Miracle-dz/UniFAR-FTUs huggingface dataset:
MODE: Mix Driven On-line Resource Demand Estimation

Adaptive performance management solutions often rely on models that require accurate resource demand measures that are estimated in an on-line manner. However it is typically not possible to directly measure resource demands at the abstraction they are needed, e.g., for a software service within an application server that is invoked by a URL. For such cases, linear regression techniques are often used to estimate resource demands. We evaluate the effectiveness of the Least Squares (LSQ) and Least Absolute Deviations (LAD) regression techniques, used extensively by others, as well as Support Vector Regression (SVR) for the purpose of demand estimation. To the best of our knowledge SVR has not yet been evaluated for computer resource demand estimation. We consider the predictive accuracy of these methods for three different real and simulated workloads. Our results demonstrate the importance of tuning the regression parameters of the techniques. We propose an on-line method named Mix Driven On-line Resource Demand Estimation (MODE) that automatically and quickly tunes the regression parameters for LSQ, LAD, and SVR to achieve their best results. The method is novel in that it relies on pre-defined workload mixes with known aggregate demand values to support the tuning exercise. We show that when employed in an on-line manner, tuning with respect to pre-defined mixes is significantly more accurate than the traditional approach of using only step by step data.
Salmonella-Induced Mucosal Lectin RegIIIβ Kills Competing Gut Microbiota

Intestinal inflammation induces alterations of the gut microbiota and promotes overgrowth of the enteric pathogen Salmonella enterica by largely unknown mechanisms. Here, we identified a host factor involved in this process. Specifically, the C-type lectin RegIIIβ is strongly upregulated during mucosal infection and released into the gut lumen. In vitro, RegIIIβ kills diverse commensal gut bacteria but not Salmonella enterica subspecies I serovar Typhimurium (S. Typhimurium). Protection of the pathogen was attributable to its specific cell envelope structure. Co-infection experiments with an avirulent S. Typhimurium mutant and a RegIIIβ-sensitive commensal E. coli strain demonstrated that feeding of RegIIIβ was sufficient for suppressing commensals in the absence of all other changes inflicted by mucosal disease. These data suggest that RegIIIβ production by the host can promote S. Typhimurium infection by eliminating inhibitory gut microbiota.
Role of Emotions and Commitment in an Influence of Remembrance Narratives: Report from the Experimental Study

The report presents results of the experimental study on relationships between arousal of emotions and commitment, and an effectiveness of the government’s remembrance policy. In measurements, the team manipulated emotions inspired in a narrative (neutral vs. positive vs. negative) and participants’ commitment to popularization of a narrative (no commitment vs. low commitment), and it observed how different conditions influenced independent variables: memorization of information, attitude change and real-life behavior. Besides presentation of collected data, the report also includes brief introduction to the theoretical background of the study (especially theoretical hypotheses which verification was project’s objective) and a short discussion of final results.
Accuracy and efficiency enhanced nonlinear model order reduction

A new method for model order reduction for an important class of nonlinear dynamical systems is proposed. The reduction technique is based on the frequency balance reduction method within a desired frequency band and a developed trajectory piecewise-linear (TPWL) model order reduction approach. Both of the methods are recently developed techniques and they can the accuracy and efficiency of the reduction method. The computational load and memory is significantly reduced when the method is used in the hybrid reduction method. The accuracy enhanced hybrid method is applicable to very large scale nonlinear models efficiently and accurately. The numerical results show the accuracy and efficiency enhancement of the proposed method
"Honey=sugar" means unhealthy: investigating how people apply knowledge to rate food's healthiness

While previous research studied the high level attributes people consider when they assess the healthiness of food they are familiar with, little work has looked at how people assess arbitrary, potentially unfamiliar, food to decide whether it is a healthy choice. Since there is a growing body of work in Ubicomp around health practices, including systems to support healthy eating, it is important to understand how people apply the knowledge they have to food decisions. In our studies we identified 8 attributes participants use for determining if they think a food is "healthy" or not. Based upon our analysis, we reflect on current system designs and propose four future design opportunities: capturing context of healthy eating, preparation and reflection on healthy eating understanding, sharing understanding and in situ information support.
A backstepping procedure for a class of underactuated system with tree structure

In the present paper, we propose to transform via a change of coordinates and control law the structure of a subclass of underactuated mechanical system in tree structure to a chain one. By this way, we allow extension of some classical control design as backstepping and feedback linearization to a class of underactuated system to which these approaches fail to be applied initially. Specially, a systematic backstepping strategy is applied to stabilize the Tora system to show the effectiveness of the procedure. However, due to the transformation, one assumption in the control scheme is verified only on a domain rather then the hole space. The control law derived from this procedure can still be applied but the stability is not global. In order to make the latter global, we propose to switch to an other control near the singularities. The simulation results for using the hybrid switching control are given.
Cognitive spectrum utilization in Ka band multibeam satellite communications

Multibeam satellite networks in Ka band have been designed to accommodate the increasing traffic demands expected in the future. However, these systems are spectrum limited due to the current spectrum allocation policies. This paper investigates the potential of applying cognitive radio techniques in satellite communications (SatCom) in order to increase the spectrum opportunities for future generations of satellite networks without interfering with the operation of incumbent services. These extra spectrum opportunities can potentially amount to 2.4 GHz of bandwidth in the downlink and 2 GHz of bandwidth in the uplink for high density fixed satellite services (HDFSS).
A cross-layer optimization framework for joint channel assignment and multicast routing in multi-channel multi-radio wireless mesh networks

ABSTRACT Existing literature on multicast routing protocols in wireless mesh networks (WMNs) from the view point of the links involved in routing are divided into two categories: schemes are aimed at multicast construction with minimal interference which is known as NP hard problem. In contrast, other methods develop network-coding-based solutions with the main objective of throughput maximization, which can effectively reduce the complexity of finding the optimal routing solution from exponential to polynomial time. The proposed framework in this paper is placed in the second category. In multi-channel multi-radio WMNs (MCMR WMNs), each node is equipped with multiple radios, each tuned on a different channel. In this paper, for the first time, we propose a cross-layer convex optimization framework for joint channel assignment and multicast throughput maximization in MCMR WMNs. The proposed method is composed of two phases: in the first phase, using cellular learning automata, channels are assigned to the links established between the radios of the nodes in a distributed fashion such that the minimal interference coefficient for each link is provided. Then, the resultant channel assignment scheme is utilized in the second phase for throughput maximization within an iterative optimization framework based on Lagrange relaxation and primal problem decomposition. We have conducted many experiments to contrast the performance of our solution against many representative approaches.
A composite algorithm for flow rate reduction and stable body trajectory generation in a hydraulic actuated quadruped robot with kinematic redundancy

A hydraulic actuator has a various merits. It has a small size and a high power-to-weight ratio. So it is able to generate high torque in small size actuator. These features of hydraulic actuator are very useful for a walking robot. However, the hydraulic actuator needs a hydraulic power-pack which supplies pressure and flow. Usually, a big and heavy power-pack is needed to generate high pressure and large flow rate. Especially this power-pack is a burden to the stand alone type robot. Therefore it is very important o minimize the size of the power-pack. In this paper, we propose a redundantly actuated quadruped robot which is driven by hydraulic actuator. Using the kinematic redundancy of each leg mechanism, a flow rate consumption minimization algorithm is proposed, which allows minimizing the size of a power-pack. We demonstrate the reduction of the flow rate of the robot through simulation and experiment.
The LLVM Instruction Set and Compilation Strategy

This document introduces the LLVM compiler infrastructure and instruction set, a simple approach that enables sophisticated code transformations at link time, runtime, and in the field. It is a pragmatic approach to compilation, interfering with programmers and tools as little as possible, while still retaining extensive high-level information from source-level compilers for later stages of an application’s lifetime. We describe the LLVM instruction set, the design of the LLVM system, and some of its key components.
Erythronium japonicum attenuates histopathological lung abnormalities in a mouse model of ovalbumin-induced asthma

Asthma is a chronic lung condition that can induce mucus hypersecretion and pulmonary obstruction and may even cause death, particularly in children and older individuals. Erythronium japonicum (E. japonicum) is a traditional herb used in Korea and East Asian countries that has been found to exert free radical scavenging activity and anti-proliferative effects in human colorectal carcinoma cells. In the present study, we evaluated the anti-asthmatic effects of an extract of E. japonicum in a mouse model of ovalbumin (OVA)-induced asthma. Female BALB/c mice were sensitized with an intraperitoneal injection of OVA and aluminum hydroxide hydrate on days 1 and 8 and then received the following treatments on days 21 to 25: i) control (no treatment), ii) sterilized tap water (given orally), iii) 1 mg/kg/day dexamethasone (administered orally), iv) 60 mg/kg/day E. japonicum extract, and v) 600 mg/kg/day E. japonicum extract. On the same days, all the mice except those in the control group were challenged 1 h later with nebulized 5% OVA for 30 min. We found that treatment with E. japonicum extract suppressed the OVA-induced increase in the number of white blood cells and decreased the IgE level in the bronchoalveolar lavage fluid samples obtained from the mice. Histopathological analysis of the lung tissues revealed that E. japonicum attenuated the asthma-related morphological changes in the mouse lung tissue, including the increased secretion of mucus in the bronchioles, eosinophil infiltration around the bronchioles and vessels, and goblet cell and epithelial cell hyperplasia. Immunohistochemical analysis revealed that treatment with E. japonicum extract suppressed the OVA-induced proliferation of T helper cells (CD4+) and B cells (CD19+) in the mouse lung tissue. Furthermore, treatment with E. japonicum extract modulated the expression of both T helper 2 cell-related factors [GATA binding protein 3 (GATA-3), tumor necrosis factor-α (TNF-α), interleukin (IL)-4, IL-5, IL-6 and IL-13], as well as that of T helper 1 cell-related factors [(interferon-γ (IFN-γ), IL-12p35 and IL-12p40]. These findings suggest that E. japonicum may potentially be used as an anti-asthmatic treatment.
Multivariate trend comparisons between autocorrelated climate series with general trend regressors

Inference regarding trends in climatic data series, including comparisons across different data sets as well as univariate trend significance tests, is complicated by the presence of serial correlation and step-changes in the mean. We review recent developments in the estimation of heteroskedasticity and autocorrelation robust (HAC) covariance estimators as they have been applied to linear trend inference, with focus on the Vogelsang-Franses (2005) nonparametric approach, which provides a unified framework for trend covariance estimation robust to unknown forms of autocorrelation up to but not including unit roots, making it especially useful for climatic data applications. We extend the Vogelsang-Franses approach to allow general deterministic regressors including the case where a step-change in the mean occurs at a known date. Additional regressors change the critical values of the Vogelsang-Franses statistic. We derive an asymptotic approximation that can be used to simulate critical values. We also outline a simple bootstrap procedure that generates valid critical values and p-values. The motivation for extending the Vogelsang-Franses approach is an application that compares climate model generated and observational global temperature data in the tropical lowerand mid-troposphere from 1958 to 2010. Inclusion of a mean shift regressor to capture the Pacific Climate Shift of 1977 causes apparently significant observed trends to become statistically insignificant, and rejection of the equivalence
Two-Layered Dynamic Key Management in Mobile and Long-Lived Cluster-Based Wireless Sensor Networks

A resource-efficient key management protocol is essential for security-sensitive applications in wireless sensor networks (WSN). Moreover, the dynamic pair-wise key and group key management protocols are also important for long-lived and mobile WSN. In this paper, a two-layered dynamic key management (TDKM) approach for cluster-based WSN (CWSN) is proposed. Both pair-wise key and group key are distributed in three rounds for key material exchange without encryption/decryption and exponentiation operations in TDKM. In theoretical analysis, TDKM is compared with other key management protocols to show its efficiency. Finally, the relationships between the number of groups and the system performance including key generation overhead, network security, and secured data transmission overhead in CWSN are analyzed.
Reachability and time-based path queries in temporal graphs

A temporal graph is a graph in which vertices communicate with each other at specific time, e.g., A calls B at 11 a.m. and talks for 7 minutes, which is modeled by an edge from A to B with starting time “11 a.m.” and duration “7 mins”. Temporal graphs can be used to model many networks with time-related activities, but efficient algorithms for analyzing temporal graphs are severely inadequate. We study fundamental problems such as answering reachability and time-based path queries in a temporal graph, and propose an efficient indexing technique specifically designed for processing these queries in a temporal graph. Our results show that our method is efficient and scalable in both index construction and query processing.
A Theoretical Comparison of Robustness in Combating CD and PMD in Fiber-Optic Systems

We provide a theoretical comparison, in terms of system outage probability, of the effectiveness of several different approaches to counteract chromatic dispersion and polarization-mode dispersion in fiber-optic systems. After shortly recalling the approach to performance evaluation, performances for various modulation formats, line coding, receiver electronic processing techniques including maximum likelihood sequence detection, and optical equalizers with various taps are superimposed and discussed.
Privacy preserving group nearest neighbor queries

User privacy in location-based services has attracted great interest in the research community. We introduce a novel framework based on a decentralized architecture for privacy preserving group nearest neighbor queries. A group nearest neighbor (GNN) query returns the location of a meeting place that minimizes the aggregate distance from a spread out group of users; for example, a group of users can ask for a restaurant that minimizes the total travel distance from them. We identify the challenges in preserving user privacy for GNN queries and provide a comprehensive solution to this problem. In our approach, users provide their locations as regions instead of exact points to a location service provider (LSP) to preserve their privacy. The LSP returns a set of candidate answers that includes the actual group nearest neighbor. We develop a private filter that determines the actual group nearest neighbor from the retrieved candidate answers without revealing user locations to any involved party, including the LSP. We also propose an efficient algorithm to evaluate GNN queries with respect to the provided set of regions (the users' imprecise locations). An extensive experimental study shows the effectiveness of our proposed technique.
Estimation of capture probabilities using generalized estimating equations and mixed effects approaches

Modeling individual heterogeneity in capture probabilities has been one of the most challenging tasks in capture–recapture studies. Heterogeneity in capture probabilities can be modeled as a function of individual covariates, but correlation structure among capture occasions should be taking into account. A proposed generalized estimating equations (GEE) and generalized linear mixed modeling (GLMM) approaches can be used to estimate capture probabilities and population size for capture–recapture closed population models. An example is used for an illustrative application and for comparison with currently used methodology. A simulation study is also conducted to show the performance of the estimation procedures. Our simulation results show that the proposed quasi-likelihood based on GEE approach provides lower SE than partial likelihood based on either generalized linear models (GLM) or GLMM approaches for estimating population size in a closed capture–recapture experiment. Estimator performance is good if a large proportion of individuals are captured. For cases where only a small proportion of individuals are captured, the estimates become unstable, but the GEE approach outperforms the other methods.
Strategizing software development: strategic management of internet service development

This paper explores a theoretical linkage between software engineering and strategic management. Software engineering is now faced with two dynamic innovation streams: technological innovation and market innovation. Harshly shook by rapid technological development and highly volatile market environments, today's software development is under the constant necessity for swift and reliable development practices and market launch in appropriate timing. In short, software development has to be more and more strategic. Based on a brief review of the existing strategic management frameworks, the paper suggests that Eisenhardt's framework of 'Strategy as Simple Rules' is highly applicable to software development practices. Through a short case study of internet service development in Japan, the paper also suggests that dual roles of beta versions, as a product and media, would play a critical role in making strategic decisions in internet service development.
Strategic teaching in a virtual context: a study on tutor training in continuing education

Adult students who start a distance-studying program need to develop a strategic learning that allows them to adapt themselves to the demands of distance education and achieve their learning goals successfully. The tutor becomes a fundamental factor when it comes to facilitating these students’ adaptation process to distance studying as well as the development of their strategic learning. In this article it is presented a qualitative study on the training process of a group of tutors in strategic distance teaching and learning; it is also presented the analysis of the changes generated in them regarding conceptual and strategic knowledge, both in an academic (as students in the course) and a professional (as tutors) level. Results point out that those tutors who managed to develop higher levels as strategic learners during the training course, accordingly developed a bigger conceptual knowledge and a better performance as strategic tutors.
mtDNA from fossils reveals a radiation of Hawaiian geese recently derived from the Canada goose (Branta canadensis)

Phylogenetic analysis of 1.35 kb of mtDNA sequence from fossils revealed a previously unknown radiation of Hawaiian geese, of which only one representative remains alive (the endangered Hawaiian goose or nene, Branta sandvicensis). This radiation is nested phylogenetically within a living species, the Canada goose (Branta canadensis) and is related most closely to the large-bodied lineage within that species. The barnacle goose (Branta leucopsis) is also nested within the Canada goose species and is related most closely to the small-bodied lineage of Canada geese. The peripheral isolation of the barnacle goose in the Palearctic apparently allowed the evolution of its distinctive plumage pattern, whereas the two Nearctic lineages of Canada geese share a primitive plumage pattern. The Hawaiian lineage of Canada geese diverged more dramatically, splitting into at least three species that differ in body size, body proportions, and flight ability. One fossil species, limited to the island of Hawaii, was related closely to the nene but was over four times larger, flightless, heavy-bodied and had a much more robust cranium. Application of a rate calibration to levels of DNA divergence suggests that this species evolved on the island of Hawaii in less than 500,000 years. This date is consistent with the potassium/argon-based age of the island of Hawaii of 430,000–500,000 years. The giant Hawaii goose resembles the moa-nalos, a group of massive, extinct, flightless ducks that lived on older Hawaiian Islands and thus is an example of convergent evolution of similar morphologies in island ecosystems.
Using Simulated Annealing to Prioritize Query Results: Some Initial Findings

The overall goal of this research is the make search engines active personal assistants using an autonomous agent. An autonomous agent monitors user actions and determines which of the remaining query results are relevant to the user. User actions can be saving, printing, or browsing a particular document. The agent then reorders the remaining query results using this new knowledge of user desires. The focus of this paper is on the first phase of the research, which explores a method for the agent to both prioritize query results and rapidly unlearn inaccurate knowledge concerning what information is relevant.