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The trend toward using increasingly large arrays of antenna elements continues. However, fitting more antennas into the limited space available on user equipment (UE) within the currently popular Frequency Range 1 spectrum presents a significant challenge. This limitation constrains the capacity scaling gains for end users, even when networks can support a higher number of antennas. To address this issue, we explore a user-centric collaborative MIMO approach, termed UE-CoMIMO, which leverages several fixed or portable devices within a personal area to form a virtually expanded antenna array. This paper develops a comprehensive mathematical framework to analyze the performance of UE-CoMIMO. Our analytical results demonstrate that UE-CoMIMO can significantly enhance the system's effective channel response within the current communication system without requiring extensive modifications. Further performance improvements can be realized by optimizing the phase shifters on the expanded antenna arrays at the collaborative devices. These findings are corroborated by ray-tracing simulations. Beyond the simulations, we implemented these collaborative devices and successfully conducted over-the-air validation in a real 5G environment, showcasing the practical potential of UE-CoMIMO. Several practical perspectives are discussed, highlighting the feasibility and benefits of this approach in real-world scenarios., Comment: 13 pages, 11 figures, this work has been submitted to IEEE for possible publication

Hyperspectral image (HSI) classification involves assigning specific labels to each pixel to identify various land cover categories. Although deep classifiers have shown high predictive accuracy in this field, quantifying their uncertainty remains a significant challenge, which hinders their application in critical contexts. This study first theoretically evaluates the applicability of \textit{Conformal Prediction} (CP), an emerging technique for uncertainty quantification, in the context of HSI classification. We then propose a conformal procedure that provides HSI classifiers with trustworthy prediction sets, offering coverage guarantees that ensure these sets contain the true labels with a user-specified probability. Building on this foundation, we introduce \textit{Spatial-Aware Conformal Prediction} (\texttt{SACP}), which incorporates essential spatial information inherent in HSIs by aggregating non-conformity scores of pixels with high spatial correlation. Both theoretical and empirical results demonstrate that \texttt{SACP} outperforms standard CP in HSI classification. The source code is accessible at \url{https://github.com/J4ckLiu/SACP}.

We report a milestone in achieving large-scale, ultrathin (~5 nm) superconducting NbN thin films on 300 mm Si wafers using a high-volume manufacturing (HVM) industrial physical vapor deposition (PVD) system. The NbN thin films possess remarkable structural uniformity and consistently high superconducting quality across the entire 300 mm Si wafer, by incorporating an AlN buffer layer. High-resolution X-ray diffraction and transmission electron microscopy analyses unveiled enhanced crystallinity of (111)-oriented {\delta}-phase NbN with the AlN buffer layer. Notably, NbN films deposited on AlN-buffered Si substrates exhibited a significantly elevated superconducting critical temperature (~2 K higher for the 10 nm NbN) and a higher upper critical magnetic field or Hc2 (34.06 T boost in Hc2 for the 50 nm NbN) in comparison with those without AlN. These findings present a promising pathway for the integration of quantum-grade superconducting NbN films with the existing 300 mm CMOS Si platform for quantum information applications.

Solidification of droplets is of great importance to various technological applications, drawing considerable attention from scientists aiming to unravel the fundamental physical mechanisms. In the case of multicomponent droplets undergoing solidification, the emergence of concentration gradients may trigger significant interfacial flows that dominate the freezing dynamics. Here, we experimentally investigate the fascinating snow-globe freezing dynamics of supercooled ethanol-water droplets. We reveal that these unique freezing dynamics are driven by solidification-induced solutal Marangoni flow within the droplets. We quantitatively characterize the concentration-dependent migration and growth dynamics of ice particles, tightly connecting them to the solutal Marangoni effect and the associated convective heat transfer. Moreover, we show that the final wrapping state of droplets can be modulated by the concentration of ethanol. Our findings may pave the way for novel insights into the physicochemical hydrodynamics of multicomponent liquids undergoing phase transitions., Comment: 5 pages, 4 figures

Feasible solutions are crucial for Integer Programming (IP) since they can substantially speed up the solving process. In many applications, similar IP instances often exhibit similar structures and shared solution distributions, which can be potentially modeled by deep learning methods. Unfortunately, existing deep-learning-based algorithms, such as Neural Diving and Predict-and-search framework, are limited to generating only partial feasible solutions, and they must rely on solvers like SCIP and Gurobi to complete the solutions for a given IP problem. In this paper, we propose a novel framework that generates complete feasible solutions end-to-end. Our framework leverages contrastive learning to characterize the relationship between IP instances and solutions, and learns latent embeddings for both IP instances and their solutions. Further, the framework employs diffusion models to learn the distribution of solution embeddings conditioned on IP representations, with a dedicated guided sampling strategy that accounts for both constraints and objectives. We empirically evaluate our framework on four typical datasets of IP problems, and show that it effectively generates complete feasible solutions with a high probability (> 89.7 \%) without the reliance of Solvers and the quality of solutions is comparable to the best heuristic solutions from Gurobi. Furthermore, by integrating our method's sampled partial solutions with the CompleteSol heuristic from SCIP, the resulting feasible solutions outperform those from state-of-the-art methods across all datasets, exhibiting a 3.7 to 33.7\% improvement in the gap to optimal values, and maintaining a feasible ratio of over 99.7\% for all datasets., Comment: Accepted to SIGKDD 2024

It is important to incorporate spatial geographic information into U.S. presidential election analysis, especially for swing states. The state-level analysis also faces significant challenges of limited spatial data availability. To address the challenges of spatial dependence and small sample sizes in predicting U.S. presidential election results using spatially dependent data, we propose a novel transfer learning framework within the SAR model, called as tranSAR. Classical SAR model estimation often loses accuracy with small target data samples. Our framework enhances estimation and prediction by leveraging information from similar source data. We introduce a two-stage algorithm, consisting of a transferring stage and a debiasing stage, to estimate parameters and establish theoretical convergence rates for the estimators. Additionally, if the informative source data are unknown, we propose a transferable source detection algorithm using spatial residual bootstrap to maintain spatial dependence and derive its detection consistency. Simulation studies show our algorithm substantially improves the classical two-stage least squares estimator. We demonstrate our method's effectiveness in predicting outcomes in U.S. presidential swing states, where it outperforms traditional methods. In addition, our tranSAR model predicts that the Democratic party will win the 2024 U.S. presidential election.

In this paper, we use the concepts of quantum entanglement and coherence to analyze the Unruh and anti-Unruh effects based on the model of Unruh-DeWitt detector. For the first time, we find that (i) the Unruh effect reduces quantum entanglement but enhances quantum coherence; (ii) the anti-Unruh effect enhances quantum entanglement but reduces quantum coherence. This surprising result refutes the notion that the Unruh effect can only destroy quantum entanglement and coherence simultaneously, and that the anti-Unruh can only protect quantum resources. Consequently, it opens up a new source for discovering experimental evidence supporting the existence of the Unruh and anti-Unruh effects., Comment: 12 pages, 2 figures

The Circular Electron Positron Collider (CEPC) has been proposed to enable more thorough and precise measurements of the properties of Higgs, W, and Z bosons, as well as to search for new physics. In response to the stringent performance requirements of the vertex detector for the CEPC, a baseline vertex detector prototype was tested and characterized for the first time using a 6 GeV electron beam at DESY II Test Beam Line 21. The baseline vertex detector prototype is designed with a cylindrical barrel structure that contains six double-sided detector modules (ladders). Each side of the ladder includes TaichuPix-3 sensors based on Monolithic Active Pixel Sensor (MAPS) technology, a flexible printed circuit, and a carbon fiber support structure. Additionally, the readout electronics and the Data Acquisition system were also examined during this beam test. The performance of the prototype was evaluated using an electron beam that passed through six ladders in a perpendicular direction. The offline data analysis indicates a spatial resolution of about 5 um, with detection efficiency exceeding 99 % and an impact parameter resolution of about 5.1 um. These promising results from this baseline vertex detector prototype mark a significant step toward realizing the optimal vertex detector for the CEPC.

Understanding the solidification dynamics of impacted water droplets is fundamental and crucial for applications, especially with the presence of frost and salt. Here, we experimentally investigate the spreading and freezing dynamics of saline droplets upon impact on a cold, frosty surface. Our findings demonstrate that the frost and salt can lead to an extended spreading of impacted droplets under specific conditions. In addition to the well-known 1/2 inertial spreading scaling law for droplets impact on a cold substrate, we observe a distinct transition to a 1/10 scaling law dominated by the capillary-viscous relation at low impacting velocities. We formulate the onset criterion for this extended spreading regime and derive a scaling dependence that captures the droplet's arrested diameter over various supercooling temperatures, by incorporating the effect of impact inertia, partial-wetting behavior and salinity. Based on the analysis, a unified model is proposed for predicting the droplet arrested diameter over a wide range of impact velocity and salinity., Comment: 10 pages, 10 figures

Manipulating radiation asymmetry of photonic structures is of particular interest in many photonic applications such as directional optical antenna, high efficiency on-chip lasers, and coherent light control. Here, we proposed a term of pseudo-polarization to reveal topological nature of radiation asymmetry in bilayer metagratings. Robust pseudo-polarization vortex with an integer topological charge exists in P-symmetry metagrating, allowing for tunable directionality ranging from -1 to 1 in synthetic parameter space. When P-symmetry-breaking, such vortex becomes pairs of C points due to the conservation law of charge, leading to the phase difference of radiation asymmetry from {\pi}/2 to 3{\pi}/2. Furthermore, topologically enabled coherent perfect absorption is robust with customized phase difference at will between two counter-propagating external light sources. This work can not only enrich the understanding of two particular topological photonic behavriors, i.e., bound state in the continuum and unidirectional guided resonance, but also provide a topological view on radiation asymmetry, opening an unexplored avenue for asymmetric light manipulation in on-chip laser, light-light switch and quantum emitters.

The proposed Circular Electron Positron Collider (CEPC) imposes new challenges for the vertex detector in terms of pixel size and material budget. A Monolithic Active Pixel Sensor (MAPS) prototype called TaichuPix, based on a column drain readout architecture, has been developed to address the need for high spatial resolution. In order to evaluate the performance of the TaichuPix-3 chips, a beam test was carried out at DESY II TB21 in December 2022. Meanwhile, the Data Acquisition (DAQ) for a muti-plane configuration was tested during the beam test. This work presents the characterization of the TaichuPix-3 chips with two different processes, including cluster size, spatial resolution, and detection efficiency. The analysis results indicate the spatial resolution better than 5 $\mu m$ and the detection efficiency exceeds 99.5 % for both TaichuPix-3 chips with the two different processes.

Here, we study quantum coherence of N-partite GHZ (Greenberger-Horne-Zeilinger) and W states in the multiverse consisting of N causally disconnected de Sitter spaces. Interestingly, N-partite coherence increases monotonically as the curvature increases, while the Unruh effect destroys multipartite coherence in Rindler spacetime. Conversely, the curvature effect destroys quantum entanglement and discord, meaning that the curvature effect is beneficial to quantum coherence and harmful to quantum correlations in the multiverse. We find that, with the increase of n expanding de Sitter spaces, N-partite coherence of GHZ state increases monotonically for any curvature, while quantum coherence of the W state decreases or increases monotonically depending on the curvature. We find a distribution relationship, which indicates that the correlated coherence of N-partite W state is equal to the sum of all bipartite correlated coherence in the multiverse. Multipartite coherence exhibits unique properties in the multiverse, which argues that it may provide some evidence for the existence of the multiverse., Comment: 26 pages, 8 figures

Hypothesis: Surfactant-free microemulsion (SFME), an emerging phenomenology that occurs in the monophasic zone of a broad category of ternary mixtures 'hydrophobe/hydrotrope/water', has attracted extensive interests due to their unique physicochemical properties. The potential of this kind of ternary fluid for solubilization and drug delivery make them promising candidates in many industrial scenarios. Experiments: Here the thermodynamic behavior of these multiscale nanodomains formed in the ternary trans-anethol/ethanol/water system over a wide range of temperatures is explored. The macroscopic physical properties of the ternary solutions are characterized, with revealing the temperature dependence of refractive index and dynamic viscosity. Findings: With increasing temperature, the ternary system shows extended areas in the monophasic zone. We demonstrate that the phase behavior and the multiscale nanodomains formed in the monophasic zone can be precisely and reversibly tuned by altering the temperature. Increasing temperature can destroy the stability of the multiscale nanodomains in equilibrium, with an exponential decay in the scattering light intensity. Nevertheless, molecular-scale aggregates and mesoscopic droplets exhibit significantly different response behaviors to temperature stimuli. The temperature-sensitive nature of the ternary SFME system provides a crucial step forward exploring and industrializing its stability.

This work proposes a novel face-swapping framework FlowFace++, utilizing explicit semantic flow supervision and end-to-end architecture to facilitate shape-aware face-swapping. Specifically, our work pretrains a facial shape discriminator to supervise the face swapping network. The discriminator is shape-aware and relies on a semantic flow-guided operation to explicitly calculate the shape discrepancies between the target and source faces, thus optimizing the face swapping network to generate highly realistic results. The face swapping network is a stack of a pre-trained face-masked autoencoder (MAE), a cross-attention fusion module, and a convolutional decoder. The MAE provides a fine-grained facial image representation space, which is unified for the target and source faces and thus facilitates final realistic results. The cross-attention fusion module carries out the source-to-target face swapping in a fine-grained latent space while preserving other attributes of the target image (e.g. expression, head pose, hair, background, illumination, etc). Lastly, the convolutional decoder further synthesizes the swapping results according to the face-swapping latent embedding from the cross-attention fusion module. Extensive quantitative and qualitative experiments on in-the-wild faces demonstrate that our FlowFace++ outperforms the state-of-the-art significantly, particularly while the source face is obstructed by uneven lighting or angle offset., Comment: arXiv admin note: text overlap with arXiv:2212.02797

Hypothesis: Multi-component droplet evaporation has received significant attention in recent years due to the broad range of applications such as material science, environmental monitoring, and pharmaceuticals. The selective evaporation induced by the different physicochemical properties of components is expected to influence the concentration distributions and the separation of mixtures, thereby leading to rich interfacial phenomena and phase interactions. Experiments: A ternary mixture system containing hexadecane, ethanol, and diethyl ether is investigated in this study. The diethyl ether exhibits both surfactant-like and co-solvent properties. Systematic experiments were performed using acoustic levitation technique to achieve a contact-less evaporation condition. The evaporation dynamics and temperature information are acquired in the experiments, using high-speed photography and infrared thermography technologies. Findings: Three distinct stages, namely, "Ouzo state", "Janus state", and "Encapsulating state", are identified for the evaporating ternary droplet in acoustic levitation. A self-sustaining periodic freezing & melting evaporation mode is reported. A theoretical model is developed to characterize the multi-stage evaporating behaviors. We demonstrate the capability to tune the evaporating behaviors by varying the initial droplet composition. This work provides a deeper understanding of the interfacial dynamics and phase transitions involved in multi-component droplets and proposes novel strategies for the design and control of droplet-based systems

We investigate experimentally and theoretically the interactions between a cavitation bubble and a hemispherical pendant oil droplet immersed in water. In experiments, the cavitation bubble is generated by a focused laser pulse right below the pendant droplet with well-controlled bubble-wall distances and bubble-droplet size ratios. By high-speed imaging, four typical interactions are observed, namely, oil droplet rupture, water droplet entrapment, oil droplet large deformation, and oil droplet mild deformation. The bubble jetting at the end of collapse and the migration of the bubble centroid are particularly different in each bubble-droplet interaction. We propose theoretical models based on the method of images for calculating the Kelvin impulse and the anisotropy parameter which quantitatively reflects the migration of the bubble centroid at the end of the collapse. Finally, we explain that a combination of the Weber number and the anisotropy parameter determines the regimes of the bubble-droplet interactions., Comment: 26 pages, 14 figures

Previous studies have shown that the Hawking effect always destroys quantum correlations and the fidelity of quantum teleportation in the Schwarzschild black hole. Here, we investigate the fidelity of quantum teleportation of Dirac fields between users in Schwarzschild spacetime. We find that, with the increase of the Hawking temperature, the fidelity of quantum teleportation can monotonically increases, monotonically decreases, or non-monotonically increases, depending on the choice of the initial state, which means that the Hawking effect can create net fidelity of quantum teleportation. This striking result banishes the extended belief that the Hawking effect of the black hole can only destroy the fidelity of quantum teleportation. We also find that quantum steering cannot fully guarantee the fidelity of quantum teleportation in Schwarzschild spacetime. This new unexpected source may provide a new idea for the experimental evidence of the Hawking effect., Comment: 16 pages, 3 figures

We propose the concept of pair-wise coherence to study the relation between the l1 norm of coherence and the quantum interference in Young's multi-photon multi-path experiments, where the input photons may be entangled each other. We find that only the local coherence of each single photon can make quantum interference and the collective coherence between photons has no contribution to quantum interference. The visibility of interference fringe is commonly less than the l1norm of coherence of the corresponding input state, suggesting that the l1 norm of coherence is only the necessary but not sufficient condition for quantum interference. We also find that the maximal fringe visibility can reach one. The optimal input states for producing the maximal visibility are presented., Comment: 12 pages, 2 figures

We study the genuine tripartite entanglement (GTE), one-tangle and two-tangle of W state of fermionic fields in the background of a Schwarzschild black hole. We find that, with the increase of the Hawking temperature, the GTE of W state first decreases and then tends to zero, while the GTE of GHZ state first decreases and then freezes. We also find that the Hawking effect can completely destroy the two-tangle of W state, whilie one-tangle first decreases and then appears freezing phenomenon with the growth of the Hawking temperature. These results are helpful to guide us to select appropriate quantum states and quantum resources to deal with relativistic quantum information tasks., Comment: 7 pages, 4 figures

We study Gaussian quantum steering in the Schwarzschild-de Sitter (SdS) spacetime that is endowed with both a black hole event horizon (BEH) and a cosmological event horizon (CEH), giving rise to two different Hawking temperatures. It is shown that the Hawking effect of the black hole always reduces the quantum steering, but the Hawking effect of the expanding universe does not always play the same role. For the first time, we find that the Hawking effect can improve quantum steering. We also find that the observer who locates in the BEH has stronger steerability than the observer who locates in CEH. Further, we study the steering asymmetry, and the conditions for two-way, one-way and no-way steering in the SdS spacetime. Finally, we study the Gaussian quantum steering in the scenario of effective equilibrium temperature. We show that quantum steering reduces monotonically with the effective temperature but now increases monotonically with the Hawking temperature of the black hole, which banishes the belief that the Hawking effect can only destroy quantum steering., Comment: 21 pages, 7 figures

In this work, we propose a semantic flow-guided two-stage framework for shape-aware face swapping, namely FlowFace. Unlike most previous methods that focus on transferring the source inner facial features but neglect facial contours, our FlowFace can transfer both of them to a target face, thus leading to more realistic face swapping. Concretely, our FlowFace consists of a face reshaping network and a face swapping network. The face reshaping network addresses the shape outline differences between the source and target faces. It first estimates a semantic flow (i.e., face shape differences) between the source and the target face, and then explicitly warps the target face shape with the estimated semantic flow. After reshaping, the face swapping network generates inner facial features that exhibit the identity of the source face. We employ a pre-trained face masked autoencoder (MAE) to extract facial features from both the source face and the target face. In contrast to previous methods that use identity embedding to preserve identity information, the features extracted by our encoder can better capture facial appearances and identity information. Then, we develop a cross-attention fusion module to adaptively fuse inner facial features from the source face with the target facial attributes, thus leading to better identity preservation. Extensive quantitative and qualitative experiments on in-the-wild faces demonstrate that our FlowFace outperforms the state-of-the-art significantly.

Expressions and facial action units (AUs) are two levels of facial behavior descriptors. Expression auxiliary information has been widely used to improve the AU detection performance. However, most existing expression representations can only describe pre-determined discrete categories (e.g., Angry, Disgust, Happy, Sad, etc.) and cannot capture subtle expression transformations like AUs. In this paper, we propose a novel fine-grained \textsl{Global Expression representation Encoder} to capture subtle and continuous facial movements, to promote AU detection. To obtain such a global expression representation, we propose to train an expression embedding model on a large-scale expression dataset according to global expression similarity. Moreover, considering the local definition of AUs, it is essential to extract local AU features. Therefore, we design a \textsl{Local AU Features Module} to generate local facial features for each AU. Specifically, it consists of an AU feature map extractor and a corresponding AU mask extractor. First, the two extractors transform the global expression representation into AU feature maps and masks, respectively. Then, AU feature maps and their corresponding AU masks are multiplied to generate AU masked features focusing on local facial region. Finally, the AU masked features are fed into an AU classifier for judging the AU occurrence. Extensive experiment results demonstrate the superiority of our proposed method. Our method validly outperforms previous works and achieves state-of-the-art performances on widely-used face datasets, including BP4D, DISFA, and BP4D+.

Using two different types of quantification for quantum steering, we study the influence of Hawking radiation on quantum steering for fermionic fields in Schwarzschild spacetime. The degradation for the steering between physically accessible observers and the generation for the steering between physically accessible and inaccessible observers induced by Hawking radiation are studied. We also reveal the difference between the two types of quantification for steering, and find some monogamy relations between steering and entanglement. Furthermore, we show the different properties between fermionic steering and bosonic steering in Schwarzschild spacetime., Comment: 22 pages, 3 figures