93,098 research outputs found
Effects of creatine supplementation on memory in healthy individuals: a systematic review and meta-analysis of randomized controlled trials
Get PDFContext: From an energy perspective, the brain is very metabolically demanding. It is well documented that creatine plays a key role in brain bioenergetics. There is some evidence that creatine supplementation can augment brain creatine stores, which could increase memory. Objective: A systematic review and meta-analysis of randomized controlled trials (RCTs) was conducted to determine the effects of creatine supplementation on memory performance in healthy humans. Data Sources: The literature was searched through the PubMed, Web of Science, Cochrane Library, and Scopus databases from inception until September 2021. Data Extraction: Twenty-three eligible RCTs were initially identified. Ten RCTs examining the effect of creatine supplementation compared with placebo on measures of memory in healthy individuals met the inclusion criteria for systematic review, 8 of which were included in the meta-analysis. Data Analysis: Overall, creatine supplementation improved measures of memory compared with placebo (standard mean difference [SMD] = 0.29, 95%CI, 0.04-0.53; I2 = 66%; P = 0.02). Subgroup analyses revealed a significant improvement in memory in older adults (66-76 years) (SMD = 0.88; 95%CI, 0.22-1.55; I2 = 83%; P = 0.009) compared with their younger counterparts (11-31 years) (SMD = 0.03; 95%CI, -0.14 to 0.20; I2 = 0%; P = 0.72). Creatine dose (≈ 2.2-20 g/d), duration of intervention (5 days to 24 weeks), sex, or geographical origin did not influence the findings. Conclusion: Creatine supplementation enhanced measures of memory performance in healthy individuals, especially in older adults (66-76 years)
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
Author, publisher and bookseller : a tripartite synergy in Nigerian book industry
Get PDFThis work is about the roles of Author, Publisher and Bookseller in Book development in
Nigeria. The paper started by delving into the history of Book Publishing in Nigeria after
which it proceeded by defining who an author, a publisher, and a bookseller is and
expatiated on the indispensable roles of these key actors in Nigerian Book Industry and in
the emerging Information Society. Furthermore, the various constraints to book
development were identified while the paper advised on how the Book Industry can be
further promoted in Nigeria. However, the paper concluded and made recommendations
on how the Book sector can help in enhancing scholarship in the country
Author inscription in The Chinese slave-girl: a story of woman's life in China
No full textThis edition includes a gift inscription by author Rev. J.A. Davis, "To Rev. A. G. Russell with the warmest regards of the author J.A. Davis."Davis, John Agnell, 1839-1897
G-Rank: Unsupervised Continuous Learn-to-Rank for Edge Devices in a P2P Network
No full textRanking algorithms in traditional search engines are powered by enormous training data sets that are meticulously engineered and curated by a centralized entity. Decentralized peer-to-peer (p2p) networks such as torrenting applications and Web3 protocols deliberately eschew centralized databases and computational architectures when designing services and features. As such, robust search-and-rank algorithms designed for such domains must be engineered specifically for decentralized networks, and must be lightweight enough to operate on consumer-grade personal devices such as a smartphone or laptop computer. We introduce G-Rank, an unsupervised ranking algorithm designed exclusively for decentralized networks. We demonstrate that accurate, relevant ranking results can be achieved in fully decentralized networks without any centralized data aggregation, feature engineering, or model training. Furthermore, we show that such results are obtainable with minimal data preprocessing and computational overhead, and can still return highly relevant results even when a user’s device is disconnected from the network. G-Rank is highly modular in design, is not limited to categorical data, and can be implemented in a variety of domains with minimal modification. The results herein show that unsupervised ranking models designed for decentralized p2p networks are not only viable, but worthy of further research.https://github.com/awrgold/G-RankComputer Scienc
Advancements in sensory-motor perception and biologically-inspired hierarchical learning for embodied intelligence
No full textFrom a biological perspective, humans possess incredible sensory, dexterous and cognitive abilities. By virtue of these abilities, humans and more broadly their sensory systems are able to adapt to environmental demands seamlessly. Switching from a biological perspective to robotic systems as embodied intelligence, achieving such adaptation is currently far from trivial. Understanding the biological mechanisms that govern and render humans proficient in interacting with their surroundings, could ultimately illuminate new pathways to replicate such human-like cognition and dexterity in machines. Inspired by the aforementioned narrative, this thesis delves into and addresses four main research areas.
The first contribution of this thesis provides novel insights into the intricate interplay of multimodal interfaces, their impact on the human sensory-motor system and their correlation to the generation of meaningful motor actions. Different sensory modalities are examined, entailing a full factorial comparison of auditory, visual and somatosensory states and their influence on motor performance. Through a series of varying complexity motor tasks with human subjects, a correlation is established between sensory states and their influence on motor actions. Results provide novel evidence of which sensory combinations contribute to enhanced task performance and how these can be harnessed.
The second contribution of this work is the derivation of a novel metric capable of quantifying motor actions stemming from the intricate human sensory-motor system. Measuring human motor performance is a complex phenomenon and the absence of a standardised metric renders inter-study comparability challenging. To this end, four motor tasks, increasing in spatial complexity, were devised to establish a correlation of which spatial variables influence motor performance. Results revealed which spatial variables had the most notable effect, highlighting that existing metrics are inadequate for modelling higher dimensions. To account for this, a novel metric is derived, capable of modelling human motor performance in full 3D space, underlining its value for quantifying commonly seen motor movements and enhancing inter-study comparability.
The third and penultimate contribution builds on the foundation laid by the preceding segments, striving to align human capabilities closer to embodied intelligence. To realise this aim, inspired by a biological standpoint, the RObotic MAnipulation Network (ROMAN) is introduced. ROMAN is a novel Hybrid Hierarchical Learning (HHL) architecture designed to address the challenges of notably complex long-horizon sequential tasks. ROMAN utilises the exploratory nature of Reinforcement Learning (RL) while simultaneously exploiting the higher-level skills of humans in the form of imitation. Consisting of a plethora of specialising skills, ROMAN's hierarchical architecture demonstrates versatility in intricate, long-horizon sequential tasks; while exhibiting robustness against various levels of sensory uncertainties. By virtue of the HHL employed, ROMAN also exhibits adaptability beyond demonstrated behaviour; featuring failure recovery capabilities and adaptation in avoiding local minima. These results underline the significance of ROMAN for autonomous manipulation tasks necessitating intelligent and adaptive behaviour.
The fourth and concluding contribution of this thesis investigates the potential of language-guided exploration in augmenting embodied intelligence. In pursuit of this goal, the Intrinsically Guided Exploration from Large Language Models (IGE-LLMs) framework is presented, capable of complementing the existing bio-inspired hierarchy of ROMAN. By harnessing LLMs as an assistive intrinsic reward source alongside the conventional RL paradigm, IGE-LLMs enhances the exploratory process to address intricate settings challenged by sparse rewards and long-horizons. Validated on environments challenged by exploration and long-horizons, IGE-LLMs exhibits notably higher performance over existing methods and is capable of complementing the shortcomings of using LLMs in isolation. Moreover, the modularity and robustness of IGE-LLMs is underscored, due to its ability to complement existing intrinsic reward methods and its insensitivity to most intrinsic scaling parameters. Finally, the framework's resilience is highlighted over existing methods when faced with increased uncertainties and horizons. Capable of fostering exploration and the ability to automate the orchestration of ROMAN's intricate macro-actions, IGE-LLMs value as a language-guided framework is underlined.
This thesis provides novel findings on harnessing human sensory-motor abilities for generating meaningful motor actions, which can be adequately measured and quantified. Ultimately, these represent the inspiration for shaping the development of a novel bio-inspired learning method to align human capabilities closer to embodied intelligence that can further be complemented and automated by eliciting language-guided exploration; tailored to address notably intricate, long-horizon tasks with sparse rewards. Nevertheless, to further narrow the gap between humans and machines, a deeper understanding of designing artificial intelligence inspired by biological insights is necessitated
A Relational Unsupervised Approach to Author Identification
No full textIn the last decades speaking and writing habits have changed.
Many works faced the author identification task by exploiting frequencybased
approaches, numeric techniques or writing style analysis. Following
the last approach we propose a technique for author identification
based on First-Order Logic. Specifically, we translate the complex data
represented by natural language text to complex (relational) patterns
that represent the writing style of an author. Then, we model an author
as the result of clustering the relational descriptions associated to the
sentences. The underlying idea is that such a model can express the typical
way in which an author composes the sentences in his writings. So,
if we can map such writing habits from the unknown-author model to
the known-author model, we can conclude that the author is the same.
Preliminary results are promising and the approach seems viable in real
contexts since it does not need a training phase and performs well also
with short texts
Nd-doped polarization maintaining all-fiber laser with dissipative soliton resonance mode-locking at 905 nm
No full textFunding Information: Manuscript received February 15, 2021; revised May 14, 2021; accepted May 27, 2021. Date of publication June 1, 2021; date of current version September 13, 2021. This work was supported by RFBR under Grant 20-32-90233. (Corresponding author: Aram A. Mkrtchyan.) Aram A. Mkrtchyan, Yuriy G. Gladush, and Kirill A. Sitnik are with the Skolkovo Institute of Science and Technology, Moscow 121205, Russia (e-mail: [email protected]; [email protected]; [email protected]). Publisher Copyright: © 1983-2012 IEEE.Moving the fiber laser emission to the region below one micron may provide a cheaper, more compact and robust alternatives to the existing solid state lasers. Here, for the first time we report a neodymium mode-locked fiber laser emitting at 905 nm in the all-fiber polarization maintaining configuration. We obtain a self-starting pulse generation in nonlinear amplifying loop mirror (NALM) cavity configuration. To suppress a dominant emission at 1064 nm corresponding to a 4-level laser scheme, we use an active fiber - 920/1064 division multiplexer - active fiber sandwich-like sequence in the NALM loop. A rectangular shape dissipative soliton had nJ energy, 30 pm spectral width and 80 ÷ 430 ps width linearly depending on the pump power. Excellent agreement with numerical simulation allowed us to recover pulse shape and width for the pulses out of autocorrelation window.Peer reviewe
Investigation of a flow field generated by a fractal grid based on experimental data and CFD simulations
No full textFractal grids generate turbulence by directly exciting many length-scales of different sizes simultaneously, rather than using the nonlinear cascade mechanism to obtain multiscale excitation, as it is the case for classical grids. These scales influence each other and show very different properties compared to all previously documented turbulent flows. In this work we present experimental wind tunnel and computer fluid dynamics (CFD) studies of the turbulent flow generated by a fractal grid under the same conditions. We did an extensive statistical study and a direct comparison between the experimentally and numerically acquired time series in order to investigate and compare one-point- and two-point-statistics. In addition we present an application of a stochastic method, so-called Langevin approach, to the experimentally and numerically acquired velocity increment time series to examine three-point-statistics in terms of Kramers-Moyal coefficients
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