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<jats:title>Abstract</jats:title><jats:p>The theory of the strong force, quantum chromodynamics, describes the proton in terms of quarks and gluons. The proton is a state of two up quarks and one down quark bound by gluons, but quantum theory predicts that in addition there is an infinite number of quark–antiquark pairs. Both light and heavy quarks, whose mass is respectively smaller or bigger than the mass of the proton, are revealed inside the proton in high-energy collisions. However, it is unclear whether heavy quarks also exist as a part of the proton wavefunction, which is determined by non-perturbative dynamics and accordingly unknown: so-called intrinsic heavy quarks<jats:sup>1</jats:sup>. It has been argued for a long time that the proton could have a sizable intrinsic component of the lightest heavy quark, the charm quark. Innumerable efforts to establish intrinsic charm in the proton<jats:sup>2</jats:sup> have remained inconclusive. Here we provide evidence for intrinsic charm by exploiting a high-precision determination of the quark–gluon content of the nucleon<jats:sup>3</jats:sup> based on machine learning and a large experimental dataset. We disentangle the intrinsic charm component from charm–anticharm pairs arising from high-energy radiation<jats:sup>4</jats:sup>. We establish the existence of intrinsic charm at the 3-standard-deviation level, with a momentum distribution in remarkable agreement with model predictions<jats:sup>1,5</jats:sup>.We confirm these findings by comparing them to very recent data on <jats:italic>Z</jats:italic>-boson production with charm jets from the Large Hadron Collider beauty (LHCb) experiment<jats:sup>6</jats:sup>.</jats:p>

<jats:title>Abstract</jats:title><jats:p>Rabi oscillations are periodic modulations of populations in two-level systems interacting with a time-varying field<jats:sup>1</jats:sup>. They are ubiquitous in physics with applications in different areas such as photonics<jats:sup>2</jats:sup>, nano-electronics<jats:sup>3</jats:sup>, electron microscopy<jats:sup>4</jats:sup> and quantum information<jats:sup>5</jats:sup>. While the theory developed by Rabi was intended for fermions in gyrating magnetic fields, Autler and Townes realized that it could also be used to describe coherent light–matter interactions within the rotating-wave approximation<jats:sup>6</jats:sup>. Although intense nanometre-wavelength light sources have been available for more than a decade<jats:sup>7–9</jats:sup>, Rabi dynamics at such short wavelengths has not been directly observed. Here we show that femtosecond extreme-ultraviolet pulses from a seeded free-electron laser<jats:sup>10</jats:sup> can drive Rabi dynamics between the ground state and an excited state in helium atoms. The measured photoelectron signal reveals an Autler–Townes doublet and an avoided crossing, phenomena that are both fundamental to coherent atom–field interactions<jats:sup>11</jats:sup>. Using an analytical model derived from perturbation theory on top of the Rabi model, we find that the ultrafast build-up of the doublet structure carries the signature of a quantum interference effect between resonant and non-resonant photoionization pathways. Given the recent availability of intense attosecond<jats:sup>12</jats:sup> and few-femtosecond<jats:sup>13</jats:sup> extreme-ultraviolet pulses, our results unfold opportunities to carry out ultrafast manipulation of coherent processes at short wavelengths using free-electron lasers.</jats:p>

<jats:title>Abstract</jats:title><jats:p>Realizing increasingly complex artificial intelligence (AI) functionalities directly on edge devices calls for unprecedented energy efficiency of edge hardware. Compute-in-memory (CIM) based on resistive random-access memory (RRAM)<jats:sup>1</jats:sup> promises to meet such demand by storing AI model weights in dense, analogue and non-volatile RRAM devices, and by performing AI computation directly within RRAM, thus eliminating power-hungry data movement between separate compute and memory<jats:sup>2–5</jats:sup>. Although recent studies have demonstrated in-memory matrix-vector multiplication on fully integrated RRAM-CIM hardware<jats:sup>6–17</jats:sup>, it remains a goal for a RRAM-CIM chip to simultaneously deliver high energy efficiency, versatility to support diverse models and software-comparable accuracy. Although efficiency, versatility and accuracy are all indispensable for broad adoption of the technology, the inter-related trade-offs among them cannot be addressed by isolated improvements on any single abstraction level of the design. Here, by co-optimizing across all hierarchies of the design from algorithms and architecture to circuits and devices, we present NeuRRAM—a RRAM-based CIM chip that simultaneously delivers versatility in reconfiguring CIM cores for diverse model architectures, energy efficiency that is two-times better than previous state-of-the-art RRAM-CIM chips across various computational bit-precisions, and inference accuracy comparable to software models quantized to four-bit weights across various AI tasks, including accuracy of 99.0 percent on MNIST<jats:sup>18</jats:sup> and 85.7 percent on CIFAR-10<jats:sup>19</jats:sup> image classification, 84.7-percent accuracy on Google speech command recognition<jats:sup>20</jats:sup>, and a 70-percent reduction in image-reconstruction error on a Bayesian image-recovery task.</jats:p>