Miracle-dz/UniFAR-FTUs huggingface dataset:
Galileo Photometry of Asteroid 951 Gaspra

Galileo images of Gaspra make it possible for the first time to determine a main-belt asteroid's photometric properties accurately by providing surface-resolved coverage over a wide range of incidence and emission angles and by extending the phase angle coverage to phases not observable from Earth. We combine Earth-based telescopic photometry over phase angles 2° ≤ α ≤ 25° with Galileo whole-disk and disk-resolved data at 33° ≤ α ≤ 51° to derive average global photometric properties in terms of Hapke's photometric model. The microscopic texture and particle phase-function behavior of Gaspra's surface are remarkably like those of other airless rocky bodies such as the Moon. The macroscopic surface roughness parameter, θ = 29°, is slightly larger than that reported for typical lunar materials. The particle single scattering albedo, ω0 = 0.36 ± 0.07, is significantly larger than for lunar materials, and the opposition surge amplitude, B0 = 1.63 ± 0.07, is correspondingly smaller. We determine a visual geometric albedo pv = 0.22 ± 0.06 for Gaspra, in close agreement with pv = 0.22 ± 0.03 estimated from Earth-based observations. Gaspra's phase integral is 0.47, and the bolometric Bond albedo is estimated to be 0.12 ± 0.03. An albedo map derived by correcting Galileo images with our average global photometric function reveals subdued albedo contrasts of ±10% or less over Gaspra's northern hemisphere. Several independent classification algorithms confirm the subtle spectral heterogeneity reported earlier (S. Mottola, M. DiMartino, M. Gonano-Beurer, H. Hoffman, and G. Neukum, 1993, Asteroids, Comets, Meteors, pp. 421-424; M. J. S. Belton et al., 1992, Science 257, 1647-1652). Whole-disk colors (0.41 ≤ λ ≤ 0.99 μm) vary systematically with longitude by about ±5%, but color differences as large as 30% occur locally. Colors vary continuously between end-member materials whose areal distribution correlates with regional topography. Infrared: violet (0.99:0.41-μm) color ratios on Gaspra are strongly correlated with local elevation, being largest at lower elevations and smaller at higher elevations. No correlation was detected between elevation and the green:violet (0.56:0.41-μm) color ratio. Bright materials with a strong 1-μm absorption occur primarily in association with craters along ridges, while darker materials with 30% weaker 1-μm signatures occur downslope. The variations of color and albedo cannot be easily explained by grain-size effects alone or by differences in photometric geometry. The trends observed are consistent with those revealed by laboratory studies of the effects of comminution, glass formation, and segregation of metal from silicate components in chondritic meteorites and also in some silicate mixtures. The relative importance of these various processes on Gaspra remains to be determined.
A simple approach to quasicrystal structure and phason defect formulation

The advantages of the cut or section method in describing quasicrystal structures and phason defects are given. The real and reciprocal quasilattice formulation is derived straightforwardly. It is shown that the linear phason strain which leads to the quasilattice distortion is equivalent to a rotation of physical space relative to the high-dimensional space. A continuous rotation of the physical space will make the quasilattice deviate from its idealized form and turn gradually into a periodic lattice. The derivation of a geometrical relationship between the icosahedral quasilattice and the corresponding b.c.c. lattice becomes simple and clear. This will be beneficial to the construction of a quasicrystal structure model by reference to the corresponding b.c.c. crystal structure.
On Learning Necessary and Sufficient Causal Graphs

The causal revolution has stimulated interest in understanding complex relationships in various fields. Most of the existing methods aim to discover causal relationships among all variables within a complex large-scale graph. However, in practice, only a small subset of variables in the graph are relevant to the outcomes of interest. Consequently, causal estimation with the full causal graph -- particularly given limited data -- could lead to numerous falsely discovered, spurious variables that exhibit high correlation with, but exert no causal impact on, the target outcome. In this paper, we propose learning a class of necessary and sufficient causal graphs (NSCG) that exclusively comprises causally relevant variables for an outcome of interest, which we term causal features. The key idea is to employ probabilities of causation to systematically evaluate the importance of features in the causal graph, allowing us to identify a subgraph relevant to the outcome of interest. To learn NSCG from data, we develop a necessary and sufficient causal structural learning (NSCSL) algorithm, by establishing theoretical properties and relationships between probabilities of causation and natural causal effects of features. Across empirical studies of simulated and real data, we demonstrate that NSCSL outperforms existing algorithms and can reveal crucial yeast genes for target heritable traits of interest.
Magnetic properties of hematite revealed by an 
ab initio
 parameterized spin model

Hematite is a canted antiferromagnetic insulator, promising for applications in spintronics. Here, we present ab initio calculations of the tensorial exchange interactions of hematite and use them to understand its magnetic properties by parameterizing a semiclassical Heisenberg spin model. Using atomistic spin dynamics simulations, we calculate the equilibrium properties and phase transitions of hematite, most notably the Morin transition. The computed isotropic and Dzyaloshinskii--Moriya interactions result in a N\'eel temperature and weak ferromagnetic canting angle that are in good agreement with experimental measurements. Our simulations show how dipole-dipole interactions act in a delicate balance with first and higher-order on-site anisotropies to determine the material's magnetic phase. Comparison with spin-Hall magnetoresistance measurements on a hematite single-crystal reveals deviations of the critical behavior at low temperatures. Based on a mean-field model, we argue that these differences result from the quantum nature of the fluctuations that drive the phase transitions.
Tomography of a Solar Plage with the Tenerife Inversion Code

We apply the Tenerife Inversion Code (TIC) to the plage spectropolarimetric observations obtained by the Chromospheric LAyer SpectroPolarimeter (CLASP2). These unprecedented data consist of full Stokes profiles in the spectral region around the Mg ii h and k lines for a single slit position, with around two thirds of the 196″ slit crossing a plage region and the rest crossing an enhanced network. A previous analysis of these data had allowed us to infer the longitudinal component of the magnetic field by applying the weak-field approximation (WFA) to the circular polarization profiles, and to assign the inferred magnetic fields to different layers of the solar atmosphere based on the results of previous theoretical radiative transfer investigations. In this work, we apply the recently developed TIC to the same data. We obtain a stratified model atmosphere that fits the intensity and circular polarization profiles at each position along the spectrograph slit and we compare our results for the longitudinal component of the magnetic field with the previously obtained WFA results, highlighting the generally good agreement in spite of the fact that the WFA is known to produce an underestimation when applied to the outer lobes of the Mg ii h and k circular polarization profiles. Finally, we use the inverted model atmospheres to give a rough estimation of the energy that could be carried by Alfvén waves propagating in the chromosphere in the plage and network regions, showing that it is sufficient to compensate for the estimated energy losses in the chromosphere of solar active regions.
Equivariant oriented homology of the affine Grassmannian

We generalize the property of small-torus equivariant K-homology of the aﬃne Grassmannian to general oriented (co)homology theory in the sense of Levine and Morel. The main tool we use is the formal aﬃne Demazure algebra associated to the aﬃne root system. More precisely, we prove that the small-torus equivariant oriented cohomology of the aﬃne Grassmannian satisﬁes the GKM condition. We also show that its dual, the small-torus equivariant homology, is isomorphic to the centralizer of the equivariant oriented cohomology of a point in the the formal aﬃne Demazure algebra.
Understanding Hindsight Goal Relabeling from a Divergence Minimization Perspective

Hindsight goal relabeling has become a foundational technique in multi-goal reinforcement learning (RL). The essential idea is that any trajectory can be seen as a sub-optimal demonstration for reaching its final state. Intuitively, learning from those arbitrary demonstrations can be seen as a form of imitation learning (IL). However, the connection between hindsight goal relabeling and imitation learning is not well understood. In this paper, we propose a novel framework to understand hindsight goal relabeling from a divergence minimization perspective. Recasting the goal reaching problem in the IL framework not only allows us to derive several existing methods from first principles, but also provides us with the tools from IL to improve goal reaching algorithms. Experimentally, we find that under hindsight relabeling, Q-learning outperforms behavioral cloning (BC). Yet, a vanilla combination of both hurts performance. Concretely, we see that the BC loss only helps when selectively applied to actions that get the agent closer to the goal according to the Q-function. Our framework also explains the puzzling phenomenon wherein a reward of (-1, 0) results in significantly better performance than a (0, 1) reward for goal reaching.
Asymptotic Inference for Multi-Stage Stationary Treatment Policy with High Dimensional Features

Dynamic treatment rules or policies are a sequence of decision functions over multiple stages that are tailored to individual features. One important class of treatment policies for practice, namely multi-stage stationary treatment policies, prescribe treatment assignment probabilities using the same decision function over stages, where the decision is based on the same set of features consisting of both baseline variables (e.g., demographics) and time-evolving variables (e.g., routinely collected disease biomarkers). Although there has been extensive literature to construct valid inference for the value function associated with the dynamic treatment policies, little work has been done for the policies themselves, especially in the presence of high dimensional feature variables. We aim to fill in the gap in this work. Specifically, we first estimate the multistage stationary treatment policy based on an augmented inverse probability weighted estimator for the value function to increase the asymptotic efficiency, and further apply a penalty to select important feature variables. We then construct one-step improvement of the policy parameter estimators. Theoretically, we show that the improved estimators are asymptotically normal, even if nuisance parameters are estimated at a slow convergence rate and the dimension of the feature variables increases with the sample size. Our numerical studies demonstrate that the proposed method has satisfactory performance in small samples, and that the performance can be improved with a choice of the augmentation term that approximates the rewards or minimizes the variance of the value function.
In our mind's eye: Visible and invisible in quantum theory, with Schr\"odinger's cat experiment

This article aims to reconsider E. Schrödinger’s famous thought experiment, the cat-paradox experiment, and its place in quantum foundations from a new perspective, grounded in the type of interpretation of quantum phenomena and quantum mechanics, which belongs to the class of interpretations designated here as “reality without realism” (RWR) interpretations. Such interpretations have not been previously brought to bear on the cat experiment, including by N. Bohr, whose interpretation in its ultimate form (as he changed his interpretation a few times) is an RWR interpretation, but who does not appear to have commented on the cat experiment. The interpretation adopted in this article follows Bohr’s interpretation, as based on two assumptions or postulates, the Heisenberg and Bohr postulates, but it adds a third postulate, the Dirac postulate. The article also introduces, in conjunction with the concept of reality without realism, the concepts of visible and invisible to thought and considers their role in the cat-paradox experiment.
Training Full Spike Neural Networks via Auxiliary Accumulation Pathway

Due to the binary spike signals making converting the traditional high-power multiply-accumulation (MAC) into a low-power accumulation (AC) available, the brain-inspired Spiking Neural Networks (SNNs) are gaining more and more attention. However, the binary spike propagation of the Full-Spike Neural Networks (FSNN) with limited time steps is prone to significant information loss. To improve performance, several state-of-the-art SNN models trained from scratch inevitably bring many non-spike operations. The non-spike operations cause additional computational consumption and may not be deployed on some neuromorphic hardware where only spike operation is allowed. To train a large-scale FSNN with high performance, this paper proposes a novel Dual-Stream Training (DST) method which adds a detachable Auxiliary Accumulation Pathway (AAP) to the full spiking residual networks. The accumulation in AAP could compensate for the information loss during the forward and backward of full spike propagation, and facilitate the training of the FSNN. In the test phase, the AAP could be removed and only the FSNN remained. This not only keeps the lower energy consumption but also makes our model easy to deploy. Moreover, for some cases where the non-spike operations are available, the APP could also be retained in test inference and improve feature discrimination by introducing a little non-spike consumption. Extensive experiments on ImageNet, DVS Gesture, and CIFAR10-DVS datasets demonstrate the effectiveness of DST.
Arrhenius Crossover Temperature of Glass-Forming Liquids Predicted by an Artificial Neural Network

The Arrhenius crossover temperature, TA, corresponds to a thermodynamic state wherein the atomistic dynamics of a liquid becomes heterogeneous and cooperative; and the activation barrier of diffusion dynamics becomes temperature-dependent at temperatures below TA. The theoretical estimation of this temperature is difficult for some types of materials, especially silicates and borates. In these materials, self-diffusion as a function of the temperature T is reproduced by the Arrhenius law, where the activation barrier practically independent on the temperature T. The purpose of the present work was to establish the relationship between the Arrhenius crossover temperature TA and the physical properties of liquids directly related to their glass-forming ability. Using a machine learning model, the crossover temperature TA was calculated for silicates, borates, organic compounds and metal melts of various compositions. The empirical values of the glass transition temperature Tg, the melting temperature Tm, the ratio of these temperatures Tg/Tm and the fragility index m were applied as input parameters. It has been established that the temperatures Tg and Tm are significant parameters, whereas their ratio Tg/Tm and the fragility index m do not correlate much with the temperature TA. An important result of the present work is the analytical equation relating the temperatures Tg, Tm and TA, and that, from the algebraic point of view, is the equation for a second-order curved surface. It was shown that this equation allows one to correctly estimate the temperature TA for a large class of materials, regardless of their compositions and glass-forming abilities.
Schmidt subspaces of block Hankel operators

In scalar-valued Hardy space, the class of Schmidt subspaces for a bounded Hankel operator are closely related to nearly $S^*$-invariant subspaces, as described by G\'{e}rard and Pushnitski. In this article, we prove that these subspaces in the context of vector-valued Hardy spaces are nearly $S^*$-invariant with finite defect in general. As a consequence, we obtain a short proof of the characterization results concerning the Schmidt subspaces in scalar-valued Hardy space in an alternative way. Thus, our work complements the work of G\'{e}rard and Pushnitski regarding the structure of Schmidt subspaces.
Does Federated Learning Really Need Backpropagation?

Federated learning (FL) is a general principle for decentralized clients to train a server model collectively without sharing local data. FL is a promising framework with practical applications, but its standard training paradigm requires the clients to backpropagate through the model to compute gradients. Since these clients are typically edge devices and not fully trusted, executing backpropagation on them incurs computational and storage overhead as well as white-box vulnerability. In light of this, we develop backpropagation-free federated learning, dubbed BAFFLE, in which backpropagation is replaced by multiple forward processes to estimate gradients. BAFFLE is 1) memory-efficient and easily fits uploading bandwidth; 2) compatible with inference-only hardware optimization and model quantization or pruning; and 3) well-suited to trusted execution environments, because the clients in BAFFLE only execute forward propagation and return a set of scalars to the server. Empirically we use BAFFLE to train deep models from scratch or to finetune pretrained models, achieving acceptable results. Code is available in https://github.com/FengHZ/BAFFLE.
Combinatorial Pen Testing (or Consumer Surplus of Deferred-Acceptance Auctions)

Pen testing is the problem of selecting high-capacity resources when the only way to measure the capacity of a resource expends its capacity. We have a set of $n$ pens with unknown amounts of ink and our goal is to select a feasible subset of pens maximizing the total ink in them. We are allowed to gather more information by writing with them, but this uses up ink that was previously in the pens. Algorithms are evaluated against the standard benchmark, i.e, the optimal pen testing algorithm, and the omniscient benchmark, i.e, the optimal selection if the quantity of ink in the pens are known. We identify optimal and near optimal pen testing algorithms by drawing analogues to auction theoretic frameworks of deferred-acceptance auctions and virtual values. Our framework allows the conversion of any near optimal deferred-acceptance mechanism into a near optimal pen testing algorithm. Moreover, these algorithms guarantee an additional overhead of at most $(1+o(1)) \ln n$ in the approximation factor of the omniscient benchmark. We use this framework to give pen testing algorithms for various combinatorial constraints like matroid, knapsack, and general downward-closed constraints and also for online environments.
Over-8-dB squeezed light generation by a broadband waveguide optical parametric amplifier toward fault-tolerant ultra-fast quantum computers

We achieved continuous-wave 8.3-dB squeezed light generation using a terahertz-order-broadband waveguide optical parametric amplifier by improving a measurement setup from our previous work [T. Kashiwazaki et al., Appl. Phys. Lett. 119, 251104 (2021)], where a low-loss periodically poled lithium niobate (PPLN) waveguide had shown 6.3-dB squeezing at a 6 THz frequency. First, to improve efficiency of the squeezed light detection, we reduced effective optical loss to about 12% by removing extra optics and changing the detection method into a low-loss balanced homodyne measurement. Second, to minimize phase-locking fluctuation, we constructed a frequency-optimized phase-locking system by comprehending its frequency responses. Finally, we found optimal experimental parameters of a measurement frequency and a pump power from their dependences for the squeezing levels. The measurement frequency was decided as 11 MHz to maximize a clearance between shot and circuit noises. Furthermore, pump power was optimized as 660 mW to get higher squeezing level while suppressing anti-squeezed-noise contamination due to an imperfection of phase locking. Note that this over-8-dB squeezing is achieved without any loss-correction and circuit-noise correction. Moreover, it is shown that the squeezing level soon after our PPLN waveguide is estimated at over 10 dB, which is thought to be mainly restricted by the waveguide loss. This broadband highly squeezed light opens the possibility to realize fault-tolerant ultra-fast optical quantum computers.
Graph Mixer Networks

In recent years, the attention mechanism has demonstrated superior performance in various tasks, leading to the emergence of GAT and Graph Transformer models that utilize this mechanism to extract relational information from graph-structured data. However, the high computational cost associated with the Transformer block, as seen in Vision Transformers, has motivated the development of alternative architectures such as MLP-Mixers, which have been shown to improve performance in image tasks while reducing the computational cost. Despite the effectiveness of Transformers in graph-based tasks, their computational efficiency remains a concern. The logic behind MLP-Mixers, which addresses this issue in image tasks, has the potential to be applied to graph-structured data as well. In this paper, we propose the Graph Mixer Network (GMN), also referred to as Graph Nasreddin Nets (GNasNets), a framework that incorporates the principles of MLP-Mixers for graph-structured data. Using a PNA model with multiple aggregators as the foundation, our proposed GMN has demonstrated improved performance compared to Graph Transformers. The source code is available publicly at https://github.com/asarigun/GraphMixerNetworks.
BabelBrain: An Open-Source Application for Prospective Modeling of Transcranial Focused Ultrasound for Neuromodulation Applications

BabelBrain is an open-source standalone graphic-user-interface application designed for studies of neuromodulation using transcranial-focused ultrasound (FUS). It calculates the transmitted acoustic field in the brain tissue, taking into account the distortion effects caused by the skull barrier. The simulation is prepared using scans from magnetic resonance imaging (MRI) and, if available, computed tomography (CT) and zero-echo time MRI scans. It also calculates the thermal effects based on a given ultrasound regime, such as the total duration of exposure, the duty cycle, and acoustic intensity. The tool is designed to work in tandem with neuronavigation and visualization software, such as 3-DSlicer. It uses image processing to prepare domains for ultrasound simulation and uses the BabelViscoFDTD library for transcranial modeling calculations. BabelBrain supports multiple GPU backends, including Metal, OpenCL, and CUDA, and works on all major operating systems including Linux, macOS, and Windows. This tool is particularly optimized for Apple ARM64 systems, which are common in brain imaging research. The article presents the modeling pipeline used in BabelBrain and a numerical study where different methods of acoustic properties mapping were tested to select the best method that can reproduce the transcranial pressure transmission efficiency reported in the literature.