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Optical Diffraction Neural Networks (DNNs), a subset of Optical Neural Networks (ONNs), show promise in mirroring the prowess of electronic networks. This study introduces the Hybrid Diffraction Neural Network (HDNN), a novel architecture that incorporates matrix multiplication into DNNs, synergizing the benefits of conventional ONNs with those of DNNs to surmount the modulation limitations inherent in optical diffraction neural networks. Utilizing a singular phase modulation layer and an amplitude modulation layer, the trained neural network demonstrated remarkable accuracies of 96.39% and 89% in digit recognition tasks in simulation and experiment, respectively. Additionally, we develop the Binning Design (BD) method, which effectively mitigates the constraints imposed by sampling intervals on diffraction units, substantially streamlining experimental procedures. Furthermore, we propose an on-chip HDNN that not only employs a beam-splitting phase modulation layer for enhanced integration level but also significantly relaxes device fabrication requirements, replacing metasurfaces with relief surfaces designed by 1-bit quantization. Besides, we conceptualized an all-optical HDNN-assisted lesion detection network, achieving detection outcomes that were 100% aligned with simulation predictions. This work not only advances the performance of DNNs but also streamlines the path towards industrial optical neural network production.

Distributed processing of large-scale graph data has many practical applications and has been widely studied. In recent years, a lot of distributed graph processing frameworks and algorithms have been proposed. While many efforts have been devoted to analyzing these, with most analyzing them based on programming models, less research focuses on understanding their challenges in distributed environments. Applying graph tasks to distributed environments is not easy, often facing numerous challenges through our analysis, including parallelism, load balancing, communication overhead, and bandwidth. In this paper, we provide an extensive overview of the current state-of-the-art in this field by outlining the challenges and solutions of distributed graph algorithms. We first conduct a systematic analysis of the inherent challenges in distributed graph processing, followed by presenting an overview of existing general solutions. Subsequently, we survey the challenges highlighted in recent distributed graph processing papers and the strategies adopted to address them. Finally, we discuss the current research trends and identify potential future opportunities.

Resonant inelastic x-ray scattering (RIXS) experiment was performed at the Mn $L_3$ edge. A 10 $\%$ Mn-doped Sr$_{3}$Ru$_{2}$O$_{7}$ compound, where the Mn$^{3+}$ ions are in the 3$d^4$ state, were probed for $dd$ excitations. The dilute doping concentration allows one to treat the dopant Mn$^{3+}$ ions as effectively free in the host ruthenium compound. The local nature of $dd$ RIXS spectroscopy permits one to use a single-site model to simulate the experimental spectra. The simulated spectra reproduces the in-plane [100] experimental RIXS spectrum. We also predict the intensity for the in-plane [110] direction and the out-of-plane spin orientation configuration [001]. Based on our single-ion model we were able to fit the experimental data to obtain the crystal field parameters, the 10Dq value, and the intra-orbital spin-flip energy 2$\mathcal{J}$(or $3J_{H}$, where $J_{H}$ is the Hund's energy) of the Mn$^{3+}$ ion. Utilizing our computed RIXS quantum transition amplitudes between the various $d$ orbitals of the Mn$^{3+}$ ion, the expression for the Kramers-Heisenberg cross section, and a self-consistent fitting procedure we also identify the energy boundaries of the non-spin-flip and spin-flip $dd$ excitations present in the experimental data. From our fitting procedure we obtain $2\mathcal{J} (3J_{H})=2.06$ eV, a value which is in excellent agreement with that computed from the free ion Racah parameters. We also identified the charge transfer boundary. In addition to predicting the microscopic parameters, we find a quantum spin-flip transition in the non-cross ($\sigma_{in}-\sigma_{out}$, $\pi_{in}-\pi_{out}$) x-ray polarization channels of the $dd$ RIXS spectra. A similar transition, was previously predicted to occur in the $\pi-\pi$ channel of the magnon spectrum in the non-collinear non-coplanar Kagome compound composed of Cu$^{2+}$ 3d$^{9}$ ion., Comment: 12 pages, 7 figures, see PDF text for full abstract info

Virtual Try-on (VTON) involves generating images of a person wearing selected garments. Diffusion-based methods, in particular, can create high-quality images, but they struggle to maintain the identities of the input garments. We identified this problem stems from the specifics in the training formulation for diffusion. To address this, we propose a unique training scheme that limits the scope in which diffusion is trained. We use a control image that perfectly aligns with the target image during training. In turn, this accurately preserves garment details during inference. We demonstrate our method not only effectively conserves garment details but also allows for layering, styling, and shoe try-on. Our method runs multi-garment try-on in a single inference cycle and can support high-quality zoomed-in generations without training in higher resolutions. Finally, we show our method surpasses prior methods in accuracy and quality.

A key open question in the study of layered superconducting nickelate films is the role that hydrogen incorporation into the lattice plays in the appearance of the superconducting state. Due to the challenges of stabilizing highly crystalline square planar nickelate films, films are prepared by the deposition of a more stable parent compound which is then transformed into the target phase via a topotactic reaction with a strongly reducing agent such as CaH$_2$. Recent studies, both experimental and theoretical, have introduced the possibility that the incorporation of hydrogen from the reducing agent into the nickelate lattice may be critical for the superconductivity. In this work, we use secondary ion mass spectrometry to examine superconducting La$_{1-x}$X$_x$NiO$_2$ / SrTiO$_3$ (X = Ca and Sr) and Nd$_6$Ni$_5$O$_{12}$ / NdGaO$_3$ films, along with non-superconducting NdNiO$_2$ / SrTiO$_3$ and (Nd,Sr)NiO$_2$ / SrTiO$_3$. We find no evidence for extensive hydrogen incorporation across a broad range of samples, including both superconducting and non-superconducting films. Theoretical calculations indicate that hydrogen incorporation is broadly energetically unfavorable in these systems, supporting our conclusion that hydrogen incorporation is not generally required to achieve a superconducting state in layered square-planar nickelates., Comment: 23 pages, 21 figures (including supplemental information)

The availability of city-scale Lidar maps enables the potential of city-scale place recognition using mobile cameras. However, the city-scale Lidar maps generally need to be compressed for storage efficiency, which increases the difficulty of direct visual place recognition in compressed Lidar maps. This paper proposes VOLoc, an accurate and efficient visual place recognition method that exploits geometric similarity to directly query the compressed Lidar map via the real-time captured image sequence. In the offline phase, VOLoc compresses the Lidar maps using a \emph{Geometry-Preserving Compressor} (GPC), in which the compression is reversible, a crucial requirement for the downstream 6DoF pose estimation. In the online phase, VOLoc proposes an online Geometric Recovery Module (GRM), which is composed of online Visual Odometry (VO) and a point cloud optimization module, such that the local scene structure around the camera is online recovered to build the \emph{Querying Point Cloud} (QPC). Then the QPC is compressed by the same GPC, and is aggregated into a global descriptor by an attention-based aggregation module, to query the compressed Lidar map in the vector space. A transfer learning mechanism is also proposed to improve the accuracy and the generality of the aggregation network. Extensive evaluations show that VOLoc provides localization accuracy even better than the Lidar-to-Lidar place recognition, setting up a new record for utilizing the compressed Lidar map by low-end mobile cameras. The code are publicly available at https://github.com/Master-cai/VOLoc., Comment: 8 pages, 7figures, ICRA 2024

Constructing earth-fixed cells with low-earth orbit (LEO) satellites in non-terrestrial networks (NTNs) has been the most promising paradigm to enable global coverage. The limited computing capabilities on LEO satellites however render tackling resource optimization within a short duration a critical challenge. Although the sufficient computing capabilities of the ground infrastructures can be utilized to assist the LEO satellite, different time-scale control cycles and coupling decisions between the space- and ground-segments still obstruct the joint optimization design for computing agents at different segments. To address the above challenges, in this paper, a multi-time-scale deep reinforcement learning (DRL) scheme is developed for achieving the radio resource optimization in NTNs, in which the LEO satellite and user equipment (UE) collaborate with each other to perform individual decision-making tasks with different control cycles. Specifically, the UE updates its policy toward improving value functions of both the satellite and UE, while the LEO satellite only performs finite-step rollout for decision-makings based on the reference decision trajectory provided by the UE. Most importantly, rigorous analysis to guarantee the performance convergence of the proposed scheme is provided. Comprehensive simulations are conducted to justify the effectiveness of the proposed scheme in balancing the transmission performance and computational complexity.

Non-terrestrial networks (NTNs) with low-earth orbit (LEO) satellites have been regarded as promising remedies to support global ubiquitous wireless services. Due to the rapid mobility of LEO satellite, inter-beam/satellite handovers happen frequently for a specific user equipment (UE). To tackle this issue, earth-fixed cell scenarios have been under studied, in which the LEO satellite adjusts its beam direction towards a fixed area within its dwell duration, to maintain stable transmission performance for the UE. Therefore, it is required that the LEO satellite performs real-time resource allocation, which however is unaffordable by the LEO satellite with limited computing capability. To address this issue, in this paper, we propose a two-time-scale collaborative deep reinforcement learning (DRL) scheme for beam management and resource allocation in NTNs, in which LEO satellite and UE with different control cycles update their decision-making policies through a sequential manner. Specifically, UE updates its policy subject to improving the value functions of both the agents. Furthermore, the LEO satellite only makes decisions through finite-step rollouts with a reference decision trajectory received from the UE. Simulation results show that the proposed scheme can effectively balance the throughput performance and computational complexity over traditional greedy-searching schemes.

When optimizing real-time systems, designers often face a challenging problem where the schedulability constraints are non-convex, non-continuous, or lack an analytical form to understand their properties. Although the optimization framework NORTH proposed in previous work is general (it works with arbitrary schedulability analysis) and scalable, it can only handle problems with continuous variables, which limits its application. In this paper, we extend the applications of the framework NORTH to problems with a hybrid of continuous and discrete variables. This is achieved in a coordinate-descent method, where the continuous and discrete variables are optimized separately during iterations. The new framework, NORTH+, improves around 20% solution quality than NORTH in experiments., Comment: Workshop on OPtimization for Embedded and ReAl-time systems (OPERA 2023) co-located with the 44th IEEE Real-Time Systems Symposium (RTSS)

In real-time systems optimization, designers often face a challenging problem posed by the non-convex and non-continuous schedulability conditions, which may even lack an analytical form to understand their properties. To tackle this challenging problem, we treat the schedulability analysis as a black box that only returns true/false results. We propose a general and scalable framework to optimize real-time systems, named Numerical Optimizer with Real-Time Highlight (NORTH). NORTH is built upon the gradient-based active-set methods from the numerical optimization literature but with new methods to manage active constraints for the non-differentiable schedulability constraints. In addition, we also generalize NORTH to NORTH+, to collaboratively optimize certain types of discrete variables (\eg priority assignments, categorical variables) with continuous variables based on numerical optimization algorithms. We demonstrate the algorithm performance with two example applications: energy minimization based on dynamic voltage and frequency scaling (DVFS), and optimization of control system performance. In these experiments, NORTH achieved $10^2$ to $10^5$ times speed improvements over state-of-the-art methods while maintaining similar or better solution quality. NORTH+ outperforms NORTH by 30\% with similar algorithm scalability. Both NORTH and NORTH+ support black-box schedulability analysis, ensuring broad applicability., Comment: Extension of a conference paper

Retail photography imposes specific requirements on images. For instance, images may need uniform background colors, consistent model poses, centered products, and consistent lighting. Minor deviations from these standards impact a site's aesthetic appeal, making the images unsuitable for use. We show that Stable Diffusion methods, as currently applied, do not respect these requirements. The usual practice of training the denoiser with a very noisy image and starting inference with a sample of pure noise leads to inconsistent generated images during inference. This inconsistency occurs because it is easy to tell the difference between samples of the training and inference distributions. As a result, a network trained with centered retail product images with uniform backgrounds generates images with erratic backgrounds. The problem is easily fixed by initializing inference with samples from an approximation of noisy images. However, in using such an approximation, the joint distribution of text and noisy image at inference time still slightly differs from that at training time. This discrepancy is corrected by training the network with samples from the approximate noisy image distribution. Extensive experiments on real application data show significant qualitative and quantitative improvements in performance from adopting these procedures. Finally, our procedure can interact well with other control-based methods to further enhance the controllability of diffusion-based methods.

We present a diffusion-based video editing framework, namely DiffusionAtlas, which can achieve both frame consistency and high fidelity in editing video object appearance. Despite the success in image editing, diffusion models still encounter significant hindrances when it comes to video editing due to the challenge of maintaining spatiotemporal consistency in the object's appearance across frames. On the other hand, atlas-based techniques allow propagating edits on the layered representations consistently back to frames. However, they often struggle to create editing effects that adhere correctly to the user-provided textual or visual conditions due to the limitation of editing the texture atlas on a fixed UV mapping field. Our method leverages a visual-textual diffusion model to edit objects directly on the diffusion atlases, ensuring coherent object identity across frames. We design a loss term with atlas-based constraints and build a pretrained text-driven diffusion model as pixel-wise guidance for refining shape distortions and correcting texture deviations. Qualitative and quantitative experiments show that our method outperforms state-of-the-art methods in achieving consistent high-fidelity video-object editing., Comment: Preprint

Vapor-pressure mismatched materials such as transition metal chalcogenides have emerged as electronic, photonic, and quantum materials with scientific and technological importance. However, epitaxial growth of vapor-pressure mismatched materials are challenging due to differences in the reactivity, sticking coefficient, and surface adatom mobility of the mismatched species constituting the material, especially sulfur containing compounds. Here, we report a novel approach to grow chalcogenides - hybrid pulsed laser deposition - wherein an organosulfur precursor is used as a sulfur source in conjunction with pulsed laser deposition to regulate the stoichiometry of the deposited films. Epitaxial or textured thin films of sulfides with variety of structure and chemistry such as alkaline metal chalcogenides, main group chalcogenides, transition metal chalcogenides and chalcogenide perovskites are demonstrated, and structural characterization reveal improvement in thin film crystallinity, and surface and interface roughness compared to the state-of-the-art. The growth method can be broadened to other vapor-pressure mismatched chalcogenides such as selenides and tellurides. Our work opens up opportunities for broader epitaxial growth of chalcogenides, especially sulfide-based thin film technological applications., Comment: 27 pages

The Logical Execution Time (LET) programming model has recently received considerable attention, particularly because of its timing and dataflow determinism. In LET, task computation appears always to take the same amount of time (called the task's LET interval), and the task reads (resp. writes) at the beginning (resp. end) of the interval. Compared to other communication mechanisms, such as implicit communication and Dynamic Buffer Protocol (DBP), LET performs worse on many metrics, such as end-to-end latency (including reaction time and data age) and time disparity jitter. Compared with the default LET setting, the flexible LET (fLET) model shrinks the LET interval while still guaranteeing schedulability by introducing the virtual offset to defer the read operation and using the virtual deadline to move up the write operation. Therefore, fLET has the potential to significantly improve the end-to-end timing performance while keeping the benefits of deterministic behavior on timing and dataflow. To fully realize the potential of fLET, we consider the problem of optimizing the assignments of its virtual offsets and deadlines. We propose new abstractions to describe the task communication pattern and new optimization algorithms to explore the solution space efficiently. The algorithms leverage the linearizability of communication patterns and utilize symbolic operations to achieve efficient optimization while providing a theoretical guarantee. The framework supports optimizing multiple performance metrics and guarantees bounded suboptimality when optimizing end-to-end latency. Experimental results show that our optimization algorithms improve upon the default LET and its existing extensions and significantly outperform implicit communication and DBP in terms of various metrics, such as end-to-end latency, time disparity, and its jitter., Comment: accepted in RTAS'24

A potential candidate technology for the development of future 6G networks has been recognized as Reconfigurable Intelligent Surface (RIS). However, due to the variation in radio link quality, traditional passive RISs only accomplish a minimal signal gain in situations with strong direct links between user equipment (UE) and base station (BS). In order to get over this fundamental restriction of smaller gain, the idea of active RISs might be a suitable solution. In contrast to current passive RIS, which simply reflects and directs signals without any additional amplification, active RISs have the ability to enhance reflected signals by the incorporation of amplifiers inside its elements. However, with additional amplifiers, apart from the relatively complex attributes of RIS-assisted arrangements, the additional energy consumption of such technologies is often disregarded. So, there might be a tradeoff between the additional energy consumption for the RIS technologies and the overall gain acquired by deploying this potential advancement. The objective of this work is to provide a primary idea of a three-layer hybrid RIS-assisted configuration that is responsive to both active and passive RIS, as well as an additional dormant or inactive state. The single RIS structure should be capable of adjusting its overall configuration in response to fluctuations in transmit power and radio link quality. Furthermore, our fabricated passive RIS-assisted structure verifies a portion of the proposed idea, with simulations highlighting its advantages over standalone passive or active RIS-assisted technologies., Comment: Accepted for presentation and publication at the 8th IEEE Asia Pacific Conference on Wireless and Mobile (APWiMob) Conference

A supermassive black hole can launch a relativistic jet when it violently disrupts a star that passes too close. Such jetted tidal disruption events (TDEs) are rare and unique tools to investigate quiescent supermassive black holes, jet physics, and circumnuclear environment at high redshift. The newly discovered TDE AT2022cmc ($z\sim 1.193$) providing rich multi-band (X-ray, UV, optical, sub-millimeter, and radio) data, has been interpreted as the fourth on-axis jetted TDE. In this work, we constrain the circumnuclear medium (CNM) density profile with both closure relation (CR) test and detailed forward shock model fit with Markov chain Monte Carlo (MCMC) approach to the multi-band (optical, sub-millimeter, and radio) data of AT2022cmc.We find that the CNM density profile of AT2022cmc is $n\propto R^{-k}$ with $k \sim 1.68$, implying a Bondi accretion in history. Furthermore, our model fit result suggests a two-component jet in AT2022cmc, indicating a similar jet physics to well-studied jetted TDE Sw J1644+57., Comment: accepted in ApJ

Chalcogen vacancies in transition metal dichalcogenides are widely acknowledged as both donor dopants and as a source of disorder. The electronic structure of sulphur vacancies in MoS2 however is still controversial, with discrepancies in the literature pertaining to the origin of the in-gap features observed via scanning tunneling spectroscopy (STS) on single sulphur vacancies. Here we use a combination of scanning tunnelling microscopy (STM) and STS to study embedded sulphur vacancies in bulk MoS2 crystals. We observe spectroscopic features dispersing in real space and in energy, which we interpret as tip position- and bias-dependent ionization of the sulphur vacancy donor due to tip induced band bending (TIBB). The observations indicate that care must be taken in interpreting defect spectra as reflecting in-gap density of states, and may explain discrepancies in the literature., Comment: 7 pages, 5 figures

Record linkage is a bedrock of quantitative social science, as analyses often require linking data from multiple, noisy sources. Off-the-shelf string matching methods are widely used, as they are straightforward and cheap to implement and scale. Not all character substitutions are equally probable, and for some settings there are widely used handcrafted lists denoting which string substitutions are more likely, that improve the accuracy of string matching. However, such lists do not exist for many settings, skewing research with linked datasets towards a few high-resource contexts that are not representative of the diversity of human societies. This study develops an extensible way to measure character substitution costs for OCR'ed documents, by employing large-scale self-supervised training of vision transformers (ViT) with augmented digital fonts. For each language written with the CJK script, we contrastively learn a metric space where different augmentations of the same character are represented nearby. In this space, homoglyphic characters - those with similar appearance such as ``O'' and ``0'' - have similar vector representations. Using the cosine distance between characters' representations as the substitution cost in an edit distance matching algorithm significantly improves record linkage compared to other widely used string matching methods, as OCR errors tend to be homoglyphic in nature. Homoglyphs can plausibly capture character visual similarity across any script, including low-resource settings. We illustrate this by creating homoglyph sets for 3,000 year old ancient Chinese characters, which are highly pictorial. Fascinatingly, a ViT is able to capture relationships in how different abstract concepts were conceptualized by ancient societies, that have been noted in the archaeological literature.

One fundamental limitation to the research of bird strike prevention is the lack of a large-scale dataset taken directly from real-world airports. Existing relevant datasets are either small in size or not dedicated for this purpose. To advance the research and practical solutions for bird strike prevention, in this paper, we present a large-scale challenging dataset AirBirds that consists of 118,312 time-series images, where a total of 409,967 bounding boxes of flying birds are manually, carefully annotated. The average size of all annotated instances is smaller than 10 pixels in 1920x1080 images. Images in the dataset are captured over 4 seasons of a whole year by a network of cameras deployed at a real-world airport, covering diverse bird species, lighting conditions and 13 meteorological scenarios. To the best of our knowledge, it is the first large-scale image dataset that directly collects flying birds in real-world airports for bird strike prevention. This dataset is publicly available at https://airbirdsdata.github.io/., Comment: 17 pages, 9 figures, 3 tables; accepted by ACCV 2022