Abstract

Finding the centrality measures of nodes in a graph is a problem of fundamental importance due to various applications from social networks, biological networks, and transportation networks. Given the large size of such graphs, it is natural to use parallelism as a recourse. Several studies show how to compute the various centrality measures of nodes in a graph on parallel architectures, including multi‐core systems and GPUs. However, as these graphs evolve and change, it is pertinent to study how to update the centrality measures on changes to the underlying graph. In this article, we show novel parallel algorithms for updating the betweenness‐ and closeness‐centrality values of nodes in a dynamic graph. Our algorithms process a batch of updates in parallel by extending the approach of handling a single update for betweenness‐ and closeness‐centrality. For the latter, we also introduce techniques based on

Abstract

This work presents a thermoelectric vibration and buckling behaviour of a bidirectional functionally graded piezoelectric porous (BD-FGPP) plate resting on the elastic foundation with arbitrary boundary conditions. The variation of the material properties in the FGPP plate is bidirectional, ie, along longitudinal and thickness directions, using the modified power law distributions. Also, the BD-FGPP plate carries various porosity distributions, ie, even, uneven, and symmetric center types. The governing equation of the FGPP plate has been obtained through Hamilton’s principle and solved through a higher-order finite element approach. The correctness and usefulness of the current technique are confirmed and compared with existing model outcomes available in the literature. A comprehensive parametric study has investigated the effect of the elastic foundations, porous exponent with various porosity distributions

Abstract

We investigate the dynamical aspects of the quantum switch and find a particular form of quantum memory emerging out of the switch action. We first analyse the loss of information in a general quantum evolution subjected to a quantum switch and propose a measure to quantify the switch-induced memory. We then derive an uncertainty relation between information loss and switch-induced memory. We explicitly consider the example of depolarising dynamics and show how it is affected by the action of a quantum switch. For a more detailed analysis, we consider both the control qubit and the final measurement on the control qubit as noisy and investigate the said uncertainty relation. Further, while deriving the Lindblad-type dynamics for the reduced operation of the switch action, we identify that the switch-induced memory actually leads to the emergence of non-Markovianity. Interestingly, we demonstrate that the emergent non-Markovianity can be explicitly attributed to the switch operation by comparing it with other standard measures of non-Markovianity. Our investigation thus paves the way forward to understanding the quantum switch as an emerging non-Markovian quantum memory.

Abstract

As quantum theory allows for information processing and computing tasks that otherwise are not possible with classical systems, there is a need and use of quantum Internet beyond existing network systems. At the same time, the realization of a desirably functional quantum Internet is hindered by fundamental and practical challenges such as high loss during transmission of quantum systems, decoherence due to interaction with the environment, fragility of quantum states, etc. We study the implications of these constraints by analyzing the limitations on the scaling and robustness of quantum Internet. Considering quantum networks, we present practical bottlenecks for secure communication, delegated computing, and resource distribution among end nodes. Motivated by the power of abstraction in graph theory (in association with quantum information theory), we consider graph-theoretic quantifiers to assess network robustness and provide critical values of communication lines for viable communication over quantum Internet. In particular, we begin by discussing limitations on usefulness of isotropic states as device-independent quantum key repeaters which otherwise could be useful for device-independent quantum key distribution. We consider some quantum networks of practical interest, ranging from satellite-based networks connecting far-off spatial locations to currently available quantum processor architectures within computers, and analyze their robustness to perform quantum information processing tasks. Some of these tasks form primitives for delegated quantum computing, e.g., entanglement distribution and quantum teleportation.

Abstract

In this paper, we propose MolOpt, the first attempt of its kind to use Multi-Agent Reinforcement Learning (MARL) for autonomous molecular geometry optimization (MGO). Typically MGO algorithms are hand-designed, but MolOpt uses MARL to learn a learned optimizer (policy) that can perform MGO without depending on other hand-designed optimizers. We cast MGO as a MARL problem, where each agent corresponds to a single atom in the molecule. MolOpt performs MGO by minimizing the forces on each atom in the molecule. Our experiments demonstrate the generalizing ability of MolOpt for MGO of Propane, Pentane, Heptane, Hexane, and Octane when trained on Ethane, Butane, and Isobutane. In terms of performance, MolOpt outperforms the MDMin optimizer and demonstrates similar performance to the FIRE optimizer. However, it does not surpass the BFGS optimizer. The results demonstrate that MolOpt has the potential to introduce innovative advancements in MGO by providing a novel approach using reinforcement learning (RL), which may open up new research directions for MGO. Overall, this work serves as a proof-of-concept for the potential of MARL in MGO.

Abstract

Computing binding affinities is of great importance in drug discovery pipeline and its prediction using advanced machine learning methods still remains a major challenge as the existing datasets and models do not consider the dynamic features of protein-ligand interactions. To this end, we have developed PLAS-20k dataset, an extension of previously developed PLAS-5k, with 97,500 independent simulations on a total of 19,500 different protein-ligand complexes. Our results show good correlation with the available experimental values, performing better than docking scores. This holds true even for a subset of ligands that follows Lipinski’s rule, and for diverse clusters of complex structures, thereby highlighting the importance of PLAS-20k dataset in developing new ML models. Along with this, our dataset is also beneficial in classifying strong and weak binders compared to docking. Further, OnionNet model has been retrained on PLAS-20k dataset and is provided as a baseline for the prediction of binding affinities. We believe that large-scale MD-based datasets along with trajectories will form new synergy, paving the way for accelerating drug discovery.

Abstract

X-rays diagnose numerous thoracic diseases; however, accurate pathology detection requires trained radiologists. The number of available experts may impede population-level patient care, delaying medical action. State-of-the-art (SOTA) machine learning methods categorize chest X-rays across numerous diseases well but do not always account for explainability. Interpretability assessments rarely focus on the conciseness and anatomical correctness of Class Activation Mapping outcomes. These models are not accurate or dependable when tested on different datasets of the same modality. They are not used because their predicted performance is not explainable. This work introduces a self-supervised and weakly supervised pretraining pipeline with an auxiliary loss and supervised fine-tuning that retains performance across datasets. We use the Chest X-ray14 (NIH CXR) dataset for pre-training and CheXpert

Abstract

Criticality, observed during second-order phase transitions, is an emergent phenomenon. The brain operates near criticality, where complex systems exhibit high correlations. The critical brain hypothesis suggests that the brain becomes an efficient learning system in this state but poor in memory, while sub-criticality enhances memory but inhibits learning. As a system approaches criticality, it develops ”domain”-like regions with competing phases and increased spatiotemporal correlations that diverge. The dynamics of these domains depend on the system’s proximity to criticality. This study investigates the phase ordering properties of a spin-lattice model derived from Alzheimer’s and cognitively normal subjects, expecting significant differences in their proximity to criticality. However, our findings show no conclusive distinction in the distal properties from criticality, as reflected in the phase ordering behavior of the Alzheimer’s and cognitively normal brain.

Abstract

A leptophobic Z ′ that does not couple with the Standard Model leptons can evade the stringent bounds from the dilepton-resonance searches. In our earlier paper [T. Arun et al., Search for the Z ′ boson decaying to a right-handed neutrino pair in leptophobic U(1) models, Phys. Rev. D, 106 (2022) 095035; arXiv:2204.02949], we presented two gauge anomaly-free U(1) models where a heavy leptophobic Z ′ is present along with right-handed neutrinos (NR). We pointed out the interesting possibility of a correlated search for Z ′ and NR at the LHC through the pp → Z ′ → NRNR channel. This channel can probe a part of the leptophobic Z ′ parameter space that cannot be otherwise probed using the standard dijet resonance searches. In this paper, we analyse the monolepton final state arising from the decay of the NR pair. We show that a leptophobic Z ′ as heavy as 7 TeV and with a gauge coupling of the order of the electroweak coupling is discoverable through this channel at the high-luminosity LHC.

Abstract

Abstract: We calculate QED corrections to the semileptonic decays H1 → H2ℓ +ℓ − where ℓ = e, µ and H1,2 are hadrons. The soft and/or collinear divergences are regulated in a gauge-invariant manner and demonstrably cancel, leaving behind a finite residue that depends on the (infrared) momentum cutoff below which a photon is considered to be indistinguishable. On resuming, the said sensitivity reduces drastically, i.e., for the NLL result as compared to the NLO one. The overall correction is negative and its magnitude is larger for a lighter lepton. For B → K(∗) decays, the corrections improve the agreement for the differential distributions, while the behavior is more complicated for Λb → Λ (∗) decays. Rather intriguingly, the corrections serve to regenerate the tension for the lepton flavor universality observables RK and RK∗ .