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This work presents the first hardware realisation of the Syndrome-Decodingin-the-Head (SDitH) signature scheme, which is a candidate in the NIST PQC process for standardising post-quantum secure digital signature schemes. SDitH’s hardness is based on conservative code-based assumptions, and it uses the Multi-Party-Computation-in-the-Head (MPCitH) construction. This is the first hardware design of a code-based signature scheme based on traditional decoding problems and only the second for MPCitH constructions, after Picnic. This work presents optimised designs to achieve the best area efficiency, which we evaluate using the Time-Area Product (TAP) metric. This work also proposes a novel hardware architecture by dividing the signature generation algorithm into two phases, namely offline and online phases for optimising the overall clock cycle count. The hardware designs for key generation, signature generation, and signature verification are parameterised for all SDitH parameters, including the NIST security levels, both syndrome decoding base fields (GF256 and GF251), and thus conforms to the SDitH specifications. The hardware design further supports secret share splitting, and the hypercube optimisation which can be applied in this and multiple other NIST PQC candidates. The results of this work result in a hardware design with a drastic reducing in clock cycles compared to the optimised AVX2 software implementation, in the range of 2-4x for most operations. Our key generation outperforms software drastically, giving a 11-17x reduction in runtime, despite the significantly faster clock speed. On Artix 7 FPGAs we can perform key generation in 55.1 Kcycles, signature generation in 6.7 Mcycles, and signature verification in 8.6 Mcycles for NIST L1 parameters, which increase for GF251, and for L3 and L5 parameters. 

Experience-dependent gene expression reshapes neural circuits, permitting the learning of knowledge and skills. Most learning involves repetitive experiences during which neurons undergo multiple stages of functional and structural plasticity. Currently, the diversity of transcriptional responses underlying dynamic plasticity during repetition-based learning is poorly understood. To close this gap, we analyzed single-nucleus transcriptomes of L2/3 glutamatergic neurons of the primary motor cortex after 3 d motor skill training or home cage control in water-restricted male mice. “Train” and “control” neurons could be discriminated with high accuracy based on expression patterns of many genes, indicating that recent experience leaves a widespread transcriptional signature across L2/3 neurons. These discriminating genes exhibited divergent modes of coregulation, differentiating neurons into discrete clusters of transcriptional states. Several states showed gene expressions associated with activity-dependent plasticity. Some of these states were also prominent in the previously published reference, suggesting that they represent both spontaneous and task-related plasticity events. Markedly, however, two states were unique to our dataset. The first state, further enriched by motor training, showed gene expression suggestive of late-stage plasticity with repeated activation, which is suitable for expected emergent neuronal ensembles that stably retain motor learning. The second state, equally found in both train and control mice, showed elevated levels of metabolic pathways and norepinephrine sensitivity, suggesting a response to common experiences specific to our experimental conditions, such as water restriction or circadian rhythm. Together, we uncovered divergent transcriptional responses across L2/3 neurons, each potentially linked with distinct features of repetition-based motor learning such as plasticity, memory, and motivation. 

Abstract Materials with tunable thermal properties enable on-demand control of temperature and heat flow, which is an integral component in the development of solid-state refrigeration, energy scavenging, and thermal circuits. Although gap-based and liquid-based thermal switches that work on the basis of mechanical movements have been an effective approach to control the flow of heat in the devices, their complex mechanisms impose considerable costs in latency, expense, and power consumption. As a consequence, materials that have multiple solid-state phases with distinct thermal properties are appealing for thermal management due to their simplicity, fast switching, and compactness. Thus, an ideal thermal switch should operate near or above room temperature, have a simple trigger mechanism, and offer a quick and large on/off switching ratio. In this study, we experimentally demonstrate that manipulating phonon scattering rates can switch the thermal conductivity of antiferroelectric PbZrO 3 bidirectionally by −10% and +25% upon applying electrical and thermal excitation, respectively. Our approach takes advantage of two separate phase transformations in PbZrO 3 that alter the phonon scattering rate in different manners. In this study, we demonstrate that PbZrO 3 can serve as a fast (<1 second), repeatable, simple trigger, and reliable thermal switch with a net switching ratio of nearly 38% from ~1.20 to ~1.65 W m −1 K −1 . 

Abstract As the length scales of materials decrease, the heterogeneities associated with interfaces become almost as important as the surrounding materials. This has led to extensive studies of emergent electronic and magnetic interface properties in superlattices 1–9 . However, the interfacial vibrations that affect the phonon-mediated properties, such as thermal conductivity 10,11 , are measured using macroscopic techniques that lack spatial resolution. Although it is accepted that intrinsic phonons change near boundaries 12,13 , the physical mechanisms and length scales through which interfacial effects influence materials remain unclear. Here we demonstrate the localized vibrational response of interfaces in strontium titanate–calcium titanate superlattices by combining advanced scanning transmission electron microscopy imaging and spectroscopy, density functional theory calculations and ultrafast optical spectroscopy. Structurally diffuse interfaces that bridge the bounding materials are observed and this local structure creates phonon modes that determine the global response of the superlattice once the spacing of the interfaces approaches the phonon spatial extent. Our results provide direct visualization of the progression of the local atomic structure and interface vibrations as they come to determine the vibrational response of an entire superlattice. Direct observation of such local atomic and vibrational phenomena demonstrates that their spatial extent needs to be quantified to understand macroscopic behaviour. Tailoring interfaces, and knowing their local vibrational response, provides a means of pursuing designer solids with emergent infrared and thermal responses.