Miracle-dz/UniFAR-FTUs huggingface dataset:
Performance Evaluation of Multicast Video Streaming via P2P Networking

the past few years, the multicast video streaming via P2P networking has become a very successful and increasingly popular in distributing multimedia content by encouraging an important number of users to act as both clients and servers. Video on demand (VoD) streaming one such service where videos are delivered to asynchronous users with minimum delay and free interconnectivity. The VoD is costly due to the limited upload capacity of the video server and traditional centralized client/server architecture. Peer-to-Peer streaming techniques are an approach to alleviate server load through peer-assisted sharing. Proxy caching is a key technique to reduce transmission cost for on-demand multimedia streaming. This innovative approach combines the advantages of both proxy caching and peer-to-peer client communications. In this paper, firstly we provide a better understanding of the basic concept of multicast video streaming, media delivery structure, streaming media storage size, bandwidth requirement, video streaming architecture and then present a novel approach for the performance evaluation of multicast video streaming via P2P networking using servers and proxy servers situated between local area networks (LANs). The results demonstrate the benefits of multicast video streaming on the server-proxy paths.
Planning with Continuous Resources in Stochastic Domains

We consider the problem of optimal planning in stochastic domains with resource constraints, where resources are continuous and the choice of action at each step may depend on the current resource level. Our principal contribution is the HAO* algorithm, a generalization of the AO* algorithm that performs search in a hybrid state space that is modeled using both discrete and continuous state variables. The search algorithm leverages knowledge of the starting state to focus computational effort on the relevant parts of the state space. We claim that this approach is especially effective when resource limitations contribute to reachability constraints. Experimental results show its effectiveness in the domain that motivates our research - automated planning for planetary exploration rovers.
Cross dissolve without cross fade: further details

Overview This document contains additional explanatory text supporting our Eurographics 2006 paper: “Cross dissolve without cross fade: preserving contrast, color and salience in image compositing”, M. Grundland, R. Vohra, G. P. Williams, and N. A. Dodgson, Computer Graphics Forum, 25(3), E. Groller and L. Szirmay-Kalos (guest editors), 2006 That paper is terse in order to fit within the ten page limit set by Eurographics. This document provides more detailed background to this research and an extended discussion of related work.
Mobile dual arm robotic workers with embedded cognition for hybrid and dynamically reconfigurable manufacturing systems

Grant Agreement No : 723616 Project Acronym : THOMAS Project Start Date : 1 st October, 2016 Consortium : UNIVERSITY OF PATRAS (LMS) PEUGEOT CITROEN AUTOMOBILES S.A. (PSA) SICK AG (SICK) FUNDATION TECHNALIA RESEARCH & INNOVATION (TECNALIA) ROBOCEPTION GMBH (ROBOCEPTION) DGH ROBOTICA, AUTOMATIZACION Y MANTENIMIENTO INDUSTRIAL SA (DGH) AERNNOVA AEROSPACE S.A. U. (AERNNOVA) INTRASOFT INTERNATIONAL SA (INTRASOFT)
Title Predicting outcomes of steady-state 13 C isotope tracing experiments with Monte Carlo sampling

Background: Carbon-13 (C) analysis is a commonly used method for estimating reaction rates in biochemical networks. The choice of carbon labeling pattern is an important consideration when designing these experiments. We present a novel Monte Carlo algorithm for finding the optimal substrate input label for a particular experimental objective (flux or flux ratio). Unlike previous work, this method does not require assumption of the flux distribution beforehand. Results: Using a large E. coli isotopomer model, different commercially available substrate labeling patterns were tested computationally for their ability to determine reaction fluxes. The choice of optimal labeled substrate was found to be dependent upon the desired experimental objective. Many commercially available labels are predicted to be outperformed by complex labeling patterns. Based on Monte Carlo Sampling, the dimensionality of experimental data was found to be considerably less than anticipated, suggesting that effectiveness of C experiments for determining reaction fluxes across a large-scale metabolic network is less than previously believed. Conclusions: While C analysis is a useful tool in systems biology, high redundancy in measurements limits the information that can be obtained from each experiment. It is however possible to compute potential limitations before an experiment is run and predict whether, and to what degree, the rate of each reaction can be resolved.
Querying Weak Labels From Oracle Unlabeled Pool Labeled Pool Object Detector Querying Strong Labels From Oracle Actively Sample Images Using Standard Methods Retrain Using Updated Labeled Pool Weak Labeled Pool

Background: Panicle density of cereal crops such as wheat and sorghum is one of the main components for plant breeders and agronomists in understanding the yield of their crops. To phenotype the panicle density effectively, researchers agree there is a significant need for computer vision-based object detection techniques. Especially in recent times, research in deep learning-based object detection shows promising results in various agricultural studies. However, training such systems usually requires a lot of bounding-box labeled data. Since crops vary by both environmental and genetic conditions, acquisition of huge amount of labeled image datasets for each crop is expensive and time-consuming. Thus, to catalyze the widespread usage of automatic object detection for crop phenotyping, a cost-effective method to develop such automated systems is essential.
Aligning an ERP system with enterprise requirements : An object-process based approach

One of the main problems in ERP implementation projects is how to align an off-the-shelf software packagewith the business processes of the enterprise implementing it. The paper proposes a requirement-driven approach, which benefits from reusing the business process design without being restricted by predefined solutions and criteria. The approach applies an iterative alignment process, which employs an algorithm that matches a model of the enterprise requirements with a model of the ERP system capabilities. The algorithm identifies possible matches between the two models and evaluates the gaps between them despite differences in their completeness and detail level. It provides the enterprise with a set of feasible combinations of requirements that can be satisfied by the ERP system as a basis for making implementation decisions. We use Object-Process Methodology (OPM) to model both the ERP system and the enterprise requirements, and utilize the pair of resulting OPM models as input for the matching algorithm. The alignment algorithm has been tested in an experimental study, whose encouraging results demonstrate the ability of the approach to provide a satisfactory solution to the problem of aligning an ERP software package with an enterprise business model. # 2005 Elsevier B.V. All rights reserved.
Pest Conference

: For centuries, coyotes have been controlled to protect livestock and/or enhance game populations. The intensity of control has varied widely and many types of control techniques have been used. The effects of these control techniques need to be evaluated to effectively resolve conflicts, to fulfill legal requirements, and to aid the development of new strategies. However, the influence of these techniques on coyote population size and structure is largely unknown. Furthermore, management decisions are often required before experimental tests can be developed, and conducting requisite experimental programs on meaningful scales are logistically prohibitive. Therefore, we developed an individual-based computer model to evaluate the effects of various control techniques on age structure including selective removal, random removal, and denning. This model is part of a larger effort to fully evaluate the effect of current management strategies on coyote populations and to eventually link this population model to a depredation model. Selective and random removal resulted in younger age structures, whereas denning produced population age structures similar to an unexploited population.
The M/M/k with Deterministic Setup Times

Capacity management, whether it involves servers in a data center, or human staff in a call center, or doctors in a hospital, is largely about balancing a resource-delay tradeoff. On the one hand, one would like to turn off servers when not in use (or send home staff that are idle) to save on resources. On the other hand, one wants to avoid the considerable setup time required to turn an ''off'' server back ''on. '' This paper aims to understand the delay component of this tradeoff, namely, what is the effect of setup time on average delay in a multi-server system? Surprisingly little is known about the effect of setup times on delay. While there has been some work on studying the M/M/k with Exponentially-distributed setup times, these works provide only iterative methods for computing mean delay, giving little insight as to how delay is affected by k, by load, and by the setup time. Furthermore, setup time in practice is much better modeled by a Deterministic random variable, and, as this paper shows, the scaling effect of a Deterministic setup time is nothing like that of an Exponentially-distributed setup time. This paper provides the first analysis of the M/M/k with Deterministic setup times. We prove a lower bound on the effect of setup on delay, where our bound is highly accurate for the common case where the setup time is much higher than the job service time. Our result is a relatively simple algebraic formula which provides insights on how delay scales with the input parameters. Our proof uses a combination of renewal theory, martingale arguments and novel probabilistic arguments, providing strong intuition on the transient behavior of a system that turns servers on and off.
Multimodal language processing

Multimodal interfaces enable more natural and effective humancomputer interaction by providing multiple channels through which input or output may pass. In order to realize their full potential, they need to support not just input from multiple modes, but synchronized integration of semantic content from different modes. This paper describes a multimodal language processing architecture which allows for declarative statement of multimodal integration strategies in a unification-based grammar formalism. The architecture is currently deployed in a working system enabling interaction with dynamic maps using speech and pen, but the approach is more general and supports a wide variety of other potential multimodal interfaces.
COVER SHEET FOR PROPOSAL TO THE NATIONAL SCIENCE FOUNDATION

REQUESTED AMOUNT $ PROPOSED DURATION (1-60 MONTHS) months REQUESTED STARTING DATE SHOW RELATED PREPROPOSAL NO., IF APPLICABLE CHECK APPROPRIATE BOX(ES) IF THIS PROPOSAL INCLUDES ANY OF THE ITEMS LISTED BELOW BEGINNING INVESTIGATOR (GPG 1.A.3) DISCLOSURE OF LOBBYING ACTIVITIES (GPG II.D.1) PROPRIETARY & PRIVILEGED INFORMATION (GPG II.D.10) NATIONAL ENVIRONMENTAL POLICY ACT (GPG II.D.10) HISTORIC PLACES (GPG II.D.10) SMALL GRANT FOR EXPLOR. RESEARCH (SGER) (GPG II.D.12) VERTEBRATE ANIMALS (GPG II.D.12) IACUC App. Date HUMAN SUBJECTS (GPG II.D.12) Exemption Subsection or IRB App. Date INTERNATIONAL COOPERATIVE ACTIVITIES: COUNTRY/COUNTRIES
Assembly pointers for variable binding in networks of spiking 1 neurons

5 Currently existing neural network models lack an important computational capability of 6 networks of neurons in the brain: the capability to reason on an abstract level, and to structure 7 information according to abstract categories. While there is hardly anything known about 8 underlying processes for that in the human brain, experimental evidence for the organization 9 of an important prerequisite for abstract reasoning has recently been found: for binding words 10 to syntactic categories, such as subject or object. Based on these experimental data from fMRI 11 recordings we propose a model for binding of semantic content to variables that is consistent 12 with these data and provides new functions to neural network computations. We provide a 13 proof of principle that this model can be implemented even in networks of spiking neurons. 14 It enables structured information retrieval and copying of information without routing, and 15 provides a basis for the emulation of more demanding cognitive computations in networks of 16 spiking neurons. [February 2, 2018, 12:04] 17
Probabilistic Generative Models

A generative model is a model that learns to imitate some patterngenerating process. That is, we have (high-dimensional) patterns x ∈ X that are generated according to a probability distribution p on X . We want to learn a model pθ that is indistinguishable from p. In particular, our model should (1) avoid missing patterns that are generated by p (this is called mode collapse) and (2) avoid generating patterns that are not supported by p. A famous example is the example of generating human faces, where we want to generate facial features proportional to how frequently they occur in real human faces.
The Impacts of Uncertainty/Variability in the C8 Exposure Assessment on Serum PFOA Concentration Predictions and the Epidemiological Association with Preeclampsia

Author(s): Avanasi Narasimhan, Raghavendhran | Advisor(s): Bartell, Scott M | Abstract: Previous studies produced the C8 exposure assessment which included PFOA release assessment, integrated fate and transport modeling, and dose reconstruction to predict the serum PFOA concentration of each individual in the C8 Health Project population from 1951 to 2008. The serum concentration predictions were used in various C8 Science Panel epidemiological studies to evaluate whether there is a ‘probable link’ between PFOA exposure and health effects. One such study analyzed the association with preeclampsia among the participants and found a moderate association (Savitz et al., 2012a). For this study, uncertainties in spatiotemporal predictions of PFOA water/air concentrations and in individual-level variables used in the dose-reconstruction and pharmacokinetic models likely resulted in some exposure measurement error, potentially affecting the validity of the epidemiological findings. The main objective of this dissertation was to analyze the impacts of different sources of input parameter uncertainty/variability in the C8 exposure assessment and study the impacts on the serum PFOA concentration predictions and the epidemiological association with preeclampsia. Monte Carlo (MC) simulation techniques were used to conduct these uncertainty analyses in this exposure assessment-environmental epidemiology model system.I found that autocorrelated and shared uncertainty in the PFOA water concentration estimates produced a highly variable set of plausible serum PFOA concentrations for study participants; however, it had less impact on the AOR of preeclampsia occurrence. Together, inter-individual variability and epistemic uncertainty in independent individual-level exposure parameters including water ingestion rates, the serum PFOA half-life, and the volume of distribution for PFOA impacted the serum PFOA concentration predictions and their association with preeclampsia moderately, with a 25% bias towards the null in the AOR of preeclampisa occurrence. Geocoding based uncertainty in residential addresses and work history together moderately impacted the rank exposure among the participants and caused a 41% bias away from the null in the AOR of preeclampsia occurrence. Future studies with complex exposure scenarios and multiple sources of variability/uncertainty might benefit from our approach of separating out the different kinds of uncertainty to better understand their individual impacts on the validity of epidemiological associations
Internet of Things‐based wireless networking for seismic applications

There is growing pressure from regulators on operators to adhere to increasingly stricter regulations related to the environment and safety. Hence, operators are required to predict and contain risks related to hydrocarbon production and their infrastructure in order to maintain their licence to operate. A deeper understanding of production optimisation and production‐related risk requires strengthened knowledge of reservoir behaviour and overburden dynamics. To accomplish this, sufficient temporal and spatial resolution is required as well as an integration of various sources of measurements. At the same time, tremendous developments are taking place in sensors, networks, and data analysis technologies. Sensors and accompanying channels are getting smaller and cheaper, and yet they offer high fidelity. New ecosystems of ubiquitous wireless communications including Internet of Things nowadays allow anyone to affordably connect to the Internet at any time and anywhere. Recent advances in cloud storage and computing combined with data analytics allow fast and efficient solutions to handle considerable amounts of data. This paper is an effort to pave the way for exploiting these three fundamental advances to create Internet of Things‐based wireless networks of seismic sensors.
A data fusion algorithm for multi-sensor dynamic system based on Interacting Multiple Model

We present an algorithm of data fusion estimation for dynamic system with multi-sensor and uncertain system models based on Kalman filtering and Interacting Multiple Model. The algorithm estimates the target state using interacting multiple model filtering method after using augmented multi-sensor fusion method. And this method is also available when the system contains different kinds of sensors or the measurement errors of different sensors are related. We test and verify the feasibility of this estimation algorithm through simulation and discuss the effect of the number of sensor on the estimation precision. Results show that, simply increasing the number of sensor cannot always improve the performance of the estimation. Type and number of sensor should be optimized in practical applications.
Nonparametric Classi(cid:12)cation with Polynomial MPMC Cascades

A new class of nonparametric algorithms for high-dimensional binary classi(cid:12)cation is presented using cascades of low dimensional polynomial structures. Construction of polynomial cascades is based on Minimax Probability Machine Classi(cid:12)cation (MPMC) [Lanckriet et al. , 2002], which results in direct estimates of classi(cid:12)cation accuracy, and provides a simple stopping criteria that does not require expensive cross-validation measures. This Polynomial MPMC Cascade (PMC) algorithm is constructed in linear time with respect to the input space dimensionality, and linear time in the number of examples, making it an attractive alternative to computationally expensive algorithms like support vector machines and standard MPMC. Experimental evidence is given showing that, compared to state-of-the-art classi(cid:12)ers, PMCs are competitive; inherently fast to compute; not prone to over(cid:12)tting; and generally yield accurate estimates of the maximum error rate on unseen data.
Fabrication and Analysis of Complex Electrospun Scaffolds and Constructs

Tissue construct mechanics are of pivotal importance in mechanically active tissues, particularly where the construct must assume a functional role immediately upon implantation and during the remodeling period. While numerous methods exist to generate porous scaffolds for tissue engineering, electrospinning has become increasingly applied as a means of generating microfibrillar scaffolds that provide surface structures roughly comparable in scale to that seen with ECM proteins such as collagen. Simple mechanical relationships have been explored in electrospun scaffolds such as simple fiber alignment to achieve planar anisotropy, however more thorough structure-function characterization has not been pursued to date. Further, the effect of cellular or other component incorporation has not been studied. An understanding of cellular function within scaffolds is critical for the design of seeded tissue constructs. For these reasons, this research aimed to gain an understanding of how structural and compositional modifications to electrospun scaffolds can result in a change in mechanical function and host response. This research question was approached through a series of directed modifications to the electrospinning process designed to alter the microenvironment within the scaffold. These micro-scale changes were related to tissue-level changes in both planar biaxial and flexural mechanical response. This knowledge was used to fabricate cellularized constructs for cardiac right ventricular outflow tract replacement in order to discern the fate and function of implanted cells within electrospun scaffolds. The results from this research can be combined with structural deterministic modeling in order to gain a more complete understanding of the mechanical and biological function of cellularized electrospun constructs and therefore help guide future work in the rational design of engineered tissues.
Innovations-Based State Estimation with ireless Sensor Networks 4858

We study a state estimation architecture for sensor networks, where several sensors transmit quantized innovations to a central estimator. Transmission is via a wireless channel, which is prone to fading leading to random packet loss. State estimation is carried out at the gateway via a time-varying Kalman filter which accounts for packet loss and quantization effects. To form the innovations at the sensors, the estimator transmits information regarding its current state estimate to the sensors. This information could be dedicated to each sensor or broadcast to all sensors. In addition, the gateway also decides upon power levels and quantization step-sizes to be used by each sensor node. Here, we adopt elements of predictive control to trade off estimation performance versus energy use.
“ FBRAM : A New Form of Memory Opti mized for 3 D Graphics ”

3,394,354 A 7/1968 Senzig . , 340/172.5 3.675,218 A 7/1972 Sechler . 340/173 3,848.234 A 11/1974 MacDonald . 340/172.5 4,056.845. A 11/1977 Churchill, Jr. . 364/200 4,086,662 A 4/1978 Itoh . 365/189 4,104,719 A 8/1978 Chu et al. . 340/172.5 4,106,109 A 8/1978 Fassbender . 365/238 4,110,842 A 8/1978 Sarkissian et al. . 365/233 4,156.290 A 5/1979 Lanza . 365/230 4,156,905 A 5/1979 Fassbender . 364/200 4,156,938 A 5/1979 Proebsting et al. . 365/63
the Paper 1077 "Write Like You: Synthesizing Your Cursive Online Chinese Handwriting via Metric-based Meta Learning"

1 MONOTONIC ATTENTION Monotonic attention mechanism [4] attends to the memory (i.e., output of content encoder [hc,1,hc,2, .,hc,N ] in our case) in a monotonic manner: if the decoder attends to the hc,ti−1 at previous decoding time step i − 1, at current decoding time step i , we begin processing memory entries starting at index ti−1, namely we calculates the score scalar of hc, j for j = ti−1, ti−1 + 1, . ,N . Then we use a logistic sigmoid function σ (·) to transform these score scalars into probabilities pi, j and sample zi, j from a Bernoulli distribution parameterized by pi, j :