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Electrified Medium Scale Traveling Ionospheric Disturbances (EMSTIDs) is one of the prominent plasma structures that affect the propagation of high frequency radio waves. Overall, seasonal variation and propagation characteristics of the EMSTIDs are widely reported in literature. However, the effects of substorms on the formation and dissipation of the EMSTIDs are not well explored. In the present study, on a moderately geomagnetically active night of 26 October 2019 (Ap=24), the airglow imager over Hanle (32.7{\deg}N, 78.9{\deg}E; Mlat. ~24.1{\deg}N), India recorded the evolution and decay of an EMSTID in the O(1D) 630.0 nm airglow images in between 13.3 UT and 15.8 UT. In addition, during the same time, a steep rise and fall of the virtual base height of the ionospheric F-layer were also recorded by a nearby digisonde over New Delhi (28.70{\deg}N, 77.10{\deg}E; Mlat. ~20.2{\deg}N). The most important aspect of the event was the occurrence of the two consecutive substorms in between 13.3 UT and 15.8 UT. To the best of our knowledge, this is the first of its kind study where we report the role of interplanetary electric field (IEF) and substorm induced electric fields on the evolution and decay of the EMSTID. This study elicits effects of the externally imposed electric fields on the mid-latitude ionospheric plasma structures and provides insight into the complex coupling between auroral and low-mid latitude region.

In this paper we study the local spectral statistics in the localised region of various random operator models, including the $d$-dimensional the Anderson model and random Schr\"odinger operators. It is already established, in the above models, that at an energy $E$, in the localised energy region of the spectrum, where the density of states $n(E) > 0$, the local eigenvalue statistics $X_E$ is a Poisson processes with intensity $n(E) \mathcal{L}$, $\mathcal{L}$ being the Lebesgue measure on $\mathbb{R}$. The question of independence of $X_E, X_{E^\prime}$ for distinct energies was partially solved in the literature. We solve it completely for all the models for which the Minami technique works., Comment: There is a major error in the paper that cannot be fixed at the moment.

In general, nighttime thermospheric 557.7 nm emission over mid-latitudes is predominantly masked by significantly larger mesospheric component, and hence, F-region plasma structures are rarely observed in this emission. This paper reports the first rare simultaneous detection of F-region plasma depleted structure in O($^1$D) 630.0 nm and O($^1$S) 557.7 nm airglow images from Hanle, India, a mid-latitude station (32.7{\deg}N, 78.9{\deg}E; Mlat. ~24.1{\deg}N) on a geomagnetically quiet night (Ap=3) of 26 June 2021. This indicates significant enhancement of thermospheric 557.7 nm emission. Interestingly, thermospheric 557.7 nm emission was not significant on the following geomagnetically quiet night as MSTID bands were only observed in 630.0 nm images. We show that enhanced dissociative recombination caused by descent of F-layer peak over the observation region coupled with the significant increase of the electron density at thermospheric 557.7 nm emission altitude enabled the detection of the plasma depleted structure on 26 June 2021.

The latest X-ray photon-counting computed tomography (PCCT) for extremity allows multi-energy high-resolution (HR) imaging for tissue characterization and material decomposition. However, both radiation dose and imaging speed need improvement for contrast-enhanced and other studies. Despite the success of deep learning methods for 2D few-view reconstruction, applying them to HR volumetric reconstruction of extremity scans for clinical diagnosis has been limited due to GPU memory constraints, training data scarcity, and domain gap issues. In this paper, we propose a deep learning-based approach for PCCT image reconstruction at halved dose and doubled speed in a New Zealand clinical trial. Particularly, we present a patch-based volumetric refinement network to alleviate the GPU memory limitation, train network with synthetic data, and use model-based iterative refinement to bridge the gap between synthetic and real-world data. The simulation and phantom experiments demonstrate consistently improved results under different acquisition conditions on both in- and off-domain structures using a fixed network. The image quality of 8 patients from the clinical trial are evaluated by three radiologists in comparison with the standard image reconstruction with a full-view dataset. It is shown that our proposed approach is essentially identical to or better than the clinical benchmark in terms of diagnostic image quality scores. Our approach has a great potential to improve the safety and efficiency of PCCT without compromising image quality., Comment: 9 figures, 5 tables

The discovery of small molecules with therapeutic potential is a long-standing challenge in chemistry and biology. Researchers have increasingly leveraged novel computational techniques to streamline the drug development process to increase hit rates and reduce the costs associated with bringing a drug to market. To this end, we introduce a quantum-classical generative model that seamlessly integrates the computational power of quantum algorithms trained on a 16-qubit IBM quantum computer with the established reliability of classical methods for designing small molecules. Our hybrid generative model was applied to designing new KRAS inhibitors, a crucial target in cancer therapy. We synthesized 15 promising molecules during our investigation and subjected them to experimental testing to assess their ability to engage with the target. Notably, among these candidates, two molecules, ISM061-018-2 and ISM061-22, each featuring unique scaffolds, stood out by demonstrating effective engagement with KRAS. ISM061-018-2 was identified as a broad-spectrum KRAS inhibitor, exhibiting a binding affinity to KRAS-G12D at $1.4 \mu M$. Concurrently, ISM061-22 exhibited specific mutant selectivity, displaying heightened activity against KRAS G12R and Q61H mutants. To our knowledge, this work shows for the first time the use of a quantum-generative model to yield experimentally confirmed biological hits, showcasing the practical potential of quantum-assisted drug discovery to produce viable therapeutics. Moreover, our findings reveal that the efficacy of distribution learning correlates with the number of qubits utilized, underlining the scalability potential of quantum computing resources. Overall, we anticipate our results to be a stepping stone towards developing more advanced quantum generative models in drug discovery.

Sparse graphs are ubiquitous in real and virtual worlds. With the phenomenal growth in semi-structured and unstructured data, sizes of the underlying graphs have witnessed a rapid growth over the years. Analyzing such large structures necessitates parallel processing, which is challenged by the intrinsic irregularity of sparse computation, memory access, and communication. It would be ideal if programmers and domain-experts get to focus only on the sequential computation and a compiler takes care of auto-generating the parallel code. On the other side, there is a variety in the number of target hardware devices, and achieving optimal performance often demands coding in specific languages or frameworks. Our goal in this work is to focus on a graph DSL which allows the domain-experts to write almost-sequential code, and generate parallel code for different accelerators from the same algorithmic specification. In particular, we illustrate code generation from the StarPlat graph DSL for NVIDIA, AMD, and Intel GPUs using CUDA, OpenCL, SYCL, and OpenACC programming languages. Using a suite of ten large graphs and four popular algorithms, we present the efficacy of StarPlat's versatile code generator., Comment: arXiv admin note: text overlap with arXiv:2305.03317

This paper deals with congestion control in a software defined network (SDN) setting. Presently, explicit router schemes, such as Explicit Congestion Notification (ECN), work in conjunction with the TCP protocol to handle congestion in a distributed manner. With the emergence of SDN and centralized control, it is possible to leverage the global view of the network state to make better congestion control decisions. In this work, we explore the advantages of bringing in global information into distributed congestion control. We propose a framework where the controller with its global view of the network actively participates in the congestion control decisions of the end TCP hosts, by setting the ECN bits of IP packets appropriately. Our framework can be deployed very easily without any change to the end node TCPs or the SDN switches. We also show 30x improvement over the TCP Cubic variant and 1.7x improvement over TCP/RED in terms of flow completion times for one implementation of this framework, using the Mininet emulator.

We show that the mapping torus of a hyperbolic group by a hyperbolic automorphism is cubulable. Along the way, we (i) give an alternate proof of Hagen and Wise's theorem that hyperbolic free-by-cyclic groups are cubulable, and (ii) extend to the case with torsion Brinkmann's thesis that a torsion-free hyperbolic-by-cyclic group is hyperbolic if and only if it does not contain $\mathbb{Z}^2$-subgroups., Comment: Incorporated referee suggestions. Final version, to appear in Geom. Topol

The present study deals with Lyra's geometry in plane symmetric metric discussed in the presence of bulk viscous fluid and one dimensional strings are assumed to be loaded with particles and the particle energy density. The variation of Hubble's parameter gives a constant value of decelerating parameter. The exact solution has been found for the plane symmetric model in Lyra's geometry in the framework of bulk viscosity and string cosmology. Also, the bulk viscous pressure is assumed to be proportional to the energy density. The physical and geometrical properties of the model are also discussed., Comment: 9 pages and 5 sections with references