Miracle-dz/UniFAR-FTUs huggingface dataset:
Cooperative Network Coded ARQ Strategies for Two-Way Relay Channels

In this paper, novel cooperative automatic repeat request (ARQ) methods with network coding are proposed for two-way relaying network where two communicating nodes are assisted by a relay node at the cooperative retransmission phase upon failed transmission of a packet. The proposed approach integrates network coding into cooperative ARQ (C-ARQ), aiming to improve the network throughput by reducing the number of retransmissions. For successive retransmission, three different methods for choosing the retransmitting node are considered. The throughputs of the methods are analyzed and compared. The analysis is based on a binary Markov channel, which takes the correlation of the channel coefficients in time into account. Analytical results show that the proposed use of network coding enhances throughput performance up to 15% compared with C-ARQ without network coding and 40% compared with traditional ARQ when the relay channel conditions are better than the direct channel between two communicating nodes. It is also observed that correlation can have a significant effect on the performance of the proposed cooperative network coded ARQ (C-NC-ARQ) approach. In particular, the proposed approach is advantageous for moderately fast to slow fading channels where channel correlation coefficient varies from 0.5 to 1.
Robust Initialization of Differential-Algebraic Equations Using Homotopy

The new operator homotopy (.) was introduced in Modelica 3.2 to improve the solution of difficult initialization problems. The background and motivation for this approach is discussed and it is demonstrated how to apply it for mechanical, electrical and fluid systems. Furthermore, it is shown at hand of several examples how an inappropriate formulation might lead to ill-posed problems.
Quantitative Phosphoproteomics Applied to the Yeast Pheromone Signaling Pathway*S

Cellular processes such as proliferation, differentiation, and adaptation to environmental changes are regulated by protein phosphorylation. Development of sensitive and comprehensive analytical methods for determination of protein phosphorylation is therefore a necessity in the pursuit of a detailed molecular view of complex biological processes. We present a quantitative modification-specific proteomic approach that combines stable isotope labeling by amino acids in cell culture (SILAC) for quantitation with IMAC for phosphopeptide enrichment and three stages of mass spectrometry (MS/MS/MS) for identification. This integrated phosphoproteomic technology identified and quantified phosphorylation in key regulator and effector proteins of a prototypical G-protein-coupled receptor signaling pathway, the yeast pheromone response. SILAC encoding of yeast proteomes was achieved by incorporation of [13C6]arginine and [13C6]lysine in a double auxotroph yeast strain. Pheromone-treated yeast cells were mixed with SILAC-encoded cells as the control and lysed, and extracted proteins were digested with trypsin. Phosphopeptides were enriched by a combination of strong cation exchange chromatography and IMAC. Phosphopeptide fractions were analyzed by LC-MS using a linear ion trap-Fourier transform ion cyclotron resonance mass spectrometer. MS/MS and neutral loss-directed MS/MS/MS analysis allowed detection and sequencing of phosphopeptides with exceptional accuracy and specificity. Of more than 700 identified phosphopeptides, 139 were differentially regulated at least 2-fold in response to mating pheromone. Among these regulated proteins were components belonging to the mitogen-activated protein kinase signaling pathway and to downstream processes including transcriptional regulation, the establishment of polarized growth, and the regulation of the cell cycle.
Vehicle-Assisted Device-to-Device Data Delivery for Smart Grid

In this paper, we propose a heterogeneous framework to deliver smart grid (SG) data cost effectively. The data generated by distributed SG loads, and generation units should be delivered to the utility control center (UCC) within the tolerated delay, which is crucial for SG applications. To this end, a heterogeneous communication framework is proposed, where the cellular network (CN) provides ubiquitous yet expensive data transmission, and vehicle-assisted device-to-device (D2D) communications are leveraged to offload the CN by delivering the delay-tolerant SG data in a store-carry-forward fashion with low cost. To improve the offloading and cost performance of the proposed framework, we put effort in the following aspects: 1) optimal forwarding schemes to optimally select vehicles to carry and forward the data; and 2) mode selection and dynamic resource allocation to maximize the amount of data delivered by D2D communications, reduce the cost of SG data delivery, and guarantee the fairness among SG users. Simulation results are given to validate proposed approaches and demonstrate that the proposed framework is efficient in saving cost for the utility and offloading the CN.
Cellular Therapy for Heart Failure

The pathogenesis of cardiomyopathy and heart failure (HF) is underpinned by complex changes at subcellular, cellular and extracellular levels in the ventricular myocardium. For all of the gains that conventional treatments for HF have brought to mortality and morbidity, they do not adequately address the loss of cardiomyocyte numbers in the remodeling ventricle. Originally conceived to address this problem, cellular transplantation for HF has already gone through several stages of evolution over the past two decades. Various cell types and delivery routes have been implemented to positive effect in preclinical models of ischemic and nonischemic cardiomyopathy, with pleiotropic benefits observed in terms of myocardial remodeling, systolic and diastolic performance, perfusion, fibrosis, inflammation, metabolism and electrophysiology. To a large extent, these salubrious effects are now attributed to the indirect, paracrine capacity of transplanted stem cells to facilitate endogenous cardiac repair processes. Promising results have also followed in early phase human studies, although these have been relatively modest and somewhat inconsistent. This review details the preclinical and clinical evidence currently available regarding the use of pluripotent stem cells and adult-derived progenitor cells for cardiomyopathy and HF. It outlines the important lessons that have been learned to this point in time, and balances the promise of this exciting field against the key challenges and questions that still need to be addressed at all levels of research, to ensure that cell therapy realizes its full potential by adding to the armamentarium of HF management.
Exact and Approximated Outage Probability Analyses for Decode-and-Forward Relaying System Allowing Intra-Link Errors

In this paper, we theoretically analyze the outage probability of decode-and-forward (DF) relaying system allowing intra-link errors (DF-IE), where the relay always forwards the decoder output to the destination regardless of whether errors are detected after decoding in the information part or not. The results apply to practical fading scenarios where all the links between the nodes suffer from independent block Rayleigh fading. The key idea of DF-IE system is that the data sequence forwarded by the relay is highly correlated with the original information sequence sent from the source, and hence with a proper joint decoding technique at the destination, the correlation knowledge can well be exploited to improve the system performance. We analyze this problem in the information theoretical framework of correlated source coding. Using the theorems for lossy source-channel separation and for source coding with side information, the exact outage probability is derived. It is then shown that the exact expression can be reduced to a simple, yet accurate approximation by replacing the theorem for source coding with side information by the Slepian-Wolf theorem. Compared with conventional DF relaying where relay keeps silent if errors are detected after decoding, DF-IE can achieve even lower outage probability. Moreover, by allowing intra-link errors, the optimal position of the relay is found to be exactly the midpoint between the source and destination. Results of the simulations are provided to verify the accuracy of the analytical results.
On Dynamic Server Provisioning in Multichannel P2P Live Streaming

To guarantee the streaming quality in live peer-to-peer (P2P) streaming channels, it is preferable to provision adequate levels of upload capacities at dedicated streaming servers, compensating for peer instability and time-varying peer upload bandwidth availability. Most commercial P2P streaming systems have resorted to the practice of overprovisioning a fixed amount of upload capacity on streaming servers. In this paper, we have performed a detailed analysis on 10 months of run-time traces from UUSee, a commercial P2P streaming system, and observed that available server capacities are not able to keep up with the increasing demand by hundreds of channels. We propose a novel online server capacity provisioning algorithm that proactively adjusts server capacities available to each of the concurrent channels, such that the supply of server bandwidth in each channel dynamically adapts to the forecasted demand, taking into account the number of peers, the streaming quality, and the channel priority. The algorithm is able to learn over time, has full Internet service provider (ISP) awareness to maximally constrain P2P traffic within ISP boundaries, and can provide differentiated streaming qualities to different channels by manipulating their priorities. To evaluate its effectiveness, our experiments are based on an implementation of the algorithm, which replays real-world traces.
Large-scale drug function prediction by integrating QIS D/sup 2/ and biospice

Quantum Intelligence System for Drug Discovery (QIS D/sup 2/) is a unique adaptive learning system designed to predict potential large-scale drug characteristics such as toxicity and efficacy. BioSpice is a set of software tools designed to represent and simulate cellular processes funded by DARPA. We show a QIS D/sup 2/ model is successfully trained, tested and validated on experimental data sets for predicting the potential in vivo effects of drug molecules in biological systems. QIS D/sup 2/ is interoperable with BioSpice. The workflow and visualization are built-in capabilities for easy-of-use. The integration of QIS D/sup 2/ and BioSpice draw on diversified technologies to deliver unique benefits for simulation and screening of potential drugs and their targets. We show that our approach leverages both structured and unstructured bioinformatics databases such as BioWarehouse and GeneWays in BioSpice to greatly enhance a QIS D/sup 2/ model. We show QIS D/sup 2/ models data from seven sources for 37,330 chemicals, performs an automatic sequence clustering using 1234 structure fragments, and accurately predict 1829 targets simultaneously.
Low-frequency rTMS promotes use-dependent motor plasticity in chronic stroke

Objective: To investigate the long-term behavioral and neurophysiologic effects of combined time-locked repetitive transcranial magnetic stimulation (rTMS) and physical therapy (PT) intervention in chronic stroke patients with mild motor disabilities. Methods: Thirty patients were enrolled in a double-blind, randomized, single-center clinical trial. Patients received 10 daily sessions of 1 Hz rTMS over the intact motor cortex. In different groups, stimulation was either real (rTMSR) or sham (rTMSS) and was administered either immediately before or after PT. Outcome measures included dexterity, force, interhemispheric inhibition, and corticospinal excitability and were assessed for 3 months after the end of treatment. Results: Treatment induced cumulative rebalance of excitability in the 2 hemispheres and a reduction of interhemispheric inhibition in the rTMSR groups. Use-dependent improvements were detected in all groups. Improvements in trained abilities were small and transitory in rTMSS patients. Greater behavioral and neurophysiologic outcomes were found after rTMSR, with the group receiving rTMSR before PT (rTMSR-PT) showing robust and stable improvements and the other group (PT-rTMSR) showing a slight improvement decline over time. Conclusion: Our findings indicate that priming PT with inhibitory rTMS is optimal to boost use-dependent plasticity and rebalance motor excitability and suggest that time-locked rTMS is a valid and promising approach for chronic stroke patients with mild motor impairment. Classification of evidence: This interventional study provides Class I evidence that time-locked rTMS before or after physical therapy improves measures of dexterity and force in the affected limb in patients with chronic deficits more than 6 months poststroke. Neurology® 2012;78:256–264
Community-based Link Prediction with Text

There has been much recent interest in generative models for graphs. The intuition behind the study of such link prediction functions is that they provide a succinct description of the process by which networks grow and evolve: a model that accurately predicts small-scale actions such as coauthorships should help us understand the global properties of the network. Previous work in social network analysis, such as LibenNowell and Kleinberg [5], has often focused on generative models that take into account only the graph structure of the network, without making any use of the individual properties of the nodes themselves. Frequently, however, much richer data is available than the link structure alone, such as text documents for coauthorship networks. In this paper, we propose a generative model for documents that produces both text and authors based on a notion of communities, which each have a distribution over authors and over topics. We demonstrate this model on the proceedings of the NIPS conference, showing improved likelihood of held-out coauthorship data. Discovering latent structure can also be useful in analyzing long term trends, such as the growth and fragmentation of communities.
Fault Tolerance through Interaction and Mutual Cooperation in Hierarchical Multi-Agent Systems

Multi-Agent Systems (MASs) are well suited for development of complex, distributed systems. In its essence MAS is a distributed system that consists of multiple agents working together to solve common problems. Failure handling is an important property of large scale MAS because the failure rate grows with both the number of the hosts, deployed agents and the duration of agent’s task execution. Numerous approaches have been introduced to deal with some aspects of the failure handling. However, absence of centralized control and large number of individual intelligent components makes it difficult to detect and to treat errors. Risk of uncontrollable fault propagation is high and can seriously impact the performance of the system. Although existing research has been extensive, it still needs to attend the MAS failure handling problem in all its aspects, which makes this topic very interesting. We propose a concept of agent interaction that enables any hierarchical MAS to become fault tolerant, regardless of the used agent
Optimize Support Vector Machine Classifier based on Evolutionary Algorithm for Breast Cancer Diagnosis

Summary Support Vector Machine has become the reference for many classification problems, because of their flexibility, and capacity to handle high dimensional data. However, the success of Support Vector Machine classifier depends on the perfect choice of the values of its parameters along with the feature subset selection. So the objective of this paper is to propose an Evolutionary Optimization Algorithm (EA) for feature selection and parameter optimization to solve this kind of the SVM depended to improve its performance accuracy. The proposed approach is compared with other approach. The results show that our approach obtained the highest classification accuracy (100%) with limit feature subset selected.
RIAACT: a robust approach to adjustable autonomy for human-multiagent teams

When human-multiagent teams act in real-time uncertain domains, adjustable autonomy (dynamic transferring of decisions between human and agents) raises three key challenges. First, the human and agents may differ significantly in their worldviews, leading to inconsistencies in their decisions. Second, these human-multiagent teams must operate and plan in real-time with deadlines with uncertain duration of human actions. Thirdly, adjustable autonomy in teams is an inherently distributed and complex problem that cannot be solved optimally and completely online. To address these challenges, our paper presents a solution for Resolving Inconsistencies in Adjustable Autonomy in Continuous Time (RIAACT). RIAACT incorporates models of the resolution of inconsistencies, continuous time planning techniques, and hybrid method to address coordination complexity. These contributions have been realized in a disaster response simulation system.
Ultrafast Time-Spread Optical BPSK Code Label Generation and Processing Based on Variable-Bandwidth Spectrum Shaper

In experiments, we demonstrated time-spread 200-Gchip/s 10-Gb/s eight-chip optical binary phase-shift keying code generation and matched filtering based on spectral amplitude and phase control. By using a bandwidth-tunable high-resolution optical spectrum controller that we have previously developed, we were able to control spectral components with 10-GHz resolution in the entire C-band, with rectangular passbands. To demonstrate the application of this spectrum controller to tunable optical label processing, we performed experiments and simulated label generation and processing by controlling the spectrum.
Non-Uniform Window Decoding Schedules for Spatially Coupled LDPC Codes

Spatially coupled low-density parity-check codes can be decoded using a graph-based message passing algorithm applied across the total length of the coupled graph. However, considering practical constraints on decoding latency and complexity, a sliding window decoding approach is normally preferred. In order to reduce decoding complexity compared with standard parallel decoding schedules, serial schedules can be applied within a decoding window. However, uniform serial schedules within a window do not provide the expected reduction in complexity. Hence, we propose non-uniform schedules (parallel and serial) based on measured improvements in the estimated bit error rate (BER). We show that these non-uniform schedules result in a significant reduction in complexity without any loss in performance. Furthermore, based on observations made using density evolution, we propose a non-uniform pragmatic decoding schedule (parallel and serial) that does not require any additional calculations (e.g., BER estimates) within the decoding process.
Salmonella: a continuing problem

Salmonella was the archetypal food poisoning organism of the 20th century. It achieved a high public profile following the salmonella-in-eggs crisis of 1988–89, but by then had been the subject of public health concern and scientific interest for over a century. Early associated with animal foods, the advent of phage typing in the 1940s began to reveal the complexity of its environmental associations. This paper explores the story of salmonella as a continuing problem in epidemiology, microbiology and public health, in the food chain, and in the kitchen.
Super-Resolution Reconstruction of 3D PET Images Using Two Respiratory-Phase Low-Dose CT Images

Positron emission tomography (PET) images usually suffer from incorrect quantification of the radioactive uptake of small lesions due to low spatial resolution. To improve the spatial resolution, we previously proposed super-resolution (SR) algorithms based on wobble scanning. The proposed algorithms, however, require mechanical motion of the patient bed or a system gantry for wobble scanning. In this paper, we propose a framework for SR reconstruction of 3D PET images based on the use of respiratory motion rather than wobble motion. As in the conventional protocol of PET imaging, gated list-mode PET data are acquired in a free breathing condition. In addition, we acquire two low-dose CT images in a breath-hold manner at exhale and inhale phases, without increasing the radiation burden to a patient. Using the two low-dose CT images, we estimate the 4D motion vector field (MVF) and correspondingly generate a virtual 4D CT image that are matched to the 4D PET image. The 3D CT images have much better spatial resolution than PET images and therefore the corresponding estimated 3D MVFs can be considered reliable for PET SR reconstruction. We then estimate space-variant point spread functions (PSFs) in the imaging field of view using a minimum number of PSFs obtained through Monte-Carlo simulations. Finally, SR reconstruction is performed by incorporating the estimated 3D MVFs and space-variant PSFs. In the SR reconstruction, to avoid the resolution degradation in the evenly spaced parallel-beam rebinning and to reduce the computational time on the graphics processing unit, we introduce a parallel-friendly spanned line of response reconstruction technique based on fan-beam reordering. The proposed framework is evaluated via Monte-Carlo simulations with the digital XCAT phantom and via experiments with several patient datasets including moving lung lesions. Both the simulation and experiment results show that the proposed framework provides much clearer organ boundaries as well as more accurate quantitative lesion information than the conventional methods, with a considerable reduction of computational time.
Overview of the oxygen isotope systematics of land snails from North America

Abstract Continental paleoclimate proxies with near-global coverage are rare. Land snail δ18O is one of the few proxies abundant in Quaternary sediments ranging from the tropics to the high Arctic tundra. However, its application in paleoclimatology remains difficult, attributable in part to limitations in published calibration studies. Here we present shell δ18O of modern small (<10 mm) snails across North America, from Florida (30°N) to Manitoba (58°N), to examine the main climatic controls on shell δ18O at a coarse scale. This transect is augmented by published δ18O values, which expand our coverage from Jamaica (18°N) to Alaska (64°N). Results indicate that shell δ18O primarily tracks the average annual precipitation δ18O. Shell δ18O increases 0.5–0.7‰ for every 1‰ increase in precipitation δ18O, and 0.3–0.7‰ for every 1°C increase in temperature. These relationships hold true when all taxa are included regardless of body size (ranging from ~1.6 to ~58 mm), ecology (herbivores, omnivores, and carnivores), or behavior (variable seasonal active periods and mobility habits). Future isotopic investigations should include calibration studies in tropical and high-latitude settings, arid environments, and along altitudinal gradients to test if the near linear relationship between shell and meteoric precipitation δ18O observed on a continental scale remains significant.