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Photoacoustic computed tomography (PACT) is a non-invasive imaging modality with wide medical applications. Conventional PACT image reconstruction algorithms suffer from wavefront distortion caused by the heterogeneous speed of sound (SOS) in tissue, which leads to image degradation. Accounting for these effects improves image quality, but measuring the SOS distribution is experimentally expensive. An alternative approach is to perform joint reconstruction of the initial pressure image and SOS using only the PA signals. Existing joint reconstruction methods come with limitations: high computational cost, inability to directly recover SOS, and reliance on inaccurate simplifying assumptions. Implicit neural representation, or neural fields, is an emerging technique in computer vision to learn an efficient and continuous representation of physical fields with a coordinate-based neural network. In this work, we introduce NF-APACT, an efficient self-supervised framework utilizing neural fields to estimate the SOS in service of an accurate and robust multi-channel deconvolution. Our method removes SOS aberrations an order of magnitude faster and more accurately than existing methods. We demonstrate the success of our method on a novel numerical phantom as well as an experimentally collected phantom and in vivo data. Our code and numerical phantom are available at https://github.com/Lukeli0425/NF-APACT.

Over the past decade, wearable computing devices (``smart glasses'') have undergone remarkable advancements in sensor technology, design, and processing power, ushering in a new era of opportunity for high-density human behavior data. Equipped with wearable cameras, these glasses offer a unique opportunity to analyze non-verbal behavior in natural settings as individuals interact. Our focus lies in predicting engagement in dyadic interactions by scrutinizing verbal and non-verbal cues, aiming to detect signs of disinterest or confusion. Leveraging such analyses may revolutionize our understanding of human communication, foster more effective collaboration in professional environments, provide better mental health support through empathetic virtual interactions, and enhance accessibility for those with communication barriers. In this work, we collect a dataset featuring 34 participants engaged in casual dyadic conversations, each providing self-reported engagement ratings at the end of each conversation. We introduce a novel fusion strategy using Large Language Models (LLMs) to integrate multiple behavior modalities into a ``multimodal transcript'' that can be processed by an LLM for behavioral reasoning tasks. Remarkably, this method achieves performance comparable to established fusion techniques even in its preliminary implementation, indicating strong potential for further research and optimization. This fusion method is one of the first to approach ``reasoning'' about real-world human behavior through a language model. Smart glasses provide us the ability to unobtrusively gather high-density multimodal data on human behavior, paving the way for new approaches to understanding and improving human communication with the potential for important societal benefits. The features and data collected during the studies will be made publicly available to promote further research., Comment: 22 pages, first three authors equal contribution

To optimize the entanglement detection, we formulate the metrologically operational entanglement condition in quantum Fisher information by maximizing the QFI on the measurement orbit. Specifically, we consider two classes of typical local observables, i.e. the local orthonormal observables and symmetric informationally complete positive operator-valued measures. Result shows that the symmetric informationally complete positive operator-valued measures are superior to local orthonormal observables in entanglement detection, which in some sense hints the yet unconfirmed generally superiority of symmetric informationally complete positive operator-valued measures in quantum information processing., Comment: 20 pages, 3 figures. Close to the published version

Finite-temperature orbital-free density functional theory (FT-OFDFT) holds significant promise for simulating warm dense matter due to its favorable scaling with both system size and temperature. However, the lack of the numerically accurate and transferable noninteracting free energy functionals results in a limit on the application of FT-OFDFT for warm dense matter simulations. Here, a nonlocal free energy functional, named XWMF, was derived by line integrals for FT-OFDFT simulations. Particularly, a designed integral path, wherein the electronic density varies from uniform to inhomogeneous, was employed to accurately describe deviations in response behavior from the uniform electron gas. The XWMF has been benchmarked by a range of warm dense matter systems including the Si, Al, H, He, and H-He mixture. The simulated results demonstrate that FT-OFDFT within XWMF achieves remarkable performance for accuracy and numerical stability. It is worth noting that XWMF exhibits a low computational cost for large-scale ab~initio simulations, offering exciting opportunities for the realistic simulations of warm dense matter systems covering a broad range of temperatures and pressures., Comment: 8 pages, 4 figures

Foundation models have rapidly evolved and have achieved significant accomplishments in computer vision tasks. Specifically, the prompt mechanism conveniently allows users to integrate image prior information into the model, making it possible to apply models without any training. Therefore, we propose a method based on foundation models and zero training to solve the tasks of photoacoustic (PA) image segmentation. We employed the segment anything model (SAM) by setting simple prompts and integrating the model's outputs with prior knowledge of the imaged objects to accomplish various tasks, including: (1) removing the skin signal in three-dimensional PA image rendering; (2) dual speed-of-sound reconstruction, and (3) segmentation of finger blood vessels. Through these demonstrations, we have concluded that deep learning can be directly applied in PA imaging without the requirement for network design and training. This potentially allows for a hands-on, convenient approach to achieving efficient and accurate segmentation of PA images. This letter serves as a comprehensive tutorial, facilitating the mastery of the technique through the provision of code and sample datasets.

We propose in this work a practical approach, by virtue of correlation matrices of the generic observables, to study the long lasting tough issue of quantum separability. Some general separability conditions are set up through constructing a measurement-induced Bloch space. In essence, these conditions are established due to the self constraint in the space of quantum states. The new approach can not only reproduce many of the prevailing entanglement criteria, but also lead to even stronger results and manifest superiority for some bound entangled states. Moreover, as a by product, the new criteria are found directly transformable to the entanglement witness operators., Comment: 25 pages, 2 figures

We investigate quantum persistence by analyzing amplitude and phase fluctuations of the wave function governed by the time-dependent free-particle Schr\"odinger equation. The quantum system is initialized with local random uncorrelated Gaussian amplitude and phase fluctuations. In analogy with classical diffusion, the persistence probability is defined as the probability that the local (amplitude or phase) fluctuations have not changed sign up to time $t$. Our results show that the persistence probability in quantum diffusion exhibits exponential-like tails. More specifically, in $d=1$ the persistence probability decays in a stretched exponential fashion, while in $d=2$ and $d=3$ as an exponential. We also provide some insights by analyzing the two-point spatial and temporal correlation functions in the limit of small fluctuations. In particular, in the long-time limit, the temporal correlation functions for both local amplitude and phase fluctuations become time-homogeneous, i.e., the zero-crossing events correspond to those of a stationary Gaussian process, with sufficiently fast-decaying power-law tail of its autocorrelation function, implying an exponential-like tail of the persistence probabilities., Comment: 41 pages, 16 figures

We explore the uncertainty relation for unitary operators in a new way and find two uncertainty equalities for unitary operators, which are minimized by any pure states. Additionally, we derive two sets of uncertainty inequalities that unveil hierarchical structures within the realm of unitary operator uncertainty. Furthermore, we examine and compare our method for unitary uncertainty relations to other prevailing formulations. We provide explicit examples for better understanding and clarity. Results show that the hierarchical unitary uncertainty relations establish strong bounds. Moreover, we investigate the higher-dimensional limit of the unitary uncertainty equalities., Comment: 16 pages, 4 figures. Close to the published version

Networks of interconnected agents are essential to study complex networked systems' state evolution, stability, resilience, and control. Nevertheless, the high dimensionality and nonlinear dynamics are vital factors preventing us from theoretically analyzing them. Recently, the dimension-reduction approaches reduced the system's size by mapping the original system to a one-dimensional system such that only one effective representative can capture its macroscopic dynamics. However, the approaches dramatically fail as the network becomes heterogeneous and has multiple community structures. Here, we bridge the gap by developing a generalized dimension reduction approach, which enables us to map the original system to a $m$-dimensional system that consists of $m$ interacting components. Notably, by validating it on various dynamical models, this approach accurately predicts the original system state and the tipping point, if any. Furthermore, the numerical results demonstrate that this approach approximates the system evolution and identifies the critical points for complex networks with time delay., Comment: 26 pages, 11 figures

We derive by lattice theory a universal quantum certainty relation for arbitrary $M$ observables in $N$-dimensional system, which provides a state-independent maximum lower bound on the direct-sum of the probability distribution vectors (PDVs) in terms of majorization relation. While the utmost lower bound coincides with $(1/N,...,1/N)$ for any two orthogonal bases, the majorization certainty relation for $M\geqslant3$ is shown to be nontrivial. The universal majorization bounds for three mutually complementary observables and a more general set of observables in dimension-2 are achieved. It is found that one cannot prepare a quantum state with PDVs of incompatible observables spreading out arbitrarily. Moreover, we obtain a complementary relation for the quantum coherence as well, which characterizes a trade-off relation of quantum coherence with different bases., Comment: 15 pages, 3 figures

The Naming Game is a classic model for studying the emergence and evolution of language within a population. In this paper, we extend the traditional Naming Game model to encompass multiple committed opinions and investigate the system dynamics on the complete graph with an arbitrarily large population and random networks of finite size. For the fully connected complete graph, the homogeneous mixing condition enables us to use mean-field theory to analyze the opinion evolution of the system. However, when the number of opinions increases, the number of variables describing the system grows exponentially. To mitigate this, we focus on a special scenario where the largest group of committed agents competes with a motley of committed groups, each of which is smaller than the largest one, while initially, most of uncommitted agents hold one unique opinion. This scenario is chosen for its recurrence in diverse societies and its potential for complexity reduction by unifying agents from smaller committed groups into one category. Our investigation reveals that when the size of the largest committed group reaches the critical threshold, most of uncommitted agents change their beliefs to this opinion, triggering a phase transition. Further, we derive the general formula for the multi-opinion evolution using a recursive approach, enabling investigation into any scenario. Finally, we employ agent-based simulations to reveal the opinion evolution and dominance transition in random graphs. Our results provide insights into the conditions under which the dominant opinion emerges in a population and the factors that influence these conditions., Comment: 15 pages, 13 figures

Effective information transmission is a central element in quantum information protocols, but the quest for optimal efficiency in channels with symmetrical characteristics remains a prominent challenge in quantum information science. In light of this challenge, we introduce a hybrid channel that encompasses thermal, magnetic, and local components, each simultaneously endowed with characteristics that enhance and diminish quantum correlations. To investigate the symmetry of this hybrid channel, we explore the quantum correlations of a simple two-qubit Heisenberg spin state, quantified using measures such as negativity, $\ell_1$-norm coherence, entropic uncertainty, and entropy functions. Our findings reveal that the hybrid channel can be adeptly tailored to preserve quantum correlations, surpassing the capabilities of its individual components. We also identify optimal parameterizations to attain maximum entanglement from mixed-entangled/separable states, even in the presence of local dephasing. Notably, various parameters and quantum features, including non-Markovianity, exhibit distinct behaviors in the context of this hybrid channel. Ultimately, we discuss potential experimental applications of this configuration.

Separability problem is a long-standing tough issue in quantum information theory. In this paper, we propose a general method to detect entanglement via arbitrary measurement $\boldsymbol{X}$, by which several novel criteria are established. The new criteria are found incorporate many of the prevailing ones in the literatures. Our method is applicable as well to the steering detection, which possesses a merit of ignoring the knowledge of involved quantum states. A concept of measurement orbit, which plays an important role in the detection of entanglement and steering, is introduced, which enlightens our understanding of uncertainty relation. Moreover, an extension of symmetric informationally complete positive operator-valued measures (SIC-POVM), viz. symmetric complete measurements (SCM), is proposed, and employed to reconstruct the quantum state analytically., Comment: 52 pages, 3 figures

Lookup table (LUT) has shown its efficacy in low-level vision tasks due to the valuable characteristics of low computational cost and hardware independence. However, recent attempts to address the problem of single image super-resolution (SISR) with lookup tables are highly constrained by the small receptive field size. Besides, their frameworks of single-layer lookup tables limit the extension and generalization capacities of the model. In this paper, we propose a framework of series-parallel lookup tables (SPLUT) to alleviate the above issues and achieve efficient image super-resolution. On the one hand, we cascade multiple lookup tables to enlarge the receptive field of each extracted feature vector. On the other hand, we propose a parallel network which includes two branches of cascaded lookup tables which process different components of the input low-resolution images. By doing so, the two branches collaborate with each other and compensate for the precision loss of discretizing input pixels when establishing lookup tables. Compared to previous lookup table-based methods, our framework has stronger representation abilities with more flexible architectures. Furthermore, we no longer need interpolation methods which introduce redundant computations so that our method can achieve faster inference speed. Extensive experimental results on five popular benchmark datasets show that our method obtains superior SISR performance in a more efficient way. The code is available at https://github.com/zhjy2016/SPLUT., Comment: accpted by ECCV2022

A family of skew information quantities is obtained, in which the well-known Wigner-Yanase skew information and quantum Fisher information stand as special cases. A transparent proof of convexity of the generalized skew information is given, implying a simple proof of the Wigner-Yanase-Dyson conjecture. We find in this work an exact skew information inequality for qubit system, which may regard as the information counterpart of the uncertainty relation. A lower bound for generalized skew information of a pair of incompatible observables in arbitrary dimension and also the upper bound for qubit system are achieved., Comment: 23 pages, 2 figures