Miracle-dz/UniFAR-FTUs huggingface dataset:
Courseware Considered Harmful

The study of DNS is an important quandary. After years of key research into local-area networks, we prove the simulation of IPv7. In order to accomplish this goal, we demonstrate that though the much-touted semantic algorithm for the understanding of thin clients by Zheng and Martin is optimal, the famous game-theoretic algorithm for the refinement of the transistor by J. M. Davis et al. [11] is maximally efficient.
Multicasting for wireless video-on-demand

We consider a video delivery problem in which a server multicasts a single video to several users over a wireless broadcast channel. Users arrive and depart asynchronously, and when a user arrives, he or she wishes to watch the video from its beginning. Under a natural stochastic model for this problem, the optimal scheduling algorithm can be described by dynamic programming, but it is too complex to be practical. We describe a suite of approximations to the optimal scheme that lead to practical scheduling algorithms. The algorithms work with partially observed state, multirate multicast, and a variety of cost functions. Numerical results show significant performance gains over previously known schemes.
A comprehensive framework for testing graphical user interfaces

The widespread recognition of the usefulness of graphical user interfaces (GUIs) has established their importance as critical components of today's software. Although the use of GUIs continues to grow, GUI testing has remained a neglected research area. Since GUIs have characteristics that are different from those of conventional software, such as user events for input and graphical output, techniques developed to test conventional software cannot be directly applied to test GUIs. This thesis develops a unified solution to the GUI testing problem with the particular goals of automation and integration of tools and techniques used in various phases of GUI testing. These goals are accomplished by developing a GUI testing framework with a GUI model as its central component. For efficiency and scalability, a GUI is represented as a hierarchy of components, each used as a basic unit of testing. The framework also includes a test coverage evaluator, test case generator, test oracle, test executor, and regression tester. The test coverage evaluator employs hierarchical, event-based coverage criteria to automatically specify what to test in a GUI and to determine whether the test suite has adequately tested the GUI. The test case generator employs plan generation techniques from artificial intelligence to automatically generate a test suite. A test executor automatically executes all the test cases on the GUI. As test cases are being executed, a test oracle automatically determines the correctness of the GUI. The test oracle employs a model of the expected state of the GUI in terms of its constituent objects and their properties. After changes are made to a GUI, a regression tester partitions the original GUI test suite into valid test cases that represent correct input/output for the modified GUI and invalid test cases that no longer represent correct input/output. The regression tester employs a new technique to reuse some of the invalid test cases by repairing them. A cursory exploration of extending the framework to handle the new testing requirements of web-user interfaces (WUIs) is also done. The framework has been implemented and experiments have demonstrated that the developed techniques are both practical and useful.
Signal Detection

In Chapter 13 we considered hypothesis testing in the context of random variables. The detector resulting in the minimum probability of error corresponds to the MAP test as developed in section 13.2. 1 or equivalently the likelihood ratio test in section 13.2. 3. In this chapter we extend those results to a class of detection problems that are central in radar, sonar and communications, involving measurements of signals over time. The generic signal detection problem that we consider corresponds to receiv­ ing a signal r(t) over a noisy channel. r(t) either contains a known deterministic pulse s(t) or it does not contain the pulse. Thus our two hypotheses are H 1 : r(t) = s(t) + w(t) H 0 : r(t) = w(t), (14.1) where w(t) is a wide-sense stationary random process. One example of a scenario in which this problem arises is in binary communication using pulse amplitude modulation. In that context the presence or absence of the pulse s(t) represents the transmission of a " one " or a " zero ". As another example, radar and sonar systems are based on transmitting a pulse and detecting the presence or absence of an echo. In our treatment in this chapter we first consider the case in which the noise is white and carry out the formulation and analysis in discrete-time which avoids some of the subtler issues associated with continuous-time white noise. We also initially treat the case in which the noise is Gaussian. In Section 14.3. 4 we extend the discussion to discrete-time Gaussian colored noise. In Section 14.3. 2 we discuss the implications when the noise is not Gaussian and in Section 14.3. 3 we discuss how the results generalize to the continuous-time case. In the signal detection task outlined above, our hypothesis test is no longer based on the measurement of a single (scalar) random variable R, but instead involves a collection of L
Breast Cancer Risk Is Associated with the Genes Encoding the DNA Double-Strand Break Repair Mre11/Rad50/Nbs1 Complex

The evolutionarily conserved Mre11-Rad50-Nbs1 (MRN) complex, consisting of proteins encoded by the genes Mre11, Rad50, and Nbs1, was recently shown to play a crucial role in DNA double-strand break (DSB) repair by recruiting the nuclear protein kinase ataxia telangiectasia mutated to DSB sites, leading to activation of this DNA repair network. Given the fact that carriers of defective mutation and polymorphic variants of ataxia telangiectasia mutated are at higher risk of developing breast cancer, we hypothesized a role of the MRN genes in determining breast cancer susceptibility. This hypothesis was examined both in a case control study of 559 breast cancer patients and 1,125 healthy women of single-nucleotide polymorphisms in Mre11, Rad50, and Nbs1 and by the in vivo detection of binding between Mre11 and BRCA1, encoded by the breast cancer susceptibility gene BRCA1. We were also interested in defining whether any association between MRN genes and breast cancer was modified by reproductive risk factors reflecting the level of estrogen exposure or susceptibility to estrogen exposure, as estrogen is known to initiate breast cancer development due to its metabolites causing DSB formation. Support for the hypothesis came from the observations that (a) one single-nucleotide polymorphism in Nbs1 was significantly associated with breast cancer risk, and a trend toward an increased risk of developing breast cancer was found in women harboring a greater number of putative high-risk genotypes of MRN genes (an adjusted odds ratio of 1.25 for each additional putative high-risk genotype; 95% confidence interval, 1.10-1.44); (b) this association between risk and the number of putative high-risk genotypes was stronger and more significant in women thought to be more susceptible to estrogen, i.e., those with no history of full-term pregnancy, those older (≥26 years of age) at first full-term pregnancy, or those having had fewer (<2) full-term pregnancies; the risk effect conferred by harboring a higher number of high-risk genotypes of MRN genes was more significant in women without a history of breast feeding; and (c) Mre11 and BRCA1 were shown to form a complex in vivo, and this interaction was increased by irradiation. This study supports the role of the MRN pathway in breast cancer development, further strengthening the suggestion that mechanisms regulating DSB repair may play a mutator role driving breast cancer pathogenesis. (Cancer Epidemiol Biomarkers Prev 2007;6(10):2024–32)
Hormone Ablation , and Chemotherapy Vascular Endothelial Growth Factor Receptor-2 Blockade , Tumor Oxygenation in Hormone-Dependent Tumors During Updated

Tumor oxygenation is critical for tumor survival as well as for response to therapy, e.g., radiation therapy. Hormone ablation therapy in certain hormone-dependent tumors and antiangiogenic therapy lead to vessel regression and have also shown beneficial effects when combined with radiation therapy. These findings are counterintuitive because vessel regression should reduce oxygen tension (pO 2) in tumors, decreasing the effectiveness of radiotherapy. Here we report on the dynamics of pO 2 and oxygen consumption in a hormone-dependent tumor following hormone ablation and during treatment with an anti-VEGFR-2 monoclonal antibody (mAb) or a combination of doxorubicin and cyclophosphamide; the latter combination is not known to cause vessel regression at doses used clinically. Androgen-dependent male mouse mammary carcinoma (Shionogi) was implanted into transparent dorsal skin-fold chambers in male severe combined immunodeficient mice. Thirteen days after the tumors were implanted, mice were treated with antiangiogenic therapy (anti-VEGFR-2 mAb, 1.4 mg/30 g body weight), hormone ablation by castration, or doxorubicin (6.5 mg/kg every 7 days) and cyclophosphamide (100 mg/kg every 7 days). A non-invasivein vivo method was used to measure pO2 profiles and to calculate oxygen consumption rates (Q O2) in tumors. Tumors treated with anti-VEGFR-2 mAb exhibited vessel regression and became hypoxic. Initial vessel regression was followed by a “second wave” of angiogenesis and increases in both pO 2 and QO2. Hormone ablation led to tumor regression followed by an increase in pO2 coincident with regrowth. Chemotherapy led to tumor growth arrest characterized by constant QO2 and elevated pO2. The increased pO2 during anti-VEGFR-2 mAb and hormone ablation therapy may explain the observed beneficial effects of combining antiangiogenic or hormone therapies with radiation treatment. Thus, understanding the microenvironmental dynamics is critical for optimal scheduling of these treatment modalities.
Darkness visible: reflections on underground ecology

1 Soil science and ecology have developed independently, making it difficult for ecologists to contribute to urgent current debates on the destruction of the global soil resource and its key role in the global carbon cycle. Soils are believed to be exceptionally biodiverse parts of ecosystems, a view confirmed by recent data from the UK Soil Biodiversity Programme at Sourhope, Scotland, where high diversity was a characteristic of small organisms, but not of larger ones. Explaining this difference requires knowledge that we currently lack about the basic biology and biogeography of micro‐organisms. 2 It seems inherently plausible that the high levels of biological diversity in soil play some part in determining the ability of soils to undertake ecosystem‐level processes, such as carbon and mineral cycling. However, we lack conceptual models to address this issue, and debate about the role of biodiversity in ecosystem processes has centred around the concept of functional redundancy, and has consequently been largely semantic. More precise construction of our experimental questions is needed to advance understanding. 3 These issues are well illustrated by the fungi that form arbuscular mycorrhizas, the Glomeromycota. This ancient symbiosis of plants and fungi is responsible for phosphate uptake in most land plants, and the phylum is generally held to be species‐poor and non‐specific, with most members readily colonizing any plant species. Molecular techniques have shown both those assumptions to be unsafe, raising questions about what factors have promoted diversification in these fungi. One source of this genetic diversity may be functional diversity. 4 Specificity of the mycorrhizal interaction between plants and fungi would have important ecosystem consequences. One example would be in the control of invasiveness in introduced plant species: surprisingly, naturalized plant species in Britain are disproportionately from mycorrhizal families, suggesting that these fungi may play a role in assisting invasion. 5 What emerges from an attempt to relate biodiversity and ecosystem processes in soil is our extraordinary ignorance about the organisms involved. There are fundamental questions that are now answerable with new techniques and sufficient will, such as how biodiverse are natural soils? Do microbes have biogeography? Are there rare or even endangered microbes?
Downlink Training Techniques for FDD Massive MIMO Systems: Open-Loop and Closed-Loop Training With Memory

The concept of deploying a large number of antennas at the base station, often called massive multiple-input multiple-output (MIMO), has drawn considerable interest because of its potential ability to revolutionize current wireless communication systems. Most literature on massive MIMO systems assumes time division duplexing (TDD), although frequency division duplexing (FDD) dominates current cellular systems. Due to the large number of transmit antennas at the base station, currently standardized approaches would require a large percentage of the precious downlink and uplink resources in FDD massive MIMO be used for training signal transmissions and channel state information (CSI) feedback. To reduce the overhead of the downlink training phase, we propose practical open-loop and closed-loop training frameworks in this paper. We assume the base station and the user share a common set of training signals in advance. In open-loop training, the base station transmits training signals in a round-robin manner, and the user successively estimates the current channel using long-term channel statistics such as temporal and spatial correlations and previous channel estimates. In closed-loop training, the user feeds back the best training signal to be sent in the future based on channel prediction and the previously received training signals. With a small amount of feedback from the user to the base station, closed-loop training offers better performance in the data communication phase, especially when the signal-to-noise ratio is low, the number of transmit antennas is large, or prior channel estimates are not accurate at the beginning of the communication setup, all of which would be mostly beneficial for massive MIMO systems.
Swarm-Bots: Swarm of Mobile Robots able to Self-Assemble and Self-Organize

Keywords: SWARM_BOTS ; [MOBOTS] ; Evolutionary Robotics Note: Sponsor: swarm-bots, OFES 01-0012-1 Reference LSA-ARTICLE-2003-006 URL: http://www.swarm-bots.org/ Record created on 2006-12-07, modified on 2016-08-08
Multi-core systems modeling for formal verification of parallel algorithms

Modeling parallel algorithms at the architecture level enables exploring side-effects of the weakly ordered nature of modern processors. Formal verification of such models with model-checking can ensure that algorithm guarantees will hold even in the presence of the most aggressive compiler and processor optimizations. This paper proposes a virtual architecture to model the effects of such optimizations. It first presents the OoOmem framework to model out-of-order memory accesses. It then presents the OoOisched framework to model the effects of out-of-order instruction scheduling. These two frameworks are explained and tested using weaklyordered memory interaction scenarios known to be affected by weak ordering. Then, modeling of user-level RCU (Read- Copy Update) synchronization algorithms is presented. It uses the virtual architecture proposed to verify that the RCU guarantees are indeed respected.
Single Dose Caffeine Protects the Neonatal Mouse Brain against Hypoxia Ischemia

In this randomized blinded study, we investigated caffeine 5 mg/kg treatment given directly after neonatal brain hypoxia ischemia. Brain morphology, behavior and key brain infiltrating immune populations were examined. Caffeine treatment significantly improves outcome when compared to phosphate buffered saline. Flow cytometric analysis of immune responses revealed no persistent immunological alterations. Given its safety caffeine emerges as a candidate for neuroprotective intervention after neonatal brain injury.
Efficient Video Object Segmentation by Graph-Cut

Segmentation of video objects from background is a popular computer vision topic and has many important applications. Most existing methods are either computationally expensive or requiring manual initialization, static cameras, and/or rigid scenes. In a previous work, we proposed a joint spatio-temporal linear regression algorithm to automatically cluster the sparse edge/corner pixels in each video frame and obtain two motion models for the object and background respectively. To label the rest pixels for object segmentation, in this paper, we propose to model the Optical-Flow residual error, color intensity residual error and temporal label consistency features, as well as color/edge orientation consistency constrains, in a graph, and apply the Graph-Cut algorithm to minimize the energy of the graph to obtain an optimal segmentation of the two motion layers boundaries. Finally the object layer is identified from the two using simple heuristics. Experimental segmentation result with videos taken by webcams is promising.
Experiment on a novel user input for computer interface utilizing tongue input for severely disabled

This paper introduces a novel passive tongue control and tracking device, which require no electrical object to be inserted to user's mouth. The device is intended to be used by the severely disabled or quadriplegic person. The device is able perform two main applications, the keyboard and mouse application. The results show that this device allow the severely disabled person to have some control in his environment, such as to turn on and control daily electrical appliances or to control the wheel chair movement. The LabVIEW programming is used for the software of this device. The system is still in the development and working prototype phase to determine the accuracy and viability of such a setup.
Precise assembly of complex beta sheet topologies from de novo designed building blocks

Design of complex alpha-beta protein topologies poses a challenge because of the large number of alternative packing arrangements. A similar challenge presumably limited the emergence of large and complex protein topologies in evolution. Here, we demonstrate that protein topologies with six and seven-stranded beta sheets can be designed by insertion of one de novo designed beta sheet containing protein into another such that the two beta sheets are merged to form a single extended sheet, followed by amino acid sequence optimization at the newly formed strand-strand, strand-helix, and helix-helix interfaces. Crystal structures of two such designs closely match the computational design models. Searches for similar structures in the SCOP protein domain database yield only weak matches with different beta sheet connectivities. A similar beta sheet fusion mechanism may have contributed to the emergence of complex beta sheets during natural protein evolution. DOI: http://dx.doi. org/10.7554/eLife.11012. 001
Improved scaling of the gross motor function measure for children with cerebral palsy: evidence of reliability and validity.

BACKGROUND AND PURPOSE This study examined the reliability, validity, and responsiveness to change of measurements obtained with a 66-item version of the Gross Motor Function Measure (GMFM-66) developed using Rasch analysis. SUBJECTS AND METHODS The validity of measurements obtained with the GMFM-66 was assessed by examining the hierarchy of items and the GMFM-66 scores for different groups of children from a stratified random community-based sample of 537 children with cerebral palsy (CP). A subset of 228 children who had been reassessed at 12 months was used to test the hypothesis that children who are young (<5 years of age) and have "mild" CP will demonstrate greater change in GMFM-66 scores than children who are older ((5 years of age) and whose CP is more severe. Data from an additional 19 children with CP who were assessed twice, one week apart, were used to examine test-retest reliability. RESULTS The overall changes in GMFM-66 scores over 12 months and a time ( severity ( age interaction supported our hypotheses. Test-retest reliability was high (intraclass correlation coefficient=.99). CONCLUSION AND DISCUSSION This study demonstrated that the GMFM-66 has good psychometric properties. By providing a hierarchical structure and interval scaling, the GMFM-66 can provide a better understanding of motor development for children with CP than the 88 item GMFM and can improve the scoring and interpretation of data obtained with the GMFM.
An experimental analysis of the Echo State Network initialization using the Particle Swarm Optimization

This article introduces a robust hybrid method for solving supervised learning tasks, which uses the Echo State Network (ESN) model and the Particle Swarm Optimization (PSO) algorithm. An ESN is a Recurrent Neural Network with the hidden-hidden weights fixed in the learning process. The recurrent part of the network stores the input information in internal states of the network. Another structure forms a free-memory method used as supervised learning tool. The setting procedure for initializing the recurrent structure of the ESN model can impact on the model performance. On the other hand, the PSO has been shown to be a successful technique for finding optimal points in complex spaces. Here, we present an approach to use the PSO for finding some initial hidden-hidden weights of the ESN model. We present empirical results that compare the canonical ESN model with this hybrid method on a wide range of benchmark problems.
A Distributed Access Point Selection Algorithm Based on No-Regret Learning for Wireless Access Networks

The proliferation of wireless access technologies offers users the possibility of choosing among multiple available wireless access networks to connect to. This paper focuses on such network selection problem in the context of IEEE 802.11 WLANs where several access points provide connection service to users. We formulate this problem as a non-cooperative game where each user tries to maximize its utility function, defined as the throughput reward minus the fee charged by the access point. We then conduct a systematic analysis on the formulated game and develop an access point selection algorithm based on no-regret learning to orient the system converges to an equilibrium state (correlated equilibrium). The proposed algorithm, which can be implemented distributedly based on local observation, is especially suited in decentralized adaptive learning environments as wireless access networks. Finally, the simulation results demonstrate the effectiveness of the proposed algorithm in achieving high system efficiency.
The co-evolution of organizational structure and individual decision-making : three applications of social network analysis

Social influences and diffusion of innovation are key factors for investigating how the social contexts of organizations evolve over time and how this co-evolution shapes organizational structure. Building on previous studies of the impact of social interdependencies on organizational structure, this dissertation aims to investigate both how organizational structure co-evolves with individual decisions in a dense-network context and how to exploit social interdependences to ease the diffusion of information throughout organizations. Intra-organizational networks will be examined by using the network tools usually applied in biology and semantic network analysis; research will also be done on creativity. Data on membership and utilization of a health club in Tuscany will be used to examine how individuals’ decisions are influenced by other subjects’ actions and an email dataset of Eni S.p.A. will be used to study how individual positions in the network impact intra-firm communication and individual decisions influence intra-firm percolation of innovation.