Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Learning for Cross-Domain Few-Shot Visual Recognition: A Survey

Deep learning has been highly successful in computer vision with large amounts of labeled data, but struggles with limited labeled training data. To address this, Few-shot learning (FSL) is proposed, but it assumes that all samples (including source and target task data, where target tasks are performed with prior knowledge from source ones) are from the same domain, which is a stringent assumption in the real world. To alleviate this limitation, Cross-domain few-shot learning (CDFSL) has gained attention as it allows source and target data from different domains and label spaces. This paper provides a comprehensive review of CDFSL at the first time, which has received far less attention than FSL due to its unique setup and difficulties. We expect this paper to serve as both a position paper and a tutorial for those doing research in CDFSL. This review first introduces the definition of CDFSL and the issues involved, followed by the core scientific question and challenge. A comprehensive review of validated CDFSL approaches from the existing literature is then presented, along with their detailed descriptions based on a rigorous taxonomy. Furthermore, this paper outlines and discusses several promising directions of CDFSL that deserve further scientific investigation, covering aspects of problem setups, applications and theories.
A boundary-layer analysis of Rayleigh-Bénard convection at large Rayleigh number

A boundary-layer analysis is presented for the two-dimensional nonlinear convection of an infinite-Prandtl-number fluid in a rectangular enclosure, in the limit of large Rayleigh numbers. Particular emphasis is given to the analysis of the periodic boundary layers, and on the removal of the singularities that appear near the corners of the cell. It is argued that this later step is necessary to ensure the correctness of the boundary-layer assumptions. Numerical values are obtained for the heat transfer and stress characteristics of the flow.
PLEX: Making the Most of the Available Data for Robotic Manipulation Pretraining

A rich representation is key to general robotic manipulation, but existing model architectures require a lot of data to learn it. Unfortunately, ideal robotic manipulation training data, which comes in the form of expert visuomotor demonstrations for a variety of annotated tasks, is scarce. In this work we propose PLEX, a transformer-based architecture that learns from task-agnostic visuomotor trajectories accompanied by a much larger amount of task-conditioned object manipulation videos -- a type of robotics-relevant data available in quantity. The key insight behind PLEX is that the trajectories with observations and actions help induce a latent feature space and train a robot to execute task-agnostic manipulation routines, while a diverse set of video-only demonstrations can efficiently teach the robot how to plan in this feature space for a wide variety of tasks. In contrast to most works on robotic manipulation pretraining, PLEX learns a generalizable sensorimotor multi-task policy, not just an observational representation. We also show that using relative positional encoding in PLEX's transformers further increases its data efficiency when learning from human-collected demonstrations. Experiments showcase \appr's generalization on Meta-World-v2 benchmark and establish state-of-the-art performance in challenging Robosuite environments.
Hall effect thruster design via deep neural network for additive manufacturing

Hall effect thrusters are one of the most versatile and popular electric propulsion systems for space use. Industry trends towards interplanetary missions arise advances in design development of such propulsion systems. It is understood that correct sizing of discharge channel in Hall effect thruster impact performance greatly. Since the complete physics model of such propulsion system is not yet optimized for fast computations and design iterations, most thrusters are being designed using so-called scaling laws. But this work focuses on rather novel approach, which is outlined less frequently than ordinary scaling design approach in literature. Using deep machine learning it is possible to create predictive performance model, which can be used to effortlessly get design of required hall thruster with required characteristics using way less computational power than design from scratch and way more flexible than usual scaling approach.
Communication-Efficient Design for Quantized Decentralized Federated Learning

Decentralized federated learning (DFL) is a variant of federated learning, where edge nodes only communicate with their one-hop neighbors to learn the optimal model. However, as information exchange is restricted in a range of one-hop in DFL, inefficient information exchange leads to more communication rounds to reach the targeted training loss. This greatly reduces the communication efficiency. In this paper, we propose a new non-uniform quantization of model parameters to improve DFL convergence. Specifically, we apply the Lloyd-Max algorithm to DFL (LM-DFL) first to minimize the quantization distortion by adjusting the quantization levels adaptively. Convergence guarantee of LM-DFL is established without convex loss assumption. Based on LM-DFL, we then propose a new doubly-adaptive DFL, which jointly considers the ascending number of quantization levels to reduce the amount of communicated information in the training and adapts the quantization levels for non-uniform gradient distributions. Experiment results based on MNIST and CIFAR-10 datasets illustrate the superiority of LM-DFL with the optimal quantized distortion and show that doubly-adaptive DFL can greatly improve communication efficiency.
Spatial causal inference in the presence of unmeasured confounding and interference

This manuscript bridges the divide between causal inference and spatial statistics, presenting novel insights for causal inference in spatial data analysis, and establishing how tools from spatial statistics can be used to draw causal inferences. We introduce spatial causal graphs to highlight that spatial confounding and interference can be entangled, in that investigating the presence of one can lead to wrongful conclusions in the presence of the other. Moreover, we show that spatial dependence in the exposure variable can render standard analyses invalid, which can lead to erroneous conclusions. To remedy these issues, we propose a Bayesian parametric approach based on tools commonly-used in spatial statistics. This approach simultaneously accounts for interference and mitigates bias resulting from local and neighborhood unmeasured spatial confounding. From a Bayesian perspective, we show that incorporating an exposure model is necessary, and we theoretically prove that all model parameters are identifiable, even in the presence of unmeasured confounding. To illustrate the approach's effectiveness, we provide results from a simulation study and a case study involving the impact of sulfur dioxide emissions from power plants on cardiovascular mortality.
An alternative approach to optimal wire cutting without ancilla qubits

Wire cutting is a technique for partitioning large quantum circuits into smaller subcircuits in such a way that observables for the original circuits can be estimated from measurements on the smaller subcircuits. Such techniques provide workarounds for the limited numbers of qubits that are available on near-term quantum devices. Wire cutting, however, introduces multiplicative factors in the number of times such subcircuits need to be executed in order to estimate desired quantum observables to desired levels of statistical accuracy. An optimal wire-cutting methodology has recently been reported that uses ancilla qubits to minimize the multiplicative factors involved as a function of the number of wire cuts. Until just recently, the best-known wire-cutting technique that did not employ ancillas asymptotically converged to the same multiplicative factors, but performed significantly worse for small numbers of cuts. This latter technique also requires inserting measurement and state-preparation subcircuits that are randomly sampled from Clifford 2-designs on a per-shot basis. This paper presents a modified wire-cutting approach for pairs of subcircuits that achieves the same optimal multiplicative factors as wire cutting aided by ancilla qubits, but without requiring ancillas. The paper also shows that, while unitary 2-designs provide a sufficient basis for satisfying the decomposition, 2-designs are not mathematically necessary and alternative unitary designs can be constructed for the decompositions that are substantially smaller in size than 2-designs. As this paper was just about to be released, a similar result was published, so we also include a comparison of the two approaches.
Comparative Evaluation of Data Decoupling Techniques for Federated Machine Learning with Database as a Service

Federated Learning (FL) is a machine learning approach that allows multiple clients to collaboratively learn a shared model without sharing raw data. However, current FL systems provide an all-in-one solution, which can hinder the wide adoption of FL in certain domains such as scientific applications. To overcome this limitation, this paper proposes a decoupling approach that enables clients to customize FL applications with specific data subsystems. To evaluate this approach, the authors develop a framework called Data-Decoupling Federated Learning (DDFL) and compare it with state-of-the-art FL systems that tightly couple data management and computation. Extensive experiments on various datasets and data management subsystems show that DDFL achieves comparable or better performance in terms of training time, inference accuracy, and database query time. Moreover, DDFL provides clients with more options to tune their FL applications regarding data-related metrics. The authors also provide a detailed qualitative analysis of DDFL when integrated with mainstream database systems.
Coordinating Fully-Cooperative Agents Using Hierarchical Learning Anticipation

Learning anticipation is a reasoning paradigm in multi-agent reinforcement learning, where agents, during learning, consider the anticipated learning of other agents. There has been substantial research into the role of learning anticipation in improving cooperation among self-interested agents in general-sum games. Two primary examples are Learning with Opponent-Learning Awareness (LOLA), which anticipates and shapes the opponent's learning process to ensure cooperation among self-interested agents in various games such as iterated prisoner's dilemma, and Look-Ahead (LA), which uses learning anticipation to guarantee convergence in games with cyclic behaviors. So far, the effectiveness of applying learning anticipation to fully-cooperative games has not been explored. In this study, we aim to research the influence of learning anticipation on coordination among common-interested agents. We first illustrate that both LOLA and LA, when applied to fully-cooperative games, degrade coordination among agents, causing worst-case outcomes. Subsequently, to overcome this miscoordination behavior, we propose Hierarchical Learning Anticipation (HLA), where agents anticipate the learning of other agents in a hierarchical fashion. Specifically, HLA assigns agents to several hierarchy levels to properly regulate their reasonings. Our theoretical and empirical findings confirm that HLA can significantly improve coordination among common-interested agents in fully-cooperative normal-form games. With HLA, to the best of our knowledge, we are the first to unlock the benefits of learning anticipation for fully-cooperative games.
X-ray optics simulation and beamline design using a hybrid method: diffraction-limited focusing mirrors

A hybrid method combining ray-tracing and wavefront propagation was recently developed for X-ray optics simulation and beamline design optimization. One major application of the hybrid method is its ability to assess the effects of figure errors on the performance of focusing mirrors. In the present work, focusing profiles of mirrors with different figure errors are simulated using three available wave optics methods: the hybrid code based on the Fourier optics approach, the stationary phase approximation and a technique based on the direct Fresnel-Kirchhoff diffraction integral. The advantages and limitations of each wave optics method are discussed. We also present simulations performed using the figure errors of an elliptical cylinder mirror measured at APS using microstitching interferometry. These results show that the hybrid method provides accurate and quick evaluation of the expected mirror performance making it a useful tool for designing diffraction-limited focusing beamlines.
Probabilistic forecasting with Factor Quantile Regression: Application to electricity trading

This paper presents a novel approach for constructing probabilistic forecasts, which combines both the Quantile Regression Averaging (QRA) method and the Principal Component Analysis (PCA) averaging scheme. The performance of the approach is evaluated on datasets from two European energy markets - the German EPEX SPOT and the Polish Power Exchange (TGE). The results indicate that newly proposed solutions yield results, which are more accurate than the literature benchmarks. Additionally, empirical evidence indicates that the proposed method outperforms its competitors in terms of the empirical coverage and the Christoffersen test. In addition, the economic value of the probabilistic forecast is evaluated on the basis of financial metrics. We test the performance of forecasting models taking into account a day-ahead market trading strategy that utilizes probabilistic price predictions and an energy storage system. The results indicate that profits of up to 10 EUR per 1 MWh transaction can be obtained when predictions are generated using the novel approach.
The Imprint of Clump Formation at High Redshift. II. The Chemistry of the Bulge

In Paper I, we showed that clumps in high-redshift galaxies, having a high star formation rate density (ΣSFR), produce disks with two tracks in the [Fe/H]–[α/Fe] chemical space, similar to that of the Milky Way’s (MW’s) thin+thick disks. Here we investigate the effect of clumps on the bulge’s chemistry. The chemistry of the MW’s bulge is comprised of a single track with two density peaks separated by a trough. We show that the bulge chemistry of an N-body + smoothed particle hydrodynamics clumpy simulation also has a single track. Star formation within the bulge is itself in the high-ΣSFR clumpy mode, which ensures that the bulge’s chemical track follows that of the thick disk at low [Fe/H] and then extends to high [Fe/H], where it peaks. The peak at low metallicity instead is comprised of a mixture of in situ stars and stars accreted via clumps. As a result, the trough between the peaks occurs at the end of the thick disk track. We find that the high-metallicity peak dominates near the mid-plane and declines in relative importance with height, as in the MW. The bulge is already rapidly rotating by the end of the clump epoch, with higher rotation at low [α/Fe]. Thus clumpy star formation is able to simultaneously explain the chemodynamic trends of the MW’s bulge, thin+thick disks, and the splash.
Bathymetry imposes a global pattern of cross-front transport in the Southern Ocean

The Southern Ocean plays an integral role in the global climate system, exchanging heat, salt, and carbon throughout the major ocean basins via the deep, fast-flowing Antarctic Circumpolar Current. The Antarctic Circumpolar Current is bounded by spatially and temporally varying fronts that partition distinct water masses. Locating and quantifying cross-front transport is crucial for understanding global patterns of inter-basin exchange; however, this is challenging because fronts are typically defined by hydrographic properties, such as temperature or salinity, which are subject to external sources and sinks, rather than by properties of the flow itself. Here we show that, when characterized by material contours that minimize deformation and cross-contour mixing, Southern Ocean fronts exhibit a global pattern of alternating poleward and equatorward transport caused by frontal meandering, which, in turn is influenced by prominent sea-floor obstacles. These results highlight the importance of bathymetric features in controlling Southern Ocean dynamics and inter-basin exchange.
How to measure work functions from aqueous solutions

The recent application of concepts from condensed-matter physics to photoelectron spectroscopy (PES) of volatile, liquid-phase systems has enabled the measurement of electronic energetics of liquids on an absolute scale. Particularly, vertical ionization energies, VIEs, of liquid water and aqueous solutions, both in the bulk and at associated interfaces, can now be accurately, precisely, and routinely determined. These IEs are referenced to the local vacuum level, which is the appropriate quantity for condensed matter with associated surfaces, including liquids. In this work, we connect this newly accessible energy level to another important surface property, namely, the solution work function, eΦliq. We lay out the prerequisites for and unique challenges of determining eΦ of aqueous solutions and liquids in general. We demonstrate – for a model aqueous solution with a tetra-n-butylammonium iodide (TBAI) surfactant solute – that concentration-dependent work functions, associated with the surface dipoles generated by the segregated interfacial layer of TBA+ and I− ions, can be accurately measured under controlled conditions. We detail the nature of surface potentials, uniquely tied to the nature of the flowing-liquid sample, which must be eliminated or quantified to enable such measurements. This allows us to refer aqueous-phase spectra to the Fermi level and to quantitatively assign surfactant-concentration-dependent spectral shifts to competing work function and electronic-structure effects, where the latter are typically associated with solute–solvent interactions in the bulk of the solution which determine, e.g., chemical reactivity. The present work describes the extension of liquid-jet PES to quantitatively access concentration-dependent surface descriptors that have so far been restricted to solid-phase measurements. Correspondingly, these studies mark the beginning of a new era in the characterization of the interfacial electronic structure of aqueous solutions and liquids more generally.
Skinned Motion Retargeting with Residual Perception of Motion Semantics & Geometry

A good motion retargeting cannot be reached without reasonable consideration of source-target differences on both the skeleton and shape geometry levels. In this work, we propose a novel Residual RETargeting network (R2ET) structure, which relies on two neural modification modules, to adjust the source motions to fit the target skeletons and shapes progressively. In particular, a skeleton-aware module is introduced to preserve the source motion semantics. A shape-aware module is designed to perceive the geometries of target characters to reduce interpenetration and contact-missing. Driven by our explored distance-based losses that explicitly model the motion semantics and geometry, these two modules can learn residual motion modifications on the source motion to generate plausible retargeted motion in a single inference without postprocessing. To balance these two modifications, we further present a balancing gate to conduct linear interpolation between them. Extensive experiments on the public dataset Mixamo demonstrate that our R2ET achieves the state-of-the-art performance, and provides a good balance between the preservation of motion semantics as well as the attenuation of interpenetration and contact-missing. Code is available at https://github.com/Kebii/R2ET.
A new invariant of equivariant concordance and results on 2-bridge knots

We study the equivariant concordance classes of two-bridge knots, providing an easy formula to compute their butterfly polynomial, and we give two different proofs that no two-bridge knot is equivariantly slice. Finally, we introduce a new invariant of equivariant concordance for strongly invertible knots. Using this invariant as an obstruction we strengthen the result on two-bridge knots, proving that their equivariant concordance order is always infinite.
Coexistence of superconductivity with partially filled stripes in the Hubbard model

Combining the complementary capabilities of two of the most powerful modern computational methods, we find superconductivity in both the electron- and hole-doped regimes of the two-dimensional Hubbard model (with next nearest neighbor hopping). In the electron-doped regime, superconductivity is weaker and is accompanied by antiferromagnetic N\'eel correlations at low doping. The strong superconductivity on the hole-doped side coexists with stripe order, which persists into the overdoped region with weaker hole density modulation. These stripe orders, neither filled as in the pure Hubbard model (no next nearest neighbor hopping) nor half-filled as seen in previous state-of-the-art calculations, vary in fillings between 0.6 and 0.8. The resolution of the tiny energy scales separating competing orders requires exceedingly high accuracy combined with averaging and extrapolating with a wide range of system sizes and boundary conditions. These results validate the applicability of this iconic model for describing cuprate high-$T_c$ superconductivity.
Destructive interactions between two counter-rotating quasi-geostrophic vortices

This paper illustrates the linear stability and the nonlinear evolution of two opposite-signed quasi-geostrophic vortices. We investigate the influence of the volume ratio between the two vortices as well as the influence of their vertical offset. Instability is always found for sufficiently close vortices. A convenient measure of the separation distance between the two vortices at their margin of stability is the horizontal gap between their two outermost edges. When the vortex volume ratio is very close to unity, the critical gap at the margin of stability tends to increase with the vertical offset. However, for volume ratios greater than 1.1, it decreases with the vertical offset. This is due to differences in the magnitude of the tilt angle of the vortices. The nonlinear evolution of unstable equilibria tends to be destructive, often breaking one vortex or both vortices into smaller vortices.
Evolution of a Stratified Turbulent Cloud under Rotation

Localized turbulence is common in geophysical flows, where the roles of rotation and stratification are paramount. In this study, we investigate the evolution of a stratified turbulent cloud under rotation. Recognizing that a turbulent cloud is composed of vortices of varying scales and shapes, we start our investigation with a single eddy using analytical solutions derived from a linearized system. Compared to an eddy under pure rotation, the stratified eddy shows the physical manifestation of a known potential vorticity mode, appearing as a static stable vortex. In addition, the expected shift from inertial waves to inertial-gravity waves is observed. In our numerical simulations of the turbulent cloud, carried out at a constant Rossby number over a range of Froude numbers, stratification causes columnar structures to deviate from vertical alignment. This deviation increases with increasing stratification, slowing the expansion rate of the cloud. The observed characteristics of these columnar structures are consistent with the predictions of linear theory, particularly in their tilt angles and vertical growth rates, suggesting a significant influence of inertial-gravity waves. Using Lagrangian particle tracking, we have identified regions where wave activity dominates over turbulence. In scenarios of milder stratification, these inertial-gravity waves are responsible for a significant energy transfer away from the turbulent cloud, a phenomenon that attenuates with increasing stratification.