417 research outputs found
The Role of Extraversion, Sensitivity to Music Reward, and Music Tempo on Word Recall
The Mozart Effect refers to the theory that exposure to classical music will make people more intelligent. The study explored whether the benefits of classic music extended to memory processes such as immediate word recall, while considering individual differences in extroversion and sensitivity to music reward. To test this, 56 first-year psychology students completed Eysenck’s Personality Inventory, the Barcelona Music Reward Questionnaire and a music experience questionnaire. Participants then were exposed to a three-minute Mozart excerpt that was either slow, regular or fast tempo, then completed an immediate recall task. A 2X2X3 ANOVA was conducted, a significant interaction effect was found for tempo X extraversion. No other significant main or interaction effects were found. Independent t-tests found low extraversion people performed significantly better after regular tempo than slow tempo music. Independent t-tests also found low extraversion people performed significantly better than high extraversion people after regular tempo music. Implications of the results are discussed
An equivalent-effect phenomenon in eddy current non-destructive testing of thin structures
The inductance/impedance due to thin metallic structures in non-destructive
testing (NDT) is difficult to evaluate. In particular, in Finite Element Method
(FEM) eddy current simulation, an extremely fine mesh is required to accurately
simulate skin effects especially at high frequencies, and this could cause an
extremely large total mesh for the whole problem, i.e. including, for example,
other surrounding structures and excitation sources like coils. Consequently,
intensive computation requirements are needed. In this paper, an
equivalent-effect phenomenon is found, which has revealed that alternative
structures can produce the same effect on the sensor response, i.e. mutual
impedance/inductance of coupled coils if a relationship (reciprocal
relationship) between the electrical conductivity and the thickness of the
structure is observed. By using this relationship, the mutual
inductance/impedance can be calculated from the equivalent structures with much
fewer mesh elements, which can significantly save the computation time. In eddy
current NDT, coils inductance/impedance is normally used as a critical
parameter for various industrial applications, such as flaw detection, coating
and microstructure sensing. Theoretical derivation, measurements and
simulations have been presented to verify the feasibility of the proposed
phenomenon
Coherent heteronuclear spin dynamics in an ultracold spin-1 mixture
We report the observation of coherent heteronuclear spin dynamics driven by
inter-species spin-spin interaction in an ultracold spinor mixture, which
manifests as periodical and well correlated spin oscillations between two
atomic species. In particular, we investigate the magnetic field dependence of
the oscillations and find a resonance behavior which depends on {\em both} the
linear and quadratic Zeeman effects and the spin-dependent interaction. We also
demonstrate a unique knob for controlling the spin dynamics in the spinor
mixture with species-dependent vector light shifts. Our finds are in agreement
with theoretical simulations without any fitting parameters.Comment: 13 pages including the supplementary materia
The Applications of Green Building Rating System in Property Management
In the time of Low-carbon economy,the thought of sustainable development has influenced every aspects of life, and the ideas of green service and environmental management has become increasingly popular in property management .Green property management is now a trend, yet necessarily the only way to meet the owner’s needs. Responding to the current call of building energy efficiency, it is inevitable in the development of property management to introduce the idea of green management, advocate green service management, and apply the green building rating system to property management, which is one distinguishing feature of modern property services.Key words: Green Building Rating System; Green Property Management; Application
Model combination by decomposition and aggregation
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Nuclear Engineering, 2004.Includes bibliographical references (p. 265-282).This thesis focuses on a general problem in statistical modeling, namely model combination. It proposes a novel feature-based model combination method to improve model accuracy and reduce model uncertainty. In this method, a set of candidate models are first decomposed into a group of components or features and then components are selected and aggregated into a composite model based on data. However, in implementing this new method, some central challenges have to be addressed, which include candidate model choice, component selection, data noise modeling, model uncertainty reduction and model locality. In order to solve these problems, some new methods are put forward. In choosing candidate models, some criteria are proposed including accuracy, diversity, independence as well as completeness and then corresponding quantitative measures are designed to quantify these criteria, and finally an overall preference score is generated for each model in the pool. Principal component analysis (PCA) and independent component analysis (ICA) are applied to decompose candidate models into components and multiple linear regression is employed to aggregate components into a composite model.(cont.) In order to reduce model structure uncertainty, a new concept of fuzzy variable selection is introduced to carry out component selection, which is able to combine the interpretability of classical variable selection and the stability of shrinkage estimators. In dealing with parameter estimation uncertainty, exponential power distribution is proposed to model unknown non-Gaussian noise and parametric weighted least-squares method is devise to estimate parameters in the context of non-Gaussian noise. These two methods are combined to work together to reduce model uncertainty, including both model structure uncertainty and parameter uncertainty. To handle model locality, i.e. candidate models do not work equally well over different regions, the adaptive fuzzy mixture of local ICA models is developed. Basically, it splits the entire input space into domains, build local ICA models within each sub-region and then combine them into a mixture model. Many different experiments are carried out to demonstrate the performance of this novel method. Our simulation study and comparison show that this new method meets our goals and outperforms existing methods in most situations.by Mingyang Xu.Ph.D
Linguistic experience acquisition for novel stimuli selectively activates the neural network of the visual word form area
The human ventral visual cortex is functionally organized into different domains that sensitively respond to different categories, such as words and objects. There is heated debate over what principle constrains the locations of those domains. Taking the visual word form area (VWFA) as an example, we tested whether the word preference in this area originates from the bottom-up processes related to word shape (the shape hypothesis) or top-down connectivity of higher-order language regions (the connectivity hypothesis). We trained subjects to associate identical, meaningless, non-word-like figures with high-level features of either words or objects. We found that the word-feature learning for the figures elicited the neural activation change in the VWFA, and learning performance effectively predicted the activation strength of this area after learning. Word-learning effects were also observed in other language areas (i.e., the left posterior superior temporal gyrus, postcentral gyrus, and supplementary motor area), with increased functional connectivity between the VWFA and the language regions. In contrast, object-feature learning was not associated with obvious activation changes in the language regions. These results indicate that high-level language features of stimuli can modulate the activation of the VWFA, providing supportive evidence for the connectivity hypothesis of words processing in the ventral occipitotemporal cortex
Multi-User Chat Assistant (MUCA): a Framework Using LLMs to Facilitate Group Conversations
Recent advancements in large language models (LLMs) have provided a new
avenue for chatbot development, while most existing research has primarily
centered on single-user chatbots that focus on deciding "What" to answer after
user inputs. In this paper, we identified that multi-user chatbots have more
complex 3W design dimensions -- "What" to say, "When" to respond, and "Who" to
answer. Additionally, we proposed Multi-User Chat Assistant (MUCA), which is an
LLM-based framework for chatbots specifically designed for group discussions.
MUCA consists of three main modules: Sub-topic Generator, Dialog Analyzer, and
Utterance Strategies Arbitrator. These modules jointly determine suitable
response contents, timings, and the appropriate recipients. To make the
optimizing process for MUCA easier, we further propose an LLM-based Multi-User
Simulator (MUS) that can mimic real user behavior. This enables faster
simulation of a conversation between the chatbot and simulated users, making
the early development of the chatbot framework much more efficient. MUCA
demonstrates effectiveness, including appropriate chime-in timing, relevant
content, and improving user engagement, in group conversations with a small to
medium number of participants, as evidenced by case studies and experimental
results from user studies
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