Miracle-dz/UniFAR-FTUs huggingface dataset:
Energy Adaptive Cluster-Head Selection for Wireless Sensor Networks

A wireless network consisting of a large number of small sensors with limited battery power can be an effective tool for gathering data in a variety of environments. Clustering sensors into groups, so that sensors communicate information only to cluster-heads and then the cluster-heads communicate the aggregated information to the base station, may save energy. In this paper, we propose an optimal energyadaptive clustering algorithm which is motivated from the LEACH protocol presented in [4]. We optimize LEACH’s random cluster-head selection algorithm to ensure the balanced energy depletion over the whole network thus prolongs the network lifetime. Simulation results show that our algorithm outperforms LEACH by about 20% to 35% when 1%, 50%, 100% of nodes die for different network sizes and topologies.
A tiny bleb at Junctional Dilatation of the Posterior Communicating Artery as a Predisposing Factor for Development of a De Novo Aneurysm

Formation of de novo aneurysm from a junctional dilatation at the origin site of the posterior communicating artery (PcomA) has been rarely reported. In this case report, three females in sixth decades of age developed a de novo aneurysm from the junctional dilatation of the PComA with a tiny bleb-like lesion over 5 years after initial presentation.
Quantifying the Capacity Limitations of Hardware Transactional Memory

Hardware transactions offer a performance advantage over software implementations by harnessing the power of existing cache coherence mechanisms which are already fast, automatic, and parallel. The source of superior performance, however, is also the root of their weakness: existing implementations of hardware transactions abort when the working set exceeds the capacity of the underlying hardware. Before we can incorporate this nascent technology into high-performing concurrent data structures, it is necessary to investigate these capacity constraints in order to better inform programmers of their abilities and limitations. This paper provides the first comprehensive empirical study of the “capacity envelope” of HTM in Intel’s Haswell and IBM’s Power8 architectures, providing what we believe is a much needed understanding of the extent to which one can use these systems to replace locks.
Decomposing Random Graphs into Few Cycles and Edges

Over 50 years ago, Erdős and Gallai conjectured that the edges of every graph on n vertices can be decomposed into O(n) cycles and edges. Among other results, Conlon, Fox and Sudakov recently proved that this holds for the random graph G(n, p) with probability approaching 1 as n → ∞. In this paper we show that for most edge probabilities G(n, p) can be decomposed into a union of n/4 + np/2 + o(n) cycles and edges w.h. p. This result is asymptotically tight.
Review of Guidance Documents for Selected Methods in Patient Centered Outcomes Research: Standards in Addressing Heterogeneity of Treatment Effectiveness in Observational and Experimental Patient Centered Outcomes Research

DISCLAIMER All statements in this report, including its findings and conclusions, are solely those of the authors and do not necessarily represent the views of the Patient-Centered Outcomes Research Institute (PCORI), its Board of Governors or Methodology Committee. PCORI has not peer-reviewed or edited this content, which was developed through a contract to support the Methodology Committee's development of a report to outline existing methodologies for conducting patient-centered outcomes research, propose appropriate methodological standards, and identify important methodological gaps that need to be addressed. The report is being made available free of charge for the information of the scientific community and general public as part of PCORI's ongoing research programs. Questions or comments about this report may be sent to PCORI at
Intellectual Property Rights, Technology Diffusion, and Agricultural Development: Cross-Country Evidence

The role of intellectual property rights (IPRs) has been extensively debated in the literature on technology transfers and agricultural production in developing countries. However, few studies offer cross-country evidence on how IPRs affect yield growth, for example, by incentivizing private-sector investment in cultivar improvement. We address this knowledge gap by testing technology diffusion patterns for six major crops using a unique dataset for the period 1961â€“2010 and an Arellanoâ€“Bond linear dynamic panel-data estimation approach. Findings indicate that both biological and legal forms of IPRs tend to promote yield gap convergence between developed and developing countries, although effects vary between crops.
A Mixed-Methods Pilot Study of the Acceptability and Effectiveness of a Brief Meditation and Mindfulness Intervention for People with Diabetes and Coronary Heart Disease

Mindfulness-based interventions can successfully target negative perseverative cognitions such as worry and thought suppression, but their acceptability and effectiveness in people with long-term conditions is uncertain. We therefore pilot tested a six-week meditation and mindfulness intervention in people (n = 40) with diabetes mellitus and coronary heart disease. We used a sequential mixed-methods approach that measured change in worry and thought suppression and qualitatively explored acceptability, feasibility, and user experience with a focus group (n = 11) and in-depth interviews (n = 16). The intervention was highly acceptable, with 90% completing ≥5 sessions. Meditation and mindfulness skills led to improved sleep, greater relaxation, and more-accepting approaches to illness and illness experience. At the end of the six-week meditation course, worry, and thought suppression were significantly reduced. Positive impacts of mindfulness-based interventions on psychological health may relate to acquisition and development of meta-cognitive skills but this needs experimental confirmation.
Complex Dynamics and Financial Fragility in an Agent-Based Model

We model an agent-based economy in which heterogeneous agents (firms and a bank) interact in the financial markets. The heterogeneity is due to the balance sheet conditions and to size. In our simulations, at the aggregate level, output displays changes in trend and volatility giving rise to complex dynamics. The average solvency and liquidity ratios peak during recessions as empirical analysis shows. At the firm level the model generates: (i) firm sizes left-skewed distributed, (ii) growth rates Laplace distributed. Furthermore, small idiosyncratic shocks can generate large aggregate fluctuations.
Improving Bitext Word Alignments via Syntax-based Reordering of English

We present an improved method for automated word alignment of parallel texts which takes advantage of knowledge of syntactic divergences, while avoiding the need for syntactic analysis of the less resource rich language, and retaining the robustness of syntactically agnostic approaches such as the IBM word alignment models. We achieve this by using simple, easily-elicited knowledge to produce syntax-based heuristics which transform the target language (e.g. English) into a form more closely resembling the source language, and then by using standard alignment methods to align the transformed bitext. We present experimental results under variable resource conditions. The method improves word alignment performance for language pairs such as English-Korean and English-Hindi, which exhibit longer-distance syntactic divergences.
Toggled RNA Aptamers Against Aminoglycosides Allowing Facile Detection of Antibiotics Using Gold Nanoparticle Assays

We have used systematic evolution of ligands by exponential enrichment (SELEX) to isolate RNA aptamers against aminoglycoside antibiotics. The SELEX rounds were toggled against four pairs of aminoglycosides with the goal of isolating reagents that recognize conserved structural features. The resulting aptamers bind both of their selection targets with nanomolar affinities. They also bind the less structurally related targets, although they show clear specificity for this class of antibiotics. We show that this lack of aminoglycoside specificity is a common property of aptamers previously selected against single compounds and described as “specific”. Broad target specificity aptamers would be ideal for sensors detecting the entire class of aminoglycosides. We have used ligand-induced aggregation of gold-nanoparticles coated with our aptamers as a rapid and sensitive assay for these compounds. In contrast to DNA aptamers, unmodified RNA aptamers cannot be used as the recognition ligand in this assay, whereas 2′-fluoro-pyrimidine derivatives work reliably. We discuss the possible application of these reagents as sensors for drug residues and the challenges for understanding the structural basis of aminoglycoside-aptamer recognition highlighted by the SELEX results.
Ink Jet Technology

Based on the nature of ink droplet generation, ink jets may be classified as continuous droplet ink jets, impulse (or ink-on-demand) jets, and electrostatically generated ink jets. The basic theory and the principles of operation are reviewed. The principal physical limitations of each system are discussed, followed by a comparison of commercial systems and a review of developmental activities related to ink jets.
Use of Anti-Retroviral Therapy in Tuberculosis Patients on Second-Line Anti-TB Regimens: A Systematic Review

Introduction Use of antiretroviral therapy (ART) during treatment of drug susceptible tuberculosis (TB) improves survival. However, data from HIV infected individuals with drug resistant TB are lacking. Second line TB drugs when combined with ART may increase drug interactions and lead to higher rates of toxicity and greater noncompliance. This systematic review sought to determine the benefit of ART in the setting of second line drug therapy for drug resistant TB. Methods We included individual patient data from studies that evaluated treatment of drug-resistant tuberculosis in HIV-1 infected individuals published between January 1980 and December of 2009. We evaluated the effect of ART on treatment outcomes, time to smear and culture conversion, and adverse events. Results Ten observational studies, including data from 217 subjects, were analyzed. Patients using ART during TB treatment had increased likelihood of cure (hazard ratio (HR) 3.4, 95% CI 1.6–7.4) and decreased likelihood of death (HR 0.4, 95% CI 0.3–0.6) during treatment for drug resistant TB. These associations remained significant in patients with a CD4 less than 200 cells/mm3 and less than 50 cells/mm3, and when correcting for drug resistance pattern. Limitations We identified only observational studies from which individual patient data could be drawn. Limitations in study design, and heterogeneity in a number of the outcomes of interest had the potential to introduce bias. Discussion While there are insufficient data to determine if ART use increases adverse drug interactions when used with second line TB drugs, ART use during treatment of drug resistant TB appears to improve cure rates and decrease risk of death. All individuals with HIV appear to benefit from ART use during treatment for TB.
The Research of Enterprise's Business Model Based on Systemic View

On the basis of literature review, this paper indicates that business model is an organic entity composed of various resource elements. The structure and the value functions of business model are determined by the number of resource elements and their ordered sequence. The amount of values acquired is also affected by the management and control abilities of resources. Therefore the core firm should strengthen the access, control and integration of resources elements, ensure the business model to evolve at a the direction of higher market value. Key words-system; business model; value; evolvement
Novel image authentication scheme based on quadtree segmentation

Abstract In this paper, a new image authentication scheme based on quadtree segmentation is presented. In this scheme, partial digital signature encryption is employed to ascertain the origin of the image. Furthermore, the quadtree structure is imposed on the design of the authentication code. The authentication code consists of the quadtree list and the mean values of the quadtree leaf nodes. The size of the authentication code is adaptively determined according to the image characteristics. In the experiments, JPEG and JPEG2000 lossy compression and malicious manipulations are employed to evaluate the validity of the proposed scheme. The experimental results show that this scheme permits some acceptable operations and can detect malicious manipulations. Furthermore, when a malicious manipulation is detected, the mean values of the quadtree leaf nodes can be applied to recover the manipulated areas.
Simultaneous identification of duplications and lateral transfers

This paper introduces a combinatorial model that incorporates duplication events as well as lateral gene transfer events (a.k.a. horizontal gene transfer events). To the best of our knowledge, this is the first such model containing both of these events. A so-called dt-scenario is used to explain differences between a gene tree T and species trees S. The model is biologically as well as mathematically sound. Among other biological considerations, the model respects the partial order of evolution implied by S by demanding that the dt-scenarios are "acyclic". We present fixed parameter tractable algorithms that count the minimum number of duplications and lateral transfers, and more generally can compute the set of pairs (t,d) where d is the minimum number of duplications required by any explanation that requires t lateral transfers. This allows us to also compute a weighted parsimony score. We also show how gene loss events can be incorporated into our model. We also give an $NP$-completeness proof which suggests that the intractability is due to the demand that the dt-scenarios be acyclic. When this condition is removed, we can show that the problem is computable in polynomial time via dynamic programming. By generating "synthetic" gene and species trees via a birth-death process, we explored the capacity of our algorithms to faithfully reconstruct the actual number of events taken place. The results are positive.
Improved methods for language model based question classification

In this paper, we propose a language model based approach to classify user questions in the context of question answering systems. As categorization paradigm, a Bayes classifier is used to determine a corresponding semantic class. We present experiments with state-of-the-art smoothing methods as well as with some improved language models. Our results indicate that the techniques proposed here provide performance superior to the standard methods, including support vector machines.
A novel prime numbers based hashing technique for minimizing collisions

Searching is a prime operation in computer science and numerous methods has been devised to make it efficient. Hashing is one such searching technique with objective of limiting the searching complexity to O (1) i.e. finding the desired item in one attempt. But achieving complexity of O (1) is quite difficult or usually not possible. This happens because there is no perfect mapping function for insertion and searching; and this imperfection of hashing function results in collisions. The algorithm and technique presented in this article minimizes the number of collisions by removing the problem of clustering. Clustering occurs when the data items congregates in one particular area thus increasing the number of collisions and results in increased number of probes to insert and search an item. During trials runs the proposed algorithm have shown considerable improvements over all major hashing algorithms in terms of performance.
Representing and Reasoning with Defaults For Learning Agents

The challenge we address is to create autonomous, inductively learning agents that exploit and modify a knowledge base. Our general approach, embodied in a continuing research program (joint with Stuart Russell), is declarative bias, i.e., to use declarative knowledge to constrain the hypothesis space in inductive learning. In previous work, we have shown that many kinds of declarative bias can be relatively efficiently represented and derived from background knowledge. We begin by observing that the default, i.e., revisable, flavor of beliefs is crucial in applications, especially for competence to improve incrementally and for information to be acquired through communication, language, and sensory perception in integrated agents. We argue that much of learning in humans consists of "learning in the small" and is nothing more nor less than acquiring new plausible premise beliefs. Thus representation of defaults and plausible knowledge should be a central question for researchers aiming to design sophisticated learning agents that exploit a knowledge base. We show that such applications pose several representational requirements that are unfamiliar to most in the machine learning community, and whose combination has not been previously addressed by the knowledge representation community. These include: prioritization-type precedence between defaults; updating with new defaults, not just new for-sure beliefs; explicit reasoning about adoption of defaults and precedence between defaults; and integration of defaults with probabilistic and statistical beliefs. We show how, for the first time, to achieve all of these requiremetats, at least partially, in one declarative formal
Wakefield induced Losses in the Manual Valves of the TESLA Cryomodule

The beam pipe of the TESLA cryomodule is closed off by manual valves on both sides of the string of 12 cavities. Compared to the manual valves with spring type rf-shield which are presently used in the linac of the TESLA Test Facility a simplified version will be needed by cost reasons for the TESLA linear collider. Therefore wakefield induced losses for two simplified valve geometries have been studied in detail. Three types of losses are distinguished: transient and resonant losses due to wake fields induced by the valves themselves and losses due to wakes induced in the cavities. The calculations have been done for two different operation modes linac operation and FEL operation. In addition the transverse kick has been calculated and compared with the strongest kick from a nine cell cavity. The results show, that the losses from a valve with simplified rf-shield are acceptable.
Multilevel Tunnelling Systems and Fractal Clustering in the Low-Temperature Mixed Alkali-Silicate Glasses

The thermal and dielectric anomalies of window-type glasses at low temperatures (T < 1 K) are rather successfully explained by the two-level systems (2LS) standard tunneling model (STM). However, the magnetic effects discovered in the multisilicate glasses in recent times, magnetic effects in the organic glasses, and also some older data from mixed (SiO2)1−x(K2O)x and (SiO2)1−x(Na2O)x glasses indicate the need for a suitable extension of the 2LS-STM. We show that—not only for the magnetic effects, but also for the mixed glasses in the absence of a field—the right extension of the 2LS-STM is provided by the (anomalous) multilevel tunnelling systems (ATS) proposed by one of us for multicomponent amorphous solids. Though a secondary type of TS, different from the standard 2LS, was invoked long ago already, we clarify their physical origin and mathematical description and show that their contribution considerably improves the agreement with the experimental data. In spite of dealing with low-temperature properties, our work impinges on the structure and statistical physics of glasses at all temperatures.
Seasonal dynamics of the relationship between landscape pattern and land surface temperature in a coastal city

The effects of land cover on LST have been found varying with season. Yet the seasonal dynamics of the relationship between metrics measured landscape pattern and LST has seldom been investigated. This study used a seasonal series of Landsat TM/ETM+ images in the year 2002 to examine the landscape pattern-LST relationship in Xiamen city. Landscape metrics and partial Pearson correlation were employed. The results showed that all the bivariate correlations between landscape metrics and LST changed with season to different extents. There were 7 consistently significant metrics across seasons for the year in Pearson correlation. All the configuration metrics lost their consistent significance in correlations with LST across seasons after partial Pearson correlation. Composition metrics of forest (PLAND3) was found to be the effective metric affecting LST consistently across seasons in the year. And the strongest bivariate correlation of PLAND3 happened in autumn for the study years.
Efficient Heuristics for Flowshop Scheduling for Minimizing the Makespan and Total Flowtime of Jobs

The problem of scheduling in permutation flowshops has been extensively investigated by many researchers. Recently, attempts are being made to consider more than one objective simultaneously and develop algorithms to obtain a set of Pareto-optimal solutions. Varadharajan et al. (2005) presented a multi-objective simulated-annealing algorithm (MOSA) for the problem of permutation-flowshop scheduling with the objectives of minimizing the makespan and the total flowtime of jobs. The MOSA uses two initial sequences obtained using heuristics, and seeks to obtain non-dominated solutions through the implementation of a probability function, which probabilistically selects the objective of minimizing either the makespan or the total flowtime of jobs. In this paper, the same problem of heuristically developing non-dominated sequences is considered. We propose an effective heuristics based on simulated annealing (SA), in which the weighted sum of the makespan and the total flowtime is used. The essences of the heuristics are in selecting the initial sequence, setting the weight and generating a solution in the search process. Using a benchmark problem provided by Taillard (1993), which was used in the MOSA, these conditions are extracted in a large-scale experiment. The non-dominated sets obtained from the existing algorithms and the proposed heuristics are compared. It was found that the proposed heuristics drastically improved the performance of finding the non-dominated frontier.
Face recognition using multi-scale PCA and Support Vector Machine

Based on Gabor wavelets, a novel multi-scale principal component analysis and support vector machine algorithm (MsPCA-SVM) for face recognition is proposed in this paper. Firstly, the Gabor wavelets transformation results including five scales and eight directions are calculated and 40 feature matrices which are reconstructed with the same scale and the same direction transform results of the different face images are obtained. Secondly, the dimensionality reduction and denoised technique with PCA are applied to form the new training samples. Finally, 40 SVMs classifiers are constructed and the vote decision strategy is used to determine the recognition results. The experimental results show that the proposed method expands the selectable range of the cumulative variance contribution rate in PCA method and the influence of the SVMs kernel parameters on the recognition rate is small. So, the SVMs kernel parameters are easy to select. Furthermore, the difficult problem to select the kernel parameters has been settled to a certain degree. In the meantime, the ideal recognition rate is obtained.