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Chiral quantum optics is a growing field of research where light-matter interactions become asymmetrically dependent on momentum and spin, offering novel control over photonic and electronic degrees of freedom. Recently, the platforms for investigating chiral light-matter interactions have expanded from laser-cooled atoms and quantum dots to various solid-state systems, such as microcavity polaritons and two-dimensional layered materials, integrated into photonic structures like waveguides, cavities, and ring resonators. In this perspective, we begin by establishing the foundation for understanding and engineering these chiral light-matter regimes. We review the cutting-edge platforms that have enabled their successful realization in recent years, focusing on solid-state platforms, and discuss the most relevant experimental challenges to fully harness their potential. Finally, we explore the vast opportunities these chiral light-matter interfaces present, particularly their ability to reveal exotic quantum many-body phenomena, such as chiral many-body superradiance and fractional quantum Hall physics., Comment: 18 pages, 6 figures

Text-conditioned generation models are commonly evaluated based on the quality of the generated data and its alignment with the input text prompt. On the other hand, several applications of prompt-based generative models require sufficient diversity in the generated data to ensure the models' capability of generating image and video samples possessing a variety of features. However, most existing diversity metrics are designed for unconditional generative models, and thus cannot distinguish the diversity arising from variations in text prompts and that contributed by the generative model itself. In this work, our goal is to quantify the prompt-induced and model-induced diversity in samples generated by prompt-based models. We propose an information-theoretic approach for internal diversity quantification, where we decompose the kernel-based entropy $H(X)$ of the generated data $X$ into the sum of the conditional entropy $H(X|T)$, given text variable $T$, and the mutual information $I(X; T)$ between the text and data variables. We introduce the \emph{Conditional-Vendi} score based on $H(X|T)$ to quantify the internal diversity of the model and the \emph{Information-Vendi} score based on $I(X; T)$ to measure the statistical relevance between the generated data and text prompts. We provide theoretical results to statistically interpret these scores and relate them to the unconditional Vendi score. We conduct several numerical experiments to show the correlation between the Conditional-Vendi score and the internal diversity of text-conditioned generative models. The codebase is available at \href{https://github.com/mjalali/conditional-vendi}{https://github.com/mjalali/conditional-vendi}.

Despite extensive microscale studies, the macroscopic mechanical properties of monolayer graphene remain underexplored. Here, we report the Young's modulus ($E$ = 1.11 $\pm$ 0.04 TPa), tensile strength (${\sigma}$ = 27.40 $\pm$ 4.36 GPa), and failure strain (${\epsilon}_f$ = 6.01 $\pm$ 0.92 %) of centimeter-scale single-crystal monolayer graphene (SCG) 'dog bone' samples with edges aligned along the zigzag (zz) direction, supported by an ultra-thin polymer (polycarbonate) film. For samples with edges along the armchair (ac) direction, we obtain $E$ = 1.01 $\pm$ 0.10 TPa, ${\sigma}$ = 20.21 $\pm$ 3.22 GPa, ${\epsilon}_f$ = 3.69 $\pm$ 0.38 %, and for chiral samples whose edges were between zz and ac, we obtain $E$= 0.75 $\pm$ 0.12 TPa, ${\sigma}$ = 23.56 $\pm$ 3.42 GPa, and ${\epsilon}_f$ = 4.53 $\pm$ 0.40 %. The SCG is grown on single crystal Cu(111) foils by chemical vapor deposition (CVD). We used a home-built 'float-on-water' (FOW) tensile testing system for tensile loading measurements that also enabled in situ crack observation. The quantized fracture mechanics (QFM) analysis predicts an edge defect size from several to tens of nanometers based on chirality and notch angle. Through Weibull analysis and given that the fatal defects are confined on the edges of macroscale samples, we projected strength ranging from 13.67 to 18.43 GPa for an A4-size SCG according to their chirality. The exceptional mechanical performance of macroscale single crystal graphene (SCG) paves the way for its widespread use in a very wide variety of applications., Comment: 76 pages, 44 figures

Early detection of lung cancer is crucial as it increases the chances of successful treatment. Automatic lung image segmentation assists doctors in identifying diseases such as lung cancer, COVID-19, and respiratory disorders. However, lung segmentation is challenging due to overlapping features like vascular and bronchial structures, along with pixel-level fusion of brightness, color, and texture. New lung segmentation methods face difficulties in identifying long-range relationships between image components, reliance on convolution operations that may not capture all critical features, and the complex structures of the lungs. Furthermore, semantic gaps between feature maps can hinder the integration of relevant information, reducing model accuracy. Skip connections can also limit the decoder's access to complete information, resulting in partial information loss during encoding. To overcome these challenges, we propose a hybrid approach using the FusionLungNet network, which has a multi-level structure with key components, including the ResNet-50 encoder, Channel-wise Aggregation Attention (CAA) module, Multi-scale Feature Fusion (MFF) block, self refinement (SR) module, and multiple decoders. The refinement sub-network uses convolutional neural networks for image post-processing to improve quality. Our method employs a combination of loss functions, including SSIM, IOU, and focal loss, to optimize image reconstruction quality. We created and publicly released a new dataset for lung segmentation called LungSegDB, including 1800 CT images from the LIDC-IDRI dataset (dataset version 1) and 700 images from the Chest CT Cancer Images from Kaggle dataset (dataset version 2). Our method achieved an IOU score of 98.04, outperforming existing methods and demonstrating significant improvements in segmentation accuracy. https://github.com/sadjadrz/FusionLungNet

By employing summary statistics obtained from Persistent Homology (PH), we investigate the influence of Redshift Space Distortion (RSD) on the topology of excursion sets formed through the super-level filtration method applied to three-dimensional matter density fields. The synthetic fields simulated by the Quijote suite in both real and redshift spaces are smoothed by accounting for the Gaussian smoothing function with different scales. The RSD leads a tendency for clusters ($\tilde{\beta}_0$) to shift towards higher thresholds, while filament loops ($\tilde{\beta}_1$) and cosmic voids ($\tilde{\beta}_2$) migrate towards lower thresholds. Notably, $\tilde{\beta}_2$ exhibits greater sensitivity to RSD compared to clusters and independent loops. As the smoothing scales increase, the amplitude of the reduced Betti number curve ($\tilde{\beta}_k$) decreases, and the corresponding peak position shifts towards the mean threshold. Conversely, the amplitude of $\tilde{\beta}_k$ remains almost unchanged with variations in redshift for $z\in[0-3]$. The analysis of persistent entropy and the overall abundance of $k$-holes indicates that the linear Kaiser effect plays a significant role compared to the non-linear effect for $R \gtrsim 30$ Mpc $h^{-1}$ at $z=0$, whereas persistent entropy proves to be a reliable measure against non-linear influences., Comment: 16 pages, 9 figures and 1 table

Bose-Fermi mixtures naturally appear in various physical systems. In semiconductor heterostructures, such mixtures can be realized, with bosons as excitons and fermions as dopant charges. However, the complexity of these hybrid systems challenges the comprehension of the mechanisms that determine physical properties such as mobility. In this study, we investigate interlayer exciton diffusion in an H-stacked WSe$_2$/WS$_2$ heterobilayer. Our measurements are performed in the dilute exciton density limit at low temperatures to examine how the presence of charges affects exciton mobility. Remarkably, for charge doping near the Mott insulator phase, we observe a giant enhancement of exciton diffusion of three orders of magnitude compared to charge neutrality. We attribute this observation to mobile valence holes, which experience a suppressed moir\'e potential due to the electronic charge order in the conduction band, and recombine with any conduction electron in a non-monogamous manner. This new mechanism emerges for sufficiently large fillings in the vicinity of correlated generalized Wigner crystal and Mott insulating states. Our results demonstrate the potential to characterize correlated electron states through exciton diffusion and provide insights into the rich interplay of bosons and fermions in semiconductor heterostructures., Comment: Main text: 19 pages, 4 figures. Supplementary Material: 9 pages, 6 figures

We consider experimentally and computationally the phenomenon of the reverse Janssen effect, involving the counterintuitive finding that the force on the base of a column containing granular particles may be larger than the weight of the granular material itself. This finding is in contrast to the common Janssen effect, for which the force on the base is smaller than the particle weight, illustrating one of the best-known differences between granular and liquid systems. We find that the reverse Janssen effect is strongly influenced by the pouring protocol: under Earth's gravitational field, we find that the reverse Janssen effect is strongly and consistently influenced by the pouring height, as well as by (to somewhat lesser degree) pouring flux. Pouring grains from the height measured in tens of particle diameters leads to a reverse Janssen effect that is an order of magnitude stronger than the one found for small pouring heights of few particle diameters. This influence of the particles' delivery protocol allows for the development of a better understanding of the general features of the reverse Janssen effect and of the comparison between experiments and simulations reported in this and previous works.

The integration of topology into photonics has generated a new design framework for constructing robust and unidirectional waveguides, which are not feasible with traditional photonic devices. Here, we overcome current barriers to the successful integration of quantum emitters such as quantum dots (QDs) into valley-Hall (VH) topological waveguides, utilising photonic defects at the topological interface to stabilise the local charge environment and inverse design for efficient topological-conventional mode conversion. By incorporating QDs within defects of VH-photonic crystals, we demonstrate the first instances of single-photon resonant fluorescence and resonant transmission spectroscopy of a quantum emitter at a topological waveguide interface. Our results bring together topological photonics with optical nonlinear effects at the single-photon level, offering a new avenue to investigate the interaction between topology and quantum nonlinear systems.

Deep neural networks (DNNs) have achieved exceptional performance across various fields by learning complex nonlinear mappings from large-scale datasets. However, they encounter challenges such as high computational costs and limited interpretability. To address these issues, hybrid approaches that integrate physics with AI are gaining interest. This paper introduces a novel physics-based AI model called the "Nonlinear Schr\"odinger Network", which treats the Nonlinear Schr\"odinger Equation (NLSE) as a general-purpose trainable model for learning complex patterns including nonlinear mappings and memory effects from data. Existing physics-informed machine learning methods use neural networks to approximate the solutions of partial differential equations (PDEs). In contrast, our approach directly treats the PDE as a trainable model to obtain general nonlinear mappings that would otherwise require neural networks. As a type of physics-AI symbiosis, it offers a more interpretable and parameter-efficient alternative to traditional black-box neural networks, achieving comparable or better accuracy in some time series classification tasks while significantly reducing the number of required parameters. Notably, the trained Nonlinear Schr\"odinger Network is interpretable, with all parameters having physical meanings as properties of a virtual physical system that transforms the data to a more separable space. This interpretability allows for insight into the underlying dynamics of the data transformation process. Applications to time series forecasting have also been explored. While our current implementation utilizes the NLSE, the proposed method of using physics equations as trainable models to learn nonlinear mappings from data is not limited to the NLSE and may be extended to other master equations of physics.

Minimally invasive robotic surgery has gained significant attention over the past two decades. Telerobotic systems, combined with robot-mediated minimally invasive techniques, have enabled surgeons and clinicians to mitigate radiation exposure for medical staff and extend medical services to remote and hard-to-reach areas. To enhance these services, teleoperated robotic surgery systems incorporating master and follower devices should offer transparency, enabling surgeons and clinicians to remotely experience a force interaction similar to the one the follower device experiences with patients' bodies. This paper presents the design and development of a three-degree-of-freedom master-follower teleoperated system for robotic catheterization. To resemble manual intervention by clinicians, the follower device features a grip-insert-release mechanism to eliminate catheter buckling and torsion during operation. The bidirectionally navigable ablation catheter is statically characterized for force-interactive medical interventions. The system's performance is evaluated through approaching and open-loop path tracking over typical circular, infinity-like, and spiral paths. Path tracking errors are presented as mean Euclidean error (MEE) and mean absolute error (MAE). The MEE ranges from 0.64 cm (infinity-like path) to 1.53 cm (spiral path). The MAE also ranges from 0.81 cm (infinity-like path) to 1.92 cm (spiral path). The results indicate that while the system's precision and accuracy with an open-loop controller meet the design targets, closed-loop controllers are necessary to address the catheter's hysteresis and dead zone, and system nonlinearities., Comment: 24 pages, 7 figures, 6 tables

While standard evaluation scores for generative models are mostly reference-based, a reference-dependent assessment of generative models could be generally difficult due to the unavailability of applicable reference datasets. Recently, the reference-free entropy scores, VENDI and RKE, have been proposed to evaluate the diversity of generated data. However, estimating these scores from data leads to significant computational costs for large-scale generative models. In this work, we leverage the random Fourier features framework to reduce the computational price and propose the Fourier-based Kernel Entropy Approximation (FKEA) method. We utilize FKEA's approximated eigenspectrum of the kernel matrix to efficiently estimate the mentioned entropy scores. Furthermore, we show the application of FKEA's proxy eigenvectors to reveal the method's identified modes in evaluating the diversity of produced samples. We provide a stochastic implementation of the FKEA assessment algorithm with a complexity $O(n)$ linearly growing with sample size $n$. We extensively evaluate FKEA's numerical performance in application to standard image, text, and video datasets. Our empirical results indicate the method's scalability and interpretability applied to large-scale generative models. The codebase is available at https://github.com/aziksh-ospanov/FKEA.

We argue that a general system of scalar fields and gauge fields manifesting vacuum degeneracy induces a principal groupoid bundle over spacetime and that the pattern of spontaneous symmetry breaking and the Higgs mechanism are encoded by the singular foliation canonically induced on the moduli space of scalar vacuum expectation values by the Lie groupoid structure. Recent mathematical results in the classification of singular foliations then provide a qualitative classification of the possible patterns of vacuum degeneracy., Comment: 7 pages, 5 figures, 1 table

We develop a new perspective on principal bundles with connection as morphisms from the tangent bundle of the underlying manifold to a classifying dg-Lie groupoid. This groupoid can be identified with a lift of the inner homomorphisms groupoid arising in \v{S}evera's differentiation procedure of Lie quasi-groupoids. Our new perspective readily extends to principal groupoid bundles, but requires an adjustment, an additional datum familiar from higher gauge theory. We show that for Lie groupoids, the additional adjustment data amounts to a Cartan connection. The resulting adjusted connections naturally provide a global formulation of the kinematical data of curved Yang-Mills-Higgs theories as described by Kotov-Strobl (arXiv:1510.07654) and Fischer (arXiv:2104.02175)., Comment: v2: 1+77 pages, full identification of adjustments with Cartan connections, presentation improved, typos fixed, references added

Event logs play a fundamental role in enabling data-driven business process analysis. Traditionally, these logs track events related to a single object, known as the case, limiting the scope of analysis. Recent advancements, such as Object-Centric Event Logs (OCEL) and Event Knowledge Graphs (EKG), capture better how events relate to multiple objects. However, attributes of objects can change over time, which was not initially considered in OCEL or EKG. While OCEL 2.0 has addressed some of these limitations, there remains a research gap concerning how attribute changes should be accommodated in EKG and how OCEL 2.0 logs can be transformed into EKG. This paper fills this gap by introducing Temporal Event Knowledge Graphs (tEKG) and defining an algorithm to convert an OCEL 2.0 log to a tEKG., Comment: 14 pages (incl. appendix)

Snapshot compressive imaging (SCI) recovers high-dimensional (3D) data cubes from a single 2D measurement, enabling diverse applications like video and hyperspectral imaging to go beyond standard techniques in terms of acquisition speed and efficiency. In this paper, we focus on SCI recovery algorithms that employ untrained neural networks (UNNs), such as deep image prior (DIP), to model source structure. Such UNN-based methods are appealing as they have the potential of avoiding the computationally intensive retraining required for different source models and different measurement scenarios. We first develop a theoretical framework for characterizing the performance of such UNN-based methods. The theoretical framework, on the one hand, enables us to optimize the parameters of data-modulating masks, and on the other hand, provides a fundamental connection between the number of data frames that can be recovered from a single measurement to the parameters of the untrained NN. We also employ the recently proposed bagged-deep-image-prior (bagged-DIP) idea to develop SCI Bagged Deep Video Prior (SCI-BDVP) algorithms that address the common challenges faced by standard UNN solutions. Our experimental results show that in video SCI our proposed solution achieves state-of-the-art among UNN methods, and in the case of noisy measurements, it even outperforms supervised solutions.

Recently, light-matter interaction has been vastly expanded as a control tool for inducing and enhancing many emergent non-equilibrium phenomena. However, conventional schemes for exploring such light-induced phenomena rely on uniform and diffraction-limited free-space optics, which limits the spatial resolution and the efficiency of light-matter interaction. Here, we overcome these challenges using metasurface plasmon polaritons (MPPs) to form a sub-wavelength optical lattice. Specifically, we report a ``nonlocal" pump-probe scheme where MPPs are excited to induce a spatially modulated AC Stark shift for excitons in a monolayer of MoSe$_2$, several microns away from the illumination spot. Remarkably, we identify nearly two orders of magnitude reduction for the required modulation power compared to the free-space optical illumination counterpart. Moreover, we demonstrate a broadening of the excitons' linewidth as a robust signature of MPP-induced periodic sub-diffraction modulation. Our results open new avenues for exploring power-efficient light-induced lattice phenomena below the diffraction limit in active chip-compatible MPP architectures.

Existing approaches for learning representations of time-series keep the temporal arrangement of the time-steps intact with the presumption that the original order is the most optimal for learning. However, non-adjacent sections of real-world time-series may have strong dependencies. Accordingly, we raise the question: Is there an alternative arrangement for time-series which could enable more effective representation learning? To address this, we propose a simple plug-and-play neural network layer called Segment, Shuffle, and Stitch (S3) designed to improve representation learning in time-series models. S3 works by creating non-overlapping segments from the original sequence and shuffling them in a learned manner that is optimal for the task at hand. It then re-attaches the shuffled segments back together and performs a learned weighted sum with the original input to capture both the newly shuffled sequence along with the original sequence. S3 is modular and can be stacked to achieve different levels of granularity, and can be added to many forms of neural architectures including CNNs or Transformers with negligible computation overhead. Through extensive experiments on several datasets and state-of-the-art baselines, we show that incorporating S3 results in significant improvements for the tasks of time-series classification, forecasting, and anomaly detection, improving performance on certain datasets by up to 68\%. We also show that S3 makes the learning more stable with a smoother training loss curve and loss landscape compared to the original baseline. The code is available at https://github.com/shivam-grover/S3-TimeSeries., Comment: Accepted to NeurIPS 2024

We report the scientific and technical queries regarding the article reported by Kim et al.1 on the mechanical properties of graphene-poly(methyl methacrylate) (PMMA) composites. Our analysis finds that the current experimental data is insufficient to fully support the conclusions presented in the article. We suggest the enhancement in Youngs modulus and strength of the graphene-PMMA laminates (GPL) samples are mainly due to the heat treatment of the polymer rather than the incorporation of graphene. The Raman spectroscopy data (as per our analysis) for the GPL samples indicates that large cracks and defects were introduced during the hot rolling process used to fabricate the graphene-PMMA composite. We believe that the queries will aid the audience in better understanding the mechanical response of graphene-PMMA composites., Comment: 14 Pages, 4 Figures

An interpretable comparison of generative models requires the identification of sample types produced more frequently by each of the involved models. While several quantitative scores have been proposed in the literature to rank different generative models, such score-based evaluations do not reveal the nuanced differences between the generative models in capturing various sample types. In this work, we attempt to solve a differential clustering problem to detect sample types expressed differently by two generative models. To solve the differential clustering problem, we propose a method called Fourier-based Identification of Novel Clusters (FINC) to identify modes produced by a generative model with a higher frequency in comparison to a reference distribution. FINC provides a scalable stochastic algorithm based on random Fourier features to estimate the eigenspace of kernel covariance matrices of two generative models and utilize the principal eigendirections to detect the sample types present more dominantly in each model. We demonstrate the application of the FINC method to large-scale computer vision datasets and generative model frameworks. Our numerical results suggest the scalability of the developed Fourier-based method in highlighting the sample types produced with different frequencies by widely-used generative models. Code is available at \url{https://github.com/buyeah1109/FINC}

Global communications have undergone a paradigm shift with the rapid expansion of low-earth orbit (LEO) satellite constellations, offering a new space era of reduced latency and ubiquitous, high-speed broadband internet access. However, the fast developments in LEO orbits pose significant challenges, particularly the coexistence with geostationary earth orbit (GEO) satellite systems. This article presents an overview of the regulatory aspects that cover the spectrum sharing in the bands allocated to the Fixed Satellite Service between geostationary networks (GSO) and non-geostationary systems (NGSO), as well as the main interference mitigation techniques for their coexistence. Our work highlights the increased potential for inter-system interference. It explores the regulatory landscape following the World Radio Conference (WRC-23). We discuss the different interference management strategies proposed for the GSO-NGSO spectral coexistence, including on-board and ground-based approaches and more advanced mitigation techniques based on beamforming. Moving onto operational aspects related to the sharing of spectrum, we introduce recent work on interference detection, identification, and mitigation and provide our vision of the emerging role of artificial intelligence (AI) in the aforementioned tasks., Comment: magazine under review

The proliferation of hate speech and offensive comments on social media has become increasingly prevalent due to user activities. Such comments can have detrimental effects on individuals' psychological well-being and social behavior. While numerous datasets in the English language exist in this domain, few equivalent resources are available for Persian language. To address this gap, this paper introduces two offensive datasets. The first dataset comprises annotations provided by domain experts, while the second consists of a large collection of unlabeled data obtained through web crawling for unsupervised learning purposes. To ensure the quality of the former dataset, a meticulous three-stage labeling process was conducted, and kappa measures were computed to assess inter-annotator agreement. Furthermore, experiments were performed on the dataset using state-of-the-art language models, both with and without employing masked language modeling techniques, as well as machine learning algorithms, in order to establish the baselines for the dataset using contemporary cutting-edge approaches. The obtained F1-scores for the three-class and two-class versions of the dataset were 76.9% and 89.9% for XLM-RoBERTa, respectively., Comment: 16 pages, 5 figures, 8 tables

Demonstrations of topological photonics have so far largely been confined to infrared wavelengths where imaging technology and access to low-dimensional quantum materials are both limited. Here, we designed and fabricated silicon nitride ring-resonator arrays to demonstrate photonic topological edge states at ~780 nm. We observed edge states corresponding to the integer quantum Hall Hamiltonian with topological protection against fabrication disorder. This demonstration extends the concept of topological edge states to the near-visible regime and paves the way for nonlinear and non-Hermitian topological photonics with the rich library of near-visible quantum emitters., Comment: 7 pages, 4 figures

In contrast to existing complex methodologies commonly employed for distilling knowledge from a teacher to a student, this paper showcases the efficacy of a simple yet powerful method for utilizing refined feature maps to transfer attention. The proposed method has proven to be effective in distilling rich information, outperforming existing methods in semantic segmentation as a dense prediction task. The proposed Attention-guided Feature Distillation (AttnFD) method, employs the Convolutional Block Attention Module (CBAM), which refines feature maps by taking into account both channel-specific and spatial information content. Simply using the Mean Squared Error (MSE) loss function between the refined feature maps of the teacher and the student, AttnFD demonstrates outstanding performance in semantic segmentation, achieving state-of-the-art results in terms of improving the mean Intersection over Union (mIoU) of the student network on the PascalVoc 2012, Cityscapes, COCO, and CamVid datasets., Comment: 9 pages, 8 figures, and 6 tables

In the realm of recommender systems, the ubiquitous adoption of deep neural networks has emerged as a dominant paradigm for modeling diverse business objectives. As user bases continue to expand, the necessity of personalization and frequent model updates have assumed paramount significance to ensure the delivery of relevant and refreshed experiences to a diverse array of members. In this work, we introduce an innovative meta-learning solution tailored to the personalization of models for individual members and other entities, coupled with the frequent updates based on the latest user interaction signals. Specifically, we leverage the Model-Agnostic Meta Learning (MAML) algorithm to adapt per-task sub-networks using recent user interaction data. Given the near infeasibility of productionizing original MAML-based models in online recommendation systems, we propose an efficient strategy to operationalize meta-learned sub-networks in production, which involves transforming them into fixed-sized vectors, termed meta embeddings, thereby enabling the seamless deployment of models with hundreds of billions of parameters for online serving. Through extensive experimentation on production data drawn from various applications at LinkedIn, we demonstrate that the proposed solution consistently outperforms the baseline models of those applications, including strong baselines such as using wide-and-deep ID based personalization approach. Our approach has enabled the deployment of a range of highly personalized AI models across diverse LinkedIn applications, leading to substantial improvements in business metrics as well as refreshed experience for our members.

We investigate both the theoretical and algorithmic aspects of likelihood-based methods for recovering a complex-valued signal from multiple sets of measurements, referred to as looks, affected by speckle (multiplicative) noise. Our theoretical contributions include establishing the first existing theoretical upper bound on the Mean Squared Error (MSE) of the maximum likelihood estimator under the deep image prior hypothesis. Our theoretical results capture the dependence of MSE upon the number of parameters in the deep image prior, the number of looks, the signal dimension, and the number of measurements per look. On the algorithmic side, we introduce the concept of bagged Deep Image Priors (Bagged-DIP) and integrate them with projected gradient descent. Furthermore, we show how employing Newton-Schulz algorithm for calculating matrix inverses within the iterations of PGD reduces the computational complexity of the algorithm. We will show that this method achieves the state-of-the-art performance.

Non-Hermitian models describe the physics of ubiquitous open systems with gain and loss. One intriguing aspect of non-Hermitian models is their inherent topology that can produce intriguing boundary phenomena like resilient higher-order topological insulators (HOTIs) and non-Hermitian skin effects (NHSE). Recently, time-multiplexed lattices in synthetic dimensions have emerged as a versatile platform for the investigation of these effects free of geometric restrictions. Despite holding broad applications, studies of these effects have been limited to static cases so far, and full dynamical control over the non-Hermitian effects has remained elusive. Here, we demonstrate the emergence of topological non-Hermitian corner states with remarkable temporal controllability and robustness in a two-dimensional photonic synthetic time lattice. Specifically, we showcase various dynamic control mechanisms for light confinement and flow, including spatial mode tapering, sequential non-Hermiticity on-off switching, dynamical corner state relocation, and light steering. Moreover, we establish the corner state's robustness in the presence of intensity modulation randomness and quantitatively determine its breakdown regime. Our findings extend non-Hermitian and topological photonic effects into higher synthetic dimensions, offering remarkable flexibility and real-time control possibilities. This opens avenues for topological classification, quantum walk simulations of many-body dynamics, and robust Floquet engineering, free from the limitations of physical geometries.