Temporal Transfer Research Articles

Using temporal information for recognizing actions from still images

In this paper we raise two important question, “1. Is temporal information beneficial in recognizing actions from still images? 2. Do we know how to take the maximum advantage from them?”. To answer these question we propose a novel transfer learning problem, Temporal To Still Image Learning (i.e., T2SIL) where we learn to derive temporal information from still images. Thereafter, we use a two-stream model where still image action predictions are fused with derived temporal predictions. In T2SIL, the knowledge transferring occurs from temporal representations of videos (e.g., Optical-flow, Dynamic Image representations) to still action images. Along with the T2SIL we propose a new action still image action dataset and a video dataset sharing the same set of classes. We explore three well established transfer learning frameworks (i.e., GANs, Embedding learning and Teacher Student Networks (TSNs)) in place of the temporal knowledge transfer method. The use of derived temporal information from our TSN and Embedding learning improves still image action recognition.

ASSESSING THE TRANSFERABILITY OF MACHINE LEARNING ALGORITHMS USING CLOUD COMPUTING AND EARTH OBSERVATION DATASETS FOR AGRICULTURAL LAND USE/COVER MAPPING

Abstract. Mapping of agricultural land use/cover was initiated since the past several decades for land use planning, change detection analysis, crop yield monitoring etc. using earth observation datasets and traditional parametric classifiers. Recently, machine learning, cloud computing, Google Earth Engine (GEE) and open source earth observation datasets widely used for fast, cost-efficient and precise agricultural land use/cover mapping and change detection analysis. Main objective of this study was to assess the transferability of the machine learning algorithms for land use/cover mapping using cloud computing and open source earth observation datasets. In this study, the Landsat TM (L5, L8) of 2018, 2009 and 1998 were selected and median reflectance of spectral bands in Kharif and Rabi season were used for the classification. In addition, three important machine learning algorithms such as Support Vector Machine with Radial Basis Function (SVM-RBF), Random forest (RF) and Classification and Regression Tree (CART) were selected to evaluate the performance in transferability for agricultural land use classification using GEE. Seven land use/cover classes such as built-up, cropland, fallow land, vegetation etc. were selected based on literature review and local land use classification scheme. In this classification, several strategies were employed such as feature extraction, feature selection, parameter tuning, sensitivity analysis on size of training samples, transferability analysis to assess the performance of the selected machine learning algorithms for land use/cover classification. The result shows that SVM-RBF outperforms the RF and CART for both spatial and temporal transferability analysis. This result is very helpful for agriculture and remote sensing scientist to suggest promising guideline to land use planner and policy-makers for efficient land use mapping, change detection analysis, land use planning and natural resource management.

Developing Cold Region Winter Weather Traffic Models and Testing Their Temporal Transferability and Model Specification

It is well known from past studies that inclement winter weather conditions such as heavy snowfall and harsh temperature affect the variation of traffic volume. In order to quantify the change in traffic volume caused by winter weather conditions, a winter weather model explaining the relationship between weather conditions and traffic volume has been generally developed. However, none of the studies in the past have evaluated the performance of the developed model in terms of temporal transferability. In addition, there is no research showing that model specification for predicting correct traffic under winter weather conditions can be optimized for different types of vehicles. This paper presents an application of dummy-variable regression to develop winter weather traffic models. In an effort to fine-tune winter weather model specification by the vehicle class, two model validation tests were conducted. First, a temporal transferability test is conducted to determine whether the model developed is suitable to be used regardless of time. Second, a model specification test is conducted to determine whether the winter weather model specification initially structured is the most appropriate format or not. To pursue this, 6 years of traffic and weather data sets from weigh-in-motion (WIM) and weather stations were collected at Highway 2A in Alberta, Canada. The study results indicate that a dummy-variable winter weather traffic model that includes a temperature variable shows good performance in terms of accuracy and temporal transferability for passenger cars. In contrast, the naïve winter weather model specification that excludes a temperature variable is preferable in predicting truck traffic volume. It is very interesting to note that even if vehicles are traveling in the same traffic stream under the same environmental conditions, different model specifications are preferable for different vehicle types to predict more accurate traffic in winter season.

Temporal transferability of marine distribution models: The role of algorithm selection

• Framework for generating marine species models transferable in time. • Objective grouping of species to deal with a high number effectively. • Evaluation of nine algorithms and definition of the most transferable. • Machine learning algorithms are more sensitive than regression-based approaches. • Time and cost-effective tool for climate change studies in large marine areas. Species distribution models (SDMs 1 Species Distribution Models (SDM) 1 ) are crucial for guiding management in a changing world. However, limited understanding of algorithm selection, ensemble weights and temporal transferability assessment undermines confidence in their predictions. Transferable predictive models, based on objective and proven selection criteria could therefore provide effective tools for defining species-environment relationships. This study developed a framework for generating SDMs in the marine environment that improves models’ temporal transferability. The methodological approach steps were: 1) Collection of predictors related to species ecology and their records and species grouping according to their ecological requirements. Twenty-one seaweeds were used as a case study. Environmental and distribution data were divided into two independent periods to evaluate temporal transferability. 2) A model for each species was built in each period with nine algorithms (Generalized Linear Model, Generalized Additive Model, Multivariate Adaptive Regression Spline, Mixture Discriminant Analysis, Classification and Regression Trees, Support Vector Machine, Flexible Discriminant Analysis, Random Forest, MAXENT) and projected into the other period. Predictor contributions to the final models were obtained. 3) Assessment of predictive performance for each model was made using the area under the receiver operating characteristic curve and true skill statistics metric for both models’ accuracy and temporal transferability capabilities. All values were over 0.8 for all groups. In turn, the geographical pattern of all models were shown to be ecologically coherent. The algorithms and their weights that fit best were used to generate transferable models over time in the marine environment and retained for each species. In general, machine learning algorithms produce models with higher sensitivity than regression-based approaches. This methodology sets the scene for further inquiries in the marine environment when developing consistent practices for model development and transferability. Results are satisfactory for broad application in marine research, allowing a comparative framework between species predictions and facilitating the use of transferable models, especially in climate change studies across large areas. In addition, the proposed methodological approach is a cost-effective tool for dealing with a high number of species in marine environments. All data are freely available, so the methodology can be reproduced for marine researchers with different objectives.

Amplitude modulation detection and temporal modulation cutoff frequency in normal hearing infants.

The goal of this study was to determine if temporal modulation cutoff frequency was mature in three-month-old infants. Normal-hearing infants and young adults were tested in a single-interval forced-choice observer-based psychoacoustic procedure. Two parameters of the temporal modulation transfer function (TMTF) were estimated to separate temporal resolution from amplitude modulation sensitivity. The modulation detection threshold (MDT) of a broadband noise amplitude modulated at 10 Hz estimated the y-intercept of the TMTF. The cutoff frequency of the TMTF, measured at a modulation depth 4 dB greater than the MDT, provided an estimate of temporal resolution. MDT was obtained in 27 of 33 infants while both MDT and cutoff frequency was obtained in 15 infants and in 16 of 16 adults. Mean MDT was approximately 10 dB poorer in infants compared to adults. In contrast, mean temporal modulation cutoff frequency did not differ significantly between age groups. These results suggest that temporal resolution is mature, on average, by three months of age in normal hearing children despite immature sensitivity to amplitude modulation. The temporal modulation cutoff frequency approach used here may be a feasible way to examine development of temporal resolution in young listeners with markedly immature sensitivity to amplitude modulation.

Detection of Gap and Modulations: Auditory Temporal Resolution Deficits in Adults Who Stutter

Background: Recent models of speech production suggest a link between speech production and perception. Persons with stuttering are known to have deficits in sensorimotor timing and exhibit auditory processing problems. Most of the earlier studies have focused on assessing temporal ordering in adults who stutter (AWS), but limited attempts have been made to document temporal resolution abilities in AWS. Methods: A group of 16 AWS and 16 age- and gender-matched adults who do not stutter (AWNS) were recruited for the study. Temporal resolution abilities were assessed using the Gap Detection Test and temporal modulation transfer function (TMTF). Results: The results revealed significant differences in TMTF between AWS and AWNS, but no differences were found in the gap detection thresholds. Conclusions: Results suggest that the sensory representations of the temporal modulations are compromised in AWS, which may contribute to the programming of rhythmic movements during speech planning.

Five-Year Experience with Fifth-to-Seventh Nerve Transfer for Smile.

Masseteric- or deep temporal-to-facial nerve transfer (five-to-seven nerve transfer) is increasingly used in facial reanimation; however, the indications and clinical variables affecting outcomes for five-to-seven nerve transfer have not been defined. The authors describe their early experience with five-to-seven nerve transfer, reporting function and patient-reported outcomes, to identify potential parameters that are predictive of outcome. The authors conducted a retrospective chart review of all patients who underwent five-to-seven nerve transfer for smile reanimation from 2012 to 2017. Age, sex, cause of facial paralysis, onset and duration of paralysis, history of adjuvant chemotherapy and/or radiation therapy, donor nerve used, adjunctive procedures, and final excursion were recorded. Standard photographs and videos, and data regarding clinical facial nerve function and patient-reported quality of life, were obtained preoperatively and postoperatively. Sixty patients were identified. Forty-seven had flaccid facial paralysis and 13 had postparalysis facial palsy. Among flaccid facial paralysis patients, masseteric nerve transfer was successful in 30 patients (88 percent) and deep temporal nerve transfer was successful in three cases (60 percent). Among postparalysis facial palsy patients, five-to-seven nerve transfer was successful in two patients (20 percent). Average quality-of-life scores improved significantly from preoperatively (46) to postoperatively (59) among flaccid facial paralysis patients with successful five-to-seven nerve transfer. Five-to-seven nerve transfer is a viable option in facial reanimation. Shorter denervation times and preoperative flaccidity are favorable predictors of outcome. Five-to-seven nerve transfer as an adjunct to primary repair appears highly effective for enhancing smile function. Five-to-seven nerve transfer results are not reliable in patients with postparalysis facial palsy. Therapeutic, IV.

Appraisal of Temporal Transferability of Cold Region Winter Weather Traffic Models for Major Highway Segments in Alberta Canada

This paper evaluates the effect of inclement weather conditions on the travel demand for three classes of vehicles for a primary highway in the province of Alberta, Canada. The demand variables are passenger cars, trucks, and total traffic. It is well known from previous studies that adverse weather conditions such as low temperatures and heavy snowfall cause variation in traffic flow patterns. A winter weather model, based on the dummy variable regression model, was developed to quantify the variations in traffic volume due to snowfall and temperature changes. To establish the relationships, vehicular data was collected from six weigh-in-motion (WIM) sites, and the weather data associated with the WIM sites was collected from nearby weather stations. The study revealed that the variation in truck traffic, due to inclement weather conditions, was insignificant compared to variation in passenger car traffic. This study also investigated the temporal transferability of the developed winter weather model to test if a model can be applied irrespective of the time when it was developed. In addition, an attempt was made to check if the model coefficients could be optimized differently for different classes of traffic for estimating correct traffic variations. To evaluate transferability, the performance of both dummy variable regression and naive (without dummy variables) models was investigated. The results revealed that the dummy variable regression models show better performance for passenger car traffic and total traffic and naive winter weather models give better results for truck traffic.

Improvements of the spatially distributed hydrological modelling using the HBV model at 1 km resolution for Norway

A robust hydrological modeling at a fine spatial resolution is a vital tool for Norway to simulate river discharges and hydrological components for climate adaptation strategies. However, it requires improvements of modelling methods, detailed observational data as input and expensive computational resources. This work aims to set up a distributed version of the HBV model with a physically based evapotranspiration scheme at 1 km resolution for mainland Norway and to calibrate/validate the model for 124 catchments using regionalized parameterizations. The Penman-Monteith equation was implemented in the HBV model and vegetation characteristics were derived from the Norwegian forest inventory combined with multi-source remote sensing data at 16 m spatial resolution. The estimated potential evapotranspiration (Ep) was compared with pan measurements and estimates from the MODerate Resolution Imaging Spectrometer (MOD16) products, the Global Land Evaporation Amsterdam Model (GLEAM) and Variable Infiltration Capacity (VIC) hydrological model. There are 5 climatic zones in Norway classified based on 4 temperature and precipitation indices. For each zone, the model was calibrated separately by optimizing a multi-objective function including the Nash-Sutcliff efficiency (NSE) and biases of selected catchments. In total, there are 85 catchments for calibration and 39 for validation. The Ep estimates showed good agreement with the measurements, GLEAM and VIC outputs. However, the MOD16 product significantly overestimates Ep compared to the other products. The discharge was well reproduced with the median daily NSE of 0.68/0.67, bias of −3%/−1%, Kling-Gupta efficiency (KGE) of 0.70/0.69 and monthly NSE of 0.80/0.78 in the calibration/validation periods. Our results showed a significant improvement compared to the previous HBV application for all catchments, with an increase of 0.08–0.16 in the median values of the daily NSE, KGE and monthly NSE. Both the temporal and spatial transferability of model parameterizations were also enhanced compared to the previous application.

Effects of species traits and environmental predictors on performance and transferability of ecological niche models

The ability of ecological niche models (ENMs) to produce robust predictions for different time frames (i.e. temporal transferability) may be hindered by a lack of ecologically relevant predictors. Model performance may also be affected by species traits, which may reflect different responses to processes controlling species distribution. In this study, we tested four primary hypotheses involving the role of species traits and environmental predictors in ENM performance and transferability. We compared the predictive accuracy of ENMs based upon (1) climate, (2) land-use/cover (LULC) and (3) ecosystem functional attributes (EFAs), and (4) the combination of these factors for 27 bird species within and beyond the time frame of model calibration. The combination of these factors significantly increased both model performance and transferability, highlighting the need to integrate climate, LULC and EFAs to improve biodiversity projections. However, the overall model transferability was low (being only acceptable for less than 25% of species), even under a hierarchical modelling approach, which calls for great caution in the use of ENMs to predict bird distributions under global change scenarios. Our findings also indicate that positive effects of species traits on predictive accuracy within model calibration are not necessarily translated into higher temporal transferability.

Temporal Transferability of Vehicle Ownership Models in the Developing World: Case Study of Dhaka, Bangladesh

Temporal transferability of model parameters is a critical issue, especially in the context of developing countries where data and resources for transport model development are extremely limited. This study investigates the temporal transferability of vehicle ownership models with special emphasis on exploring the effect of model structure on temporal transferability. The performance of potential updating methods for making the models more transferable are also compared. The household survey data collected from Dhaka, Bangladesh in 2005 and 2010 have been used in this regard. Different forms of random utility and count regression models of car, motorcycle, and bicycle ownership have been developed using income and household size, and number of workers, children, and licensed drivers as explanatory variables. The temporal transferability of each model between the two time periods has been compared rigorously using statistical tests. Results indicate that the multinomial logit model has better temporal transferability than the count regression models. In relation to model updating, the combined transfer estimation method for model updating is found to perform better than the Bayesian updating. The findings can provide useful guidance during application of a pre-existing model in the context of a developing country.

Processing of fast amplitude modulations in bat auditory cortex matches communication call-specific sound features.

Bats use a large repertoire of calls for social communication. In the bat Phyllostomus discolor, social communication calls are often characterized by sinusoidal amplitude and frequency modulations with modulation frequencies in the range of 100-130 Hz. However, peaks in mammalian auditory cortical modulation transfer functions are typically limited to modulation frequencies below 100 Hz. We investigated the coding of sinusoidally amplitude modulated sounds in auditory cortical neurons in P. discolor by constructing rate and temporal modulation transfer functions. Neuronal responses to playbacks of various communication calls were additionally recorded and compared with the neurons' responses to sinusoidally amplitude-modulated sounds. Cortical neurons in the posterior dorsal field of the auditory cortex were tuned to unusually high modulation frequencies: rate modulation transfer functions often peaked around 130 Hz (median: 87 Hz), and the median of the highest modulation frequency that evoked significant phase-locking was also 130 Hz. Both values are much higher than reported from the auditory cortex of other mammals, with more than 51% of the units preferring modulation frequencies exceeding 100 Hz. Conspicuously, the fast modulations preferred by the neurons match the fast amplitude and frequency modulations of prosocial, and mostly of aggressive, communication calls in P. discolor. We suggest that the preference for fast amplitude modulations in the P. discolor dorsal auditory cortex serves to reliably encode the fast modulations seen in their communication calls. NEW & NOTEWORTHY Neural processing of temporal sound features is crucial for the analysis of communication calls. In bats, these calls are often characterized by fast temporal envelope modulations. Because auditory cortex neurons typically encode only low modulation frequencies, it is unclear how species-specific vocalizations are cortically processed. We show that auditory cortex neurons in the bat Phyllostomus discolor encode fast temporal envelope modulations. This property improves response specificity to communication calls and thus might support species-specific communication.

Robust uncertainty assessment of the spatio-temporal transferability of glacier mass and energy balance models

Abstract. Energy and mass-balance modelling of glaciers is a key tool for climate impact studies of future glacier behaviour. By incorporating many of the physical processes responsible for surface accumulation and ablation, they offer more insight than simpler statistical models and are believed to suffer less from problems of stationarity when applied under changing climate conditions. However, this view is challenged by the widespread use of parameterizations for some physical processes which introduces a statistical calibration step. We argue that the reported uncertainty in modelled mass balance (and associated energy flux components) are likely to be understated in modelling studies that do not use spatio-temporal cross-validation and use a single performance measure for model optimization. To demonstrate the importance of these principles, we present a rigorous sensitivity and uncertainty assessment workflow applied to a modelling study of two glaciers in the European Alps, extending classical best guess approaches. The procedure begins with a reduction of the model parameter space using a global sensitivity assessment that identifies the parameters to which the model responds most sensitively. We find that the model sensitivity to individual parameters varies considerably in space and time, indicating that a single stated model sensitivity value is unlikely to be realistic. The model is most sensitive to parameters related to snow albedo and vertical gradients of the meteorological forcing data. We then apply a Monte Carlo multi-objective optimization based on three performance measures: model bias and mean absolute deviation in the upper and lower glacier parts, with glaciological mass balance data measured at individual stake locations used as reference. This procedure generates an ensemble of optimal parameter solutions which are equally valid. The range of parameters associated with these ensemble members are used to estimate the cross-validated uncertainty of the model output and computed energy components. The parameter values for the optimal solutions vary widely, and considering longer calibration periods does not systematically result in better constrained parameter choices. The resulting mass balance uncertainties reach up to 1300 kg m−2, with the spatial and temporal transfer errors having the same order of magnitude. The uncertainty of surface energy flux components over the ensemble at the point scale reached up to 50 % of the computed flux. The largest absolute uncertainties originate from the short-wave radiation and the albedo parameterizations, followed by the turbulent fluxes. Our study highlights the need for due caution and realistic error quantification when applying such models to regional glacier modelling efforts, or for projections of glacier mass balance in climate settings that are substantially different from the conditions in which the model was optimized.

Temporal Transferability of Pine Forest Attributes Modeling Using Low-Density Airborne Laser Scanning Data

This study assesses model temporal transferability using airborne laser scanning (ALS) data acquired over two different dates. Seven forest attributes (i.e. stand density, basal area, squared mean diameter, dominant diameter, tree dominant height, timber volume, and total tree biomass) were estimated using an area-based approach in Mediterranean Aleppo pine forests. Low-density ALS data were acquired in 2011 and 2016 while 147 forest inventory plots were measured in 2013, 2014, and 2016. Single-tree growth models were used to generate concomitant field data for 2011 and 2016. A comparison of five selection techniques and five regression methods were performed to regress field observations against ALS metrics. The selection of the best regression models fitted for each stand attribute, and separately for both 2011 and 2016, was performed following an indirect approach. Model performance and temporal transferability were analyzed by extrapolating the best fitted models from 2011 to 2016 and inversely from 2016 to 2011 using the direct approach. Non-parametric support vector machine with radial kernel was the best regression method with average relative % root mean square error differences of 2.13% for 2011 models and 1.58% for 2016 ones.

Testing Temporal Stability of Recreation Values

Temporal stability of non-market values help to demonstrate the validity of value estimates and the reliability of benefit transfers, however evidence of value stability over time is limited. A literature review focused on meta-analysis studies shows that it is standard practice to adjust values for CPI changes between temporal periods, but that in about half of cases additional value increases over time were identified as significant. One implication is that benefit transfer may not be reliable in those cases. In this study temporal stability of recreation values for the Coorong in Australia has been assessed by travel cost model (TCM) and contingent valuation (CVM) experiments repeated seven years apart. The case study results identified temporal transfer errors were larger with the TCM (62%) than with the CVM (19%). Testing showed that the TCM models and values were significantly different over the seven year period, but the CVM models and values were not, even though the same recreation good was involved. These mixed results indicate that stated preference values may be less sensitive to temporal effects than revealed preference values.

Predicting Future Driving Risk of Crash-Involved Drivers Based on a Systematic Machine Learning Framework.

The objective of this paper is to predict the future driving risk of crash-involved drivers in Kunshan, China. A systematic machine learning framework is proposed to deal with three critical technical issues: 1. defining driving risk; 2. developing risky driving factors; 3. developing a reliable and explicable machine learning model. High-risk (HR) and low-risk (LR) drivers were defined by five different scenarios. A number of features were extracted from seven-year crash/violation records. Drivers’ two-year prior crash/violation information was used to predict their driving risk in the subsequent two years. Using a one-year rolling time window, prediction models were developed for four consecutive time periods: 2013–2014, 2014–2015, 2015–2016, and 2016–2017. Four tree-based ensemble learning techniques were attempted, including random forest (RF), Adaboost with decision tree, gradient boosting decision tree (GBDT), and extreme gradient boosting decision tree (XGboost). A temporal transferability test and a follow-up study were applied to validate the trained models. The best scenario defining driving risk was multi-dimensional, encompassing crash recurrence, severity, and fault commitment. GBDT appeared to be the best model choice across all time periods, with an acceptable average precision (AP) of 0.68 on the most recent datasets (i.e., 2016–2017). Seven of nine top features were related to risky driving behaviors, which presented non-linear relationships with driving risk. Model transferability held within relatively short time intervals (1–2 years). Appropriate risk definition, complicated violation/crash features, and advanced machine learning techniques need to be considered for risk prediction task. The proposed machine learning approach is promising, so that safety interventions can be launched more effectively.

Two-Point Method for Measuring the Temporal Modulation Transfer Function

The temporal modulation transfer function (TMTF) has been proposed to estimate the temporal resolution abilities of listeners with normal hearing and listeners with hearing loss. The TMTF data of patients would be useful for clinical diagnosis and for adjusting the hearing instruments at clinical and fitting sites. However, practical application is precluded by the long measurement time of the conventional method, which requires several measurement points. This article presents a new method to measure the TMTF that requires only two measurement points. Experiments were performed to estimate the TMTF of normal listeners and listeners with hearing loss to demonstrate that the two-point method can estimate the TMTF parameter and the conventional method. Sixteen normal hearing and 21 subjects with hearing loss participated, and the difference between the estimated TMTF parameters and measurement time were compared. The TMTF parameters (the peak sensitivity Lps and cutoff frequency fcutoff) estimated by the conventional and two-point methods showed significantly high correlations: the correlation coefficient for Lps was 0.91 (t(45) = 14.3; p < 10) and that for fcutoff was 0.89 (t(45) = 13.2; p < 10). There were no fixed and proportional biases. Therefore, the estimated values were in good agreement. Moreover, there was no systematic bias depending on the subject's profile. The measurement time of the two-point method was approximately 10 min, which is approximately one-third that of the conventional method. The two-point method enables the introduction of TMTF measurement in clinical diagnosis.

Effect of musical training on psychophysical abilities and working memory in children

Introduction: Music involves fine modulations of intensity, frequency, and temporal aspects and musicians can distinguish these fine variations due to their extensive training. The long-term musical practice has been found to result in the improvement of various auditory and cognitive skills. The aim of the present investigation was to study the effect of musical training on psychophysical abilities and working memory in children. Methods: Total of 30 participants in the age range of 10 to 15 years were recruited for the study. They were equally divided into two groups. Group I had children trained in Carnatic music for more than three years and group II included 15 children with no formal musical training. Psychophysical abilities were assessed using all three frequency, intensity, and temporal parameters. Frequency discrimination and intensity discrimination was assessed through differential limen of frequency (DLF) and differential limen of intensity (DLI) at 2000 and 4000 Hz. Temporal parameters were assessed through a gap detection test (GDT), temporal modulation transfer function (TMTF), and duration discrimination test (DDT). Working memory was assessed using digit span and operation span test respectively. Results: Results showed that children with musical training had significantly better scores for DLI, DLF, GDT, and DDT. However, there was no significant difference in TMTF scores between the groups. Further, a significant better score for digit span task was obtained by children with musical training. But no significant difference was noted for operational span task between the groups. Conclusion: To conclude, the study shows the importance of musical training in fine-tuning the auditory system and its significance in ameliorating auditory psychoacoustic skills and auditory memory.

When Shared Autonomous Electric Vehicles Meet Microgrids: Citywide Energy-Mobility Orchestration

Motivated by the ever-increasing interconnection of energy and mobility service systems at the urban scale, we develop a cross-disciplinary analytics framework to understand citywide mobility-energy synergy. In particular, we investigate the potential of operating shared autonomous electric vehicles (SAEVs) for improving the self-sufficiency and resilience of solar-powered urban microgrids. We propose models and analytics to characterize the dynamics of the SAEV-microgrid service systems, which were largely overlooked by the literature on service operations and vehicle-grid integration (VGI) analysis. We develop a space-time-energy network representation of SAEVs. Then we formulate linear program models to incorporate an array of main operational decisions interconnecting the mobility and energy systems. The models can efficiently handle large volumes of data and decisions and capture various urban heterogeneities. We also propose an ``N-1'' resilience-constrained fleet dispatch problem. Combining eight sources data of New York City, our results show that even a moderate-sized SAEV fleet can improve microgrid self-sufficiency via the spatial transfer of electricity, which complements conventional VGI, whereas enlisting more SAEVs substitutes conventional VGI by means of providing temporal transfer of electricity. We also quantify the potential of SAEVs for solar power utilization, peak electricity import reduction and ramping mitigation. In addition, microgrid resilience can be significantly enhanced by SAEVs, but the actual resilience level varies by microgrids and by the hour when grid contingency occurs. The SAEV fleet operator can further maintain the resilience of key microgrid areas at their maximum achievable level without significantly compromising the fleet repositioning efficiency. Our models and findings demonstrate the promising and rich potential in deepening the integration of urban mobility and energy service systems towards a smart-city future.

Neural coding of the sound envelope is changed in the inferior colliculus immediately following acoustic trauma.

Sensorineural hearing loss is often accompanied by difficulties with understanding speech in fluctuating backgrounds, suggesting that neural coding of complex sound features, such as the sound envelope, is impaired. Here, we studied how temporal and rate coding of the envelope is affected in the inferior colliculus immediately after acoustic trauma. Neural activity in response to amplitude-modulated noise was recorded from the inferior colliculus of the guinea pig, before and immediately after a 1-hr 11-kHz acoustic trauma. Units with a characteristic frequency (CF) below the trauma frequency (<11kHz) showed increased response gains, a measure for temporal coding of the sound envelope, especially at low modulation frequencies (≤128Hz). Units with a CF>11kHz, which had large acoustic trauma-induced threshold shifts, had decreased response gains to amplitude-modulated noise. Shapes of temporal modulation transfer functions shifted toward a higher proportion of low-pass shapes in low-CF units, and to less band-pass shapes in high-CF units. Furthermore, driven firing rates decreased, especially at high modulation frequencies for high-CF units. The observed changes occurred immediately following trauma and were thus a result of the immediate trauma-induced damage to the auditory system. If also present in human subjects, reduced response gains in high-frequency units could disrupt coding of consonants and consequently impair speech understanding in noisy environments. Moreover, the enhanced temporal coding by low-CF units of the low modulation frequencies could overly amplify responses to low-frequency noise, further deteriorating listening in noise.