Miracle-dz/UniFAR-FTUs huggingface dataset:
Source-Channel Rate Optimization for Progressive Image Transmission Over Block Fading Relay Channels

In this paper, we are concerned with the design and analysis of joint source-channel coding schemes for block fading channels with relay-assisted distributed spatial diversity. Assuming a progressive image coder with a constraint on the transmission bandwidth, we formulate a joint source-channel rate allocation scheme that maximizes the expected source throughput. Specifically, using Gaussian as well as BPSK inputs on flat Rayleigh fading channels, we lower bound the average packet error rate by the corresponding mutual information outage probability, and derive the average throughput expression as a function of channel code rates as well as channel SNR for both a frequency-division multiplexing-based baseline system without relaying, and a half-duplex relay system with a decode-and-forward protocol. At high signal-to-noise ratio (SNR), for the systems considered in this paper, we show that our rate optimization problem is a convex function of the channel code rates, and we show that a known recursive algorithm can be used to predict the performance of both systems. IEEE Transactions on Communications (June 2010) This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Research Laboratories, Inc.; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Research Laboratories, Inc. All rights reserved. Copyright c ©Mitsubishi Electric Research Laboratories, Inc., 2010 201 Broadway, Cambridge, Massachusetts 02139
Minimizing Learning Behavior in Repeated Real-Effort Tasks

In this paper, we discuss learning behavior and the heterogeneity of subjects' ability to perform in real-effort tasks. Afterwards, we present a novel variant of Erkal et al.'s (2011) encryption real-effort task which aims to minimize learning behavior in repeated settings. In the task, participants encrypt words into numbers. In our variant, we apply a double-randomization mechanism to minimize learning and heterogeneity. Existing experiments with repeated real-effort tasks find a performance increase of 12-14% between the first and second half. By contrast, our task mitigates learning behavior down to 2% between the first and second half. The data show that subjects show a small heterogeneity in performance.
Source simulation for tsunamis; lessons learned from fault rupture modeling of the Cascadia subduction zone, North America

Three benchmark cases are proposed to study tsunamis generated by underwater landslides. Two distinct numerical models are applied to each benchmark case. Each model involves distinct center of mass motions and rates of landslide deformation. Computed tsunami amplitudes agree reasonably well for both models, although there are differences that remain to be explained. One of the benchmark cases is compared to laboratory experiments. The agreement is quite good with the models. Other researchers are encouraged to employ these benchmark cases, in future experimental or numerical work. Science of Tsunami azards, Vol 18, NO. 2 (2000) page 107 performed laboratory experiments. comnutational or exnerimental effort. Each case is two-dimensional in order reduce rigure 1: Definition sketch of the simulation domain in II and GW Models, and of initial landslide parameters We compare results from two distinct numerical models. We hope that this work will promote future numerical andexperimental comparisons. The comparisons made here are by no means the end ofthis effort.
International Conference on Information Systems ( ICIS ) December 2003 Defining Open Source Software Project Success

Information systems success is one of the most widely used dependent variables in information systems research. In this paper, we identify a range of measures that can be used to assess the success of open source software (OSS) projects. We identify measures based on a review of the literature, a consideration of the OSS development process and an analysis of the opinions of OSS developers. For each measure, we provide examples of how they might be used in a study of OSS development.
An overview of the BIOASQ large-scale biomedical semantic indexing and question answering competition

George Tsatsaronis (george.tsatsaronis@biotec.tu-dresden.de) Georgios Balikas (georgios.balikas@imag.fr) Prodromos Malakasiotis (rulller@gmail.com) Ioannis Partalas (ioannis.partalas@imag.fr) Matthias Zschunke (mzschunke@transinsight.com) Michael R Alvers (malvers@transinsight.com) Dirk Weissenborn (dirk.weissenborn@gmail.com) Anastasia Krithara (akrithara@iit.demokritos.gr) Sergios Petridis (eserxio@gmail.com) Dimitris Polychronopoulos (dpolychr@gmail.com) Yannis Almirantis (yalmir@bio.demokritos.gr) John Pavlopoulos (annis@aueb.gr) Nicolas Baskiotis (nicolas.baskiotis@lip6.fr) Patrick Gallinari (Patrick.Gallinari@lip6.fr) Thierry Artiéres (thierry.artieres@lip6.fr) Axel Ngonga (axel.ngonga@gmail.com) Norman Heino (heino@informatik.uni-leipzig.de) Eric Gaussier (eric.gaussier@imag.fr) Liliana Barrio-Alvers (lalvers@transinsight.com) Michael Schroeder (ms@biotec.tu-dresden.de) Ion Androutsopoulos (ion@aueb.gr) Georgios Paliouras (paliourg@iit.demokritos.gr)
SOCIAL NETWORKING AND INDIVIDUAL OUTCOMES BEYOND THE MEAN FIELD CASE

This paper examines social interactions when social networking may be endogenous. It employs a linear-quadratic model that accommodates contextual effects and endogenous interactions, that is local ones where individuals react to the decisions of their neighbors, and global ones, where individuals react to the mean decision in the economy, both with a lag. Unlike the simple V AR(1) form of the structural model, the planner’s problem involves intertemporal optimization and leads to a system of linear difference equations with expectations. It also highlights an asset-like property of socially optimal outcomes in every period which helps characterize the shadow values of connections among agents. Endogenous networking is easiest to characterize when individuals choose weights of social attachment to other agents. It is much harder to do so when networking is discrete. The paper also poses the inverse social interactions problem, that is whether it is possible to design a social network whose agents’ decisions will obey an arbitrarily specified variance covariance matrix.
Model checking of multiuser power control algorithm for digital subscriber lines

A stainless steel wire for springs is proposed which has a colored coating of resinous paint thereon. The wire is coated with a paint, dried and baked to form a coating and is then drawn to a desired diameter. The coating has a good adhesiveness and good heat resistance. The stainless steel wire may be nickel-plated before painting.
Distributed Key Management Scheme Based on ECC for Heterogeneous Sensor Networks

Routing-driven key management schemes integrated with centralized routing for heterogeneous sensor networks (HSNs) mainly have two shortcomings: high communication energy consumption, and large storage requirements. To address the above problems, we propose a distributed key management scheme for HSNs. Taking advantage of distributed routing protocol, the scheme achieves to reduce communication cost and improve adaptive capacity. In the key agreement process, the scheme utilizes Identity-based Encrypt to reduce storage requirement, implement message authentication, and enhance key agreement efficiency. Comparison and theory analysis show that the proposed scheme the scheme has lower storage requirement and communication energy consumption, and better security and adaptive characteristic.
Deep Latent Low-Rank Fusion Network for Progressive Subspace Discovery

Low-rank representation is powerful for recover-ing and clustering the subspace structures, but it cannot obtain deep hierarchical information due to the single-layer mode. In this paper, we present a new and effective strategy to extend the sin-gle-layer latent low-rank models into multi-ple-layers, and propose a new and progressive Deep Latent Low-Rank Fusion Network (DLRF-Net) to uncover deep features and struc-tures embedded in input data. The basic idea of DLRF-Net is to refine features progressively from the previous layers by fusing the subspaces in each layer, which can potentially obtain accurate fea-tures and subspaces for representation. To learn deep information, DLRF-Net inputs shallow fea-tures of the last layers into subsequent layers. Then, it recovers the deeper features and hierar-chical information by congregating the projective subspaces and clustering subspaces respectively in each layer. Thus, one can learn hierarchical sub-spaces, remove noise and discover the underlying clean subspaces. Note that most existing latent low-rank coding models can be extended to multi-layers using DLRF-Net. Extensive results show that our network can deliver enhanced perfor-mance over other related frameworks.
International Journal of Computer Science and Informatics International

-Now a day's mobile devices are multi-functional devices capable of hosting a broad range of applications for both business and consumer use. Personal Digital Assistants (PDA) and the ever-growing category of smart phones allow people to access the Internet for e-mail, instant messaging, text messaging and Web browsing, as well as work documents, contact lists and more. This paper presents an overview of mobile devices and also the operating systems used in the devices.
language teaching

This paper extends the use of Generalizability Theory to the measurement of extemporaneous L2 speech through the lens of speech perception. Using six datasets of previous studies, it reports on G studies –a method of breaking down measurement variance–and D studies –a predictive study of the impact on reliability when modifying the number of raters, items, or other facets that assist the field in adopting measurement designs that include comprehensibility, accentedness, and intelligibility. When data from a single audio sample per learner were subjected to D-studies, we find that both semantic differential and rubric scales for comprehensibility were reliable at the . 90 level with about 15 trained raters or 50 untrained crowdsourced raters. In order to offer generalizable and dependable evaluations, empirically informed recommendations are given, including considerations for the number of speech samples rated, or the granularity of the scales for various assessment and research purposes.
Explicit Transport Error Notification ( ETEN ) for Error-Prone Wireless and Satellite Networks – Summary

This paper is a summary of the BBN Technical Report No. 8333, “Explicit Transport Error Notification for Error-Prone Wireless and Satellite Networks. ” In this study we discuss two types of Explicit Transport Error Notification (ETEN) mechanisms: (i) per-packet mechanisms that notify endpoints of each detected corruption; and (ii) cumulative mechanisms that notify endpoints of aggregate corruption statistics. We have implemented the proposed mechanisms in the ns-2 simulator. We present simulation results on performance gains achievable for TCP Reno and TCP SACK, using ETEN mechanisms over a wide range of bit error rates and traffic conditions. We compare TCP Reno and TCP SACK enhanced with ETEN mechanisms against TCP Westwood, which uses a bandwidth estimation strategy in place of the traditional AIMD congestion avoidance algorithm. We discuss two issues related to the practical deployment of ETEN mechanisms: corruption detection mechanisms (and their co-operation with ETEN-based recovery in the transport layer) and security aspects. We include recommendations for further work.
ScholarWorks@UMass Amherst

Wireless and satellite networks often have non-negligible packet corruption rates that can signiﬁcantly degrade TCP performance. This is due to TCP’s assumption that every packet loss is an indication of network congestion (causing TCP to reduce the transmission rate). This problem has received much attention in the literature. In this paper, we take a broad look at the problem of enhancing TCP performance under corruption losses, and include a discussion of the key issues. The main contributions of this paper are: ( i ) a conﬁrmation of previous studies that show the reduction of TCP performance in the face of corruption loss, and in addition a plausible upper bound achievable with perfect knowledge of the cause of loss, ( ii ) a classiﬁcation of the potential mitigation space, and ( iii ) the introduction of a promising new mitigation that employs rich cumulative information from intermediate nodes in a path to form a better congestion response. We ﬁrst illustrate the performance implications of corruption-based loss for a variety of networks via simulation. In addition, we show a rough upper bound on the performance gains a TCP could get if it could perfectly determine the cause of each segment loss – independent of any speciﬁc mechanism for TCP to learn the root cause of packet loss. Next, we provide a taxonomy of potential practical classes of mitigations that TCP end-points and intermediate network elements can cooperatively use to decrease the performance impact of corruption-based loss. Finally, we brieﬂy consider a potential mitigation, called cumulative explicit transport error notiﬁcation (CETEN), which covers a portion of the solution space previously unexplored. CETEN is shown to be a promising mitigation strategy, but a strategy with numerous formidable practical hurdles still to overcome.
Computational Visual Media Computational Visual Media

This paper proposes a kernel-blending connection approximated by a neural network (KBNN) for image classiﬁcation. A kernel mapping connection structure, guaranteed by the function approximation theorem, is devised to blend feature extraction and feature classiﬁcation through neural network learning. First, a feature extractor learns features from the raw images. Next, an automatically constructed kernel mapping connection maps the feature vectors into a feature space. Finally, a linear classiﬁer is used as an output layer of the neural network to provide classiﬁcation results. Furthermore, a novel loss function involving a cross-entropy loss and a hinge loss is proposed to improve the generalizability of the neural network. Experimental results on three well-known image datasets illustrate that the proposed method has good classiﬁcation accuracy and generalizability.
Shallow Unorganized Neural Networks using Smart Neuron Model for Visual Perception

The recent success of Deep Neural Networks (DNNs) has revealed the significant capability of neural computing in many challenging applications. Although DNNs are derived from emulating biological neurons, there still exist doubts over whether or not DNNs are the final and best model to emulate the mechanism of human intelligence. In particular, there are two discrepancies between computational DNN models and the observed facts of biological neurons. First, human neurons are interconnected randomly, while DNNs need carefully-designed architectures to work properly. Second, human neurons usually have a long spiking latency (~100ms) which implies that not many layers can be involved in making a decision, while DNNs could have hundreds of layers to guarantee high accuracy. In this paper, we propose a new computational model, namely shallow unorganized neural networks (SUNNs), in contrast to ANNs/DNNs. The proposed SUNNs differ from standard ANNs or DNNs in three fundamental aspects: 1) SUNNs are based on an adaptive neuron cell model, Smart Neurons, that allows each artificial neuron cell to adaptively respond to its inputs rather than carrying out a fixed weighted-sum operation like the classic neuron model in ANNs/DNNs; 2) SUNNs can cope with computational tasks with very shallow architectures; 3) SUNNs have a natural topology with random interconnections, as the human brain does, and as proposed by Turing's B-type unorganized machines. We implemented the proposed SUNN architecture and tested it on a number of unsupervised early stage visual perception tasks. Surprisingly, such simple shallow architectures achieved very good results in our experiments. The success of our new computational model makes it the first workable example of Turing's B-Type unorganized machine that can achieve comparable or better performance against the state-of-the-art algorithms.
Voxel-Wise Brain Graphs from Diffusion MRI: Intrinsic Eigenspace Dimensionality and Application to fMRI

Structural brain graphs are conventionally limited to deﬁn-ing nodes as gray matter regions from an atlas, with edges reﬂecting the density of axonal projections between pairs of nodes. Here we explicitly model the entire set of voxels within a brain mask as nodes of high-resolution, subject-speciﬁc graphs. We deﬁne the strength of local voxel-to-voxel connections using diffusion tensors and orientation distribution functions derived from diffusion-weighted MRI data. We study the graphs’ Laplacian spectral properties on data from the Human Connectome Project. We then assess the extent of inter-subject variability of the Laplacian eigenmodes via a procrustes validation scheme. Finally, we demonstrate the extent to which functional MRI data are shaped by the underlying anatomical structure via graph signal processing. The graph Laplacian eigen-modes manifest highly resolved spatial proﬁles, reﬂecting distributed patterns that correspond to major white matter pathways. We show that the intrinsic dimensionality of the eigenspace of such high-resolution graphs is only a mere fraction of the graph dimensions. By projecting task and resting-state data on low-frequency graph Laplacian eigenmodes, we show, ﬁrstly, that brain activity can be well approximated by a small subset of low-frequency components, and secondly, that spectral graph energy pro-ﬁles differ under different functional loads. The proposed graphs open new avenues in studying the brain, be it, by exploring their organisational properties via graph or spectral graph theory, or by treating them as the scaffold on which brain function is observed at the individual level.
Extreme Low Light Requirement for Algae Growth Underneath Sea Ice: A Case Study From Station Nord, NE Greenland: MINIMUM LIGHT REQUIREMENT FOR ICE ALGAE

Microalgae colonizing the underside of sea ice in spring are a key component of the Arctic foodweb as they drive early primary production and transport of carbon from the atmosphere to the ocean interior. Onset of the spring bloom of ice algae is typically limited by the availability of light, and the current consensus is that a few tens-of-centimeters of snow is enough to prevent sufficient solar radiation to reach underneath the sea ice. We challenge this consensus, and investigated the onset and the light requirement of an ice algae spring bloom, and the importance of snow optical properties for light penetration. Colonization by ice algae began in May under >1 m of first-year sea ice with 1 m thick snow cover on top, in NE Greenland. The initial growth of ice algae began at extremely low irradiance (<0.17 lmol photons m s) and was documented as an increase in Chlorophyll a concentration, an increase in algal cell number, and a viable photosynthetic activity. Snow thickness changed little during May (from 110 to 91 cm), however the snow temperature increased steadily, as observed from automated high-frequency temperature profiles. We propose that changes in snow optical properties, caused by temperature-driven snow metamorphosis, was the primary driver for allowing sufficient light to penetrate through the thick snow and initiate algae growth below the sea ice. This was supported by radiative-transfer modeling of light attenuation. Implications are an earlier productivity by ice algae in Arctic sea ice than recognized previously.
Agronomic Potential of Using Precipitated Calcium Carbonate on Early Plant Growth and Soil Quality in the Intermountain West - Greenhouse Studies

Storage and management of large piles of precipitated calcium carbonate (PCC) from sugarbeet processing are a challenge in the western US. Potential uses of this product on surrounding agricultural lands in western NE, eastern WY and northeast CO requires an evaluation of chemical and agronomic impacts of PCC on soils and crop growth. A preliminary greenhouse study was conducted in Scottsbluff, NE using 10 soils from the 3 states. Soils were mixed with 11, 22, 33 and 44 Mg ha-1 rates of PCC to test the early plant growth of sugarbeet, corn, and dry bean in addition to determining soil chemical characteristics. Chemical analysis of PCC from the three processing factories indicates that PCC provides some nitrogen and phosphorus, in addition to some iron, depending on rate. Application of four rates of PCC to neutral to slightly alkali soils neither improved nor negatively impacted the soil chemical characteristics. Dry matter of the three crops after 7 weeks showed no significant effects of PCC. Future utilization of PCC in this region will require further research based on longterm investigations of possible effects of PCC on soil chemical characteristics and plant growth under field conditions. Additional
The OO-binary relationship model

Conventional conceptual models like the Binary Relationship Model (also known as NIAM) or the Entity-Relationship Model do not fit well with the promising object oriented database systems. In this paper we show that is possible to turn such a conventional conceptual model (in particular the Binary Relationship Model) into a truly object oriented conceptual model which combines the assets of the conventional model with the advantages of the object oriented approach.
Energy-Efﬁcient Resource Allocation for Dynamic Priority-Based Vehicular Mobile-Health Communications

—Owing to the developments of wireless communication technologies, mobile health (M-Health) has been postulated as a promising means to improve healthcare quality and save lives in emergencies.However,usingwirelesscommunicationsinM-Health facesthefollowingchallenges.First,thewirelesstransmissionmaygenerateelectromagneticinterference(EMI)andtriggercritical malfunctionstothemedicaldevices.Second,differenttypesofM-Health applications and time-varying patient conditions require dynamic quality of service (QoS). Third, energy efﬁciency (EE) should be optimized to guarantee the reliability of M-Health services, considering the limitations of the existing battery technologies. To address these challenges, this paper investigates the joint channel and power resource allocation problem for vehicular M-Health communications. The problem is formulated as a mixed-integer nonlinear program (MINLP) to maximize the system EE with the consideration of EMI constraints on medical equipment and dynamic QoS requirements of medical users. To ﬁnd possible solutions, we reformulate the MINLP problem by relaxing the integer variables and transforming the objective to convex forms. Based on the dual-decomposition method, we ﬁrst obtain the optimal power allocation and then recover the channel allocation variables to integers. To satisfy the QoS requirements, we develop two channel allocation strategies, i.e., the QoS fulﬁllment strategy and the QoS compensation strategy. The swap-blocking allocation concept is introduced to guarantee the optimality of the obtained solutions.Simulationresultsshowthattheproposedresourceallocation schemeimprovesthesystemEEandservicesatisfactiondegree.