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The Software-Defined Networking (SDN) paradigm has significantly improved network efficiency by integrating machine learning (ML) capabilities into the control plane (CP). This integration allows adaptive management of network resources in response to dynamic traffic conditions. However, the significant geographical distance between the CP and data plane (DP) causes considerable round-trip latency, measured in milliseconds, which poses a major challenge to time-sensitive traffic. To address this challenge, our research proposes a novel in-network Reinforcement Learning (RL) inference framework. This framework extends programmability from the CP to the DP, enabling timely and precise control of network resources to meet the stringent Quality of Service (QoS) requirements of mission-critical applications. The in-network RL inference is implemented using match-action tables within the Protocol Independent Switch Architecture (PISA) framework in the DP and validated through hardware deployment of protocol-independent packet processors (P4). To allocate bandwidth precisely based on QoS requirements, a P4 meter extern is used to differentiate the unique demands of individual traffic flows. Our enhanced deepdeterministic policy gradient (eDDPG)-based RL inference achieves superior performance with minimal processing overhead, reducing latency by 88.7% and jitter by 89.1% compared to systems without RL inference. 

While 2D metal-organic hybrids have emerged as promising solar absorbers due to their improved moisture stability, their inferior transport properties limit their potential translation into devices. We report a new hybrid containing 2-(2-ammonioethyl)pyridine [(2-AEP)+], forming a 2D hybrid with the composition (2-AEP)2PbI4. The organic bilayer comprises of (2-AEP)+, which arrange in a face-to-face stacking that promotes π-π interactions between neighboring pyridyl rings. We also demonstrate the structural diversity of 2-(2-aminoethyl)pyridine-based lead iodide hybrids in solution-processed films. This report highlights the importance of solution-processing conditions when trying to obtain single-phase films of hybrids containing dibasic organic species. 

High performance carbon and glass fibers are widely used as reinforcements in composite material systems for aerospace, automotive, and defense applications. Modifications to fiber surface treatment (sizing) is one of the ways to improve the strength of fibers and hence the overall longitudinal tensile strength of the composite. Single fiber tensile tests at the millimeter scale are typically used to characterize the effect of sizing on fiber strength. However, the characteristic length-scale governing the composite failure due to a cluster of fiber breaks is in the micro-scales. To access such micro-scale gage-lengths, we aim to employ indenters of varying radii to transversely load fibers and use scanning electron microscope (SEM) with digital image correlation (DIC) to measure strains at these lengthscales. The use of DIC technique requires creation of a uniform, random, and high contrast speckle pattern on the fiber surface such as that shown in Figure 1. In this work, we investigate the formation of sub-microscale speckle pattern on carbon fiber surface via sputter deposition and pulsed laser deposition techniques (PLD) using Gold-Palladium (Au-Pd) and Niobium-doped SrTiO3 (Nb:STO) targets respectively. Different processing conditions are investigated for both sputter deposition: sputtering current and coating duration, and PLD: number of pulses respectively to create sub-micron scale patterns viable for micro-DIC on both sized and unsized carbon fibers. By varying the deposition conditions and SEM-imaging the deposited patterns on fibers, successful pattern formation at sub-micron scale is demonstrated for both as-received sized and unsized IM7 carbon fibers of average diameter 5.2 µm via sputter deposition and PLD respectively. 

Abstract We present photometric and spectroscopic data for SN 2022joj, a nearby peculiar Type Ia supernova (SN Ia) with a fast decline rate (Δm_15,B= 1.4 mag). SN 2022joj shows exceedingly red colors, with a value of approximatelyB−V≈ 1.1 mag during its initial stages, beginning from 11 days before maximum brightness. As it evolves, the flux shifts toward the blue end of the spectrum, approachingB−V≈ 0 mag around maximum light. Furthermore, at maximum light and beyond, the photometry is consistent with that of typical SNe Ia. This unusual behavior extends to its spectral characteristics, which initially displayed a red spectrum and later evolved to exhibit greater consistency with typical SNe Ia. Spectroscopically, we find strong agreement between SN 2022joj and double detonation models with white dwarf masses of around 1M_⊙and a thin He shell between 0.01 and 0.05M_⊙. Moreover, the early red colors are explained by line-blanketing absorption from iron peak elements created by the double detonation scenario in similar mass ranges. The nebular spectra in SN 2022joj deviate from expectations for double detonation, as we observe strong [Feiii] emission instead of [Caii] lines as anticipated, though this is not as robust a prediction as early red colors and spectra. The fact that as He shells get thinner these SNe start to look more like normal SNe Ia raises the possibility that this is the triggering mechanism for the majority of SNe Ia, though evidence would be missed if the SNe are not observed early enough. 

State-of-the-art deep reading comprehension models are dominated by recurrent neural nets. Their sequential nature is a natural fit for language, but it also precludes parallelization within an instances and often becomes the bottleneck for deploying such models to latency critical scenarios. This is particularly problematic for longer texts. Here we present a convolutional architecture as an alternative to these recurrent architectures. Using simple dilated convolutional units in place of recurrent ones, we achieve results comparable to the state of the art on two question answering tasks, while at the same time achieving up to two orders of magnitude speedups for question answering.