Miracle-dz/UniFAR-FTUs huggingface dataset:
Ultrasound Plane Pose Regression: Assessing Generalized Pose Coordinates in the Fetal Brain

In obstetric ultrasound (US) scanning, the learner’s ability to mentally build a three-dimensional (3D) map of the fetus from a two-dimensional (2D) US image represents a significant challenge in skill acquisition. We aim to build a US plane localization system for 3D visualization, training, and guidance without integrating additional sensors. This work builds on top of our previous work, which predicts the six-dimensional (6D) pose of arbitrarily oriented US planes slicing the fetal brain with respect to a normalized reference frame using a convolutional neural network (CNN) regression network. Here, we analyze in detail the assumptions of the normalized fetal brain reference frame and quantify its accuracy with respect to the acquisition of transventricular (TV) standard plane (SP) for fetal biometry. We investigate the impact of registration quality in the training and testing data and its subsequent effect on trained models. Finally, we introduce data augmentations and larger training sets that improve the results of our previous work, achieving median errors of $2.97~mm$ and 6.63° for translation and rotation, respectively.
Sub-Shot-Noise Light Sources: A Quiet Revolution in Light Control

Abstract A review of current research trends in the control and measurement of the statistical properties of light is given. This review is limited to squeezing, sub-Poisson light, and methods for measuring properties of the resulting stabilized light. Basic descriptions of the statistical properties of light, electronic noise, and behavior are given. The results are used to illustrate how the unique properties of these optical sources and detection techniques may be used to improve the precision of the analytical measurement.
Rich Action-Semantic Consistent Knowledge for Early Action Prediction

Early action prediction (EAP) aims to recognize human actions from a part of action execution in ongoing videos, which is an important task for many practical applications. Most prior works treat partial or full videos as a whole, ignoring rich action knowledge hidden in videos, i.e., semantic consistencies among different partial videos. In contrast, we partition original partial or full videos to form a new series of partial videos and mine the Action-Semantic Consistent Knowledge (ASCK) among these new partial videos evolving in arbitrary progress levels. Moreover, a novel Rich Action-semantic Consistent Knowledge network (RACK) under the teacher-student framework is proposed for EAP. Firstly, we use a two-stream pre-trained model to extract features of videos. Secondly, we treat the RGB or flow features of the partial videos as nodes and their action semantic consistencies as edges. Next, we build a bi-directional semantic graph for the teacher network and a single-directional semantic graph for the student network to model rich ASCK among partial videos. The MSE and MMD losses are incorporated as our distillation loss to enrich the ASCK of partial videos from the teacher to the student network. Finally, we obtain the final prediction by summering the logits of different subnetworks and applying a softmax layer. Extensive experiments and ablative studies have been conducted, demonstrating the effectiveness of modeling rich ASCK for EAP. With the proposed RACK, we have achieved state-of-the-art performance on three benchmarks. The code is available at https://github.com/lily2lab/RACK.git.
Satellite Clustering for Non-Terrestrial Networks: Concept, Architectures, and Applications

Recently, megaconstellations with a massive number of low Earth orbit (LEO) satellites are being considered as a possible solution for providing global coverage due to relatively low latency and high throughput compared to geosynchronous orbit (GEO) satellites. However, as the number of satellites and operators participating in the LEO constellation increases, inter-satellite interference will become more severe, which may yield marginal improvement or even decrement in network throughput. In this article, we introduce the concept of satellite clusters that can enhance network performance through satellites’ cooperative transmissions. The characteristics, formation types, and transmission schemes for the satellite clusters are highlighted. Simulation results evaluate the impact of clustering from coverage and capacity perspectives, showing that when the number of satellites is large, the performance of clustered networks outperforms the unclustered ones. The viable network architectures of the satellite cluster are proposed based on the 3rd Generation Partnership Project (3GPP) standard. Finally, the future applications of clustered satellite networks are discussed.
Machine Translation for Accessible Multi-Language Text Analysis

English is the international standard of social research, but scholars are increasingly conscious of their responsibility to meet the need for scholarly insight into communication processes globally. This tension is as true in computational methods as any other area, with revolutionary advances in the tools for English language texts leaving most other languages far behind. In this paper, we aim to leverage those very advances to demonstrate that multi-language analysis is currently accessible to all computational scholars. We show that English-trained measures computed after translation to English have adequate-to-excellent accuracy compared to source-language measures computed on original texts. We show this for three major analytics—sentiment analysis, topic analysis, and word embeddings—over 16 languages, including Spanish, Chinese, Hindi, and Arabic. We validate this claim by comparing predictions on original language tweets and their backtranslations: double translations from their source language to English and back to the source language. Overall, our results suggest that Google Translate, a simple and widely accessible tool, is effective in preserving semantic content across languages and methods. Modern machine translation can thus help computational scholars make more inclusive and general claims about human communication.
The Chromosphere Underneath a Coronal Bright Point

Coronal bright points (CBPs) are sets of small-scale coronal loops, connecting opposite magnetic polarities, primarily characterized by their enhanced extreme-ultraviolet (EUV) and X-ray emission. Being ubiquitous, they are thought to play an important role in heating the solar corona. We aim at characterizing the barely explored chromosphere underneath CBPs, focusing on the related spicular activity and on the effects of small-scale magnetic flux emergence on CBPs. We used high-resolution observations of a CBP in Hβ and Fe i 617.3 nm from the Swedish 1 m Solar Telescope in coordination with the Solar Dynamics Observatory. This work presents the first high-resolution observation of spicules imaged in Hβ. The spicules were automatically detected using advanced image processing techniques, which were applied to the Dopplergrams derived from Hβ. Here we report their abundant occurrence close to the CBP “footpoints” and find that the orientation of such spicules is aligned along the EUV loops, indicating that they constitute a fundamental part of the whole CBP magnetic structure. Spatiotemporal analysis across multiple channels indicates that there are coronal propagating disturbances associated with the studied spicules, producing transient EUV intensity variations of the individual CBP loops. Two small-scale flux emergence episodes appearing below the CBP were analyzed, one of them leading to quiet-Sun Ellerman bombs and enhancing the nearby spicular activity. This paper presents unique evidence of the tight coupling between the lower and upper atmosphere of a CBP, thus helping to unravel the dynamic phenomena underneath CBPs and their impact on the latter.
ARcode: HPC Application Recognition Through Image-encoded Monitoring Data

Knowing HPC applications of jobs and analyzing their performance behavior play important roles in system management and optimizations. The existing approaches detect and identify HPC applications through machine learning models. However, these approaches rely heavily on the manually extracted features from resource utilization data to achieve high prediction accuracy. In this study, we propose an innovative application recognition method, ARcode, which encodes job monitoring data into images and leverages the automatic feature learning capability of convolutional neural networks to detect and identify applications. Our extensive evaluations based on the dataset collected from a large-scale production HPC system show that ARcode outperforms the state-of-the-art methodology by up to 18.87% in terms of accuracy at high confidence thresholds. For some specific applications (BerkeleyGW and e3sm), ARcode outperforms by over 20% at a confidence threshold of 0.8.
Computational budget optimization for Bayesian parameter estimation in heavy-ion collisions

Bayesian parameter estimation provides a systematic approach to compare heavy ion collision models with measurements, leading to constraints on the properties of nuclear matter with proper accounting of experimental and theoretical uncertainties. Aside from statistical and systematic model uncertainties, interpolation uncertainties can also play a role in Bayesian inference, if the model’s predictions can only be calculated at a limited set of model parameters. This uncertainty originates from using an emulator to interpolate the model’s prediction across a continuous space of parameters. In this work, we study the trade-oﬀs between the emulator (interpolation) and statistical uncertainties. We perform the analysis using spatial eccentricities from the TRENTo model of initial conditions for nuclear collisions. Given a ﬁxed computational budget, we study the optimal compromise between the number of parameter samples and the number of collisions simulated per parameter sample. For the observables and parameters used in the present study, we ﬁnd that the best constraints are achieved when the number of parameter samples is slightly smaller than the number of collisions simulated per parameter sample.
State-constrained Optimization Problems under Uncertainty: A Tensor Train Approach

We propose an algorithm to solve optimization problems constrained by partial (ordinary) differential equations under uncertainty, with almost sure constraints on the state variable. To alleviate the computational burden of high-dimensional random variables, we approximate all random fields by the tensor-train decomposition. To enable efficient tensor-train approximation of the state constraints, the latter are handled using the Moreau-Yosida penalty, with an additional smoothing of the positive part (plus/ReLU) function by a softplus function. We derive theoretical bounds on the constraint violation in terms of the Moreau-Yosida regularization parameter and smoothing width of the softplus function. This result also proposes a practical recipe for selecting these two parameters. When the optimization problem is strongly convex, we establish strong convergence of the regularized solution to the optimal control. We develop a second order Newton type method with a fast matrix-free action of the approximate Hessian to solve the smoothed Moreau-Yosida problem. This algorithm is tested on benchmark elliptic problems with random coefficients, optimization problems constrained by random elliptic variational inequalities, and a real-world epidemiological model with 20 random variables. These examples demonstrate mild (at most polynomial) scaling with respect to the dimension and regularization parameters.
On the Relationship Between Information-Theoretic Privacy Metrics And Probabilistic Information Privacy

Information-theoretic (IT) measures based on $f$-divergences have recently gained interest as a measure of privacy leakage as they allow for trading off privacy against utility using only a single-value characterization. However, their operational interpretations in the privacy context are unclear. In this paper, we relate the notion of probabilistic information privacy (IP) to several IT privacy metrics based on $f$-divergences. We interpret probabilistic IP under both the detection and estimation frameworks and link it to differential privacy, thus allowing a precise operational interpretation of these IT privacy metrics. We show that the $\chi^2$-divergence privacy metric is stronger than those based on total variation distance and Kullback-Leibler divergence. Therefore, we further develop a data-driven empirical risk framework based on the $\chi^2$-divergence privacy metric and realized using deep neural networks. This framework is agnostic to the adversarial attack model. Empirical experiments demonstrate the efficacy of our approach.
FENDI: Toward High-Fidelity Entanglement Distribution in the Quantum Internet

A quantum network distributes quantum entanglements between remote nodes, and is key to many applications in secure communication, quantum sensing and distributed quantum computing. This paper explores the fundamental trade-off between the throughput and the quality of entanglement distribution in a multi-hop quantum repeater network. Compared to existing work which aims to heuristically maximize the entanglement distribution rate (EDR) and/or entanglement fidelity, our goal is to characterize the maximum achievable worst-case fidelity, while satisfying a bound on the maximum achievable expected EDR between an arbitrary pair of quantum nodes. This characterization will provide fundamental bounds on the achievable performance region of a quantum network, which can assist with the design of quantum network topology, protocols and applications. However, the task is highly non-trivial and is NP-hard as we shall prove. Our main contribution is a fully polynomial-time approximation scheme to approximate the achievable worst-case fidelity subject to a strict expected EDR bound, combining an optimal fidelity-agnostic EDR-maximizing formulation and a worst-case isotropic noise model. The EDR and fidelity guarantees can be implemented by a post-selection-and-storage protocol with quantum memories. By developing a discrete-time quantum network simulator, we conduct simulations to show the characterized performance region (the approximate Pareto frontier) of a network, and demonstrate that the designed protocol can achieve the performance region while existing protocols exhibit a substantial gap.
Low-energy hole subband dispersions in a cylindrical Ge nanowire: the effects of the nanowire growth direction

We examine the validity of the spherical approximation γs=(2γ2+3γ3)/5 in the Luttinger–Kohn Hamiltonian in calculating the subband dispersions of the hole gas. We calculate the realistic hole subband dispersions (without the spherical approximation) in a cylindrical Ge nanowire by using quasi-degenerate perturbation theory. The realistic low-energy hole subband dispersions have a double-well anticrossing structure, that consists with the spherical approximation prediction. However, the realistic subband dispersions are also nanowire growth direction dependent. When the nanowire growth direction is restricted in the (100) crystal plane, the detailed growth direction dependences of the subband parameters are given. We find the spherical approximation is good approximation, it can nicely reproduce the real result in some special growth directions.
Integrated Planning of Multi-energy Grids: Concepts and Challenges

In order to meet ever-stricter climate targets and achieve the eventual decarbonization of the energy supply of German industrial metropolises, the focus is on gradually phasing out nuclear power, then coal and gas combined with the increased use of renewable energy sources and employing hydrogen as a clean energy carrier. While complete electrification of the energy supply of households and the transportation sector may be the ultimate goal, a transitional phase is necessary as such massive as well as rapid expansion of the electrical distribution grid is infeasible. Additionally, German industries have expressed their plans to use hydrogen as their primary strategy in meeting carbon targets. This poses challenges to the existing electrical, gas, and heating distribution grids. It becomes necessary to integrate the planning and developing procedures for these grids to maximize efficiencies and guarantee security of supply during the transition. The aim of this paper is thus to highlight those challenges and present novel concepts for the integrated planning of the three grids as one multi-energy grid.
Alfv\'en wave experiments with liquid rubidium in a pulsed magnetic field

Magnetic fields are key ingredients for heating the solar corona to temperatures of several million Kelvin. A particularly important region with respect to this is the so-called magnetic canopy below the corona, where sound and Alfv\'en waves have roughly the same speed and can, therefore, easily transform into each other. We present the results of an Alfv\'en-wave experiment with liquid rubidium carried out in a pulsed field of up to 63 T. At the critical point of 54 T, where the speeds of Alfv\'en waves and sound coincide, a new 4 kHz signal appears in addition to the externally excited 8 kHz torsional wave. This emergence of an Alfv\'en wave with a doubled period is in agreement with the theoretical predictions of a parametric resonance between the two wave types. We also present preliminary results from numerical simulations of Alfv\'en and magneto-sonic waves using a compressible MHD code.
Improving Sketch Colorization using Adversarial Segmentation Consistency

We propose a new method for producing color images from sketches. Current solutions in sketch colorization either necessitate additional user instruction or are restricted to the"paired"translation strategy. We leverage semantic image segmentation from a general-purpose panoptic segmentation network to generate an additional adversarial loss function. The proposed loss function is compatible with any GAN model. Our method is not restricted to datasets with segmentation labels and can be applied to unpaired translation tasks as well. Using qualitative, and quantitative analysis, and based on a user study, we demonstrate the efficacy of our method on four distinct image datasets. On the FID metric, our model improves the baseline by up to 35 points. Our code, pretrained models, scripts to produce newly introduced datasets and corresponding sketch images are available at https://github.com/giddyyupp/AdvSegLoss.
An NDN-Enabled Fog Radio Access Network Architecture with Distributed In-Network Caching

To meet the increasing demands of next-generation cellular networks (e.g., 6G), advanced networking technologies must be incorporated. On one hand, the Fog Radio Access Network (F-RAN), has been proposed as an enhancement to the Cloud Radio Access Network (C-RAN). On the other hand, efficient network architectures, such as Named Data Networking (NDN), have been recognized as prominent Future Internet candidates. Nevertheless, the interplay between F-RAN and NDN warrants further investigation. In this paper, we propose an NDN-enabled F-RAN architecture featuring a strategy for distributed in-network caching. Through a simulation study, we demonstrate the superiority of the proposed in-network caching strategy in comparison with baseline caching strategies in terms of network resource utilization, cache hits, and fronthaul channel usage.
Revealing the supercritical dynamics of dusty plasmas and their liquidlike to gaslike dynamical crossover

Dusty plasmas represent a powerful playground to study the collective dynamics of strongly coupled systems with important interdisciplinary connections to condensed matter physics. Due to the pure Yukawa repulsive interaction between dust particles, dusty plasmas do not display a traditional liquid-vapor phase transition, perfectly matching the definition of a supercritical fluid. Using molecular dynamics simulations, we verify the supercritical nature of dusty plasmas and reveal the existence of a dynamical liquid-like to gas-like crossover which perfectly matches the salient features of the Frenkel line in classical supercritical fluids. We present several diagnostics to locate this dynamical crossover spanning from local atomic connectivity, shear relaxation dynamics, velocity autocorrelation function, heat capacity, and various transport properties. All these different criteria well agree with each other and are able to successfully locate the Frenkel line in both 2D and 3D dusty plasmas. In addition, we propose the unity ratio of the instantaneous transverse sound speed $C_T$ to the average particle speed $\bar{v}_{p}$, i.e., $C_T / \bar{v}_{p} = 1$, as a new diagnostic to identify this dynamical crossover. Finally, we observe an emergent degree of universality in the collective dynamics and transport properties of dusty plasmas as a function of the screening parameter and dimensionality of the system. Intriguingly, the temperature of the dynamical transition is independent of the dimensionality, and it is found to be always $20$ times of the corresponding melting point. Our results open a new path for the study of single particle and collective dynamics in plasmas and their interrelation with supercritical fluids in general.
Surface optical properties of clean Cu(110) and Cu(110)-(2×1)-O

The surface optical and electronical properties of Cu(110) surfaces were studied by using reflectance anisotropy spectroscopy (RAS) together with angle-resolved photoemission spectroscopy (ARUPS). On the clean surface, a structure in the optical spectra at 2.1 eV is assigned to transitions between occupied p-type and unoccupied s-type surface states occurring at the $\overline{Y}$ point of the surface Brillouin zone. Another structure at 4.2 eV is associated with a transition at the $\overline{X}$ point between a surface resonance (occupied) split off from the bulk d bands and a p-derived surface state (unoccupied). The oxygen-induced $(2\ifmmode\times\else\texttimes\fi{}1)$ surface exhibits a double peak in the reflectance anisotropy which can be explained partly by transitions from oxygen induced d- to empty p-derived states. Surface modified bulk d-states are responsible for parts of the features around 2 eV and features at higher energies.
Coupled Physics-informed Neural Networks for Inferring Solutions of Partial Differential Equations with Unknown Source Terms

—Physics-informed neural networks (PINNs) provide a transformative development for approximating the solutions to partial di ﬀ erential equations (PDEs). This work proposes a coupled physics-informed neural network (C-PINN) for the nonhomogeneous PDEs with unknown dynamical source terms, which is used to describe the systems with external forces and cannot be well approximated by the existing PINNs. In our method, two neural networks, NetU and NetG , are proposed. NetU is constructed to generate a quasi-solution satisfying PDEs under study. NetG is used to regularize the training of NetU . Then, the two networks are integrated into a data-physics-hybrid cost function. Finally, we propose a hierarchical training strategy to optimize and couple the two networks. The performance of C-PINN is proved by approximating several classical PDEs.