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 $a$ is in the range of $3\lambda\sim 50\lambda$ we can obtain the obvious diffraction patterns. (2) when the ratio of $\frac{d+a}{a}=n (n=1, 2, 3,\cdot\cdot\cdot)$, order $2n, 3n, 4n,\cdot\cdot\cdot$ are missing in diffraction pattern. (3)When the ratio of $\frac{d+a}{a}\neq n (n=1, 2, 3,\cdot\cdot\cdot)$, there isn't missing order in diffraction pattern. (4) We also find a new quantum mechanics effect that the slit thickness $c$ 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., $^{60}$Fe) resulting from the metal-enriched ejecta of nearby (within $\sim 100$ pc) supernova explosions. The majority of neutron stars in our Solar neighborhood remain to be discovered. Here we report the discovery of the nearest ($127.7 \pm 0.3$ 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 ($M_{\rm vis}=0.70\pm 0.05\ M_{\odot}$) and determine the mass function of the invisible object $f(M)=0.525 \pm 0.004\ M_{\odot}$, i.e., the mass of the unseen compact object is $M_{\rm inv} \geq 1.26 \pm 0.03\ M_{\odot}$. The excess UV emission due to a hot supramassive white dwarf is absent. Hence, it is likely that J2354 harbors a neutron star. J2354 is X-ray dim (the$0.1$--$2.4$keV luminosity$<10^{30}\ {\rm erg\ s^{-1}}$) since it is not detected in the ROSAT all-sky surveys in X-ray. One-hour exceptionally sensitive radio follow-up observations with FAST, the largest single-dish radio telescope, failed to reveal any radio pulsating signals (the potential pulse power at$1.4$GHz is$<6.8\times 10^{23}\ {\rm erg\ s^{-1}}$). 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