918 research outputs found
Generating Hermite polynomial excited squeezed states by means of conditional measurements on a beam splitter
A scheme for conditional generating a Hermite polynomial excited squeezed
vacuum states (HESVS) is proposed. Injecting a two-mode squeezed vacuum state
(TMSVS) into a beam splitter (BS) and counting the photons in one of the output
channels, the conditional state in the other output channel is just a HESVS. To
exhibit a number of nonclassical effects and non-Guassianity, we mainly
investigate the photon number distribution, sub-Poissonian distribution,
quadrature component distribution, and quasi-probability distribution of the
HPESVS. We find that its nonclassicality closely relates to the control
parameter of the BS, the squeezed parameter of the TMSVS, and the photon number
of conditional measurement. These further demonstrate that performing the
conditional measurement on a BS is an effective approach to generate
non-Guassian state.Comment: 8 pages, 8 figures. arXiv admin note: text overlap with
arXiv:quant-ph/9703039 by other author
Ultrathin Acoustic Parity-Time Symmetric Metasurface Cloak
Invisibility or unhearability cloaks have beenmade possible by using metamaterials enabling light or sound to flow around obstacle
without the trace of reflections or shadows. Metamaterials are known for being flexible building units that can mimic a host of
unusual and extreme material responses, which are essential when engineering artificial material properties to realize a coordinate
transforming cloak. Bending and stretching the coordinate grid in space require stringent material parameters; therefore, small
inaccuracies and inevitablematerial losses become sources for unwanted scattering that are decremental to the desired effect.These
obstacles further limit the possibility of achieving a robust concealment of sizeable objects from either radar or sonar detection. By
using an elaborate arrangement of gain and lossy acousticmedia respecting parity-time symmetry, we built a one-way unhearability
cloak able to hide objects seven times larger than the acoustic wavelength. Generally speaking, our approach has no limits in terms
of working frequency, shape, or size, specifically though we demonstrate how, in principle, an object of the size of a human can be
hidden from audible sound
Postnatal dysregulation of Notch signal disrupts dendrite development of adult-born neurons in the hippocampus and contributes to memory impairment
Deficits in the Notch pathway are involved in a number of neurologic diseases associated with mental retardation or/and dementia. The mechanisms by which Notch dysregulation are associated with mental retardation and dementia are poorly understood. We found that Notch1 is highly expressed in the adult-born immature neurons in the hippocampus of mice. Retrovirus mediated knockout of notch1 in single adult-born immature neurons decreases mTOR signaling and compromises their dendrite morphogenesis. In contrast, overexpression of Notch1 intracellular domain (NICD), to constitutively activate Notch signaling in single adult-born immature neurons, promotes mTOR signaling and increases their dendrite arborization. Using a unique genetic approach to conditionally and selectively knockout notch 1 in the postnatally born immature neurons in the hippocampus decreases mTOR signaling, compromises their dendrite morphogenesis, and impairs spatial learning and memory. Conditional overexpression of NICD in the postnatally born immature neurons in the hippocampus increases mTOR signaling and promotes dendrite arborization. These data indicate that Notch signaling plays a critical role in dendrite development of immature neurons in the postnatal brain, and dysregulation of Notch signaling in the postnatally born neurons disrupts their development and thus contributes to the cognitive deficits associated with neurological diseases
Five Classification of Mammography Images Based on Deep Cooperation Convolutional Neural Network
Mammography is currently the preferred imaging method for breast cancer screening. Masses and calcification are the main positive signs of mammography. Due to the variable appearance of masses and calcification, a significant number of breast cancer cases are missed or misdiagnosed if it is only depended on the radiologists’ subjective judgement. At present, most of the studies are based on the classical Convolutional Neural Networks (CNN), which uses the transfer learning to classify the benign and malignant masses in the mammography images. However, the CNN is designed for natural images which are substantially different from medical images. Therefore, we propose a Deep Cooperation CNN (DCCNN) to classify mammography images of a data set into five categories including benign calcification, benign mass, malignant calcification, malignant mass and normal breast. The data set consists of 695 normal cases from DDSM, 753 calcification cases and 891 mass cases from CBIS-DDSM. Finally, DCCNN achieves 91% accuracy and 0.98 AUC on the test set, whose performance is superior to VGG16, GoogLeNet and InceptionV3 models. Therefore, DCCNN can aid radiologists to make more accurate judgments, greatly reducing the rate of missed and misdiagnosis
A novel strategy for rapid identification of the fruits of Illicium verum and Illicium anisatum using electronic nose and tongue technology
Purpose: To develop an effective and rapid strategy for the identification of fruits of I. verum and I. anisatum based on their odor and taste.Methods: Electronic nose (E-nose) and electronic tongue (E-tongue) technology was used to identify the fruits of I. verum (FIV) and I. anisatum (FIA). Samples of FIA, FIV, and FIA : FIV mixtures in different proportions (1 : 3, 1 : 1, and 3 : 1) were prepared to evaluate the identification abilities of E-nose and Etongue methods. Samples were powdered and sifted through a standard sieve (aperture size 355 ± 13 μm) for E-nose analysis. Each sample was refluxed with water for 1 h before E-tongue analysis. The acquired data were analyzed by principal component analysis (PCA) and discriminant factor analysis (DFA).Results: Based on the signals acquired by E-nose and E-tongue analyses, a total of 90 data points each were used for PCA. The three principal component values for E-nose analysis were PC1 = 93.89 %, PC2 = 6.08 %, and PC3 = 0.03 %, and those for E-tongue analysis were PC1 = 98.72 %, PC2 = 0.68 %, and PC3 = 0.57 %. The sample data were significantly divided into two groups representing FIV and FIA. Furthermore, E-nose and E-tongue assessments combined with PCA and DFA analyses effectively identified FIV, FIA and their mixtures.Conclusion: The use of E-nose and E-tongue technology is an effective and rapid strategy to identify the fruits of I. verum and I. anisatum and their mixtures. This strategy may also offer an effective method for detection of adulterants.Keywords: Illicium verum, Illicium anisatum, Discrimination, Electronic nose, Electronic tongue, Safety, Principal component analysis, Discriminant factor analysi
Quantum theory of electronic double-slit diffraction
The phenomena of electron, neutron, atomic and molecular diffraction have
been studied by many experiments, and these experiments are explained by some
theoretical works. In this paper, we study electronic double-slit diffraction
with quantum mechanical approach. We can obtain the results: (1) When the slit
width is in the range of we can obtain the obvious
diffraction patterns. (2) when the ratio of , order are missing in
diffraction pattern. (3)When the ratio of , there isn't missing order in diffraction pattern. (4) We
also find a new quantum mechanics effect that the slit thickness has a
large affect to the electronic diffraction patterns. We think all the
predictions in our work can be tested by the electronic double-slit diffraction
experiment.Comment: 9pages, 14figure
Quantifying & Modeling Multimodal Interactions: An Information Decomposition Framework
The recent explosion of interest in multimodal applications has resulted in a
wide selection of datasets and methods for representing and integrating
information from different modalities. Despite these empirical advances, there
remain fundamental research questions: How can we quantify the interactions
that are necessary to solve a multimodal task? Subsequently, what are the most
suitable multimodal models to capture these interactions? To answer these
questions, we propose an information-theoretic approach to quantify the degree
of redundancy, uniqueness, and synergy relating input modalities with an output
task. We term these three measures as the PID statistics of a multimodal
distribution (or PID for short), and introduce two new estimators for these PID
statistics that scale to high-dimensional distributions. To validate PID
estimation, we conduct extensive experiments on both synthetic datasets where
the PID is known and on large-scale multimodal benchmarks where PID estimations
are compared with human annotations. Finally, we demonstrate their usefulness
in (1) quantifying interactions within multimodal datasets, (2) quantifying
interactions captured by multimodal models, (3) principled approaches for model
selection, and (4) three real-world case studies engaging with domain experts
in pathology, mood prediction, and robotic perception where our framework helps
to recommend strong multimodal models for each application.Comment: Code available at: https://github.com/pliang279/PI
The Nearest Neutron Star Candidate in a Binary Revealed by Optical Time-domain Surveys
Recent studies have revealed the global deposition on Earth of radioactive
elements (e.g., Fe) resulting from the metal-enriched ejecta of nearby
(within pc) supernova explosions. The majority of neutron stars in
our Solar neighborhood remain to be discovered. Here we report the discovery of
the nearest ( pc) neutron star candidate in the single-lined
spectroscopic binary LAMOST J235456.76+335625.7 (hereafter J2354). Utilizing
the multi-epoch spectra and high-cadence periodic light curves, we measure the
mass of the visible star () and determine
the mass function of the invisible object ,
i.e., the mass of the unseen compact object is $M_{\rm inv} \geq 1.26 \pm 0.03\
M_{\odot}0.12.4<10^{30}\ {\rm erg\ s^{-1}}1.4<6.8\times 10^{23}\ {\rm erg\ s^{-1}}$). Hence, the
neutron star candidate in J2354 can only be discovered via our time-resolved
observations. The alternative scenario involving a nearby supramassive cold
white dwarf cannot be fully excluded. Our discovery demonstrates a promising
way to unveil the missing population of backyard inactive neutron stars or
supramassive cold white dwarfs in binaries by exploring the optical time
domain, thereby facilitating understanding of the supernovae explosion and
metal-enrichment history in our Solar neighborhood.Comment: 35 pages, 8 figures, to be submitte
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