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Abstract The detection of high-energy neutrino signals from the nearby Seyfert galaxy NGC 1068 provides us with an opportunity to study nonthermal processes near the center of supermassive black holes. Using the IceCube and latest Fermi-LAT data, we present general multimessenger constraints on the energetics of cosmic rays and the size of neutrino emission regions. In the photohadronic scenario, the required cosmic-ray luminosity should be larger than ∼1%−10% of the Eddington luminosity and the emission radius should be ≲15R_Sin low-βplasma and ≲3R_Sin high-βplasma. The leptonic scenario overshoots the NuSTAR or Fermi-LAT data for any emission radii we consider, and the required gamma-ray luminosity is much larger than the Eddington luminosity. The beta-decay scenario also violates not only the energetics requirement but also gamma-ray constraints, especially when the Bethe–Heitler and photomeson production processes are consistently considered. Our results rule out the leptonic and beta-decay scenarios in a nearly model-independent manner and support hadronic mechanisms in magnetically powered coronae if NGC 1068 is a source of high-energy neutrinos. 

Artificial Intelligence (AI) has permeated various domains but is limited by the bottlenecks imposed by data transfer latency inherent in contemporary memory technologies. Matrix multiplication, crucial for neural network training and inference, can be significantly expedited with a complexity of O(1) using Resistive RAM (RRAM) technology, instead of the conventional complexity of O(n2). This positions RRAM as a promising candidate for the efficient hardware implementation of machine learning and neural networks through in-memory computation. However, RRAM manufacturing technology remains in its infancy, rendering it susceptible to soft errors, potentially compromising neural network accuracy and reliability. In this paper, we propose a syndrome-based error correction scheme that employs selective weighted checksums to correct double adjacent column errors in RRAM. The error correction is done on the output of the matrix multiplication thus ensuring correct operation for any number of errors in two adjacent columns. The proposed codes have low redundancy and low decoding latency, making it suitable for high throughput applications. This schemeuses a repeating weight based structure that makes it scalable to large RRAM matrix sizes. 

Applications involving machine learning and neural networks have become increasingly essential in the AI revolution. Emerging trends in Resistive RAM technologies provide high-speed, low-cost, scalable solutions for such applications. These RRAM cells provide efficient and sophisticated memory hardware structures for machine-learning applications. However, it is difficult to achieve reliable multilevel cell storage capacity in these memory technologies due to the occurrence of soft and hard errors. As these memories can store multi-bits per cell, exploring limited magnitude symbols(multi-bit) error correction in RRAM is important. This paper proposes a new syndrome-based double error correcting code that divides the syndromes into groups and, uses addition and XOR operations to correct double limited magnitude errors in the RRAM cells. The key idea is to use the built-in current summing capability of RRAM cells to perform the addition operations that are used for the error correction thereby greatly reducing the overhead of the decoding logic needed to implement the ECC. This effectively avoids the need for explicit adder hardware in the decoding logic making it smaller and faster than conventional ECC codes with similar error-correcting capability. Experimental results show that the proposed code reduces the number of check symbols and significantly reduces the decoder area and power by using the RRAM cells to perform the addition. 

Abstract Many measurements at the LHC require efficient identification of heavy-flavour jets, i.e. jets originating from bottom (b) or charm (c) quarks. An overview of the algorithms used to identify c jets is described and a novel method to calibrate them is presented. This new method adjusts the entire distributions of the outputs obtained when the algorithms are applied to jets of different flavours. It is based on an iterative approach exploiting three distinct control regions that are enriched with either b jets, c jets, or light-flavour and gluon jets. Results are presented in the form of correction factors evaluated using proton-proton collision data with an integrated luminosity of 41.5 fb -1 at  √s = 13 TeV, collected by the CMS experiment in 2017. The closure of the method is tested by applying the measured correction factors on simulated data sets and checking the agreement between the adjusted simulation and collision data. Furthermore, a validation is performed by testing the method on pseudodata, which emulate various mismodelling conditions. The calibrated results enable the use of the full distributions of heavy-flavour identification algorithm outputs, e.g. as inputs to machine-learning models. Thus, they are expected to increase the sensitivity of future physics analyses.