Miracle-dz/UniFAR-FTUs huggingface dataset:
Alpha Phase Determines Successful Lexical Decision in Noise

Psychophysical target detection has been shown to be modulated by slow oscillatory brain phase. However, thus far, only low-level sensory stimuli have been used as targets. The current human electroencephalography (EEG) study examined the influence of neural oscillatory phase on a lexical-decision task performed for stimuli embedded in noise. Neural phase angles were compared for correct versus incorrect lexical decisions using a phase bifurcation index (BI), which quantifies differences in mean phase angles and phase concentrations between correct and incorrect trials. Neural phase angles in the alpha frequency range (8–12 Hz) over right anterior sensors were approximately antiphase in a prestimulus time window, and thus successfully distinguished between correct and incorrect lexical decisions. Moreover, alpha-band oscillations were again approximately antiphase across participants for correct versus incorrect trials during a later peristimulus time window (∼500 ms) at left-central electrodes. Strikingly, lexical decision accuracy was not predicted by either event-related potentials (ERPs) or oscillatory power measures. We suggest that correct lexical decisions depend both on successful sensory processing, which is made possible by the alignment of stimulus onset with an optimal alpha phase, as well as integration and weighting of decisional information, which is coupled to alpha phase immediately following the critical manipulation that differentiated words from pseudowords. The current study constitutes a first step toward characterizing the role of dynamic oscillatory brain states for higher cognitive functions, such as spoken word recognition.
Out of the Shadows: Understanding Sexual Addiction

Sex is at the core of our identities. And when it becomes a compulsion, it can unravel our lives. Out of the Shadows is the premier work on sexual addiction, written by a pioneer in its treatment. Revised and updated to include the latest research-and to address the exploding phenomenon of cybersex addiction-this third edition identifies the danger signs, explains the dynamics, and describes the consequences of sexual addiction and dependency. With practical wisdom and spiritual clarity, it points the way out of the shadows of sexual compulsion and back into the light and fullness of life.
Collaborative corporate social responsibility: A case study examination of the international public relations agency involvement in the United Nations Global Compact

Purpose – This purpose of this paper is to examine how two, American‐based, international public relations agencies came to participate in the United Nations Global Compact. The global compact is an initiative which brings together more than 4,000 member organizations in over 100 countries to address some of the world's most pressing issues. Specifically, this paper seeks to identify: the events that prompt these senior‐level executives, as members of their agency's dominant coalition, to initiate participation in the global compact; what obstacles within the agency present challenges to joining the global compact; and how each agency integrates the compact principles into its work.Design/methodology/approach – Grunig's situational theory of publics is used as a framework to create a single, embedded case study which integrates three subunits of analysis: in‐depth interviews, and primary and secondary document analysis. In‐depth interviews are conducted with one senior‐level executive/counselor from each .
Sensitivity to the visual field origin of natural image patches in human low-level visual cortex

Asymmetries in the response to visual patterns in the upper and lower visual fields (above and below the centre of gaze) have been associated with ecological factors relating to the structure of typical visual environments. Here, we investigated whether the content of the upper and lower visual field representations in low-level regions of human visual cortex are specialised for visual patterns that arise from the upper and lower visual fields in natural images. We presented image patches, drawn from above or below the centre of gaze of an observer navigating a natural environment, to either the upper or lower visual fields of human participants (n = 7) while we used functional magnetic resonance imaging (fMRI) to measure the magnitude of evoked activity in the visual areas V1, V2, and V3. We found a significant interaction between the presentation location (upper or lower visual field) and the image patch source location (above or below fixation); the responses to lower visual field presentation were significantly greater for image patches sourced from below than above fixation, while the responses in the upper visual field were not significantly different for image patches sourced from above and below fixation. This finding demonstrates an association between the representation of the lower visual field in human visual cortex and the structure of the visual input that is likely to be encountered below the centre of gaze.
Stakeholders, politics and power: Towards an understanding of stakeholder identification and salience in government

Purpose – This paper seeks to explore the meaning of the stakeholder concept in politics, using theories drawn from the fields of management, political science and public relations. In particular, the theory of stakeholder identification and salience is to be used as a framework.Design/methodology/approach – This qualitative study involved in‐depth interviews with 23 politicians and political advisers in Western Australia and was conducted in the style of a grounded theory investigation.Findings – The results indicate that in politics, as in business, controversy and confusion continue to surround the stakeholder concept. Participants thought “stakeholder” should imply possession of a legitimate interest in an issue, but did not believe this was always the case. There is a gulf between how politicians think stakeholder status should be accorded in principle and what happens in reality. In practice, power seems to play a far greater role than legitimacy in determining stakeholder salience among political d.
HySAC: A Hybrid Delivery System with Adaptive Content Management for IPTV Networks

The emergence of new platforms providing Internet protocol television (IPTV) services, such as Google TV [1] and Apple TV [2] is expected to revolutionize the way multimedia services are provided on the Internet. However, the existing network infrastructure falls well short of meeting the resource requirements to provide satisfactory levels of such services. This has led the service providers like Google to contemplate deploying large farms of network cables to transport the traffic generated by their IPTV services. However, the huge cost involved in achieving their target, means careful resource planning and meeting users' expectations are essential. In this paper, we propose an effective adaptive HYbrid delivery System Adaptive Content management (HySAC), which dynamically selects the mode of video delivery from unicast, multicast and peer-to-peer (P2P) based on the popularity of the respective videos. The videos are dynamically classified into high, medium and low popularity groups and, they are delivered using multicast, P2P and unicast methods respectively. HySAC reduces the start-up delay as the mode of delivery is well defined and the resource availability to support the delivery is guaranteed. We have evaluated the performance of HySAC on a realistic simulated environment and presented experimental results; they indicate that HySAC performs significantly better than pure unicast, multicast and P2P mechanisms. This paper also provides a mechanism to classify the videos into different popularity classes.
An Example-Based Super-Resolution Algorithm for Selfie Images

A selfie is typically a self-portrait captured using the front camera of a smartphone. Most state-of-the-art smartphones are equipped with a high-resolution (HR) rear camera and a low-resolution (LR) front camera. As selfies are captured by front camera with limited pixel resolution, the fine details in it are explicitly missed. This paper aims to improve the resolution of selfies by exploiting the fine details in HR images captured by rear camera using an example-based super-resolution (SR) algorithm. HR images captured by rear camera carry significant fine details and are used as an exemplar to train an optimal matrix-value regression (MVR) operator. The MVR operator serves as an image-pair priori which learns the correspondence between the LR-HR patch-pairs and is effectively used to super-resolve LR selfie images. The proposed MVR algorithm avoids vectorization of image patch-pairs and preserves image-level information during both learning and recovering process. The proposed algorithm is evaluated for its efficiency and effectiveness both qualitatively and quantitatively with other state-of-the-art SR algorithms. The results validate that the proposed algorithm is efficient as it requires less than 3 seconds to super-resolve LR selfie and is effective as it preserves sharp details without introducing any counterfeit fine details.
How does clustering depend on environment?

According to the current paradigm, galaxies form and reside in extended cold dark matter (CDM) halos and in turn are key tracers of cosmological structure. Understanding how different types of galaxies occupy halos of different masses is one of the major challenges facing extragalactic astrophysics. The observed galaxy properties depend on the environment surrounding the galaxy. Within the framework of most galaxy formation models the environmental dependence of the galaxy population is mainly due to the change of the halo mass function with large-scale environment. Such models make precise predictions for how galaxy clustering should depend on environment. We will illustrate this by presenting analytical models of dark matter and galaxy clustering along with results obtained from numerical simulations. With these results we can hope to obtain a better understanding of the link between galaxies and dark matter and thereby constrain galaxy formation models.To search for other articles by the author(s) go to: http://adsabs.harvard. edu/abstract_service. html
Exploiting the kernel trick to correlate fragment ions for peptide identification via tandem mass spectrometry

MOTIVATION The correlation among fragment ions in a tandem mass spectrum is crucial in reducing stochastic mismatches for peptide identification by database searching. Until now, an efficient scoring algorithm that considers the correlative information in a tunable and comprehensive manner has been lacking. RESULTS This paper provides a promising approach to utilizing the correlative information for improving the peptide identification accuracy. The kernel trick, rooted in the statistical learning theory, is exploited to address this issue with low computational effort. The common scoring method, the tandem mass spectral dot product (SDP), is extended to the kernel SDP (KSDP). Experiments on a dataset reported previously demonstrate the effectiveness of the KSDP. The implementation on consecutive fragments shows a decrease of 10% in the error rate compared with the SDP. Our software tool, pFind, using a simple scoring function based on the KSDP, outperforms two SDP-based software tools, SEQUEST and Sonar MS/MS, in terms of identification accuracy. SUPPLEMENTARY INFORMATION http://www.jdl. ac.cn/user/yfu/pfind/index.html
Load scheduling with price uncertainty and temporally-coupled constraints in smart grids

Summary form only given. Recent years have witnessed the significant growth in electricity consumption. The emerging smart grid aims to address the ever-increasing load through appropriate scheduling, i.e., to shift the energy demand from peak to off-peak periods by pricing tariffs as incentives. Under the real-time pricing environment, due to the uncertainty of future prices, load scheduling is formulated as an optimization problem with expectation and temporally-coupled constraints. Instead of resorting to stochastic dynamic programming that is generally prohibitive to be explicitly solved, we propose dual decomposition and stochastic gradient to solve the problem. That is, the primal problem is firstly dually decomposed into a series of separable subproblems, and then the price uncertainty in each subproblem is addressed by stochastic gradient based on the statistical knowledge of future prices. In addition, we propose an online approach to further alleviate the impact of price prediction error. Numerical results are provided to validate our theoretical analysis.
Autism and Asperger syndrome: Living with Asperger's syndrome

An informal test of social know-how and its uses Challenged to write about problems which face adults with Asperger's syndrome, I would like to begin by challenging readers to a simple test. The aim of the test will become clear later. For the moment, think of it as a test of your knowledge of human relations. I have offered the stories to my autistic friends with the following explanation and reassurance: ‘I am interested in your opinions so that I can compare them with others. There are no precise right or wrong answers to this test, so you cannot fail it’. The test was composed in 1974 for some young men whose parents were members of the American National Society for Autistic Children. It was my first attempt at trying to understand the frustrations of adolescents who no longer fitted the stereotype of younger autistic children. After constructing stories involving some ordinary social interactions mingled with unusual interactions, I presented them first to a class of normal college students. They compared their answers and challenged me to a lively debate. After a few revisions, I was satisfied that the stories were understood consistently by normal students, and that unconventional answers might help pin-point problems in my autistic subjects. The autistic subjects were most co-operative, taking a lot of trouble and risking humiliation in order to be helpful to people like themselves, that is, high-functioning autistic individuals who might also be classified under the term Asperger's syndrome.
Histone Deacetylase 6 Inhibition Compensates for the Transport Deficit in Huntington's Disease by Increasing Tubulin Acetylation

A defect in microtubule (MT)-based transport contributes to the neuronal toxicity observed in Huntington's disease (HD). Histone deacetylase (HDAC) inhibitors show neuroprotective effects in this devastating neurodegenerative disorder. We report here that HDAC inhibitors, including trichostatin A (TSA), increase vesicular transport of brain-derived neurotrophic factor (BDNF) by inhibiting HDAC6, thereby increasing acetylation at lysine 40 of α-tubulin. MT acetylation in vitro and in cells causes the recruitment of the molecular motors dynein and kinesin-1 to MTs. In neurons, acetylation at lysine 40 of α-tubulin increases the flux of vesicles and the subsequent release of BDNF. We show that tubulin acetylation is reduced in HD brains and that TSA compensates for the transport- and release-defect phenotypes that are observed in disease. Our findings reveal that HDAC6 inhibition and acetylation at lysine 40 of α-tubulin may be therapeutic targets of interest in disorders such as HD in which intracellular transport is altered.
Towards energy-aware scheduling in data centers using machine learning

As energy-related costs have become a major economical factor for IT infrastructures and data-centers, companies and the research community are being challenged to find better and more efficient power-aware resource management strategies. There is a growing interest in "Green" IT and there is still a big gap in this area to be covered. In order to obtain an energy-efficient data center, we propose a framework that provides an intelligent consolidation methodology using different techniques such as turning on/off machines, power-aware consolidation algorithms, and machine learning techniques to deal with uncertain information while maximizing performance. For the machine learning approach, we use models learned from previous system behaviors in order to predict power consumption levels, CPU loads, and SLA timings, and improve scheduling decisions. Our framework is vertical, because it considers from watt consumption to workload features, and cross-disciplinary, as it uses a wide variety of techniques. We evaluate these techniques with a framework that covers the whole control cycle of a real scenario, using a simulation with representative heterogeneous workloads, and we measure the quality of the results according to a set of metrics focused toward our goals, besides traditional policies. The results obtained indicate that our approach is close to the optimal placement and behaves better when the level of uncertainty increases.
Chimeric Rabies Virus-Like Particles Containing Membrane-Anchored GM-CSF Enhances the Immune Response against Rabies Virus

Rabies remains an important public health threat in most developing countries. To develop a more effective and safe vaccine against rabies, we have constructed a chimeric rabies virus-like particle (VLP), which containing glycoprotein (G) and matrix protein (M) of rabies virus (RABV) Evelyn-Rokitnicki-Abelseth (ERA) strain, and membrane-anchored granulocyte-macrophage colony-stimulating factor (GM-CSF), and it was named of EVLP-G. The immunogenicity and protective efficacy of EVLP-G against RABV were evaluated by intramuscular administration in a mouse model. The EVLP-G was successfully produced in insect cells by coinfection with three recombinant baculoviruses expressing G, M, and GM-CSF, respectively. The membrane-anchored GM-CSF possesses a strong adjuvant activity. More B cells and dendritic cells (DCs) were recruited and/or activated in inguinal lymph nodes in mice immunized with EVLP-G. EVLP-G was found to induce a significantly increased RABV-specific virus-neutralizing antibody and elicit a larger and broader antibody subclass responses compared with the standard rabies VLP (sRVLP, consisting of G and M). The EVLP-G also elicited significantly more IFN-γ- or IL-4-secreting CD4+ and CD8+ T cells than the sRVLP. Moreover, the immune responses induced by EVLP-G protect all vaccinated mice from lethal challenge with RABV. These results suggest that EVLP-G has the potential to be developed as a novel vaccine candidate for the prevention and control of animal rabies.
The maximum mutual information between the output of a discrete symmetric channel and several classes of Boolean functions of its input

We prove the Courtade-Kumar conjecture, for several classes of n-dimensional Boolean functions, for all $n \geq 2$ and for all values of the error probability of the binary symmetric channel, $0 \leq p \leq 1/2$. This conjecture states that the mutual information between any Boolean function of an n-dimensional vector of independent and identically distributed inputs to a memoryless binary symmetric channel and the corresponding vector of outputs is upper-bounded by $1-\operatorname{H}(p)$, where $\operatorname{H}(p)$ represents the binary entropy function. That is, let $\mathbf{X}=[X_1 \ldots X_n]$ be a vector of independent and identically distributed Bernoulli(1/2) random variables, which are the input to a memoryless binary symmetric channel, with the error probability in the interval $0 \leq p \leq 1/2$ and $\mathbf{Y}=[Y_1 \ldots Y_n]$ the corresponding output. Let $f:\{0,1\}^n \rightarrow \{0,1\}$ be an n-dimensional Boolean function. Then, $\operatorname{MI}(f(X),Y) \leq 1-\operatorname{H}(p)$. Our proof employs Karamata's theorem, concepts from probability theory, transformations of random variables and vectors and algebraic manipulations.
An adaptive enrichment algorithm for advection‐dominated problems

We are interested in developing a numerical framework well suited for advection–diffusion problems when the advection part is dominant. In that case, given Dirichlet type boundary condition, it is well known that a boundary layer develops. To resolve correctly this layer, standard methods consist in increasing the mesh resolution and possibly increasing the formal accuracy of the numerical method. In this paper, we follow another path: we do not seek to increase the formal accuracy of the scheme but, by a careful choice of finite element, to lower the mesh resolution in the layer. Indeed the finite element representation we choose is locally the sum of a standard one plus an enrichment. This paper proposes such a method and with several numerical examples, we show the potential of this approach. In particular, we show that the method is not very sensitive to the choice of the enrichment and develop an adaptive algorithm to automatically choose the enrichment functions.Copyright © 2012 John Wiley & Sons, Ltd.
Electronic Reprint Applied Crystallography Web-ice: Integrated Data Collection and Analysis for Macromolecular Crystallography Applied Crystallography Web-ice: Integrated Data Collection and Analysis for Macromolecular Crystallography

Author(s) of this paper may load this reprint on their own web site or institutional repository provided that this cover page is retained. Republication of this article or its storage in electronic databases other than as specified above is not permitted without prior permission in writing from the IUCr. Many research topics in condensed matter research, materials science and the life sciences make use of crystallographic methods to study crystalline and non-crystalline matter with neutrons, X-rays and electrons. Articles published in the Journal of Applied Crystallography focus on these methods and their use in identifying structural and diffusion-controlled phase transformations, structure–property relationships, structural changes of defects, interfaces and surfaces, etc. Developments of instrumentation and crystallographic apparatus, theory and interpretation, numerical analysis and other related subjects are also covered. The journal is the primary place where crystallographic computer program information is published. New software tools are introduced to facilitate diffraction experiments involving large numbers of crystals. While existing programs have long provided a framework for lattice indexing, Bragg spot integration, and symmetry determination, these initial data processing steps often require significant manual effort. This limits the timely availability of data analysis needed for high-throughput procedures, including the selection of the best crystals from a large sample pool, and the calculation of optimal data collection parameters to assure complete spot coverage with minimal radiation damage. To make these protocols more efficient, a network of software applications and application servers has been developed, collectively known as Web-Ice. When the package is installed at a crystallography beamline, a programming interface allows the beamline control software (e.g. Blu-Ice, DCSS) to trigger data analysis automatically. Results are organized based on a list of samples that the user provides, and are examined within a Web page, accessible both locally at the beamline and remotely. Optional programming interfaces permit the user to control data acquisition through the Web browser. The system as a whole is implemented to support multiple users and multiple processors, and can be expanded to provide additional scientific functionality. Web-Ice has a distributed architecture consisting of several stand-alone software components working together via a well defined interface. Other synchrotrons or institutions may integrate selected components or the whole of Web-Ice with their own data acquisition software. Updated information about current developments may be obtained at