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Holography is an essential technique of generating three-dimensional images. Recently, quantum holography with undetected photons (QHUP) has emerged as a groundbreaking method capable of capturing complex amplitude images. Despite its potential, the practical application of QHUP has been limited by susceptibility to phase disturbances, low interference visibility, and limited spatial resolution. Deep learning, recognized for its ability in processing complex data, holds significant promise in addressing these challenges. In this report, we present an ample advancement in QHUP achieved by harnessing the power of deep learning to extract images from single-shot holograms, resulting in vastly reduced noise and distortion, alongside a notable enhancement in spatial resolution. The proposed and demonstrated deep learning QHUP (DL-QHUP) methodology offers a transformative solution by delivering high-speed imaging, improved spatial resolution, and superior noise resilience, making it suitable for diverse applications across an array of research fields stretching from biomedical imaging to remote sensing. DL-QHUP signifies a crucial leap forward in the realm of holography, demonstrating its immense potential to revolutionize imaging capabilities and pave the way for advancements in various scientific disciplines. The integration of DL-QHUP promises to unlock new possibilities in imaging applications, transcending existing limitations and offering unparalleled performance in challenging environments.

Optical neural networks present distinct advantages over traditional electrical counterparts, such as accelerated data processing and reduced energy consumption. While coherent light is conventionally employed in optical neural networks, our study proposes harnessing spatially incoherent light in all-optical Fourier neural networks. Contrary to numerical predictions of declining target recognition accuracy with increased incoherence, our experimental results demonstrate a surprising outcome: improved accuracy with incoherent light. We attribute this unexpected enhancement to spatially incoherent light's ability to alleviate experimental errors like diffraction rings, laser speckle, and edge effects. Our controlled experiments introduced spatial incoherence by passing monochromatic light through a spatial light modulator featuring a dynamically changing random phase array. These findings underscore partially coherent light's potential to optimize optical neural networks, delivering dependable and efficient solutions for applications demanding consistent accuracy and robustness across diverse conditions., Comment: 19 pages,5 figures

In a recent work \cite{Liu:2023gzf}, I proposed a method to challenge the calculations of genus-$g$, bulk $n$-point, $b$-boundary, $c$-crosscap functions with $x$ boundary operators $\mathcal{F}_{g,n,b,c}^{x}$ of two-dimensional conformal field theories (CFT$_2$) by expanding them as infinite linear combinations of genus-$g$, $(n+b+c)$-point bulk correlation functions. Applying free field resolutions of chiral modules is an effective method for obtaining infinite linear coefficients in the expansion. We review free field resolution conjectures of dominant irreducible $\widehat{g}_{k}$ highest-weight modules and higher-rank parafermionic $\widehat{g}_{k}/ \widehat{u(1)}^{r}$ modules \cite{Bouwknegt:1989xa, Bouwknegt:1990fb, Bouwknegt:1990wa}, and apply them to corresponding Ishibashi states., Comment: 14 pages + references. V2: Minor corrections + reference added

Large language models (LLM) have demonstrated remarkable abilities in generating natural language, understanding user instruction, and mimicking human language use. These capabilities have garnered considerable interest in applications such as role-playing. However, the process of collecting individual role scripts (or profiles) data and manually evaluating the performance can be costly. We introduce a framework that uses prompts to leverage the state-of-the-art (SOTA) LLMs to construct role-playing dialogue datasets and evaluate the role-playing performance. Additionally, we employ recall-oriented evaluation Rouge-L metric to support the result of the LLM evaluator.

We present Tencent's ads recommendation system and examine the challenges and practices of learning appropriate recommendation representations. Our study begins by showcasing our approaches to preserving prior knowledge when encoding features of diverse types into embedding representations. We specifically address sequence features, numeric features, and pre-trained embedding features. Subsequently, we delve into two crucial challenges related to feature representation: the dimensional collapse of embeddings and the interest entanglement across different tasks or scenarios. We propose several practical approaches to address these challenges that result in robust and disentangled recommendation representations. We then explore several training techniques to facilitate model optimization, reduce bias, and enhance exploration. Additionally, we introduce three analysis tools that enable us to study feature correlation, dimensional collapse, and interest entanglement. This work builds upon the continuous efforts of Tencent's ads recommendation team over the past decade. It summarizes general design principles and presents a series of readily applicable solutions and analysis tools. The reported performance is based on our online advertising platform, which handles hundreds of billions of requests daily and serves millions of ads to billions of users.

Although model editing has shown promise in revising knowledge in Large Language Models (LLMs), its impact on the inherent capabilities of LLMs is often overlooked. In this work, we reveal a critical phenomenon: even a single edit can trigger model collapse, manifesting as significant performance degradation in various benchmark tasks. However, benchmarking LLMs after each edit, while necessary to prevent such collapses, is impractically time-consuming and resource-intensive. To mitigate this, we propose using perplexity as a surrogate metric, validated by extensive experiments demonstrating changes in an edited model's perplexity are strongly correlated with its downstream task performances. We further conduct an in-depth study on sequential editing, a practical setting for real-world scenarios, across various editing methods and LLMs, focusing on hard cases from our previous single edit studies. The results indicate that nearly all examined editing methods result in model collapse after only few edits. To facilitate further research, we have utilized GPT-3.5 to develop a new dataset, HardEdit, based on those hard cases. This dataset aims to establish the foundation for pioneering research in reliable model editing and the mechanisms underlying editing-induced model collapse. We hope this work can draw the community's attention to the potential risks inherent in model editing practices., Comment: Accepted at Findings of ACL 2024

We propose a method to challenge the calculation of genus-$g$, bulk $n$-point, $b$-boundary, $c$-crosscap correlation functions with $x$ boundary operators $\mathcal{F}_{g,n,b,c}^{x}$ in two-dimensional conformal field theories (CFT$_2$). We show that $\mathcal{F}_{g,n,b,c}^{x}$ are infinite linear combinations of genus-$g$, bulk $(n+b+c)$-point functions $\mathcal{F}_{g,(n+b+c)}$, and try to obtain the linear coefficients in this work. We show the existence of a single pole structure in the linear coefficients at degenerate limits. A practical method to obtain the infinite linear coefficients is the free field realizations of Ishibashi states. We review the results in Virasoro minimal models $\mathcal{M}(p,p')$ and extend it to the $N=1$ minimal models $\mathcal{SM}(p,p')$., Comment: V4. Minor Revision

Ni is the second most abundant element in the Earth's core. Yet, its effects on the inner core's structure and formation process are usually disregarded because of its electronic and size similarity with Fe. Using ab initio molecular dynamics simulations, we find that the bcc phase can spontaneously crystallize in liquid Ni at temperatures above Fe's melting point at inner core pressures. The melting temperature of Ni is shown to be 700-800 K higher than that of Fe at 323-360 GPa. hcp, bcc, and liquid phase relation differ for Fe and Ni. Ni can be a bcc stabilizer for Fe at high temperatures and inner core pressures. A small amount of Ni can accelerate Fe's crystallization at core pressures. These results suggest Ni may substantially impact the structure and formation process of the solid inner core.

One of the most poorly understood aspects of low-mass star formation is how multiple-star systems are formed. Here we present the results of Atacama Large Millimeter/submillimeter Array (ALMA) Band-6 observations towards a forming quadruple protostellar system, G206.93-16.61E2, in the Orion B molecular cloud. ALMA 1.3 mm continuum emission reveals four compact objects, of which two are Class I young stellar objects (YSOs), and the other two are likely in prestellar phase. The 1.3 mm continuum emission also shows three asymmetric ribbon-like structures that are connected to the four objects, with lengths ranging from $\sim$500 au to $\sim$2200 au. By comparing our data with magneto-hydrodynamic (MHD) simulations, we suggest that these ribbons trace accretion flows and also function as gas bridges connecting the member protostars. Additionally, ALMA CO J=2-1 line emission reveals a complicated molecular outflow associated with G206.93-16.61E2 with arc-like structures suggestive of an outflow cavity viewed pole-on., Comment: The paper was accepted by APJL

Radio Access Network faces challenges from privacy and flexible wide area and local area network access. RAN is limited from providing local service directly due to centralized design of cellular network and concerns of user privacy and data security. DecentRAN or Decentralized Radio Access Network offers an alternative perspective to cope with the emerging demands of 5G Non-public Network and the hybrid deployment of 5GS and Wi-Fi in the campus network. Starting from Public key as an Identity, independent mutual authentication between UE and RAN are made possible in a privacy-preserving manner. With the introduction of decentralized architecture and network functions using blockchain and smart contracts, DecentRAN has ability to provide users with locally managed, end-to-end encrypted 5G NPN and the potential connectivity to Local Area Network via campus routers. Furthermore, the performance regarding throughput and latency are discussed, offering the deployment guidance for DecentRAN.

We present a statistical study of a sample of 17 hub-filament-system (HFS) clouds of high-mass star formation using high-angular resolution ($\sim$1-2 arcsecond) ALMA 1.3mm and 3mm continuum data. The sample includes 8 infrared (IR)-dark and 9 IR-bright types, which correspond to an evolutionary sequence from the IR-dark to IR-bright stage. The central massive clumps and their associated most massive cores are observed to follow a trend of increasing mass ($M$) and mass surface density ($\Sigma$) with evolution from IR-dark to IR-bright stage. In addition, a mass-segregated cluster of young stellar objects (YSOs) are revealed in both IR-dark and IR-bright HFSs with massive YSOs located in the hub and the population of low-mass YSOs distributed over larger areas. Moreover, outflow feedback in all HFSs are found to escape preferentially through the inter-filamentary diffuse cavities, suggesting that outflows would render a limited effect on the disruption of the HFSs and ongoing high-mass star formation therein. From the above observations, we suggest that high-mass star formation in the HFSs can be described by a multi-scale mass accretion/transfer scenario, from hub-composing filaments through clumps down to cores, that can naturally lead to a mass-segregated cluster of stars., Comment: Accepted for publication in MNRAS; 16 pages, 8 figures, and 3 tables

The nature of complex eigenenergy enables unique band topology to the non-Hermitian (NH) lattice systems. Recently, there has been a fast growing interest in the elusive winding and braiding topologies of the NH single and double bands, respectively. Here, we explore the even more intricate NH multi-band topology and present the first experimental characterization of the three-band braid relations by acoustic systems. Based on a concise tight-binding model, we design a ternary cavity-tube structure equipped with a highly controllable unidirectional coupler, through which the acoustic NH Bloch bands are experimentally reproduced in a synthetic dimension. We identify the NH three-band braid relations from both the perspectives of eigenvalues and eigenstates, including a noncommutative braid relation and a swappable braid relation. Our results could promote the understanding of NH Bloch band topology and pave the way toward designing new devices for manipulating acoustic states., Comment: 5 figures

Topological features embedded in ancient braiding and knotting arts endow significant impacts on our daily life and even cutting-edge science. Recently, fast growing efforts are invested to the braiding topology of complex Bloch bands in non-Hermitian systems. This new classification of band topology goes far beyond those established in Hermitian counterparts. Here, we present the first acoustic realization of the topological non-Hermitian Bloch braids, based on a two-band model easily accessible for realizing any desired knot structure. The non-Hermitian bands are synthesized by a simple binary cavity-tube system, where the long-range, complex-valued, and momentum-resolved couplings are accomplished by a well-controlled unidirectional coupler. In addition to directly visualizing various two-band braiding patterns, we unambiguously observe the highly-elusive topological phase transitions between them. Not only do our results provide a direct demonstration for the non-Hermitian band topology, but also the experimental techniques open new avenues for designing unconventional acoustic metamaterials., Comment: Accepted by PRL