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  1. We report a search for a heavy neutral lepton (HNL) that mixes predominantly withντ. The search utilizes data collected with the Belle detector at the KEKB asymmetric energye+ecollider. The data sample was collected at and just below the center-of-mass energies of theϒ(4S)andϒ(5S)resonances and has an integrated luminosity of915fb1, corresponding to(836±12)×106e+eτ+τevents. We search for production of the HNL (denotedN) in the decayτπNfollowed by its decay viaNμ+μντ. The search focuses on the parameter-space region in which the HNL is long-lived, so that theμ+μoriginate from a common vertex that is significantly displaced from the collision point of the KEKB beams. Consistent with the expected background yield, one event is observed in the data sample after application of all the event-selection criteria. We report limits on the mixing parameter of the HNL with theτneutrino as a function of the HNL mass.

    Published by the American Physical Society2024 
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    Free, publicly-accessible full text available June 1, 2025
  2. A<sc>bstract</sc>

    We report a search for the charged-lepton flavor violation in Υ(2S) →τ±(=e, μ) decays using a 25 fb1Υ(2S) sample collected by the Belle detector at the KEKBe+easymmetric-energy collider. We find no evidence for a signal and set upper limits on the branching fractions ($$ \mathcal{B} $$B) at 90% confidence level. We obtain the most stringent upper limits:$$ \mathcal{B} $$B(Υ(2S)→ μτ±)<0.23×106and$$ \mathcal{B} $$B(Υ(2S)→ eτ±)<1.12×106.

     
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  3. A low-energy hardware implementation of deep belief network (DBN) architecture is developed using near-zero energy barrier probabilistic spin logic devices (p-bits), which are modeled to real- ize an intrinsic sigmoidal activation function. A CMOS/spin based weighted array structure is designed to implement a restricted Boltzmann machine (RBM). Device-level simulations based on precise physics relations are used to validate the sigmoidal relation between the output probability of a p-bit and its input currents. Characteristics of the resistive networks and p-bits are modeled in SPICE to perform a circuit-level simulation investigating the performance, area, and power consumption tradeoffs of the weighted array. In the application-level simulation, a DBN is implemented in MATLAB for digit recognition using the extracted device and circuit behavioral models. The MNIST data set is used to assess the accuracy of the DBN using 5,000 training images for five distinct network topologies. The results indicate that a baseline error rate of 36.8% for a 784x10 DBN trained by 100 samples can be reduced to only 3.7% using a 784x800x800x10 DBN trained by 5,000 input samples. Finally, Power dissipation and accuracy tradeoffs for probabilistic computing mechanisms using resistive devices are identified. 
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