Arbitrary Modulation of Average Dwell Time in Discrete-Time Markov Chains based on Tunneling Magnetoresistance Effect

Stochastic processes (SPs) are widely used in many real-world fields, especially AI algorithms and models. A discrete-time Markov chain (DTMC) is a fundamental SP where the probability of each event depends only on the state attained in the previous event. DTMC is extensively used in signal processing, information theory and speech processing, but the hardware generation of DTMC remains hindered by the difficulty in modulating the averaged dwell times (ADTs), i.e. the average time that the DTMC stays at one state. In this paper, we propose a two-step procedure to modulate the ADTs of a DTMC generated from one magnetic tunnel junction (MTJ), without being limited by additional restrictions such as a fixed ratio between the ADTs, widening the applications scope of the MTJ-based DTMC. This method has been verified via mathematical derivation and electrical characterization. The generation throughput and power consumption can also be conveniently modulated. This procedure provides a new hardware solution for the generation of stochastic signal in semiconductor IC chips

Brain age predicted using graph convolutional neural network explains neurodevelopmental trajectory in preterm neonates

OBJECTIVES: Dramatic brain morphological changes occur throughout the third trimester of gestation. In this study, we investigated whether the predicted brain age (PBA) derived from graph convolutional network (GCN) that accounts for cortical morphometrics in third trimester is associated with postnatal abnormalities and neurodevelopmental outcome. METHODS: In total, 577 T1 MRI scans of preterm neonates from two different datasets were analyzed; the NEOCIVET pipeline generated cortical surfaces and morphological features, which were then fed to the GCN to predict brain age. The brain age index (BAI; PBA minus chronological age) was used to determine the relationships among preterm birth (i.e., birthweight and birth age), perinatal brain injuries, postnatal events/clinical conditions, BAI at postnatal scan, and neurodevelopmental scores at 30 months. RESULTS: Brain morphology and GCN-based age prediction of preterm neonates without brain lesions (mean absolute error [MAE]: 0.96 weeks) outperformed conventional machine learning methods using no topological information. Structural equation models (SEM) showed that BAI mediated the influence of preterm birth and postnatal clinical factors, but not perinatal brain injuries, on neurodevelopmental outcome at 30 months of age. CONCLUSIONS: Brain morphology may be clinically meaningful in measuring brain age, as it relates to postnatal factors, and predicting neurodevelopmental outcome. CLINICAL RELEVANCE STATEMENT: Understanding the neurodevelopmental trajectory of preterm neonates through the prediction of brain age using a graph convolutional neural network may allow for earlier detection of potential developmental abnormalities and improved interventions, consequently enhancing the prognosis and quality of life in this vulnerable population. KEY POINTS: •Brain age in preterm neonates predicted using a graph convolutional network with brain morphological changes mediates the pre-scan risk factors and post-scan neurodevelopmental outcomes. •Predicted brain age oriented from conventional deep learning approaches, which indicates the neurodevelopmental status in neonates, shows a lack of sensitivity to perinatal risk factors and predicting neurodevelopmental outcomes. •The new brain age index based on brain morphology and graph convolutional network enhances the accuracy and clinical interpretation of predicted brain age for neonates

Markov Chain Signal Generation based on Single Magnetic Tunnel Junction

Markov chain (MC) is a stochastic model that describes a sequence of events where the probability of each event depends only on the previous state. Such memoryless property makes MC widely used in machine learning and encryption, but the hardware implementation of MC generation remains challenging. This paper presents a hardware solution for generating MC signals using only one industry-ready magnetic tunnel junction (MTJ). High quality standard MC signal has been generated with low error and good randomness. The proposed solution also demonstrates the potential in increasing the generation speed. The presented solution offers a hardware-friendly implementation of MC signal in semiconductor chips

Study of the P-wave charmonium state \chi_{cJ} in \psi(2S) decays

The processes $\psi(2S)\to \gamma \pi^+ \pi^-$, $\gamma K^+ K^-$ and $\gamma p \bar{p}$ have been studied using a sample of $3.7 \times 10^6$ produced $\psi(2S)$ decays. We determine the total width of the $\chi_{c0}$ to be $\Gamma^{tot}_{\chi_{c0}} = 14.3\pm 2.0\pm 3.0$ MeV. We present the first measurement of the branching fraction $B(\chi_{c0} \to p \bar{p}) = (16.3 \pm 4.4 \pm 5.4)\times 10^{-5}$, where the first error is statistical and the second one systematic. Branching fractions of $\chi_{c0,2} \to \pi^+ \pi^-$ and $K^+ K^-$ are also reported.Comment: 10 pages, revtex, 3 figures, 2 table

Measurement of proton electromagnetic form factors in e+e−→ppˉe^+e^- \to p\bar{p} in the energy region 2.00-3.08 GeV

The process of $e^+e^- \rightarrow p\bar{p}$ is studied at 22 center-of-mass energy points ($\sqrt{s}$) from 2.00 to 3.08 GeV, exploiting 688.5~pb$^{-1}$ of data collected with the BESIII detector operating at the BEPCII collider. The Born cross section~($\sigma_{p\bar{p}}$) of $e^+e^- \rightarrow p\bar{p}$ is measured with the energy-scan technique and it is found to be consistent with previously published data, but with much improved accuracy. In addition, the electromagnetic form-factor ratio ($|G_{E}/G_{M}|$) and the value of the effective ($|G_{\rm{eff}}|$), electric ($|G_E|$) and magnetic ($|G_M|$) form factors are measured by studying the helicity angle of the proton at 16 center-of-mass energy points. $|G_{E}/G_{M}|$ and $|G_M|$ are determined with high accuracy, providing uncertainties comparable to data in the space-like region, and $|G_E|$ is measured for the first time. We reach unprecedented accuracy, and precision results in the time-like region provide information to improve our understanding of the proton inner structure and to test theoretical models which depend on non-perturbative Quantum Chromodynamics

Synaptic 1/f noise injection for overfitting suppression in hardware neural networks

Overfitting is a common and critical challenge for neural networks trained with limited dataset. The conventional solution is software-based regularization algorithms such as Gaussian noise injection. Semiconductor noise, such as 1/f noise, in artificial neuron/synapse devices, which is often regarded as undesirable disturbance to the hardware neural networks (HNNs), could also play a useful role in suppressing overfitting, but that is as yet unexplored. In this work, we proposed the idea of using 1/f noise injection to suppress overfitting in different neural networks, and demonstrated that: (i) 1/f noise could suppress the overfitting in Multilayer Perceptron (MLP) and long short-term memory (LSTM); (ii) 1/f noise and Gaussian noise performs similarly for the MLP but differently for the LSTM; (iii) The superior performance of 1/f noise on LSTM can be attributed to its intrinsic long range dependence. This work reveals that 1/f noise, which is common in semiconductor devices, can be a useful solution to suppress the overfitting in HNNs, and more importantly, further evidents that the imperfectness of semiconductor devices is a rich mine of solutions to boost the development of brain-inspired hardware technologies in the AI era

First observations of hc→h_c \to hadrons

Based on $(4.48 \pm 0.03) \times 10^{8}$ $\psi(3686)$ events collected with the BESIII detector, five $h_c$ hadronic decays are searched for via process $\psi(3686) \to \pi^0 h_c$. Three of them, $h_c \to p \bar{p} \pi^+ \pi^-$, $\pi^+ \pi^- \pi^0$, and $2(\pi^+ \pi^-) \pi^0$ are observed for the first time, with statistical significances of 7.4$\sigma$, $4.9\sigma$, and 9.1$\sigma$, and branching fractions of $(2.89\pm0.32\pm0.55)\times10^{-3}$, $(1.60\pm0.40\pm0.32)\times10^{-3}$, and $(7.44\pm0.94\pm1.56)\times10^{-3}$, respectively, where the first uncertainties are statistical and the second systematic. No significant signal is observed for the other two decay modes, and the corresponding upper limits of the branching fractions are determined to be $B(h_c \to 3(\pi^+ \pi^-) \pi^0)<8.7\times10^{-3}$ and $B(h_c \to K^+ K^- \pi^+ \pi^-)<5.8\times10^{-4}$ at 90% confidence level.Comment: 17 pages, 16 figure

Study of J/ψJ/\psi and ψ(3686)→Σ(1385)0Σˉ(1385)0\psi(3686)\rightarrow\Sigma(1385)^{0}\bar\Sigma(1385)^{0} and Ξ0Ξˉ0\Xi^0\bar\Xi^{0}

We study the decays of $J/\psi$ and $\psi(3686)$ to the final states $\Sigma(1385)^{0}\bar\Sigma(1385)^{0}$ and $\Xi^0\bar\Xi^{0}$ based on a single baryon tag method using data samples of $(1310.6 \pm 7.0) \times 10^{6}$ $J/\psi$ and $(447.9 \pm 2.9) \times 10^{6}$ $\psi(3686)$ events collected with the BESIII detector at the BEPCII collider. The decays to $\Sigma(1385)^{0}\bar\Sigma(1385)^{0}$ are observed for the first time. The measured branching fractions of $J/\psi$ and $\psi(3686)\rightarrow\Xi^0\bar\Xi^{0}$ are in good agreement with, and much more precise, than the previously published results. The angular parameters for these decays are also measured for the first time. The measured angular decay parameter for $J/\psi\rightarrow\Sigma(1385)^{0}\bar\Sigma(1385)^{0}$, $\alpha =-0.64 \pm 0.03 \pm 0.10$, is found to be negative, different to the other decay processes in this measurement. In addition, the "12\% rule" and isospin symmetry in the $J/\psi$ and $\psi(3686)\rightarrow\Xi\bar\Xi$ and $\Sigma(1385)\bar{\Sigma}(1385)$ systems are tested.Comment: 11 pages, 7 figures. This version is consistent with paper published in Phys.Lett. B770 (2017) 217-22

Observation of Ds+→pnˉD^+_s\rightarrow p\bar{n} and confirmation of its large branching fraction

The baryonic decay $D^+_s\rightarrow p\bar{n}$ is observed, and the corresponding branching fraction is measured to be $(1.21\pm0.10\pm0.05)\times10^{-3}$, where the first uncertainty is statistical and second systematic. The data sample used in this analysis was collected with the BESIII detector operating at the BEPCII $e^+e^-$ double-ring collider with a center-of-mass energy of 4.178~GeV and an integrated luminosity of 3.19~fb$^{-1}$. The result confirms the previous measurement by the CLEO Collaboration and is of greatly improved precision, which may deepen our understanding of the dynamical enhancement of the W-annihilation topology in the charmed meson decays

Observation and study of the decay J/ψ→ϕηη′J/\psi\rightarrow\phi\eta\eta'

We report the observation and study of the decay $J/\psi\rightarrow\phi\eta\eta'$ using $1.3\times{10^9}$ $J/\psi$ events collected with the BESIII detector. Its branching fraction, including all possible intermediate states, is measured to be $(2.32\pm0.06\pm0.16)\times{10^{-4}}$. We also report evidence for a structure, denoted as $X$, in the $\phi\eta'$ mass spectrum in the $2.0-2.1$ GeV/$c^2$ region. Using two decay modes of the $\eta'$ meson ($\gamma\pi^+\pi^-$ and $\eta\pi^+\pi^-$), a simultaneous fit to the $\phi\eta'$ mass spectra is performed. Assuming the quantum numbers of the $X$ to be $J^P = 1^-$, its significance is found to be 4.4$\sigma$, with a mass and width of $(2002.1 \pm 27.5 \pm 21.4)$ MeV/$c^2$ and $(129 \pm 17 \pm 9)$ MeV, respectively, and a product branching fraction $\mathcal{B}(J/\psi\rightarrow\eta{}X)\times{}\mathcal{B}(X\rightarrow\phi\eta')=(9.8 \pm 1.2 \pm 1.7)\times10^{-5}$. Alternatively, assuming $J^P = 1^+$, the significance is 3.8$\sigma$, with a mass and width of $(2062.8 \pm 13.1 \pm 7.2)$ MeV/$c^2$ and $(177 \pm 36 \pm 35)$ MeV, respectively, and a product branching fraction $\mathcal{B}(J/\psi\rightarrow\eta{}X)\times{}\mathcal{B}(X\rightarrow\phi\eta')=(9.6 \pm 1.4 \pm 2.0)\times10^{-5}$. The angular distribution of $J/\psi\rightarrow\eta{}X$ is studied and the two $J^P$ assumptions of the $X$ cannot be clearly distinguished due to the limited statistics. In all measurements the first uncertainties are statistical and the second systematic.Comment: 10 pages, 6 figures and 4 table