Fine-grained Time Research Articles

User Behavior Analysis and Video Popularity Prediction on a Large-Scale VoD System

Understanding streaming user behavior is crucial to the design of large-scale Video-on-Demand (VoD) systems. In this article, we begin with the measurement of individual viewing behavior from two aspects: the temporal characteristics and user interest. We observe that active users spend more hours on each active day, and their daily request time distribution is more scattered than that of the less active users, while the inter-view time distribution differs negligibly between two groups. The common interest in popular videos and the latest uploaded videos is observed in both groups. We then investigate the predictability of video popularity as a collective user behavior through early views. In the light of the limitations of classical approaches, the Autoregressive-Moving-Average (ARMA) model is employed to forecast the popularity dynamics of individual videos at fine-grained time scales, thus achieving much higher prediction accuracy. When applied to video caching, the ARMA-assisted Least Frequently Used (LFU) algorithm can outperform the Least Recently Used (LRU) by 11--16%, the well-tuned LFU by 6--13%, and the LFU is only 2--4% inferior to the offline LFU in terms of hit ratio.

Using developmental rate variation to establish a fine‐grained sequence of ossification center differentiation

Mouse embryos from inbred strains exhibit a large range of phenotypic diversity, despite tight controls placed on genetic variation and environmental conditions. Individuals vary in terms of body size and the progress of developmental morphology, even within litters. This indicates that individual embryos develop at different rates. Staging atlases such as those developed by Kaufman and Theiler provide details of the morphological features and developmental processes evident on successive days of development, but do not offer a systematic method for determining if two embryos represent similar developmental stages within more fine‐grained time periods. To ensure that studies of developmental processes like tissue interactions and gene expression events that occur within restricted periods of a few hours are not confounded by inter‐individual variation in rates of development, precise temporal control is needed to sequentially order and contextualize data collected from individual embryos. We collected and compared temporal sequences of ossification center appearance and development using two measures of individual development: size, measured as crown‐rump length, and shape, using the eMOSS staging system (http://www.limbstaging.crg.es/) that generates estimates of individual ages based on the 2D outline of the hindlimb.Using crown‐rump length and the eMOSS system, we demonstrate significant developmental variation within litters, and significant disparity between an embryo's age based on the age at collection and age generated from eMOSS. Using eMOSS, we are able to elucidate the sequence of bone development in unprecedented detail. We recorded the appearance of ossification centers by scoring the presence of the critical osteoblast differentiation gene Osterix in OSX‐GFP mice sacrificed at 6 hour increments from E12.0 to E15.0 and scored the appearance of calcified bone by staining C57Bl/6 embryos between E14.0 and E16.0 with calcein. We improved the understanding of the temporal sequence and precise timing of these developmental events by generating an ossification center development sequence that details the appearance of bones according to the overall body size, and more importantly, the developmental hour at which they appeared.Support or Funding InformationThis work is supported in part by the NIH: Craniosynostosis Network P01HD078233 & R01DE018500, R01DE022988; NSF BCS‐1650824; and the Burroughs Wellcome Fund.This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.

Multisensory temporal processing in own-body contexts: plausibility of hand ownership does not improve visuo-tactile asynchrony detection.

Tracking one's own body is essential for environmental interaction, and involves integrating multisensory cues with stored information about the body's typical features. Exactly how multisensory information is integrated in own-body perception is still unclear. For example, Ide and Hidaka (Exp Brain Res 228:43-50, 2013) found that participants made less precise visuo-tactile temporal order judgments (TOJ) when viewing hands in a plausible orientation (upright; typical for one's own hand) compared to an implausible orientation (rotated 180°). This suggests that viewing one's own body relaxes the precision for perceived visuo-tactile synchrony. In contrast, visuo-proprioceptive research shows improvements for multisensory temporal perception near one's own body in asynchrony detection tasks, implying an increase in precision. Hence, it is unclear whether viewed hand orientation generally modulates the ability to detect small asynchronies between vision and touch, or if this effect is specific to TOJ tasks. We investigated whether viewed hand orientation affects detection of visuo-tactile asynchrony. In two experiments, participants viewed model hands in anatomically plausible or implausible orientations. In one experiment, we stroked the hands to induce the rubber hand illusion. Participants were asked to detect short delays (40-280ms) between vision (an LED flash on the model hand) and touch (a tap to fingertip of the participant's hidden hand) in a two-interval forced-choice task. Bayesian analyses show that our data provide strong evidence that viewed hand orientation does not affect visuo-tactile asynchrony detection. This study suggests the mechanisms for fine-grained time perception differ between visuo-tactile and visuo-proprioceptive contexts.

Effective fine-grained location prediction based on user check-in pattern in LBSNs

Location-Based Social Networks (LBSNs) have built bridges between virtual space and real-world mobility in recent years. The massive check-in data generated in LBSNs makes it possible to predict users' future check-in location, which has proved meaningful for e-commerce developments. Existing studies mainly focus on predicting the next check-in location with a coarse granularity, which only shows limited performance in practical scenarios. In this paper, we propose a comprehensive approach based on user check-in pattern to predict users' future check-in location at any fine-grained time in LBSNs. Firstly, users' check-in pattern involving time periodicity, global popularity and personal preference are analyzed. Secondly, we extract multiple features related to user check-in pattern and explore the predictive power of each individual feature. Thirdly, a set of features are combined into a supervised scoring model and a classification model respectively for predicting user's check-in location at a fine-grained time in the future. Finally, extensive experiments on three real-world Foursquare datasets are carefully designed to verify the effectiveness of the proposed approach. Experimental results show that our approach outperforms both baseline methods and state-of-the-art methods on various evaluation metrics.

In-service crosstalk monitoring, tracing and lightpath re-optimization for space-division multiplexing optical networks.

In recent years, to enlarge the single-mode fibers (SMFs) transmission capacity, researchers focused on the dimension of space, which is a new degree of freedom that is being considered for optical fiber communication beyond WDM. Space-division multiplexing (SDM), including mode-division multiplexing (MDM) using multimode fibers (MMFs) or few-mode fibers (FMFs), and core multiplexing using multicore fibers (MCFs), has attracted much recent attention. In an SDM system, high-density spatial channels are tightly packed into a single fiber, thus making crosstalk among cores or modes a critical challenge due to fiber imperfections, bending, and twisting. Previous studies have mostly been confined to the routing algorithms for crosstalk reduction but few focuses on the in-service crosstalk monitoring, tracing and quality-of-transmission (QoT)-oriented lightpath re-optimization. In this paper, we proposed novel in-service crosstalk monitoring and tracing (CMT) method and algorithm using fine-grained optical time slice monitoring channels for crosstalk reduction in SDM optical networks. Benefitting from the large amount of fine-grained channels provided by optical time slices, it becomes possible for every source node to allocate a dedicated monitoring time slice carrying the traffic and path information for each connection. Crosstalk monitoring and tracing can be realized by extracting the information contained in these monitoring time slices. Simulation results shows that the proposed CMT method and algorithm can obtain acceptable performance in large-scale network scenarios. Furthermore, we also proposed a quality-of-transmission (QoT)-oriented lightpath re-optimization mechanism based on in-service crosstalk monitoring and tracing to maintain a high level of QoT. Finally, we designed a prototype experiment to validate our proposed in-service crosstalk monitoring method. Results show that this method can realize in-service crosstalk monitoring, tracing and lightpath re-optimization over a seven-core fiber based transmission system, and the crosstalk with a minimum value of -37.9 dB can be monitored and successfully traced.

The Effect of Hearing Loss on Novel Word Learning in Infant- and Adult-Directed Speech.

Relatively little is known about how young children with hearing impairment (HI) learn novel words in infant- and adult-directed speech (ADS). Infant-directed speech (IDS) supports word learning in typically developing infants relative to ADS. This study examined how children with normal hearing (NH) and children with HI learn novel words in IDS and ADS. It was predicted that IDS would support novel word learning in both groups of children. In addition, children with HI were expected to be less proficient word learners as compared with their NH peers. A looking-while-listening paradigm was used to measure novel word learning in 16 children with sensorineural HI (age range 23.2 to 42.1 months) who wore either bilateral hearing aids (n = 10) or bilateral cochlear implants (n = 6) and 16 children with NH (age range 23.1 to 42.1 months) who were matched for gender, chronological age, and maternal education level. Two measures of word learning were assessed (accuracy and reaction time). Each child participated in two experiments approximately 1 week apart, one in IDS and one in ADS. Both groups successfully learned the novel words in both speech type conditions, as evidenced by children looking at the correct picture significantly above chance. As a group, children with NH outperformed children with HI in the novel word learning task; however, there were no significant differences between performance on IDS versus ADS. More fine-grained time course analyses revealed that children with HI, and particularly children who use hearing aids, had more difficulty learning novel words in ADS, compared with children with NH. The pattern of results observed in the children with HI suggests that they may need extended support from clinicians and caregivers, through the use of IDS, during novel word learning. Future research should continue to focus on understanding the factors (e.g., device type and use, age of intervention, audibility, acoustic characteristics of input, etc.) that may influence word learning in children with HI in both IDS and ADS.

SugarMate

Inferring abnormal glucose events such as hyperglycemia and hypoglycemia is crucial for the health of both diabetic patients and non-diabetic people. However, regular blood glucose monitoring can be invasive and inconvenient in everyday life. We present SugarMate, a first smartphone-based blood glucose inference system as a temporary alternative to continuous blood glucose monitors (CGM) when they are uncomfortable or inconvenient to wear. In addition to the records of food, drug and insulin intake, it leverages smartphone sensors to measure physical activities and sleep quality automatically. Provided with the imbalanced and often limited measurements, a challenge of SugarMate is the inference of blood glucose levels at a fine-grained time resolution. We propose Md3RNN, an efficient learning paradigm to make full use of the available blood glucose information. Specifically, the newly designed grouped input layers, together with the adoption of a deep RNN model, offer an opportunity to build blood glucose models for the general public based on limited personal measurements from single-user and grouped-users perspectives. Evaluations on 112 users demonstrate that Md3RNN yields an average accuracy of 82.14%, significantly outperforming previous learning methods those are either shallow, generically structured, or oblivious to grouped behaviors. Also, a user study with the 112 participants shows that SugarMate is acceptable for practical usage.

Demystifying the Imperfect Client-Side Cache Performance of Mobile Web Browsing

The web browser is one of the most significant applications on mobile devices such as smartphones. However, the user experience of mobile web browsing is undesirable because of the slow resource loading. To improve the performance of web resource loading, client-side cache has been adopted as a key mechanism. However, the existing passive measurement studies cannot comprehensively characterize the “ client-side ” cache performance of mobile web browsing. For example, most of these studies mainly focus on client-side implementations but not server-side configurations, suffer from biased user behaviors, and fail to study “ miscached ” resources. To address these issues, in this article, we present a proactive approach to making a comprehensive measurement study on client-side cache performance. The key idea of our approach is to proactively crawl resources from hundreds of websites periodically with a fine-grained time interval. Thus, we are able to uncover the resource update history and cache configurations at the server side, and analyze the cache performance in various time granularities. Based on our collected data, we build a new cache analysis model and study the upper bound of how high percentage of resources could potentially be cached and how effectively the caching works in practice. We report detailed analysis results of different websites and various types of web resources, and identify the problems caused by unsatisfactory cache performance. In particular, we identify two major problems— Redundant Transfer and Miscached Resource , which lead to unsatisfactory cache performance. We investigate three main root causes: Same Content , Heuristic Expiration , and Conservative Expiration Time , and discuss what mobile web developers can do to mitigate those problems.

Time Management for Monte Carlo Tree Search

Monte Carlo Tree Search (MCTS) is a popular approach for tree search in a variety of games. While MCTS allows for fine-grained time control, not much has been published on time management for MCTS programs under tournament conditions. This paper first investigates the effects of various time-management strategies on playing strength in the challenging game of Go. A number of domain-independent strategies are then tested in the domains Connect-4, Breakthrough, Othello, and Catch the Lion. We consider strategies taken from the literature as well as newly proposed and improved ones. Strategies include both semi-dynamic strategies that decide about time allocation for each search before it is started, and dynamic strategies that influence the duration of each move search while it is already running. Furthermore, we analyze the effects of time management strategies on the distribution of time over the moves of an average game, allowing us to partly explain their performance. In the experiments, the domain-independent strategy STOP provides a significant improvement over the state of the art in Go, and is the most effective time management strategy tested in all five domains.

A hybrid ICT-solution for smart meter data analytics

Smart meters are increasingly used worldwide. Smart meters are the advanced meters capable of measuring energy consumption at a fine-grained time interval, e.g., every 15 min. Smart meter data are typically bundled with social economic data in analytics, such as meter geographic locations, weather conditions and user information, which makes the data sets very sizable and the analytics complex. Data mining and emerging cloud computing technologies make collecting, processing, and analyzing the so-called big data possible. This paper proposes an innovative ICT-solution to streamline smart meter data analytics. The proposed solution offers an information integration pipeline for ingesting data from smart meters, a scalable platform for processing and mining big data sets, and a web portal for visualizing analytics results. The implemented system has a hybrid architecture of using Spark or Hive for big data processing, and using the machine learning toolkit, MADlib, for doing in-database data analytics in PostgreSQL database. This paper evaluates the key technologies of the proposed ICT-solution, and the results show the effectiveness and efficiency of using the system for both batch and online analytics.

Learning Individual Behavior Using Sensor Data: The Case of GPS Traces and Taxi Drivers

The ubiquitous deployment of mobile and sensor technologies has led to both the capacity to observe human behavior in physical (offline) settings as well as to record it. This provides researchers with a new lens to study and better understand the individual decision processes that were previously unobserved. In this paper, we study decision making behavior of 11,196 taxi drivers in a large Asian city using a rich data set consisting of 10.6 million fine-grained GPS trip records. These records include detailed taxi GPS trajectories, taxi occupancy data (i.e., whether a taxi was occupied with a passenger or was vacant) and taxi drivers’ daily incomes. This capacity to use data where occupancy of the taxi is known is a distinctive feature of our data set and sets this work apart from prior work which has attempted to study driver behavior. The specific decision we focus on pertains to actions drivers take to find new passengers after they have dropped off their current passengers. In particular, we study the role of information derivable from the GPS trace data (e.g., where passengers are dropped off, where passengers are picked up, longitudinal taxicab travel history with fine-grained time stamps) observable by or made available to drivers in enabling them to learn the distribution of demand for their services over space and time. We conduct our study using a heterogeneous Bayesian learning model. We find strong heterogeneity in individual learning behavior and driving decisions, which is significantly associated with individual economic outcomes. Drivers with higher incomes benefit significantly from their ability to learn from not only demand information directly observable in the local market, but also aggregate information on demand flows across markets. Interestingly, our policy simulations indicate information that is noisy at the individual level becomes valuable after being aggregated across various spatial and temporal dimensions. Moreover, the value of information does not increase monotonically with the scale and frequency of information sharing. Finally, our study has important welfare implications in that efficient information sharing leads to an income increase among all drivers, instead of a redistribution of income between different types of drivers. Our work allows us not only to explain driver decision making behavior using these detailed behavioral traces, but also to prescribe information sharing strategy for the firm in order to improve the overall market efficiency.

The time course of individual face recognition: A pattern analysis of ERP signals

An extensive body of work documents the time course of neural face processing in the human visual cortex. However, the majority of this work has focused on specific temporal landmarks, such as N170 and N250 components, derived through univariate analyses of EEG data. Here, we take on a broader evaluation of ERP signals related to individual face recognition as we attempt to move beyond the leading theoretical and methodological framework through the application of pattern analysis to ERP data. Specifically, we investigate the spatiotemporal profile of identity recognition across variation in emotional expression. To this end, we apply pattern classification to ERP signals both in time, for any single electrode, and in space, across multiple electrodes. Our results confirm the significance of traditional ERP components in face processing. At the same time though, they support the idea that the temporal profile of face recognition is incompletely described by such components. First, we show that signals associated with different facial identities can be discriminated from each other outside the scope of these components, as early as 70ms following stimulus presentation. Next, electrodes associated with traditional ERP components as well as, critically, those not associated with such components are shown to contribute information to stimulus discriminability. And last, the levels of ERP-based pattern discrimination are found to correlate with recognition accuracy across subjects confirming the relevance of these methods for bridging brain and behavior data. Altogether, the current results shed new light on the fine-grained time course of neural face processing and showcase the value of novel methods for pattern analysis to investigating fundamental aspects of visual recognition.

Reactive and proactive control in bilingual word production: An investigation of influential factors

The present study examined how reactive control (indexed by switching costs) and proactive control (indexed by mixing costs) during bilingual language production was modulated by three factors reflected by different time-courses of stimulus presentation. In three experiments, unbalanced Chinese–English bilinguals named digits in Chinese or English according to a naming cue. In Experiment 1, switching costs reduced when participants had longer preparation time to select the target language to name digits (during the Cue-Stimulus interval, CSI), indicating that longer preparation time helps overcome reactive inhibition. In addition, mixing costs declined drastically at a longer preparation time, indicating that a tiny amount of preparation time allows bilinguals to overcome costs associated with proactively preparing two languages. In Experiment 2, the stimuli were presented prior to the cues, so that participants were given different amounts of time to activate the target lexical nodes in both languages before they were informed of the naming language (during the Stimulus-Cue interval, SCI). Symmetrical switching and mixing costs were observed, indicating that bilinguals can strategically boost activation of the target lexical item in the second language (L2) and attempt to equalize it with its translation equivalent in the native language (L1), when they know previously the specific lexical items to be prepared in two languages. In Experiment 3, different Response-Cue intervals (RCIs) were provided after participants named a digit. It was found that the switching cost asymmetry was more prominent when the time to resolve competition was shorter, while the mixing cost asymmetry emerged only with the longest waiting time. These findings provide the first piece of evidence for the dissipation of the reactive inhibition over time, and suggest that longer preparation would allow the proactive control mechanism to be sensitive the relative proficiency levels of the two languages, leading to stronger proactive control on the dominant language. Taken together, the findings in the present study suggest the dynamic nature of reactive and proactive control in unbalanced bilinguals and have important implications for the current models of bilingual language production, which do not explicitly distinguish the two types of control or address how they adapt to the fine-grained time course of the situation.

Context-driven expectations about focus alternatives

What is conveyed by a sentence frequently depends not only on the descriptive content carried by its words, but also on implicit alternatives determined by the context of use. Four visual world eye-tracking experiments examined how alternatives are generated based on aspects of the discourse context and used in interpreting sentences containing the focus operators only and also. Experiment 1 builds on previous reading time studies showing that the interpretations of only sentences are constrained by alternatives explicitly mentioned in the preceding discourse, providing fine-grained time course information about the expectations triggered by only. Experiments 2 and 3 show that, in the absence of explicitly mentioned alternatives, lexical and situation-based categories evoked by the context are possible sources of alternatives. While Experiments 1–3 all demonstrate the discourse dependence of alternatives, only explicit mention triggered expectations about alternatives that were specific to sentences with only. By comparing only with also, Experiment 4 begins to disentangle expectations linked to the meanings of specific operators from those generalizable to the class of focus-sensitive operators. Together, these findings show that the interpretation of sentences with focus operators draws on both dedicated mechanisms for introducing alternatives into the discourse context and general mechanisms associated with discourse processing.

Deciphering the mechanism behind Fibroblast Growth Factor (FGF) induced biphasic signal-response profiles.

BackgroundThe Fibroblast Growth Factor (FGF) pathway is driving various aspects of cellular responses in both normal and malignant cells. One interesting characteristic of this pathway is the biphasic nature of the cellular response to some FGF ligands like FGF2. Specifically, it has been shown that phenotypic behaviors controlled by FGF signaling, like migration and growth, reach maximal levels in response to intermediate concentrations, while high levels of FGF2 elicit weak responses. The mechanisms leading to the observed biphasic response remains unexplained.ResultsA combination of experiments and computational modeling was used to understand the mechanism behind the observed biphasic signaling responses. FGF signaling involves a tertiary surface interaction that we captured with a computational model based on Ordinary Differential Equations (ODEs). It accounts for FGF2 binding to FGF receptors (FGFRs) and heparan sulfate glycosaminoglycans (HSGAGs), followed by receptor-phosphorylation, activation of the FRS2 adapter protein and the Ras-Raf signaling cascade. Quantitative protein assays were used to measure the dynamics of phosphorylated ERK (pERK) in response to a wide range of FGF2 ligand concentrations on a fine-grained time scale for the squamous cell lung cancer cell line H1703. We developed a novel approach combining Particle Swarm Optimization (PSO) and feature-based constraints in the objective function to calibrate the computational model to the experimental data. The model is validated using a series of extracellular and intracellular perturbation experiments. We demonstrate that in silico model predictions are in accordance with the observed in vitro results.ConclusionsUsing a combined approach of computational modeling and experiments we found that competition between binding of the ligand FGF2 to HSGAG and FGF receptor leads to the biphasic response. At low to intermediate concentrations of FGF2 there are sufficient free FGF receptors available for the FGF2-HSGAG complex to enable the formation of the trimeric signaling unit. At high ligand concentrations the ligand binding sites of the receptor become saturated and the trimeric signaling unit cannot be formed. This insight into the pathway is an important consideration for the pharmacological inhibition of this pathway.

Interference between conversation and a concurrent visuomotor task.

We report 2 experiments during which participants conversed with either a confederate (Experiment 1) or a close friend (Experiment 2) while tracking a moving target on a computer screen. In both experiments, talking led to worse performance on the tracking task than listening. We attribute this finding to the increased cognitive demands of speech planning and monitoring. Growth curve analyses of task performance during the beginning and end of conversation segments revealed dynamical changes in the impact of conversation on visuomotor task performance, with increasing impact during the beginning of speaking segments and decreasing impact during the beginning of listening segments. At the end of speaking and listening segments, this pattern reversed. These changes became more pronounced with increased difficulty of the task. Together, these results show that the planning and monitoring aspects of conversation require the majority of the attentional resources that are also used for nonlinguistic visuomotor tasks. The fact that similar results were obtained when conversing with both a confederate and a friend indicates that our findings apply to a wide range of conversational situations. This is the first study to show the fine-grained time course of the shifting attentional demands of conversation on a concurrently performed visuomotor task.

Energy supply planning and supply chain optimization under uncertainty

In energy supply planning and supply chain design, the coupling between long-term planning decisions like capital investment and short-term operation decisions like dispatching present a challenge, waiting to be tackled by systems and control engineers. The coupling is further complicated by uncertainties, which may arise from several sources including the market, politics, and technology. This paper addresses the coupling in the context of energy supply planning and supply chain design. We first discuss a simple two-stage stochastic program formulation that addresses optimization of an energy supply chain in the presence of uncertainties. The two-stage formulation can handle problems in which all design decisions are made up front and operating parameters act as ‘recourse’ decisions that can be varied from one time period to next based on realized values of uncertain parameters. The design of a biodiesel production network in the Southeastern region of the United States is used as an illustrative example. The discussion then moves on to a more complex multi-stage, multi-scale stochastic decision problem in which periodic investment/policy decisions are made on a time scale orders of magnitude slower than that of operating decisions. The problem of energy capacity planning is introduced as an example. In the particular problem we examine, annual acquisition of energy generation capacities of various types are coupled with hourly energy production and dispatch decisions. The increasing role of renewable sources like wind and solar necessitates the use of a fine-grained time scale for accurate assessment of their values. Use of storage intended to overcome the limitations of intermittent sources puts further demand on the modeling and optimization. Numerical challenges that arise from the multi-scale nature and uncertainties are reviewed and some possible modeling and numerical solution approaches are discussed.

The syntactic complexity of Russian relative clauses.

Although syntactic complexity has been investigated across dozens of studies, the available data still greatly underdetermine relevant theories of processing difficulty. Memory-based and expectation-based theories make opposite predictions regarding fine-grained time course of processing difficulty in syntactically constrained contexts, and each class of theory receives support from results on some constructions in some languages. Here we report four self-paced reading experiments on the online comprehension of Russian relative clauses together with related corpus studies, taking advantage of Russian’s flexible word order to disentangle predictions of competing theories. We find support for key predictions of memory-based theories in reading times at RC verbs, and for key predictions of expectation-based theories in processing difficulty at RC-initial accusative noun phrase (NP) objects, which corpus data suggest should be highly unexpected. These results suggest that a complete theory of syntactic complexity must integrate insights from both expectation-based and memory-based theories.

Energy Supply Chain Optimization: A Challenge for Control Engineers?

Energy systems engineering problems are oftentimes complicated by factors like large amounts of uncertainties and multi-scale nature of decisions. This paper examines a particular aspect of energy systems engineering problems that gives rise to such complications: The coupling between long-term planning decisions like capital investment and policy and shorter-term decisions like production capacity operation and logistics. The paper starts with the discussion of a simple two-stage stochastic program that addresses optimization of an energy supply chain in the presence of uncertainties. The two-stage formulation can handle problems in which all design decisions are made up front and operating parameters act as ‘recourse' decisions that can be varied from one time period to another based on realized values of uncertain parameters. The design of a biodiesel production network in the Southeastern region of the United States is used as an illustrative example. The discussion then moves on to a more complex multi-stage, multi-scale stochastic decision problem in which periodic investment / policy decisions are to be made on a time-scale orders of magnitude slower than that of operating decisions. The problem of energy policy modeling is introduced as an example. In the particular energy policy modeling problem we examine, annual acquisition of energy generation capacities of various types are coupled with hourly energy production and dispatch decisions. The increasing role of renewable sources like wind and solar necessitates the use of a fine-grained time scale for accurate assessment of their values. The use of storage intended to overcome the limitations of the intermittent sources puts further demand on the modeling. Numerical challenges that arise from the multi-scale nature and uncertainties are reviewed and some possible modeling and solution approaches are discussed. Approximate dynamic programming is proposed as a promising algorithmic strategy to handle such challenges.

CPS: Operating system architecture for efficient network resource management with control-theoretic packet scheduler

The efficient network resource management is one of the important topics in a real-time system. In this paper, we present a practical network resource management framework, control-theoretic packet scheduler (CPS) system. Using our framework, an operating system can schedule both input and output streams accurately and efficiently. Our framework adopts very portable feedback control theory for efficiency and accuracy. The CPS system is able to operate independent of the internal network protocol state, and it is designed to schedule packet streams in fine-grained time intervals to meet the resource requirement. This approach simplifies the design of the CPS system, and leads us to obtain the intended output bandwidth. We implemented our prototype system in Linux, and measured the performance of the network resource management system under various network QoS constraints. The distinctive features of our principles are as follows: It is robust and accurate, and its operation is independent of internal network protocols.