Miracle-dz/UniFAR-FTUs huggingface dataset:
Simulation of economic actors using limitedly rational autonomous agents

We model individual economic actors using intelligent agents, then allow the agents self-interestedly to choose what products to produce and consume. All transactions are voluntary. Use of agents, rather than statistical demand and supply functions, enables the direct computation of every action by every agent, thus permitting the tracing of all economic activities to the principled behavior embedded in the agents. Another feature of this work is that the agents carefully manage self-reﬂection (i.e., they balance thinking about what they will do with actually doing it). An economy emerges as the summation of the actions of the participating agents. Equilibrium is reached when the aggregate supply of goods and services equals the aggregate demand for them, both in terms of type and of quantity. We thus have the ability to tie aggregate activity to individual decisions, giving us a general equilibrium model based on methodological individualism. Equilibrium modeling has in the past invariably ignored the existence and behavior of individual actors. We demonstrate one method of addressing this deﬁcit. We focus on the emergence of organizations to manage coordinated applications of resources.
Transmission power adaptations for data collision avoidance in wireless ad hoc networks

The paper proposes a fragmentation-based MAC protocol with power control, named F-RCRC MAC protocol, for wireless ad hoc networks to avoid the Large Interference Range Collision LIRC problem, which is a kind of hidden terminal problem. F-RCRC designs a new interframe space, called FIFS, to reduce the overhead caused by the fragmentation scheme. Taking advantage of fragmentation, the design of FIFS can effectively avoid the hidden STAs from interfering with the receiver's reception. F-RCRC can reduce the energy consumption and increase the spatial reuse due to the controlled transmission power. Simulation results show that F-RCRC performs much better than the related work in terms of network throughput as well as energy efficiency.
Kaposi's Sarcoma-Associated Herpesvirus Induction of AP-1 and Interleukin 6 during Primary Infection Mediated by Multiple Mitogen-Activated Protein Kinase Pathways

ABSTRACT Kaposi's sarcoma is an angioproliferative disseminated tumor of endothelial cells linked to infection with Kaposi's sarcoma-associated herpesvirus (KSHV). AP-1 transcription factors are involved in diverse biological processes, including infection and replication of viruses, cell growth, oncogenesis, angiogenesis, and invasion of cancer cells. Here we show that KSHV activates AP-1 during primary infection. The activation of AP-1 at the early stage of KSHV infection is mainly mediated by virus entry events. Concurrently, KSHV infection strongly activates MEK, JNK, and to a lesser extent, p38 mitogen-activated protein kinase (MAPK) pathways. Specific inhibitors or dominant negative constructs of MEK and JNK completely abolish AP-1 activation by KSHV, while those of p38 reduce it by half. Furthermore, individual MAPK pathways differentially regulate KSHV activation of AP-1 components. KSHV activation of AP-1 leads to the transcriptional induction of interleukin 6 (IL-6), which is inhibited by inhibitors or dominant negative constructs of MAPK pathways. Together, these results demonstrate that KSHV induces AP-1 and IL-6 during primary infection by modulating multiple MAPK pathways. Because of the diverse roles of IL-6, AP-1, and MAPK pathways in viral infection and tumor induction and promotion, these results have important implications in the pathogenesis of KSHV-induced malignancies.
Performance modeling and analysis of high throughput wireless media access with QoS in noisy channel for different traffic conditions

The high throughput wireless extensions based on IEEE 802.11 or wireless-fidelity (Wi-Fi) support varieties of physical and media access control (MAC) sublayer features to boost up the physical data rate in wireless media. These include multiple input multiple out (MIMO) spatial multiplexing and spatial diversity, channel bonding, short guard intervals, advanced modulation and coding schemes (MCS), frame aggregations and block acknowledgements; for different Wi-Fi extensions like IEEE 802.11n, IEEE 802.11ac and IEEE 802.11ad. The existing studies show that although such physical extensions improve data rates, they have internal trade-offs in channel error and sustainability that directly impact the MAC layer frame aggregation and block acknowledgement performance. In this paper, we model the impact of the channel errors over MAC layer channel access with frame aggregation and block acknowledgement, considering the standard IEEE 802.11 service class differentiation for quality of service (QoS). The evolution of aggregated frame transmission has been modeled using a three dimension Markov chain diagram, considering channel error from physical layer and different traffic conditions. The model is validated through simulation results. The mathematical model is further explored to observe and analyze the impact of channel error over the aggregated frame based MAC scheduling with different QoS performance parameters, like channel throughput, frame loss probability and channel access delay. We observe that frame aggregation sometimes shows negative impact on channel access performance that demands the need for designing an adaptive aggregation strategy.
Cell Stem Cell Clinical Progress Human ESC-Derived Dopamine Neurons Show Similar Preclinical Efficacy and Potency to Fetal Neurons when Grafted in a Rat Model of Parkinson ’ s Disease

General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. • Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal ? Graphical Abstract Highlights Transplants of hESC-DA survive long term and restore DA neurotransmission in vivo The functional potency of hESC-DA is similar to human fetal midbrain DA neurons hESC-DA are capable of long-distance, target-specific innerva-tion of the host brain The axonal outgrowth capacity of hESC-DA meets the requirements for use in humans In Brief Grealish et al. provide preclinical evidence that hESC-derived dopamine neu-rons are functionally equivalent to those derived from fetal tissue, supporting continued development of hESC-derived cells as a clinical approach for the treatment of Parkinson's disease. SUMMARY Considerable progress has been made in generating fully functional and transplantable dopamine neu-rons from human embryonic stem cells (hESCs). Before these cells can be used for cell replacement therapy in Parkinson's disease (PD), it is important to verify their functional properties and efficacy in animal models. Here we provide a comprehensive preclinical assessment of hESC-derived midbrain dopamine neurons in a rat model of PD. We show long-term survival and functionality using clinically relevant MRI and PET imaging techniques and demonstrate efficacy in restoration of motor function with a potency comparable to that seen with human fetal dopamine neurons. Furthermore, we show that hESC-derived dopamine neurons can project sufficiently long distances for use in humans, fully regenerate midbrain-to-forebrain projections, and innervate correct target structures. This provides strong preclinical support for clinical translation of hESC-derived dopamine neurons using approaches similar to those established with fetal cells for the treatment of Parkinson's disease.
An application approach of “cluster-classification” in cancer scan images and gene expressions

Medical professionals need a reliable prediction methodology to diagnose cancer and distinguish between the different stages in cancer. Classification is a data mining techniques it mainly classify the dataset based on certain specific criteria. Clustering is another type grouping based on the similarity. These algorithms are applied to cancer dataset to group the patients into either “Carcinoma in situ” (beginning stage) or “Malignant potential” group. Pre-processing techniques have been applied to prepare the raw dataset and identify the relevant attributes for classification. Random test samples have been selected from the pre-processed data to obtain the results. The results are presented and discussed.
A Method for Classifying Emotion of Text based on Emotional Dictionaries for Emotional Reading

Representing emotional expressions in text-to-speech synthesis is an interesting subject. The ultimate purpose of our research is to develop an automatic reading system which reads text aloud such as novels with emotion. Our strategy for constructing the system is that we classify the emotion of a text in perspective based on the distribution of emotional words, and classify the emotion of a sentence based on the emotion of nouns, adjectives or verbs composed in the sentence instead of understanding the meaning of the text, and synthesize speech using partially optimum prosodic parameters.
miRNA in the Regulation of Skeletal Muscle Adaptation to Acute Endurance Exercise in C57Bl/6J Male Mice

MicroRNAs (miRNAs) are evolutionarily conserved small non-coding RNA species involved in post-transcriptional gene regulation. In vitro studies have identified a small number of skeletal muscle-specific miRNAs which play a crucial role in myoblast proliferation and differentiation. In skeletal muscle, an acute bout of endurance exercise results in the up-regulation of transcriptional networks that regulate mitochondrial biogenesis, glucose and fatty acid metabolism, and skeletal muscle remodelling. The purpose of this study was to assess the expressional profile of targeted miRNA species following an acute bout of endurance exercise and to determine relationships with previously established endurance exercise responsive transcriptional networks. C57Bl/6J wild-type male mice (N = 7/group) were randomly assigned to either sedentary or forced-endurance exercise (treadmill run @ 15 m/min for 90 min) group. The endurance exercise group was sacrificed three hours following a single bout of exercise. The expression of miR- 181, 1, 133, 23, and 107, all of which have been predicted to regulate transcription factors and co-activators involved in the adaptive response to exercise, was measured in quadriceps femoris muscle. Endurance exercise significantly increased the expression of miR-181, miR-1, and miR-107 by 37%, 40%, and 56%, respectively, and reduced miR-23 expression by 84% (P≤0.05 for all), with no change in miR-133. Importantly, decreased expression of miRNA-23, a putative negative regulator of PGC-1α was consistent with increased expression of PGC-1α mRNA and protein along with several downstream targets of PGC-1α including ALAS, CS, and cytochrome c mRNA. PDK4 protein content remains unaltered despite an increase in its putative negative regulator, miR-107, and PDK4 mRNA expression. mRNA expression of miRNA processing machinery (Drosha, Dicer, and DGCR8) remained unchanged. We conclude that miRNA-mediated post-transcriptional regulation is potentially involved in the complex regulatory networks that govern skeletal muscle adaptation to endurance exercise in C57Bl/6J male mice.
Variability in Assembly and Competing Systems: Effect on Performance and Recovery.

Abstract We examine the effect of variability on the performance of an assembly system where two stations feed another station and a competing system where two independent stations are fed by a single station. We study the impact of variability in processing times, variability due to unreliable workstations, and variability due to imperfect yield. The above impact is examined for both PUSH and PULL manufacturing environments. We also discuss design features of the two configurations that could be used to recover part of the output lost due to variability.
Application of Multiple Imputation using the Two-Fold Fully Conditional Specification Algorithm in Longitudinal Clinical Data

Electronic health records of longitudinal clinical data are a valuable resource for health care research. One obstacle of using databases of health records in epidemiological analyses is that general practitioners mainly record data if they are clinically relevant. We can use existing methods to handle missing data, such as multiple imputation (MI), if we treat the unavailability of measurements as a missing-data problem. Most software implementations of MI do not take account of the longitudinal and dynamic structure of the data and are difficult to implement in large databases with millions of individuals and long follow-up. Nevalainen, Kenward, and Virtanen (2009, Statistics in Medicine 28: 3657–3669) proposed the two-fold fully conditional specification algorithm to impute missing data in longitudinal data. It imputes missing values at a given time point, conditional on information at the same time point and immediately adjacent time points. In this article, we describe a new command, twofold, that implements the two-fold fully conditional specification algorithm. It is extended to accommodate MI of longitudinal clinical records in large databases.
Performance-aware software rejuvenation strategies in a queueing system

This paper deals with the performance analysis of a queueing system with rejuvenation controls. In particular, we describe the arrival process by the Markov-modulated Poisson process (MMPP) and consider a gradually degraded sever system. Under this situation, we discuss the system throughput of the queueing system and the optimal rejuvenation strategy maximizing the throughput based on the semi-Markov decision process in the case where the arrival and service rates are observable.
Speed estimation of induction motor based on neural network

According to the mathematic model of induction motor, a new method of speed estimation based on the neural network was presented in this paper. Based on the traditional adaptive system, the neural network replaces the original organization used in the speed-sensorless vector control system. A kind of new method of speed estimation based on the neural network, and the structure and learning algorithm of the neural network were proposed. The designed method has the advantages of simple structure and good robustness. The simulation result showed that the new method could accurately track the variation of the motor speed and had good performance.
OpenFlow based control for re-routing with differentiated flows in Data Center Networks

Data Center Networks need densely interconnected topologies to provide high bandwidth for various cloud computing services. It is required to fully utilize the bandwidth resource in such a network with varying traffic patterns. We propose a flow-based edge-to-edge rerouting scheme for the Data Center Networks which has the mix of large flows and short flows. We focus on rerouting the large flows to alternative paths in the event of congestion because small flows are short-lived and shifting them between paths will increase their latency and overhead. The proposed scheme has several important features: 1) It provides adaptive re-routing of flows to efficiently spread the load in accordance with the changing load conditions. 2) It ensures effective traffic redistribution upon link failures. We develop a prototype and demonstrate the effectiveness of the proposed mechanism on OpenFlow test bed.
Visual summarization for place-of-interest by social-contextual constrained geo-clustering

With the rapid development of social networks, more and more users choose to share their own photos with their friends. Especially, users prefer to share the photos they took during traveling, thus there emerges many user generated content for place-of-interests (POIs). So based on the user contributed photos, we can summarize each POI by mining location-of-interest (LOI, which represents the attractive viewpoints of POI) and selecting some representative images from them. It is important for scheduling a traveling, and in this paper, an effective POI summarization approach is proposed by an improved geo-clustering with visual and views verification, which helps us to have a representative and comprehensive perception for POI. In our approach, we firstly collect POI related photos from social media, and filter the raw data by the combination of tags and geo-locations. Secondly, we mine LOIs for each POI by the improved geo-location clustering method. Finally, we employ visual and views verification to select images from LOIs to summarize the POI. We conduct a series of experiments based on Flcoickr dataset. Experimental results demonstrate the effectiveness of our proposed method.
Stunt driving via policy search

To explore or exploit? In this paper, we discuss the long-standing exploration-exploration dilemma in context of designing a learning controller for stunt-style driving with scarce samples. By making an efficient use of a single demonstration by an expert, our algorithm leverages our intuitive understanding of driving to extract a coarse dynamics model from the collected driving data, then formulate the policy search in a setting of gradient update with a specially designed cost function. Both theoretical and empirical results are detailed and discussed.
Extreme Environment Effects on Cognitive Functions: A Longitudinal Study in High Altitude in Antarctica

This paper focuses on the impact of long-term Antarctic conditions on cognitive processes. Behavioral responses and event-related potentials were recorded during an auditory distraction task and an attention network paradigm. Participants were members of the over-wintering crew at Concordia Antarctic Research Station. Due to the reduced partial pressure of oxygen this environment caused moderate hypoxia. Beyond the hypoxia, the fluctuation of sunshine duration, isolation and confinement were the main stress factors of this environment. We compared 6 measurement periods completed during the campaign. Behavioral responses and N1/MMN (mismatch negativity), N1, N2, P3, RON (reorientation negativity) event-related potential components have been analyzed. Reaction time decreased in both tasks in response to repeated testing during the course of mission. The alerting effect increased, the inhibition effect decreased and the orienting effect did not change in the ANT task. Contrary to our expectations the N2, P3, RON components related to the attentional functions did not show any significant changes. Changes attributable to early stages of information processing were observed in the ANT task (N1 component) but not in the distraction task (N1/MMN). The reaction time decrements and the N1 amplitude reduction in ANT task could be attributed to sustained effect of practice. We conclude that the Antarctic conditions had no negative impacts on cognitive activity despite the presence of numerous stressors.
Modeling and analyzing architectural change with alloy

Although adaptivity based on reconfiguration has the potential to improve dependability of systems, the cost of a failed attempt at reconfiguration is prohibitive in precisely the applications where high dependability is required. Existing work on formal modeling and verification of architectural reconfigurations partly achieve the goal of ensuring correctness, however the formalisms used often lack tool support and the ensuing models have uncertain relation to a concrete implementation. Thus a practical way to ensure with formal certainty that specific architectural changes are correct remains a barrier to the uptake of reconfiguration techniques in industry. Using the Alloy language and associated tool, we propose a practical way to formally model and analyze runtime architectural change expressed as architectural scripts. Our evaluation shows the performance to be acceptable; our experience that the modelling language is convenient and expressive, and that our model accurately repesents the implementation it is used to reason about.
Compressive Sensing Using the Entropy Functional

In most compressive sensing problems l1 norm is used during the signal reconstruction process. In this article the use of entropy functional is proposed to approximate the l1 norm. A modified version of the entropy functional is continuous, differentiable and convex. Therefore, it is possible to construct globally convergent iterative algorithms using Bregman's row action D-projection method for compressive sensing applications. Simulation examples are presented.
Studies on H-Translocating ATPases in Plants of Varying Resistance to Salinity : II. K Strongly Promotes Development of Membrane Potential in Vesicles from Cotton Roots.

Mg(2+)-ATP-dependent H(+)-translocation has been studied in membrane vesicles derived from the roots of Gossypium hirsutum L. var. Acala San Jose 2. Establishment of a positive membrane potential was followed by measuring SCN(-) accumulation; establishment of DeltapH across the vesicle membranes by measuring quinacrine fluorescence quenching. High specificity for ATP was shown, and H(+)-translocation was oligomycin stable. The pH profile for H(+)-translocation showed an optimum at 5.5. The relationship between SCN(-) accumulation and ATP concentration was approximately Michaelian; the apparent K(m) was 0.7 millimolar. K-2-(N-morpholino)ethanesulfonic acid strongly promoted ATP-dependent SCN(-) uptake (up to 180% stimulation). The effect was not given by Na-Mes. Carbonyl cyanide p-trifluoromethoxyphenylhydrazone totally inhibited SCN(-) accumulation, both in the presence and absence of K-2(N-morpholino)ethanesulfonic acid. Vanadate at 200 micromolar inhibited SCN(-) uptake by about 10 to 40% in the absence of K(+), but more strongly in its presence (about 60%). NO(3) (-) at 100 millimolar inhibited initial rate of quinacrine quenching by about 25%. The NO(3) (-) insensitive fraction was activated by K(+); and inhibited by 200 micromolar vanadate to about 40%, provided K(+) was present. Saline conditions during the growth of the plants had no appreciable effect on the observed characteristics of H(+)-translocation.
Antioxidant, Immunomodulating, and Microbial-Modulating Activities of the Sustainable and Ecofriendly Spirulina

The highly nutritional and ecofriendly Spirulina (Arthrospira platensis) has hypolipidemic, hypoglycemic, and antihypertensive properties. Spirulina contains functional compounds, such as phenolics, phycocyanins, and polysaccharides, with antioxidant, anti-inflammatory, and immunostimulating effects. Studies conducted on Spirulina suggest that it is safe in healthy subjects, but attitude to eating probably affects the acceptability of Spirulina containing foods. Although the antioxidant effect of Spirulina is confirmed by the intervention studies, the concerted modulation of antioxidant and inflammatory responses, suggested by in vitro and animal studies, requires more confirmation in humans. Spirulina supplements seem to affect more effectively the innate immunity, promoting the activity of natural killer cells. The effects on cytokines and on lymphocytes' proliferation depend on age, gender, and body weight differences. In this context, ageing and obesity are both associated with chronic low grade inflammation, immune impairment, and intestinal dysbiosis. Microbial-modulating activities have been reported in vitro, suggesting that the association of Spirulina and probiotics could represent a new strategy to improve the growth of beneficial intestinal microbiota. Although Spirulina might represent a functional food with potential beneficial effects on human health, the human interventions used only supplements. Therefore, the effect of food containing Spirulina should be evaluated in the future.