Noisy unsharp measurements incorporated in quantum information protocols may hinder performance, reducing the quantum advantage. However, we show that, unlike projective measurements which completely destroy quantum correlations between nodes in quantum networks, sequential applications of noisy measurements can mitigate the adverse impact of noise in the measurement device on quantum information processing tasks. We demonstrate this in the case of concentrating entanglement on chosen nodes in quantum networks via noisy measurements performed by assisting qubits. In the case of networks with a cluster of three or higher number of qubits, we exhibit that sequentially performing optimal unsharp measurements on the assisting qubits yields localizable entanglement between two nodes akin to that obtained by optimal projective measurements on the same assisting qubits. Furthermore, we find that the proposed approach using consecutive noisy measurements can potentially be used to prepare desired states that are resource for specific quantum schemes. We also argue that assisting qubits have greater control over the qubits on which entanglement is concentrated via unsharp measurements, in contrast to sharp measurement-based protocols, which may have implications for secure quantum communication.

This study explores the effects of introducing a symmetry breaking disorder on the dynamics of a system invariant under particle permutation. The disorder forces quantum states, confined to the $N+1$ dimensional completely symmetric space to penetrate the exponentially large $2^N$ dimensional Hilbert space of $N$ particles. In particular, we focus on the quantum kicked top as a Floquet system of $N$ qubits, and use linear entropy, measuring single qubit entanglement, to investigate the changes in the time scales and values of saturation when disorder is introduced. In the near-integrable regime of the kicked top, we study the robustness of quantum revivals to disorder. We also find that a classical calculation yields the quantum single qubit entanglement to remarkable accuracy in the disorder free limit. The disorder, on the other hand, is modeled in the form of noise which again fits well with the numerical calculations. We measure the extent to which the dynamics is retained within the symmetric subspace and its spreading to the full Hilbert space using different quantities. We show that increasing disorder drives the system to a chaotic phase in full Hilbert space, as also supported by the spectral statistics. We find that there is robustness to disorder in the system, and this is a function of how chaotic the kicked top is.

In recent years, machine learning potentials (MLPs) have become indispensable tools in physics, chemistry, and materials science, driving the development of software packages for molecular dynamics (MD) simulations and related applications. These packages, typically built on specific machine learning frameworks such as TensorFlow, PyTorch, or JAX, face integration challenges when advanced applications demand communication across different frameworks. The previous TensorFlow-based implementation of DeePMD-kit exemplified these limitations. In this work, we introduce DeePMD-kit version 3, a significant update featuring a multi-backend framework that supports TensorFlow, PyTorch, JAX, and PaddlePaddle backends, and demonstrate the versatility of this architecture through the integration of other MLPs packages and of Differentiable Molecular Force Field. This architecture allows seamless backend switching with minimal modifications, enabling users and developers to integrate DeePMD-kit with other packages using different machine learning frameworks. This innovation facilitates the development of more complex and interoperable workflows, paving the way for broader applications of MLPs in scientific research.

We investigate solvable models of heat transport between a pair of quantum mechanical systems initialized at two different temperatures. At time $t=0$, a weak interaction is switched on between the systems, and we study the resulting energy transport. Focusing on the heat current as the primary observable, we analyze both the transient dynamics and the long-time behavior of the system. We demonstrate that simple toy models - including Random Matrix Theory like models ({\it RMT models}) and Schwarzian like models ({\it conformal models}) - can capture many generic features of heat transport, such as transient current peaks and the emergence of non-equilibrium steady state (NESS). For these models, we derive a variety of exact results characterizing the short time transients, long time approach to NESS and thermal conductivity. Finally, we show how these features appear in a more realistic solvable model, the Double-Scaled SYK (DSSYK) model. We demonstrate that the DSSYK model interpolates between the seemingly distinct toy models discussed earlier, with the toy models in turn providing a useful lens through which to understand the rich features of DSSYK.

Four Wheeled Mecanum Robot (FWMR) possess the capability to move in any direction on a plane making it a cornerstone system in modern industrial operations. Despite the extreme maneuverability offered by FWMR, the practical implementation or real-time simulation of Mecanum wheel robots encounters substantial challenges in trajectory tracking control. In this research work, we present a finite-time control law using backstepping technique to perform stabilization and trajectory tracking objectives for a FWMR system. A rigorous stability proof is presented and explicit computation of the finite-time is provided. For tracking objective, we demonstrate the results taking an S-shaped trajectory inclined towards collision avoidance applications. Simulation validation in real time using Gazebo-ROS on a Mecanum robot model is carried out which complies with the theoretical results.

We propose a protocol for concentrating bipartite entanglement over a qubit-qudit system from arbitrary number of qubit-qudit states via a truncation of the Hilbert space corresponding to the subsystem containing the qubits to a space that hosts a single qubit. We achieve this truncation via a multi-qubit measurement in the generalized XZ basis, and show that the protocol is effectively deterministic. We also design a repetitive two-qubit measurement protocol, where the measurements on the qubit-parts is performed taking two qubit-qudit system at a time, and establish its equivalence with the protocol involving the multi-qubit measurement. We show that a concentration of entanglement is possible in each round of two-qubit measurements in the latter scheme, and derive lower and upper bounds of the entanglement concentrated after a given number of rounds of measurements, where the entanglement of the initial qubit-qudit systems are not-necessarily equal. We apply the repetitive two-qubit measurement protocol to concentrate entanglement using arbitrary two-qubit states with unequal entanglement, and discuss the entanglement properties of the multi-qubit state created in this process. We also show that the protocol can be used to create generalized GHZ states on arbitrary number of qubits, which highlights the possibility of creating maximally entangled qubit pairs via qubit-local projection measurements.

With the recent developments in neural networks, there has been a resurgence in algorithms for the automatic generation of simulation ready electronic circuits from hand-drawn circuits. However, most of the approaches in literature were confined to classify different types of electrical components and only a few of those methods have shown a way to rebuild the circuit schematic from the scanned image, which is extremely important for further automation of netlist generation. This paper proposes a real-time algorithm for the automatic recognition of hand-drawn electrical circuits based on object detection and circuit node recognition. The proposed approach employs You Only Look Once version 5 (YOLOv5) for detection of circuit components and a novel Hough transform based approach for node recognition. Using YOLOv5 object detection algorithm, a mean average precision (mAP0.5) of 98.2% is achieved in detecting the components. The proposed method is also able to rebuild the circuit schematic with 80% accuracy with a near-real time performance of 0.33s per schematic generation.

The effects of disorder and chaos on quantum many-body systems can be superficially similar, yet their interplay has not been sufficiently explored. This work finds a continuous phase transition when disorder breaks permutation symmetry, with details of the transition being controlled by the degree of chaos in the clean limit. The system changes from an area law entangled phase in the permutation symmetric subspace where collective variables exist to volume law entanglement in the full Hilbert space, beyond a critical strength of the disorder. The critical strength tends to zero when the original disorder free system is fully chaotic. We study this mainly via the scaling of the collective spin of non-equilibrium states which transit to have properties of what has been dubbed "deep Hilbert space". This has potential implications for general many body physics, as well as technologies such as transmon qubits.

Understanding events necessitates grasping their temporal context, which is often not explicitly stated in natural language. For example, it is not a trivial task for a machine to infer that a museum tour may last for a few hours, but can not take months. Recent studies indicate that even advanced large language models (LLMs) struggle in generating text that require reasoning with temporal commonsense due to its infrequent explicit mention in text. Therefore, automatically mining temporal commonsense for events enables the creation of robust language models. In this work, we investigate the capacity of LLMs to extract temporal commonsense from text and evaluate multiple experimental setups to assess their effectiveness. Here, we propose a temporal commonsense extraction pipeline that leverages LLMs to automatically mine temporal commonsense and use it to construct TComQA, a dataset derived from SAMSum and RealNews corpora. TComQA has been validated through crowdsourcing and achieves over 80\% precision in extracting temporal commonsense. The model trained with TComQA also outperforms an LLM fine-tuned on existing dataset of temporal question answering task.

From the perspective of data reduction, the notions of minimal sufficient and complete statistics together play an important role in determining optimal statistics (estimators). The classical notion of sufficiency and completeness are not adequate in many robust estimations that are based on different divergences. Recently, the notion of generalized sufficiency based on a generalized likelihood function was introduced in the literature. It is important to note that the concept of sufficiency alone does not necessarily produce optimal statistics (estimators). Thus, in line with the generalized sufficiency, we introduce a generalized notion of completeness with respect to a generalized likelihood function. We then characterize the family of probability distributions that possesses completeness with respect to the generalized likelihood function associated with the density power divergence (DPD). Moreover, we show that the family of distributions associated with the logarithmic density power divergence (LDPD) is not complete. Further, we extend the Lehmann-Scheffé theorem and the Basu's theorem for the generalized likelihood estimation. Subsequently, we obtain the generalized uniformly minimum variance unbiased estimator (UMVUE) for the $\mathcal{B^{(\alpha)}}$-family. Further, we derive an formula of the asymptotic expected deficiency (AED) that is used to compare the performance between the minimum density power divergence estimator (MDPDE) and the generalized UMVUE for $\mathcal{B^{(\alpha)}}$-family. Finally, we provide an application of the developed results in stress-strength reliability model.

An indispensable step toward understanding magnetic interactions in rare-earth magnets is to determine the spatially anisotropic single-ion properties set by crystal electric field (CEF) physics. The CEF Hamiltonian yields a discrete energy spectrum governed by a set of parameters reflecting the local site symmetry of the magnetic ion. However, experimentally determining these parameters, especially for ones at low-symmetry sites remains highly challenging. In this work, we directly measure the CEF level splittings of CsErSe2 under magnetic fields using optical spectroscopy. This enables us to determine the CEF parameters and to predict the metamagnetic-like transition arising from a level-crossing in the ground state. We also identify a level-crossing in the first excited state that leads to a non-monotonic Zeeman splitting, which strongly influences the temperature and field dependence of the magnetization. Our results highlight the capacity of single-ion physics to drive rich and unanticipated phenomena in rare-earth magnetic insulators under applied magnetic field.

Characterizing large noisy multiparty quantum states using genuine multiparty entanglement is a challenging task. In this paper, we calculate lower bounds of genuine multiparty entanglement localized over a chosen multiparty subsystem of multi-qubit stabilizer states in the noiseless and noisy scenario. In the absence of noise, adopting a graph-based technique, we perform the calculation for arbitrary graph states as representatives of the stabilizer states, and show that the graph operations required for the calculation has a polynomial scaling with the system size. As demonstrations, we compute the localized genuine multiparty entanglement over subsystems of large graphs having linear, ladder, and square structures. We also extend the calculation for graph states subjected to single-qubit Markovian or non-Markovian Pauli noise on all qubits, and demonstrate, for a specific lower bound of the localizable genuine multiparty entanglement corresponding to a specific Pauli measurement setup, the existence of a critical noise strength beyond which all of the post measured states are biseparable. The calculation is also useful for arbitrary large stabilizer states under noise due to the local unitary connection between stabilizer states and graph states. We demonstrate this by considering a toric code defined on a square lattice, and computing a lower bound of localizable genuine multiparty entanglement over a non-trivial loop of the code. Similar to the graph states, we show the existence of the critical noise strength in this case also, and discuss its interesting features.

In this paper of a research based project, using Bidirectional Long Short-Term Memory (BiLSTM) networks, we provide a novel Fight Scene Detection (FSD) model which can be used for Movie Highlight Generation Systems (MHGS) based on deep learning and Neural Networks . Movies usually have Fight Scenes to keep the audience amazed. For trailer generation, or any other application of Highlight generation, it is very tidious to first identify all such scenes manually and then compile them to generate a highlight serving the purpose. Our proposed FSD system utilises temporal characteristics of the movie scenes and thus is capable to automatically identify fight scenes. Thereby helping in the effective production of captivating movie highlights. We observe that the proposed solution features 93.5% accuracy and is higher than 2D CNN with Hough Forests which being 92% accurate and is significantly higher than 3D CNN which features an accuracy of 65%.

DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials (MLP) known as Deep Potential (DP) models. This package, which was released in 2017, has been widely used in the fields of physics, chemistry, biology, and material science for studying atomistic systems. The current version of DeePMD-kit offers numerous advanced features such as DeepPot-SE, attention-based and hybrid descriptors, the ability to fit tensile properties, type embedding, model deviation, Deep Potential - Range Correction (DPRc), Deep Potential Long Range (DPLR), GPU support for customized operators, model compression, non-von Neumann molecular dynamics (NVNMD), and improved usability, including documentation, compiled binary packages, graphical user interfaces (GUI), and application programming interfaces (API). This article presents an overview of the current major version of the DeePMD-kit package, highlighting its features and technical details. Additionally, the article benchmarks the accuracy and efficiency of different models and discusses ongoing developments.

Efficient cooling is vital for the performance and reliability of modern systems such as electronics, nuclear reactors, and industrial equipment. Jet impingement cooling is widely used for its high local heat transfer rates. Accurate estimation of convective heat transfer coefficient (CHTC) is essential for design, simulation, and control of thermal systems. However, estimating spatially varying CHTCs from limited and noisy temperature data poses a challenging inverse problem. This study presents a physics-informed neural network (PINN) framework to estimate both averaged and spatially varying CHTCs at the fluid-solid interface in a jet impingement setup at Reynolds number 5000. The model uses sparse and noisy temperature data from within the solid and embeds the transient heat conduction equation along with boundary and initial conditions into its loss function. This enables inference of unknown boundary parameters without explicit modeling of the fluid domain. Validation is performed using synthetic temperature data from high-fidelity conjugate heat transfer (CHT) simulations. The framework is tested under various additive Gaussian noise levels (up to 30 percent) and sampling rates 0.25 to 4.0 per second. For noise levels up to 10% and sampling rates of 0.5 per second or higher, estimated CHTCs match CHT-derived benchmarks with relative errors below 8 percent. Even under high-noise scenarios, the framework maintains predictive accuracy when time resolution is sufficient. These results highlight the method's robustness to noise and sparse data, offering a scalable alternative to traditional inverse methods, experimental measurements, or full CHT modeling for estimating boundary thermal parameters in real-world cooling applications.

Stochastic resonance (SR) is a phenomenon by which the presence of noise in a non-linear system allows for detection of a weak sub-threshold signal, or in a bi-stable system allows for sub-coercive switching between the two states. Simple theory suggests that SR occurs when the Kramers rate (rk) of the bistable system, which is a function of noise and applied voltage, is twice the drive frequency (fsignal). Here, we demonstrate the synchronous switching of polarization with a sub-coercive voltage waveform, in a thin film ferroelectric lead zirconium titanate (PZT) capacitor through SR. We employ independent figures of merit (FOM) such as cross-covariance, output power and signal-to-noise ratio to experimentally identify the optimal noise for synchronous switching. We further experimentally measure the Kramers time in the ferroelectric, and show that FOMs indeed peak near the noise predicted by the SR condition. We also model the device characteristics using the stochastic Time Dependent Landau Ginzburg (TDGL) formulation, and capture the experimentally observed polarization switching under application of sub-coercive voltage, assisted by noise. Finally, we show a proof-of-concept implementation of detecting sub-threshold frequency-shift-key signals (FSK) in noisy communication channels using our ferroelectric PZT devices.

Capsule Network (CapsNet) has shown significant improvement in understanding the variation in images along with better generalization ability compared to traditional Convolutional Neural Network (CNN). CapsNet preserves spatial relationship among extracted features and apply dynamic routing to efficiently learn the internal connections between capsules. However, due to the capsule structure and the complexity of the routing mechanism, it is non-trivial to accelerate CapsNet performance in its original form on Field Programmable Gate Array (FPGA). Most of the existing works on CapsNet have achieved limited acceleration as they implement only the dynamic routing algorithm on FPGA, while considering all the processing steps synergistically is important for real-world applications of Capsule Networks. Towards this, we propose a novel two-step approach that deploys a full-fledged CapsNet on FPGA. First, we prune the network using a novel Look-Ahead Kernel Pruning (LAKP) methodology that uses the sum of look-ahead scores of the model parameters. Next, we simplify the nonlinear operations, reorder loops, and parallelize operations of the routing algorithm to reduce CapsNet hardware complexity. To the best of our knowledge, this is the first work accelerating a full-fledged CapsNet on FPGA. Experimental results on the MNIST and F-MNIST datasets (typical in Capsule Network community) show that the proposed LAKP approach achieves an effective compression rate of 99.26% and 98.84%, and achieves a throughput of 82 FPS and 48 FPS on Xilinx PYNQ-Z1 FPGA, respectively. Furthermore, reducing the hardware complexity of the routing algorithm increases the throughput to 1351 FPS and 934 FPS respectively. As corroborated by our results, this work enables highly performance-efficient deployment of CapsNets on low-cost FPGA that are popular in modern edge devices.

We study the geometry of probability distributions with respect to a generalized family of Csisz\'ar $f$-divergences. A member of this family is the relative $\alpha$-entropy which is also a R\'enyi analog of relative entropy in information theory and known as logarithmic or projective power divergence in statistics. We apply Eguchi's theory to derive the Fisher information metric and the dual affine connections arising from these generalized divergence functions. This enables us to arrive at a more widely applicable version of the Cram\'{e}r-Rao inequality, which provides a lower bound for the variance of an estimator for an escort of the underlying parametric probability distribution. We then extend the Amari-Nagaoka's dually flat structure of the exponential and mixer models to other distributions with respect to the aforementioned generalized metric. We show that these formulations lead us to find unbiased and efficient estimators for the escort model. Finally, we compare our work with prior results on generalized Cram\'er-Rao inequalities that were derived from non-information-geometric frameworks.

We describe a search for gravitational waves from compact binaries with at least one component with mass 0.2 $M_\odot$ -- $1.0 M_\odot$ and mass ratio $q \geq 0.1$ in Advanced LIGO and Advanced Virgo data collected between 1 November 2019, 15:00 UTC and 27 March 2020, 17:00 UTC. No signals were detected. The most significant candidate has a false alarm rate of 0.2 $\mathrm{yr}^{-1}$. We estimate the sensitivity of our search over the entirety of Advanced LIGO's and Advanced Virgo's third observing run, and present the most stringent limits to date on the merger rate of binary black holes with at least one subsolar-mass component. We use the upper limits to constrain two fiducial scenarios that could produce subsolar-mass black holes: primordial black holes (PBH) and a model of dissipative dark matter. The PBH model uses recent prescriptions for the merger rate of PBH binaries that include a rate suppression factor to effectively account for PBH early binary disruptions. If the PBHs are monochromatically distributed, we can exclude a dark matter fraction in PBHs $f_\mathrm{PBH} \gtrsim 0.6$ (at 90% confidence) in the probed subsolar-mass range. However, if we allow for broad PBH mass distributions we are unable to rule out $f_\mathrm{PBH} = 1$. For the dissipative model, where the dark matter has chemistry that allows a small fraction to cool and collapse into black holes, we find an upper bound f_{\mathrm{DBH}} < 10^{-5} on the fraction of atomic dark matter collapsed into black holes.

The relative $\alpha$-entropy is the R\'enyi analog of relative entropy and arises prominently in information-theoretic problems. Recent information geometric investigations on this quantity have enabled the generalization of the Cram\'{e}r-Rao inequality, which provides a lower bound for the variance of an estimator of an escort of the underlying parametric probability distribution. However, this framework remains unexamined in the Bayesian framework. In this paper, we propose a general Riemannian metric based on relative $\alpha$-entropy to obtain a generalized Bayesian Cram\'{e}r-Rao inequality. This establishes a lower bound for the variance of an unbiased estimator for the $\alpha$-escort distribution starting from an unbiased estimator for the underlying distribution. We show that in the limiting case when the entropy order approaches unity, this framework reduces to the conventional Bayesian Cram\'{e}r-Rao inequality. Further, in the absence of priors, the same framework yields the deterministic Cram\'{e}r-Rao inequality.