Stream Model Research Articles

350 Investigating the Architecture of Speech Processing Pathways in the Brain

OBJECTIVES/GOALS: Speech production requires mapping between sound-based and motor-based neural representations of a word – accomplished by learning internal models. However, the neural bases of these internal models remain unclear. The aim of this study is to provide experimental evidence for these internal models in the brain during speech production. METHODS/STUDY POPULATION: 16 healthy human adults were recruited for this electrooculography speech study. 20 English pseudowords were designed to vary on confusability along specific features of articulation (place vs manner). All words were controlled for length and voicing. Three task conditions were performed: speech perception, covert and overt speech production. EEG was recorded using a 64-channel Biosemi ActiveTwo system. EMG was recorded on the orbicularis orbis inferior and neck strap muscles. Overt productions were recorded with a high-quality microphone to determine overt production onset. EMG during was used to determine covert production onset. Neuroimaging: Representational Similarity Analysis (RSA), was used to probe the sound- and motor-based neural representations over sensors and time for each task. RESULTS/ANTICIPATED RESULTS: Production (motor) and perception (sound) neural representations were calculated using a cross-validated squared Euclidean distance metric. The RSA results in the speech perception task show a strong selectivity around 150ms, which is compatible with recent human electrocorticography findings in human superior temporal gyrus. Parietal sensors showed a large difference for motor-based neural representations, indicating a strong encoding for production related processes, as hypothesized by previous studies on the ventral and dorsal stream model of language. Temporal sensors, however, showed a large change for both motor- and sound-based neural representations. This is a surprising result since temporal regions are believed to be primarily engaged in perception (sound-based) processes. DISCUSSION/SIGNIFICANCE: This study used neuroimaging (EEG) and advanced multivariate pattern analysis (RSA) to test models of production (motor-) and perception (sound-) based neural representations in three different speech task conditions. These results show strong feasibility of this approach to map how the perception and production processes interact in the brain.

Faces and Voices Processing in Human and Primate Brains: Rhythmic and Multimodal Mechanisms Underlying the Evolution and Development of Speech.

While influential works since the 1970s have widely assumed that imitation is an innate skill in both human and non-human primate neonates, recent empirical studies and meta-analyses have challenged this view, indicating other forms of reward-based learning as relevant factors in the development of social behavior. The visual input translation into matching motor output that underlies imitation abilities instead seems to develop along with social interactions and sensorimotor experience during infancy and childhood. Recently, a new visual stream has been identified in both human and non-human primate brains, updating the dual visual stream model. This third pathway is thought to be specialized for dynamics aspects of social perceptions such as eye-gaze, facial expression and crucially for audio-visual integration of speech. Here, we review empirical studies addressing an understudied but crucial aspect of speech and communication, namely the processing of visual orofacial cues (i.e., the perception of a speaker’s lips and tongue movements) and its integration with vocal auditory cues. Along this review, we offer new insights from our understanding of speech as the product of evolution and development of a rhythmic and multimodal organization of sensorimotor brain networks, supporting volitional motor control of the upper vocal tract and audio-visual voices-faces integration.

Suitable dam site identification using GIS-based MCDA: a case study of Chemoga watershed, Ethiopia

Ethiopia is Africa's second-most populous country, after Nigeria, and is primarily a farming community with low productivity that is heavily reliant on rain-fed agriculture. Water scarcity, global warming, and rising population all necessitate more effective water conservation methods. As a result, the demand for dams is increasing dramatically in order to provide the community with safe drinking water, electricity, and irrigation to ensure food security. The goal of this study was to use remote sensing and geographic information system (GIS) techniques in conjunction with the dam suitability stream model and multi-criteria decision analysis to identify potential sites for multi-purpose dam construction. The study used six influencing factors to find suitable dam sites, with the model's suitability stream and overall suitability output maps proposed and evaluated as a result. Based on the topography and land use, the results showed that three proposed dam sites in the upper part of the watershed are likely preferable for irrigation, fishery, and clean drinking water supply. The three proposed dam sites in the watershed's lower reaches, however, are better suited to hydropower generation. In addition, remote sensing and GIS are useful in dam/reservoir site selection because they allow decision-makers to create, manipulate, and manage relevant thematic layers.

RAPT360: Reinforcement Learning-Based Rate Adaptation for 360-Degree Video Streaming With Adaptive Prediction and Tiling

Tile-based rate adaption can improve the quality of experience (QoE) for adaptive 360-degree video streaming under constrained network conditions, which, however, is a challenging problem due to the requirements of accurate prediction for users’ viewports and optimal bitrate allocation for tiles. In this paper, we propose a strategy that deploys reinforcement learning-based Rate Adaptation with adaptive Prediction and Tiling for 360-degree video streaming, named RAPT360, to address these challenges. Specifically, to improve the accuracy of the state-of-the-art viewport prediction approaches, we fit the time-varying Laplace distribution-based probability density function of the prediction error for different prediction lengths. On the basis of that, we develop a viewport identification method to determine the viewport area of a user depending on the buffer occupancy, where the obtained viewport can cover the real viewport with any given probability confidence level. We then propose a viewport-aware adaptive tiling scheme to improve the bandwidth efficiency, where three types of tile granularities are allocated according to the shape and position of the 2-D projection of that viewport. By establishing an adaptive streaming model and QoE metric specific to 360-degree videos, we finally formulate the rate adaptation problem for tile-based 360-degree video streaming as a non-linear discrete optimization problem that targets at maximizing the long-term user QoE under a bandwidth-constrained network. To efficiently solve this problem, we model the rate adaptation logic as a Markov decision process (MDP) and employ the deep reinforcement learning (DRL)-based algorithm to dynamically learn the optimal bitrate allocation of tiles. Extensive experimental results show that RAPT360 achieves a performance gain of at least 1.47 dB on average chunk QoE, including a video quality improvement of at least 1.33 dB, in comparison to the existing strategies for tile-based adaptive 360-degree video streaming.

Design optimization of industrial energy systems with energy consumption relaxation models for coupling process units and utility streams

Industrial energy systems, such as in the petrochemical and iron energy-intensive industries, are a utility hub to convert fuels into various forms of energy and then supply them to satisfy the energy requirements from process units. In conventional industrial energy systems design, the forms and quantity of energy required from process units are generally considered as deterministic. As a result, the optimization boundary of energy systems design is reduced in advance, leading to sub-optimal design results. In this study, energy interchangeability in process units is introduced to relax the bounds of energy requirements. These energy interchangeabilities can lead to different distribution patterns of energy requirement. In order to quantify the energy interchangeability of process units, energy consumption relaxation models (ECRMs) are presented. Two kinds of energy interchangeabilities are considered and investigated, one is the heat-based energy interchange between fuels and steam, another is the work-based energy interchange between electricity and steam. A candidate superstructure of industrial energy systems and a mixed integer nonlinear programming (MINLP) framework are then reformulated to conduct design optimization of the energy system to minimize the total annual cost (TAC). Lastly, the proposed method and MINLP optimization framework are applied to a large-scale refinery site for the design optimization of the energy system. Compared with the conventional optimized design, the TAC and annual CO2 emission are decreased by 10.96% and 19,845 tons, respectively.

Tectonic and Climatic Control on Quaternary Exhumation in the Eastern Pamir Domes, Western China: Insights From Geomorphic Approaches

The Kongur Shan and Muztaghata massifs, bounded by the Kongur Shan extensional system (KES), represent tectonic and topographic anomalies in the eastern Pamir region. They are ideal examples to study how normal faulting and surface erosion influence Quaternary exhumation of the dome system. We apply multiple geomorphic parameters, including hypsometric integral, stream length-gradient index, drainage basin shape, drainage basin asymmetry and ratio of valley floor width to valley height, for the catchments on both sides of the range. We first evaluated the validity of various indices and chose three active tectonic-sensitive indices to establish a newly-integrated parameter (Iat) that is used to measure relative intensities of tectonic activity in active orogens. Results suggest stronger tectonic activity west of the domes along the Kongur Shan normal fault (KSF) and Muji dextral strike-slip fault, compared to the eastern side, along the Ghez and Kalagile faults. This first-order observation reflects tectonic control on the topographic development of the domal structure, consistent with eastward crustal tilting, attested by older thermochronology ages to the east. On the western flank of the range, stronger tectonic activity occurs mostly on the Muji fault, Kingata Tagh - Kongur Shan fault segment, as well as along the western and southern Muztaghata segments of the Kongur Shan fault. This is consistent with field investigations of Quaternary offsets of landforms, which suggest continuous activity of the Muji fault and KSF since the late Miocene. Average basin-wide erosion rates derived from stream power models are highest near the Kongur Shan dome, and gradually decrease southwards and northwards, in agreement with the spatial pattern of long-term exhumation rates in the footwall of the KSF obtained by low-temperature thermochronology data. Positive correlation between exhumation/erosion rates and extensional rates along the active faults located west of the domes indicates that extensional deformation likely plays a dominant role in controlling focused dome exhumation/erosion. However, considering peaked exhumation/erosion rates, stronger rock resistivity and steeper glacial landforms, attest to the important role of glacial buzz-saw in reshaping the recent dome’s landscape.

Evaluation of the Rock Uplift Pattern in the Central Yunnan Subblock, SE Tibetan Plateau: Based on the Bedrock Channel Profile

The uplift pattern of the southeastern Tibetan Plateau is strongly related to the topographic evolution stemming from the India–Eurasia collision. However, whether strain is localized along major faults that bound large tectonic blocks or is accommodated across regions has been strongly debated. In this study, we used stream power incision models to obtain the distribution pattern of the channel steepness indices to understand the rock uplift pattern across the area, as increased channel steepness indices often correlate with the rock uplift rates. In this study, the river longitudinal profiles were analyzed to obtain the distribution of the channel steepness indices in the Central Yunnan subblock. The results suggested very weak correlations between the steepness indices and the lithology, precipitation, sediment flux, or channel concavity indices. Along the Xiaojiang strike–slip fault and the interior subblock, the uplift rate was slower, while the northern part had uplifted faster and was controlled by thrust fault systems. The channel steepness increased gradually from south to north. Thus, the distribution pattern of the normalized channel steepness, ksn, index within the Central Yunnan subblock provides notable support for the argument for the thrusting transformation-limited extrusion model of the Tibetan Plateau.

Approximability of Monotone Submodular Function Maximization under Cardinality and Matroid Constraints in the Streaming Model

Approximability of Monotone Submodular Function Maximization under Cardinality and Matroid Constraints in the Streaming Model

An Optimal Streaming Algorithm for Submodular Maximization with a Cardinality Constraint

We study the problem of maximizing a nonmonotone submodular function subject to a cardinality constraint in the streaming model. Our main contribution is a single-pass (semi) streaming algorithm that uses roughly [Formula: see text] memory, where k is the size constraint. At the end of the stream, our algorithm postprocesses its data structure using any off-line algorithm for submodular maximization and obtains a solution whose approximation guarantee is [Formula: see text], where α is the approximation of the off-line algorithm. If we use an exact (exponential time) postprocessing algorithm, this leads to [Formula: see text] approximation (which is nearly optimal). If we postprocess with the state-of-the-art offline approximation algorithm, whose guarantee is [Formula: see text], we obtain a 0.2779-approximation in polynomial time, improving over the previously best polynomial-time approximation of 0.1715. It is also worth mentioning that our algorithm is combinatorial and deterministic, which is rare for an algorithm for nonmonotone submodular maximization, and enjoys a fast update time of [Formula: see text] per element.

Mass Indices of Meteoric Bodies: I. Formation Model of Meteoroid Streams

Mass Indices of Meteoric Bodies: I. Formation Model of Meteoroid Streams

Hybrid dynamic chunk ensemble model for multi-class data streams

In the analysis more specifically in the classification of continuous data stream using machine learning algorithms joint occurrence of concept drift and imbalanced issue becomes more provocative. Also, imbalance issue is again more challenging when the data stream is multi-class with minority class and that is too with data-difficulty factors. Incremental learning with ensemble models found more promising in handling theses issues. But most of the approaches are for two-class data streams which can’t be utilized for multiclass data streams. In this paper we have designed hybrid dynamic chunk ensemble model (HDCEM) for the classification of multi-class insect-data stream for handling imbalance and concept drift issue. To deal with imbalance issue we have proposed effective split bagging algorithm which has achieved better performance on minority class recall and F-measure on arriving dynamic chunks of data from multi-class data stream. HDCEM model can adapt to abrupt and gradual drift because it has combined features of both online and chunk-based learning together. It has achieved average 78% minority class recall in abrupt insect data stream and 71% in gradual drift insect stream.

Effects of lake‐groundwater interaction on the thermal regime of a sub‐alpine headwater stream

AbstractStream thermal regimes are critical to the stability of freshwater habitats. There is growing concern that climate change will result in stream warming due to rising air temperatures, decreased shading in forested areas due to wildfires, and changes in streamflow. Groundwater plays an important role in controlling stream temperatures in mountain headwaters, where it makes up a considerable portion of discharge. This study investigated the controls on the thermal regime of a headwater stream, and the surrounding groundwater processes, in a catchment on the eastern slopes of the Canadian Rocky Mountains. Groundwater discharge to the headwater spring is partially sourced by a seasonal lake. Spring, stream and lake temperature, water level, discharge and chemistry data were used to build a conceptual model of the system. Meteorological data was used to set up a stream temperature model. This study presents a unique example of an indirectly lake‐headed stream, that is, a lake that only has transient subsurface hydrologic connections to the stream and no surface connections. The interaction of groundwater and lake water, and the subsurface connectivity between the lake and the headwater spring determine the resulting stream temperature. Radiation dominated the non‐advective fluxes in the stream energy balance. Sensible and latent heat fluxes play a secondary role, but their effects generally cancel out. During snowfall events, the latent heat associated with melting of direct snowfall onto the water surface was responsible for rapid stream cooling. An increase in advective inputs from groundwater and hillslope pathways did not result in observed cooling of stream water during rainfall events. The results from this study will assist water resource and fisheries managers in adapting to stream temperature changes under a warming climate.

K-Center Clustering with Outliers in Sliding Windows

Metric k-center clustering is a fundamental unsupervised learning primitive. Although widely used, this primitive is heavily affected by noise in the data, so a more sensible variant seeks for the best solution that disregards a given number z of points of the dataset, which are called outliers. We provide efficient algorithms for this important variant in the streaming model under the sliding window setting, where, at each time step, the dataset to be clustered is the window W of the most recent data items. For general metric spaces, our algorithms achieve O1 approximation and, remarkably, require a working memory linear in k+z and only logarithmic in |W|. For spaces of bounded doubling dimension, the approximation can be made arbitrarily close to 3. For these latter spaces, we show, as a by-product, how to estimate the effective diameter of the window W, which is a measure of the spread of the window points, disregarding a given fraction of noisy distances. We also provide experimental evidence of the practical viability of the improved clustering and diameter estimation algorithms.

A Contemporary Survey on Live Video Streaming from a Computation-Driven Perspective

Live video streaming services have experienced significant growth since the emergence of social networking paradigms in recent years. In this scenario, adaptive bitrate streaming communications transmitted on web protocols provide a convenient and cost-efficient facility to serve various multimedia platforms over the Internet. In these communication models, video content is delivered optimally, possibly transcoded, edited automatically, and cached temporarily by network elements along the path. To this end, the computational capabilities of various network elements are considered as major resources to be optimized for service quality improvements. This article provides a contemporary survey of cutting-edge live video streaming studies from a computation-driven perspective. First, an overview of the global standards, system architectures, and streaming protocols is presented. Next, hierarchical computation-driven models of live video streaming are anatomized, including cloud-, edge-, and peer-to-peer-based solutions. Cutting-edge studies are then reviewed to discover the advances they have made in improving system performance in multiple areas. Finally, open challenges are presented to direct future research in this field.

From the Fire: A Deeper Look at the Phoenix Stream

Abstract We use 6 yr of data from the Dark Energy Survey to perform a detailed photometric characterization of the Phoenix stellar stream, a 15° long, thin, dynamically cold, low-metallicity stellar system in the Southern Hemisphere. We use natural splines, a nonparametric modeling technique, to simultaneously fit the stream track, width, and linear density. This updated stream model allows us to improve measurements of the heliocentric distance (17.4 ± 0.1 (stat.) ±0.8 (sys.) kpc) and distance gradient (−0.009 ± 0.006 kpc deg−1) of Phoenix, which corresponds to a small change of 0.13 ± 0.09 kpc in heliocentric distance along the length of the stream. We measure linear intensity variations on degree scales, as well as deviations in the stream track on ∼2° scales, suggesting that the stream may have been disturbed during its formation and/or evolution. We recover three peaks and one gap in linear intensity along with fluctuations in the stream track. Compared to other thin streams, the Phoenix stream shows more fluctuations and, consequently, the study of Phoenix offers a unique perspective on gravitational perturbations of stellar streams. We discuss possible sources of perturbations to Phoenix, including baryonic structures in the Galaxy and dark matter subhalos.

A Composite Event Model for Semantic Trajectory Data Streams

A Composite Event Model for Semantic Trajectory Data Streams

Tapping stream tracking model using computer vision and deep learning to minimize slag carry-over in basic oxygen furnace

<abstract> <p>This paper describes a system that can automatically determine the result of the slag dart input to the converter during tapping of basic oxygen furnace (BOF), by directly observing and tracking the behavior of the pouring molten steel at the tapping hole after the dart is injected. First, we propose an algorithm that detects and tracks objects, then automatically calculates the width of the tapping stream from slag-detection system (SDS) images collected in real time. Second, we develop a time-series model that can determine whether the slag dart was properly seated on the tap hole; this model uses the sequential width and brightness data of the tapping stream. To test the model accuracy, an experiment was performed using SDS data collected in a real BOF. When the number of sequential images was 11 and oversampling was 2:1, the classification accuracy in the test data set was 99.61%. Cases of success and failure of dart injection were quantified in connection with operation data such as ladle weight and tilt angle. A pilot system was constructed; it increases the reliability of prevention of slag carry-over during tapping, and can reduce the operator's workload by as much as 30%. This system can reduce the secondary refining cost by reducing the dart-misclassification rate, and thereby increase the productivity of the steel mill. Finally, the system can contribute to real-time process control and management by automatically linking the task of determining the input of darts to the work of minimizing slag carry-over in a BOF.</p> </abstract>

Aeroelastična analiza lopatice vetroturbine sa ravnim lopaticama vertikalne ose

To prevent flutter phenomena in a wind turbine, minimize vibration and increase the blades' life, a systematic analysis is required to investigate the effects between the cyclic aerodynamic loads and the structural performance of the turbine. A dynamic analysis of a straight-bladed vertical axis wind turbine (SB-VAWT) blade is investigated in this paper, and a simplified approach for the energy equations of an Eulerian beam subjected to twist and transverse bending deflections is introduced. The aerodynamic loads are estimated using the double multiple stream tube models. They are introduced into the dynamic model in the aeroelastic coupling, where the structural displacements are fed back to update the aerodynamic loads by utilizing the average acceleration method for the numerical integration of the equations. Reduced order modeling is then imposed based on the first modes of vibration. It is found that the structural displacement has little effect on the aerodynamic loads, and SBVAWTs experience higher transverse displacements compared with those in curved-blade VAWTs.

Research on the Business Model of Live Stream Marketing on e-Commerce Platform

With the popularization of mobile Internet and the improvement of streaming media technology, live streaming marketing has become a popular marketing method. In China, live stream marketing helped e-commerce merchants earn more than 100 billion yuan in sales revenue in 2018. This study focuses on analysing the differences in business models of different types of live stream marketing. Using the method of case study, the paper adopts a business model canvas to study the process of value creation, value transfer and value acquisition of anchor-oriented live stream marketing and brand-oriented live stream marketing respectively, and carries out the comparative analysis. The results of the case study summarize and compare the characteristics of the two live stream marketing business models, which makes up for the deficiency of discussion on live broadcast marketing business models in existing studies and provides suggestions regarding how to choose live stream marketing types for e-commerce platform merchants.

Chapter 1 - The connectional anatomy of the temporal lobe

The idea of a temporal lobe separated from the rest of the hemisphere by reason of its unique structural and functional properties is a clinically useful artifact. While the temporal lobe can be safely defined as the portion of the cerebrum lodged in the middle cranial fossa, the pattern of its connections is a more revealing description of its functional subdivisions and specific contribution to higher cognitive functions. This chapter provides an historical overview of the anatomy of the temporal lobe and an updated framework of temporal lobe connections based on tractography studies of human and nonhuman primates and patients with brain disorders. Compared to monkeys, the human temporal lobe shows a relatively increased connectivity with perisylvian frontal and parietal regions and a set of unique intrinsic connections, which may have supported the evolution of working memory, semantic representation, and language in our species. Conversely, the decreased volume of the anterior (limbic) interhemispheric temporal connections in humans is related to a reduced reliance on olfaction and a partial transference of functions from the anterior commissure to the posterior corpus callosum. Overall the novel data from tractography suggest a revision of current dual stream models for visual and auditory processing.