convolution model with a stationary IRF (e.g., the convolution kernel) describing evolution of total hemoglobin (HbT, reflective of dilation dynamics) with respect to Ca2+, resulted in a poor fit to the data. However, the HbT time-course was well predicted, pixel-by-pixel, by a weighted sum of Ca2+and NE time-courses. The weighting coefficients, calculated using linear regression, varied smoothly across the cortical space. Consistent with this result, modeling HbT as a weighted sum of stationary Ca2+- and NE-specific IRFs convolved with the respective time-courses dramatically improved the fit compared to the invariant IRF. In both the linear regression and the Double-IRF convolution models, Ca2+and NE weighting was positive and negative, respectively. In contrast to NE, ACh was largely redundant with Ca2+and therefore did not improve HbT estimation. Because NE covaried with arousal, we observed instances of the diminished hemodynamic coherence between cortical regions during high arousal despite coherent behavior of the underlying neuronal Ca2+activity. We conclude that while neurovascular coupling with respect to neuronal Ca2+is a dynamic and seemingly complex phenomenon, hemodynamic fluctuations can be captured by a simple linear model with stationary IRFs with respect to the underlying dilatory and constrictive forces. In the current study, these forces were captured by the positive Ca2+(dilation) and negative NE (constriction) coefficients. Without accounting for NE neuromodulation and the associated vasoconstriction, diminished hemodynamic coherence, commonly referred to as ″functional (dys)connectivity″ in BOLD fMRI studies, can be falsely interpreted as neuronal desynchronizations.Item Sparse multi-task inverse covariance estimation for connectivity analysis in EEG source space(IEEE, 2019-03) Liu, Feng; Stephen, Emily P.; Prerau, Michael J.; Purdon, Patrick L.Understanding how different brain areas interact to generate complex behavior is a primary goal of neuroscience research. One approach, functional connectivity analysis, aims to characterize the connectivity patterns within brain networks. In this paper, we address the problem of discriminative connectivity, i.e. determining the differences in network structure under different experimental conditions. We introduce a novel model called Sparse Multi-task Inverse Covariance Estimation (SMICE) which is capable of estimating a common connectivity network as well as discriminative networks across different tasks. We apply the method to EEG signals after solving the inverse problem of source localization, yielding networks defined on the cortical surface. We propose an efficient algorithm based on the Alternating Direction Method of Multipliers (ADMM) to solve SMICE. We apply our newly developed framework to find common and discriminative connectivity patterns for α-oscillations during the Sleep Onset Process (SOP) and during Rapid Eye Movement (REM) sleep. Even though both stages exhibit a similar α-oscillations, we show that the underlying networks are distinct.Item Propofol disrupts alpha dynamics in functionally distinct thalamocortical networks during loss of consciousness(Proceedings of the National Academy of Sciences, 2023-03-14) Weiner, Veronica S.; Zhou, David W.; Kahali, Pegah; Stephen, Emily P.; Peterfreund, Robert A.; Aglio, Linda S.; Szabo, Michele D.; Eskandar, Emad N.; Salazar-Gomez, Andrés F.; Sampson, Aaron L.; Cash, Sydney S.; Brown, Emery N.; Purdon, Patrick L.During propofol-induced general anesthesia, alpha rhythms measured using electroencephalography undergo a striking shift from posterior to anterior, termed anteriorization, where the ubiquitous waking alpha is lost and a frontal alpha emerges. The functional significance of alpha anteriorization and the precise brain regions contributing to the phenomenon are a mystery. While posterior alpha is thought to be generated by thalamocortical circuits connecting nuclei of the sensory thalamus with their cortical partners, the thalamic origins of the propofol-induced alpha remain poorly understood. Here, we used human intracranial recordings to identify regions in sensory cortices where propofol attenuates a coherent alpha network, distinct from those in the frontal cortex where it amplifies coherent alpha and beta activities. We then performed diffusion tractography between these identified regions and individual thalamic nuclei to show that the opposing dynamics of anteriorization occur within two distinct thalamocortical networks. We found that propofol disrupted a posterior alpha network structurally connected with nuclei in the sensory and sensory associational regions of the thalamus. At the same time, propofol induced a coherent alpha oscillation within prefrontal cortical areas that were connected with thalamic nuclei involved in cognition, such as the mediodorsal nucleus. The cortical and thalamic anatomy involved, as well as their known functional roles, suggests multiple means by which propofol dismantles sensory and cognitive processes to achieve loss of consciousness.Item Latent neural dynamics encode temporal context in speech(Elsevier BV, 2023-09-15) Stephen, Emily P.; Li, Yuanning; Metzger, Sean; Oganian, Yulia; Chang, Edward F.Direct neural recordings from human auditory cortex have demonstrated encoding for acoustic-phonetic features of consonants and vowels. Neural responses also encode distinct acoustic amplitude cues related to timing, such as those that occur at the onset of a sentence after a silent period or the onset of the vowel in each syllable. Here, we used a group reduced rank regression model to show that distributed cortical responses support a low-dimensional latent state representation of temporal context in speech. The timing cues each capture more unique variance than all other phonetic features and exhibit rotational or cyclical dynamics in latent space from activity that is widespread over the superior temporal gyrus. We propose that these spatially distributed timing signals could serve to provide temporal context for, and possibly bind across time, the concurrent processing of individual phonetic features, to compose higher-order phonological (e.g. word-level) representations.Item Electrographic seizures during low-current thalamic deep brain stimulation in mice(Elsevier BV, 2024) Flores, Francisco J.; Dalla Betta, Isabella; Tauber, John; Schreier, David R.; Stephen, Emily P.; Wilson, Matthew A.; Brown, Emery N.BACKGROUND: Deep brain stimulation of the central thalamus (CT-DBS) has potential for modulating states of consciousness, but it can also trigger electrographic seizures, including poly-spike-wave trains (PSWT). OBJECTIVES: To report the probability of inducing PSWTs during CT-DBS in awake, freely-moving mice. METHODS: Mice were implanted with electrodes to deliver unilateral and bilateral CT-DBS at different frequencies while recording electroencephalogram (EEG). We titrated stimulation current by gradually increasing it at each frequency until a PSWT appeared. Subsequent stimulations to test arousal modulation were performed at the current one step below the current that caused a PSWT during titration. RESULTS: In 2.21% of the test stimulations (10 out of 12 mice), CT-DBS caused PSWTs at currents lower than the titrated current, including currents as low as 20 μA. CONCLUSION: Our study found a small but significant probability of inducing PSWTs even after titration and at relatively low currents. EEG should be closely monitored for electrographic seizures when performing CT-DBS in both research and clinical settings.Item State space oscillator models for neural data analysis(IEEE, 2018-07) Beck, Amanda M.; Stephen, Emily P.; Purdon, Patrick L.Neural oscillations reflect the coordinated activity of neuronal populations across a wide range of temporal and spatial scales, and are thought to play a significant role in mediating many aspects of brain function, including atten- tion, cognition, sensory processing, and consciousness. Brain oscillations are typically analyzed using frequency domain methods such as nonparametric spectral analysis, or time domain methods based on linear bandpass filtering. A typical analysis might seek to estimate the power within an oscillation sitting within a particular frequency band. A common approach to this problem is to estimate the signal power within that band, in frequency domain using the power spectrum, or in time domain by estimating the power or variance in a bandpass filtered signal. A major conceptual flaw in this approach is that neural systems, like many physiological or physical systems, have inherent broad-band 1/P' dynamics, whether or not an oscillation is present. Calculating power-in-band, or power in a bandpass filtered signal, can therefore be misleading, since such calculations do not distinguish between broadband power within the band of interest, and true underlying oscillations. In this paper, we present an approach for analyzing neural oscillations using a combination of linear oscillatory models. We estimate the parameters of these models using an expectation maximization (EM) algorithm, and employ AIC to select the appropriate model and identify the oscillations present in the data. We demonstrate the application of this method to univariate electroencephalogram (EEG) data recorded at quiet rest and during propofol-induced unconsciousness.Item Prediction of optimal facial electromyographic sensor configurations for human-machine interface control(IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2018-08-01) Vojtech, Jennifer M.; Cler, Gabriel J.; Stepp, Cara E.Surface electromyography (sEMG) is a promising computer access method for individuals with motor impairments. However, optimal sensor placement is a tedious task requiring trial-and-error by an expert, particularly when recording from facial musculature likely to be spared in individuals with neurological impairments. We sought to reduce the sEMG sensor configuration complexity by using quantitative signal features extracted from a short calibration task to predict human-machine interface (HMI) performance. A cursor control system allowed individuals to activate specific sEMG-targeted muscles to control an onscreen cursor and navigate a target selection task. The task was repeated for a range of sensor configurations to elicit a range of signal qualities. Signal features were extracted from the calibration of each configuration and examined via a principle component factor analysis in order to predict the HMI performance during subsequent tasks. Feature components most influenced by the energy and the complexity of the EMG signal and muscle activity between the sensors were significantly predictive of the HMI performance. However, configuration order had a greater effect on performance than the configurations, suggesting that non-experts can place sEMG sensors in the vicinity of usable muscle sites for computer access and healthy individuals will learn to efficiently control the HMI system.Item Automated relative fundamental frequency algorithms for use with neck-surface accelerometer signals(Elsevier BV, 2022-03) Groll, Matti D.; Vojtech, Jennifer M.; Hablani, Surbhi; Mehta, Daryush D.; Buckley, Daniel P.; Noordzij, J. Pieter; Stepp, Cara E.OBJECTIVE: Relative fundamental frequency (RFF) has been suggested as a potential acoustic measure of vocal effort. However, current clinical standards for RFF measures require time-consuming manual markings. Previous semi-automated algorithms have been developed to calculate RFF from microphone signals. The current study aimed to develop fully automated algorithms to calculate RFF from neck-surface accelerometer signals for ecological momentary assessment and ambulatory monitoring of voice. METHODS: Training a set of 2646 /vowel-fricative-vowel/ utterances from 317 unique speakers, with and without voice disorders, was used to develop automated algorithms to calculate RFF values from neck-surface accelerometer signals. The algorithms first rejected utterances with poor vowel-to-noise ratios, then identified fricative locations, then used signal features to determine voicing boundary cycles, and finally calculated corresponding RFF values. These automated RFF values were compared to the clinical gold-standard of manual RFF calculated from simultaneously collected microphone signals in a novel test set of 639 utterances from 77 unique speakers. RESULTS: Automated accelerometer-based RFF values resulted in an average mean bias error (MBE) across all cycles of 0.027 ST, with an MBE of 0.152 ST and -0.252 ST in the offset and onset cycles closest to the fricative, respectively. CONCLUSION: All MBE values were smaller than the expected changes in RFF values following successful voice therapy, suggesting that the current algorithms could be used for ecological momentary assessment and ambulatory monitoring via neck-surface accelerometer signals.Item Ability-based methods for personalized keyboard generation(MDPI AG, 2022-08) Mitchell, Claire L.; Cler, Gabriel J.; Fager, Susan K.; Contessa, Paola; Roy, Serge H.; De Luca, Gianluca; Kline, Joshua C.; Vojtech, Jennifer M.This study introduces an ability-based method for personalized keyboard generation, wherein an individual's own movement and human-computer interaction data are used to automatically compute a personalized virtual keyboard layout. Our approach integrates a multidirectional point-select task to characterize cursor control over time, distance, and direction. The characterization is automatically employed to develop a computationally efficient keyboard layout that prioritizes each user's movement abilities through capturing directional constraints and preferences. We evaluated our approach in a study involving 16 participants using inertial sensing and facial electromyography as an access method, resulting in significantly increased communication rates using the personalized keyboard (52.0 bits/min) when compared to a generically optimized keyboard (47.9 bits/min). Our results demonstrate the ability to effectively characterize an individual's movement abilities to design a personalized keyboard for improved communication. This work underscores the importance of integrating a user's motor abilities when designing virtual interfaces.Item Empirical evaluation of the role of vocal fold collision on relative fundamental frequency in voicing offset(Elsevier BV, 2022-11-03) Groll, Matti D.; Peterson, Sean D.; Zañartu, Matías; Vojtech, Jennifer M.; Stepp, Cara E.OBJECTIVES: Relative fundamental frequency (RFF) is an acoustic measure of changes in fundamental frequency during voicing transitions. The physiological mechanisms underlying RFF remain unclear. Recent modeling suggests that changes in RFF during voicing offset are due to decreases in overall system stiffness as a direct result of the cessation of vocal fold collision. To evaluate this finding empirically, here we examined whether variable timing between the end of vocal fold collision and the final voicing cycle used to calculate RFF explained the variability in RFF across individual voicing offset utterances. METHODS: RFF during voicing offset was calculated from /ifi/ utterances produced by 35 participants under endoscopy, with and without vocal effort. RFF was calculated via two methods, in which utterances were aligned by (1) the end of vocal fold collision, or (2) the end of voicing. Analyses of variance were used to determine the effects of vocal effort and RFF method on the mean and standard deviation of RFF. RESULTS: Aligning by vocal fold collision resulted in statistically significantly lower standard deviations. RFF means were statistically higher using the collision method; however, the degree of vocal effort was statistically significant regardless of the method. CONCLUSIONS: These results provide empirical evidence to support that decreases in RFF during voicing offset are a result of decreases in system stiffness due to termination of vocal fold collision.Item Prediction of voice fundamental frequency and intensity from surface electromyographic signals of the face and neck(MDPI AG, 2022-12) Vojtech, Jennifer M.; Mitchell, Claire L.; Raiff, Laura; Kline, Joshua C.; De Luca, GianlucaSilent speech interfaces (SSIs) enable speech recognition and synthesis in the absence of an acoustic signal. Yet, the archetypal SSI fails to convey the expressive attributes of prosody such as pitch and loudness, leading to lexical ambiguities. The aim of this study was to determine the efficacy of using surface electromyography (sEMG) as an approach for predicting continuous acoustic estimates of prosody. Ten participants performed a series of vocal tasks including sustained vowels, phrases, and monologues while acoustic data was recorded simultaneously with sEMG activity from muscles of the face and neck. A battery of time-, frequency-, and cepstral-domain features extracted from the sEMG signals were used to train deep regression neural networks to predict fundamental frequency and intensity contours from the acoustic signals. We achieved an average accuracy of 0.01 ST and precision of 0.56 ST for the estimation of fundamental frequency, and an average accuracy of 0.21 dB SPL and precision of 3.25 dB SPL for the estimation of intensity. This work highlights the importance of using sEMG as an alternative means of detecting prosody and shows promise for improving SSIs in future development.Item Special Lagrangian submanifolds in K3-fibered Calabi-Yau 3-foldsLin, Yu-Shen; Chiu, Shih-KaiItem A convergence result for mean curvature flow of totally real submanifoldsLin, Yu-Shen; Collins, Tristan; Jacob, AdamItem Animal conservation attitudes and perceived cuteness(2023-10-21) Bertolami, I.; Freimuth, E.; Kan, K.; Ma, C.; Mejia, I.; Ren, Z.,; Mello-Goldner, D.Item Assessing the relationship between the fundamental attribution error and individualism(2023-10-21) Kamel, H.; Aldabbagh, H.; Cheng, J.; Pittie, R.; Li, X.; Ruan, Y.; Mello-Goldner, D.Item Conformity in the classroom: authority vs. student confederates(2023-10-21) Chen, J.; Fortner, T.; Korn, K.; Purandare, K.; Sha, R.; Silvestri, K.; Mello-Goldner, D.Item Effects of product placement on consumer preference(2023-10-21) Chow, D.; Lee, Y.; Mejia, C.; Ramirez, J.; Yussef, M.; Zhen, A.; Mello-Goldner, D.