Miracle-dz/UniFAR-FTUs huggingface dataset:
Adversarial Feature Augmentation for Cross-domain Few-shot Classification

Existing methods based on meta-learning predict novel-class labels for (target domain) testing tasks via meta knowledge learned from (source domain) training tasks of base classes. However, most existing works may fail to generalize to novel classes due to the probably large domain discrepancy across domains. To address this issue, we propose a novel adversarial feature augmentation (AFA) method to bridge the domain gap in few-shot learning. The feature augmentation is designed to simulate distribution variations by maximizing the domain discrepancy. During adversarial training, the domain discriminator is learned by distinguishing the augmented features (unseen domain) from the original ones (seen domain), while the domain discrepancy is minimized to obtain the optimal feature encoder. The proposed method is a plug-and-play module that can be easily integrated into existing few-shot learning methods based on meta-learning. Extensive experiments on nine datasets demonstrate the superiority of our method for cross-domain few-shot classification compared with the state of the art. Code is available at https://github.com/youthhoo/AFA_For_Few_shot_learning
SoK: Explainable Machine Learning for Computer Security Applications

Explainable Artificial Intelligence (XAI) aims to improve the transparency of machine learning (ML) pipelines. We systematize the increasingly growing (but fragmented) microcosm of studies that develop and utilize XAI methods for defensive and offensive cybersecurity tasks. We identify 3 cybersecurity stakeholders, i.e., model users, designers, and adversaries, who utilize XAI for 4 distinct objectives within an ML pipeline, namely 1) XAI-enabled user assistance, 2) XAI-enabled model verification, 3) explanation verification & robustness, and 4) offensive use of explanations. Our analysis of the literature indicates that many of the XAI applications are designed with little understanding of how they might be integrated into analyst workflows – user studies for explanation evaluation are conducted in only 14% of the cases. The security literature sometimes also fails to disentangle the role of the various stakeholders, e.g., by providing explanations to model users and designers while also exposing them to adversaries. Additionally, the role of model designers is particularly minimized in the security literature. To this end, we present an illustrative tutorial for model designers, demonstrating how XAI can help with model verification. We also discuss scenarios where interpretability by design may be a better alternative. The systematization and the tutorial enable us to challenge several assumptions, and present open problems that can help shape the future of XAI research within cybersecurity.
Individual Tree Detection in Large-Scale Urban Environments using High-Resolution Multispectral Imagery

We introduce a novel deep learning method for detection of individual trees in urban environments using high-resolution multispectral aerial imagery. We use a convolutional neural network to regress a confidence map indicating the locations of individual trees, which are localized using a peak finding algorithm. Our method provides complete spatial coverage by detecting trees in both public and private spaces, and can scale to very large areas. We performed a thorough evaluation of our method, supported by a new dataset of over 1,500 images and almost 100,000 tree annotations, covering eight cities, six climate zones, and three image capture years. We trained our model on data from Southern California, and achieved a precision of 73.6% and recall of 73.3% using test data from this region. We generally observed similar precision and slightly lower recall when extrapolating to other California climate zones and image capture dates. We used our method to produce a map of trees in the entire urban forest of California, and estimated the total number of urban trees in California to be about 43.5 million. Our study indicates the potential for deep learning methods to support future urban forestry studies at unprecedented scales.
Reconstructing the Lost Eclipse Events of the Saros Spiral Applying the Draconic Gearing on the Antikythera Mechanism - the Impact of the Gearing Errors on the Pointers’ Position

We present new observations concerning the procedure for the reconstruction of the lost eclipse events engraved in the Saros spiral cells of the Antikythera Mechanism. For the reconstructed eclipse events we applied the assumed, albeit missing, Draconic gearing of the Antikythera Mechanism, which was probably a part of the Mechanism gearing, representing the fourth lunar motion, the Draconic cycle. The Draconic gearing is very critical for the eclipse prediction and defines whether an eclipse will occur. For our research we created a program which presents the phase of the four lunar cycles and the position of the Draconic pointer relative to the ecliptic limits. After calibrating the software according to the preserved eclipse events, the lost eclipse events of the Saros spiral were calculated and discussed. The procedure for the calculation of the events times by using solely the Mechanism is also presented. The eccentricity error of a gear which is preserved on the ancient prototype is discussed. An experimental setup facilitated the analysis of the mechanical characteristics of gears with triangular teeth and the errors. The experimental study of the gears errors revealed the strong impact the Antikythera Mechanism pointers have on the results.
Collisional instabilities of neutrinos and their interplay with fast flavor conversion in compact objects

Fast and collisional ﬂavor instabilities possibly occur in the neutrino decoupling regions of core-collapse supernovae and neutron-star mergers. To gain a better understanding of the relevant ﬂavor dynamics, we numerically solve for the collisionally unstable evolution in a homogeneous, anisotropic model. In these calculations collisional instability is precipitated by unequal neutrino and antineutrino scattering rates. We compare the solutions obtained using neutral-current and charged-current interactions. We then study the nonlinear development of fast instabilities subjected to asymmetric scattering rates, ﬁnding evidence that the onset of collisional instability is hastened by fast oscillations. We also discuss connections to other recent works on collision-aﬀected fast ﬂavor conversion.
Realization of the Brazil-nut effect in charged colloids without external driving

Significance Understanding the spatial ordering of dispersed particles caused by gravitational fields is relevant for applications as diverse as the separation of particles, water purification, the isolation of cells from blood, the shelf-life of paints and inks, and stabilizing emulsions. When external energy is provided, unusual density distributions have been observed: Heavier particles float on top of lighter particles. We provide a lacking experimental demonstration that this effect can also occur in Brownian colloidal systems, without external energy input, in dispersions of charged particles in a low-polar oil. Additionally, we demonstrate that density-profile shapes in mixtures of charged colloids, even if out of equilibrium, can give valuable microscopic information on the equilibrium constants determining the particle charge.
The Role of Binarity and Stellar Rotation in the Split Main Sequence of NGC 2422

In addition to the extended main-sequence turnoffs widely found in young and intermediate-age (∼600 Myr to 2 Gyr old) star clusters, some younger clusters even exhibit split main sequences (MSs). Different stellar rotation rates are proposed to account for the bifurcated MS pattern, with red and blue MSs (rMS and bMS) populated by fast and slowly rotating stars, respectively. Using photometry from Gaia Early Data Release 3, we report a Galactic open cluster with a bifurcated MS, NGC 2422 (∼90 Myr). We exclude the possibilities that the bifurcated MS pattern is caused by photometric noise or differential reddening. We aim to examine if stellar rotation can account for the split MS. We use spectra observed with the Canada–France–Hawaii Telescope and the Southern African Large Telescope and directly measured vsini , the projected rotational velocities, for stars populating the bMS and rMS. We find that their vsini values are weakly correlated with their loci in the color–magnitude diagram because of contamination caused by a large fraction of rMS stars with low projected rotational velocities. Based on the spectral energy distribution fitting method, we suggest that these slowly rotating stars at the rMS may hide a binary companion, which breaks the expected vsini –color correlation. Future time-domain studies focusing on whether these slowly rotating stars are radial velocity variables are crucial to test the roles of stellar rotation and binarity in generating the split MSs.
Space-borne atom interferometric gravitational wave detections. Part III. Eccentricity on dark sirens

Eccentricity of the inspiraling compact binaries can greatly improve the distance inference and source localization of dark sirens. In this paper, we continue the research for the space-borne atom interferometric gravitational-wave detector AEDGE and investigate the effects of eccentricity on the dark sirens observed by AEDGE in the mid-band. We simulate five types of typical compact binaries with component mass ranging from 1-100 M ⊙. The largest improvement for both distance inference and localization can be as much as 1.5–3 orders of magnitude. We then construct the catalogs of dark sirens observed by AEDGE in five years. We find eccentricity is crucial to the detection of golden binary black holes (BBH) whose host galaxy can be uniquely identified. With only 5–10 golden dark BBHs one can obtain a 2 percent precision measurement of H 0 which is sufficient to arbitrate the Hubble tension. Regardless of eccentricity, AEDGE can also observe tens of golden binary neutron stars (BNS) and neutron star-black hole binaries (NSBH) with unique host galaxies. These golden dark sirens can serve as early warnings for the follow-up observations of gravitational waves in the high frequency band as well as the search of their electromagnetic counterparts. Our results show eccentricity is a crucial factor in the detection, data analysis, and application of GWs with the atom interferometers in the mid-band.
Not All GPUs Are Created Equal: Characterizing Variability in Large-Scale, Accelerator-Rich Systems

Scientists are increasingly exploring and utilizing the massive parallelism of general-purpose accelerators such as GPUs for scientific breakthroughs. As a result, datacenters, hyperscalers, national computing centers, and supercomputers have procured hardware to support this evolving application paradigm. These systems contain hundreds to tens of thousands of accelerators, enabling peta- and exa-scale levels of compute for scientific workloads. Recent work demonstrated that power management (PM) can impact application performance in CPU-based HPC systems, even when machines have the same architecture and SKU (stock keeping unit). This variation occurs due to manufacturing variability and the chip's PM. However, while modern HPC systems widely employ accelerators such as GPUs, it is unclear how much this variability affects applications. Accordingly, we seek to characterize the extent of variation due to GPU PM in modern HPC and supercomputing systems. We study a variety of applications that stress different GPU components on five large-scale computing centers with modern GPUs: Oak Ridge's Summit, Sandia's Vortex, TACC's Frontera and Longhorn, and Livermore's Corona. These clusters use a variety of cooling methods and GPU vendors. In total, we collect over 18,800 hours of data across more than 90% of the GPUs in these clusters. Regardless of the application, cluster, GPU vendor, and cooling method, our results show significant variation: 8% (max 22%) average performance variation even though the GPU architecture and vendor SKU are identical within each cluster, with outliers up to 1.5× slower than the median GPU. These results highlight the difficulty in efficiently using existing GPU clusters for modern HPC and scientific workloads, and the need to embrace variability in future accelerator-based systems.
Positivity of infinite-dimensional linear systems

In this paper, we investigate the well-posedness and positivity property of infinite-dimensional linear system with unbounded input and output operators. In particular, we characterize the internal and external positivity for this class of systems. This latter effort is motivated in part by a complete description of well-posed positive control/observation systems. An interesting feature of positive well-posed linear systems is that weak regularity and strong regularity, in the sense of Salamon and Weiss, are equivalent. Moreover, we provide sufficient conditions for zero-class admissibility for positive semigroups. In the context of positive perturbations of positive semigroups, we establish two perturbation results, namely the Desch-Schappacher perturbation and the Staffans-Weiss perturbation. As for illustration, these findings are applied to investigate the existence and uniqueness of a positive mild solution of the linear Boltzmann equation with non-local boundary conditions on finite network.
Improving Speech Emotion Recognition Through Focus and Calibration Attention Mechanisms

Attention has become one of the most commonly used mechanisms in deep learning approaches. The attention mechanism can help the system focus more on the feature space’s critical regions. For example, high amplitude regions can play an important role for Speech Emotion Recognition (SER). In this paper, we identify misalignments between the attention and the signal amplitude in the existing multi-head self-attention. To improve the attention area, we propose to use a Focus-Attention (FA) mechanism and a novel Calibration-Attention (CA) mechanism in combination with the multi-head self-attention. Through the FA mechanism, the network can detect the largest amplitude part in the segment. By employing the CA mechanism, the network can modulate the information flow by assigning different weights to each attention head and improve the utilization of surrounding contexts. To evaluate the proposed method, experiments are performed with the IEMOCAP and RAVDESS datasets. Experimental results show that the proposed framework significantly outperforms the state-of-the-art approaches on both datasets.
VRBubble: Enhancing Peripheral Awareness of Avatars for People with Visual Impairments in Social Virtual Reality

Social Virtual Reality (VR) is growing for remote socialization and collaboration. However, current social VR applications are not accessible to people with visual impairments (PVI) due to their focus on visual experiences. We aim to facilitate social VR accessibility by enhancing PVI’s peripheral awareness of surrounding avatar dynamics. We designed VRBubble, an audio-based VR technique that provides surrounding avatar information based on social distances. Based on Hall’s proxemic theory, VRBubble divides the social space with three Bubbles—Intimate, Conversation, and Social Bubble—generating spatial audio feedback to distinguish avatars in different bubbles and provide suitable avatar information. We provide three audio alternatives: earcons, verbal notifications, and real-world sound effects. PVI can select and combine their preferred feedback alternatives for different avatars, bubbles, and social contexts. We evaluated VRBubble and an audio beacon baseline with 12 PVI in a navigation and a conversation context. We found that VRBubble significantly enhanced participants’ avatar awareness during navigation and enabled avatar identification in both contexts. However, VRBubble was shown to be more distracting in crowded environments.
A Data-Driven Statistical-Stochastic Model for Effective Ensemble Forecast of Complex Turbulent Systems

In this paper, we propose a system of statistical-stochastic equations to predict the response of the mean and variance statistics under perturbations of the initial condition and external forcing. The proposed modeling framework extends the purely statistical modeling approach that is practically limited to the homogeneous statistical regime for high-dimensional state variables. The new closure system allows one to overcome several practical issues that emerge in the non-homogeneous statistical regimes. First, the proposed ensemble modeling that couples the mean statistics and stochastic ﬂuctuations naturally produces positive-deﬁnite covariance matrix estimation, which is a challenging issue that hampers the purely statistical modeling approaches. Second, the proposed closure model, which embeds a non-Markovian neural-network model for the unresolved ﬂuxes such that the variance of the dynamics is consistent, overcomes the inherent instability of the stochastic ﬂuctuation dynamics. Eﬀectively, the proposed framework extends the classical stochastic parametric modeling paradigm for the unresolved dynamics to a semi-parametric parameterization with a residual Long-Short-Term-Memory neural network architecture. Third, based on empirical information theory, we provide an eﬃcient and eﬀective training procedure by ﬁtting a loss function that measures the diﬀerences between response statistics. Supporting numerical examples are provided with the Lorenz-96 model, a system of ODEs that admits the characteristic of turbulent dynamics with both homogeneous and inhomogeneous statistical regimes. In the latter case, we will see the eﬀectiveness of the statistical prediction even though the resolved Fourier modes corresponding to the leading mean energy and variance spectra do not coincide.
Greedy permanent magnet optimization

A number of scientific fields rely on placing permanent magnets in order to produce a desired magnetic field. We have shown in recent work that the placement process can be formulated as sparse regression. However, binary, grid-aligned solutions are desired for realistic engineering designs. We now show that the binary permanent magnet problem can be formulated as a quadratic program with quadratic equality constraints, the binary, grid-aligned problem is equivalent to the quadratic knapsack problem with multiple knapsack constraints, and the single-orientation-only problem is equivalent to the unconstrained quadratic binary problem. We then provide a set of simple greedy algorithms for solving variants of permanent magnet optimization, and demonstrate their capabilities by designing magnets for stellarator plasmas. The algorithms can a-priori produce sparse, grid-aligned, binary solutions. Despite its simple design and greedy nature, we provide an algorithm that compares with or even outperforms the state-of-the-art algorithms while being substantially faster, more flexible, and easier to use.
Systematic strain-induced bandgap tuning in binary III–V semiconductors from density functional theory

The modification of the nature and size of bandgaps for III-V semiconductors is of strong interest for optoelectronic applications. Strain can be used to systematically tune the bandgap over a wide range of values and induce indirect-to-direct transition (IDT), direct-to-indirect transition (DIT), and other changes in bandgap nature. Here, we establish a predictive first-principles approach, based on density functional theory, to analyze the effect of uniaxial, biaxial, and isotropic strain on the bandgap. We show that systematic variation is possible. For GaAs, DITs are observed at 1.56% isotropic compressive strain and 3.52% biaxial tensile strain, while for GaP an IDT is found at 2.63% isotropic tensile strain. We additionally propose a strategy for the realization of direct-to-indirect transition by combining biaxial strain with uniaxial strain. Further transition points are identified for strained GaSb, InP, InAs, and InSb and compared to the elemental semiconductor silicon. Our analyses thus provide a systematic and predictive approach to strain-induced bandgap tuning in binary III-V semiconductors.
Scaling laws for electron kinetic effects in tokamak scrape-off layer plasmas

Tokamak edge (scrape-off layer (SOL)) plasmas can exhibit non-local transport in the direction parallel to the magnetic field due to steep temperature gradients. This effect along with its consequences has been explored at equilibrium for a range of conditions, from sheath-limited to detached, using the 1D kinetic electron code SOL-KiT, where the electrons are treated kinetically and compared to a self-consistent fluid model. Line-averaged suppression of the kinetic heat flux (compared to Spitzer-Härm) of up to 50% is observed, contrasting with up to 98% enhancement of the sheath heat transmission coefficient, γ e . Simple scaling laws in terms of basic SOL parameters for both effects are presented. By implementing these scalings as corrections to the fluid model, we find good agreement with the kinetic model for target electron temperatures. It is found that the strongest kinetic effects in γ e are observed at low-intermediate collisionalities, and tend to increase (keeping upstream collisionality fixed) at increasing upstream densities and temperatures. On the other hand, the heat flux suppression is found to increase monotonically as upstream collisionality decreases. The conditions simulated encompass collisionalities relevant to current and future tokamaks.
Flopping-mode electron dipole spin resonance in the strong-driving regime

Achieving high ﬁdelity control of spin qubits with conventional electron dipole spin resonance (EDSR) requires large magnetic ﬁeld gradients of about 1 mT nm − 1 , which also couple the qubit to charge noise, and large drive amplitudes of order 1 mV. The ﬂopping-mode is an alternative method to drive EDSR of an electron in a double quantum dot, where the large displacement between both dots increases the driving eﬃciency. We propose to operate the ﬂopping-mode in the strong-driving regime to use the full magnetic ﬁeld diﬀerence between the two dots. In simulations, the reduced required magnetic ﬁeld gradients suppress the inﬁdelity contribution of charge noise by more than two orders of magnitude, while providing Rabi frequencies of up to 60 MHz. However, the near degeneracy of the conduction band in silicon introduces a valley degree of freedom that can degrade the performance of the strong-driving mode. This necessitates a valley-dependent pulse optimization and makes operation to the strong-driving regime questionable. t c 13 µ gradients ∆ B x 17 3 mT and ∆ B z = 4 . 32 mT at a total ﬁeld of B z 207 mT, an of A = 2 . 1 µ eV simulating quasistatic noise of standard deviation σ ǫ / ~ = 2 . 5 µ coherent simulation of the ﬂopping-mode qubit including the valley splitting but without The same The Inﬁdelity (c) and the Leakage (d) almost leakage inﬁdelity.