28,701 research outputs found
Advantages of nonclassical pointer states in postselected weak measurements
We investigate, within the weak measurement theory, the advantages of
non-classical pointer states over semi-classical ones for coherent, squeezed
vacuum, and Schr\"{o}inger cat states. These states are utilized as pointer
state for the system operator with property ,
where represents the identity operator. We calculate the ratio
between the signal-to-noise ratio (SNR) of non-postselected and postselected
weak measurements. The latter is used to find the quantum Fisher information
for the above pointer states. The average shifts for those pointer states with
arbitrary interaction strength are investigated in detail. One key result is
that we find the postselected weak measurement scheme for non-classical pointer
states to be superior to semi-classical ones. This can improve the precision of
measurement process.Comment: 8 pages, 5 figure
Memories of the future
The year is 2020. Sheffield Universityâs MSc in Electronic & Digital Library Management has been running for 10 years. What paths have its graduatesâ careers taken
Which Factors Can Contribute to the Success of Environmental and Animal Protection Projects in Donation-based Crowdfunding? A Neural Network Model
The crowdfunding industry has developed rapidly in recent years, the existing research shows that crowdfunding can help in many fields such as entrepreneurship, creative products, or donations. Due to global meteorological issues, more and more people are paying attention to the environment and animal protection. However, fundraising in these areas has been the biggest problem, the emergence of donation crowdfunding (DCF) can alleviate this dilemma. Currently, in academia, there is still less research focused on crowdfunding for environmental and animal protection. This paper aims to study the factors influencing the successful financing of environmental and animal protection projects in the DCF.
This paper analyses 700 DCF environmental and animal protection projects in China as samples, and creatively introduces financial transparency scoring indicators. Through binary logistic regression, financial transparency was found to be the most critical positive factor affecting project success. At the same time, donors receive NPO-initiated projects well, and the number of donors can also positively impact the results. However, the excessive description of the projects can have the opposite effect. This study also introduced a neural network model, and found that the neural network model can optimize the discriminant accuracy of the traditional binary logistic regression model
Identification Techniques Applied to a Passive Elasto-magnetic Suspension
The paper presents an experimental passive elasto-magnetic suspension based on rare-earth permanent magnets, characterized by negligible dependence on mass of its natural frequency. The nonlinear behaviour of this system, equipped with a traditional linear elastic spring coupled to a magnetic spring, is analysed in time domain, for non-zero initial conditions, and in frequency domain, by applying sweep excitations to the test rig base. The dynamics of the system is very complex in dependence of the magnetic contribution, showing both hardening behaviour in the elasto-magnetic setup, and softening motion amplitude dependent behaviour in the purely magnetic case. Hence it is necessary to adopt nonlinear identification techniques, such as non-parametric restoring force mapping method and direct parametric estimation technique, in order to identify the system parameters in the different configurations. Finally, it is discussed the ability of identified versus analytical models in reproducing the nonlinear dependency of frequency on motion amplitude and the presence of jump phenomen
Dietary glycaemic index, glycaemic load and head and neck cancer risk: A pooled analysis in an international consortium
High dietary glycaemic index (GI) and glycaemic load (GL) may increase cancer risk. However, limited information was available on GI and/or GL and head and neck cancer (HNC) risk. We conducted a pooled analysis on 8 case-control studies (4081 HNC cases; 7407 controls) from the International Head and Neck Cancer Epidemiology (INHANCE) consortium. We estimated the odds ratios (ORs) and 95% confidence intervals (CIs) of HNC, and its subsites, from fixed- or mixed-effects logistic models including centre-specific quartiles of GI or GL. GI, but not GL, had a weak positive association with HNC (O
Latent Space Model for Multi-Modal Social Data
With the emergence of social networking services, researchers enjoy the
increasing availability of large-scale heterogenous datasets capturing online
user interactions and behaviors. Traditional analysis of techno-social systems
data has focused mainly on describing either the dynamics of social
interactions, or the attributes and behaviors of the users. However,
overwhelming empirical evidence suggests that the two dimensions affect one
another, and therefore they should be jointly modeled and analyzed in a
multi-modal framework. The benefits of such an approach include the ability to
build better predictive models, leveraging social network information as well
as user behavioral signals. To this purpose, here we propose the Constrained
Latent Space Model (CLSM), a generalized framework that combines Mixed
Membership Stochastic Blockmodels (MMSB) and Latent Dirichlet Allocation (LDA)
incorporating a constraint that forces the latent space to concurrently
describe the multiple data modalities. We derive an efficient inference
algorithm based on Variational Expectation Maximization that has a
computational cost linear in the size of the network, thus making it feasible
to analyze massive social datasets. We validate the proposed framework on two
problems: prediction of social interactions from user attributes and behaviors,
and behavior prediction exploiting network information. We perform experiments
with a variety of multi-modal social systems, spanning location-based social
networks (Gowalla), social media services (Instagram, Orkut), e-commerce and
review sites (Amazon, Ciao), and finally citation networks (Cora). The results
indicate significant improvement in prediction accuracy over state of the art
methods, and demonstrate the flexibility of the proposed approach for
addressing a variety of different learning problems commonly occurring with
multi-modal social data.Comment: 12 pages, 7 figures, 2 table
Generating Multimode Entangled Microwaves with a Superconducting Parametric Cavity
In this Letter, we demonstrate the generation of multimode entangled states
of propagating microwaves. The entangled states are generated by parametrically
pumping a multimode superconducting cavity. By combining different pump
frequencies, applied simultaneously to the device, we can produce different
entanglement structures in a programable fashion. The Gaussian output states
are fully characterized by measuring the full covariance matrices of the modes.
The covariance matrices are absolutely calibrated using an in situ microwave
calibration source, a shot noise tunnel junction. Applying a variety of
entanglement measures, we demonstrate both full inseparability and genuine
tripartite entanglement of the states. Our method is easily extensible to more
modes.Comment: 5 pages, 1 figures, 1 tabl
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