Miracle-dz/UniFAR-FTUs huggingface dataset:
Anytime-Valid Tests of Conditional Independence Under Model-X

We propose a sequential, anytime-valid method to test the conditional independence of a response $Y$ and a predictor $X$ given a random vector $Z$. The proposed test is based on e-statistics and test martingales, which generalize likelihood ratios and allow valid inference at arbitrary stopping times. In accordance with the recently introduced model-X setting, our test depends on the availability of the conditional distribution of $X$ given $Z$, or at least a sufficiently sharp approximation thereof. Within this setting, we derive a general method for constructing e-statistics for testing conditional independence, show that it leads to growth-rate optimal e-statistics for simple alternatives, and prove that our method yields tests with asymptotic power one in the special case of a logistic regression model. A simulation study is done to demonstrate that the approach is competitive in terms of power when compared to established sequential and nonsequential testing methods, and robust with respect to violations of the model-X assumption.
Physically constrained neural networks to solve the inverse problem for neuron models

Systems biology and systems neurophysiology in particular have recently emerged as powerful tools for a number of key applications in the biomedical sciences. Nevertheless, such models are often based on complex combinations of multiscale (and possibly multiphysics) strategies that require ad hoc computational strategies and pose extremely high computational demands. Recent developments in the field of deep neural networks have demonstrated the possibility of formulating nonlinear, universal approximators to estimate solutions to highly nonlinear and complex problems with significant speed and accuracy advantages in comparison with traditional models. After synthetic data validation, we use so-called physically constrained neural networks (PINN) to simultaneously solve the biologically plausible Hodgkin-Huxley model and infer its parameters and hidden time-courses from real data under both variable and constant current stimulation, demonstrating extremely low variability across spikes and faithful signal reconstruction. The parameter ranges we obtain are also compatible with prior knowledge. We demonstrate that detailed biological knowledge can be provided to a neural network, making it able to fit complex dynamics over both simulated and real data.
2D Reconstruction of Magnetotail Electron Diffusion Region Measured by MMS

Models for collisionless magnetic reconnection in near‐Earth space are distinctly characterized as 2D or 3D. In 2D kinetic models, the frozen‐in law for the electron fluid is usually broken by laminar dynamics involving structures set by the electron orbit size, while in 3D models the width of the electron diffusion region is broadened by turbulent effects. We present an analysis of in situ spacecraft observations from the Earth's magnetotail of a fortuitous encounter with an active reconnection region, mapping the observations onto a 2D spatial domain. While the event likely was perturbed by low‐frequency 3D dynamics, the structure of the electron diffusion region remains consistent with results from a 2D kinetic simulation. As such, the event represents a unique validation of 2D kinetic, and laminar reconnection models.
Non-Abelian topological order in noncentrosymmetric superconductors with broken time-reversal symmetry

Department of Physics and Astronomy, Clemson University, Clemson, South Carolina, 29634, USA(Dated: July 15, 2010)We consider two-dimensional non-centrosymmetric superconductors, in which the order parameteris a mixture of s-wave and p-wave parts, in the presence of an externally induced Zeeman splitting.We derive the conditions under which the system is in a non-Abelian phase. By considering thenon-degenerate zero-energy Majorana solutions of the Bogoliubov-de Gennes (BdG) equations for avortex and by constructing a topological invariant, we show that the condition for the non-Abelianphase to exist is completely independent of the triplet pairing amplitude. The existence conditionfor the non-Abelian phase derived from the real space solutions of the BdG equations involves thePfaan of the BdG Hamiltonian at k= 0, which is completely insensitive to the magnitude of the p-wave component of the order parameter. We arrive at the same conclusion by using the appropriatetopological invariant for this case. This is in striking contrast to the analogous condition for thetime-reversal invariant topological phases, in which the amplitude of the p-wave component must belarger than the amplitude of the s-wave piece of the order parameter. As a by-product, we establishthe intrinsic connection between the Pfaan of the BdG Hamiltonian at k= 0 (which arises at theBdG approach) and the relevant Z topological invariant.
Accurate and Efficient Stereo Matching via Attention Concatenation Volume

Stereo matching is a fundamental building block for many vision and robotics applications. An informative and concise cost volume representation is vital for stereo matching of high accuracy and efficiency. In this article, we present a novel cost volume construction method, named attention concatenation volume (ACV), which generates attention weights from correlation clues to suppress redundant information and enhance matching-related information in the concatenation volume. The ACV can be seamlessly embedded into most stereo matching networks, the resulting networks can use a more lightweight aggregation network and meanwhile achieve higher accuracy. We further design a fast version of ACV to enable real-time performance, named Fast-ACV, which generates high likelihood disparity hypotheses and the corresponding attention weights from low-resolution correlation clues to significantly reduce computational and memory cost and meanwhile maintain a satisfactory accuracy. Furthermore, we design a highly accurate network ACVNet and a real-time network Fast-ACVNet based on our ACV and Fast-ACV respectively, which achieve state-of-the-art performance on several benchmarks.
Classical setting : line bundles and linear series

Notation and Conventions. - One: Ample Line Bundles and Linear Series. - to Part One. - 1 Ample and Nef Line Bundles. - 2 Linear Series. - 3 Geometric Manifestations of Positivity. - 4 Vanishing Theorems. - 5 Local Positivity. - Appendices. - A Projective Bundles. - B Cohomology and Complexes. - B.1 Cohomology. - B.2 Complexes. - References. - Glossary of Notation.
NeRF-Loc: Transformer-Based Object Localization Within Neural Radiance Fields

Neural Radiance Fields (NeRFs) have become a widely-applied scene representation technique in recent years, showing advantages for robot navigation and manipulation tasks. To further advance the utility of NeRFs for robotics, we propose a transformer-based framework, NeRF-Loc, to extract 3D bounding boxes of objects in NeRF scenes. NeRF-Loc takes a pre-trained NeRF model and camera view as input and produces labeled, oriented 3D bounding boxes of objects as output. Using current NeRF training tools, a robot can train a NeRF environment model in real-time and, using our algorithm, identify 3D bounding boxes of objects of interest within the NeRF for downstream navigation or manipulation tasks. Concretely, we design a pair of paralleled transformer encoder branches, namely the coarse stream and the fine stream, to encode both the context and details of target objects. The encoded features are then fused together with attention layers to alleviate ambiguities for accurate object localization. We have compared our method with conventional RGB(-D) based methods that take rendered RGB images and depths from NeRFs as inputs. Our method is better than the baselines.
Sensitivity mapping of TBL wall-pressure spectra with CFD turbulence models for wind tunnel test result prediction

In the present work, an attempt is made to map the sensitivity of the existing zero pressure gradient (ZPG) turbulent boundary layer (TBL) wall-pressure spectrum models with different TBL parameters, and eventually, with different Reynolds Averaged Navier Stokes (RANS) turbulence models, simulated in OpenFOAM and ANSYS Fluent solvers. This study will help future researchers to choose a particular RANS turbulence model vis-à-vis a particular wall-spectrum model in order to obtain a reasonably accurate wind tunnel result predicting capability. First, the best-predicting pressure spectrum models are selected by comparing them with wind tunnel test data. Next, considering the experimental TBL parameters as benchmarks, errors in RANS-produced data are estimated. Furthermore, wall-pressure spectra are calculated following semi-empirical spectrum models using TBL parameter feed obtained from experiments and computational fluid dynamics (CFD) simulations. Finally, sensitivity mapping is performed between spectrum models and the RANS models, with different normalized wall-normal distances ( 𝑦 + ).
Electro-optic properties of ZrO2, HfO2 and LiNbO3 ferroelectric phases: A comprehensive and comparative study with density functional theory

We report the Pockels electro-optic properties of ZrO2 and HfO2 orthorhombic Pbc21 and rhombohedral R3m ferroelectric phases, and we compare them to the well-known LiNbO3 Pockels material from density functional theory calculations using the CRYSTAL suite of programs. Specifically, three essential processes are explicitly investigated: The electronic, the ionic (or vibrational), and the piezoelectric contributions. Our calculations reveal that the ionic part coming from the low frequency phonon modes contributes the most to the electro-optic coefficients of rhombohedral LiNbO3 and of orthorhombic ZrO2 and HfO2. While these low-frequency modes show zero contribution to Pockels coefficients for the rhombohedral phase of the latter compounds.
On the Complexity of Deterministic Nonsmooth and Nonconvex Optimization

In this paper, we present several new results on minimizing a nonsmooth and nonconvex function under a Lipschitz condition. Recent work shows that while the classical notion of Clarke stationarity is computationally intractable up to some sufficiently small constant tolerance, the randomized first-order algorithms find a $(\delta, \epsilon)$-Goldstein stationary point with the complexity bound of $\tilde{O}(\delta^{-1}\epsilon^{-3})$, which is independent of dimension $d \geq 1$~\citep{Zhang-2020-Complexity, Davis-2022-Gradient, Tian-2022-Finite}. However, the deterministic algorithms have not been fully explored, leaving open several problems in nonsmooth nonconvex optimization. Our first contribution is to demonstrate that the randomization is \textit{necessary} to obtain a dimension-independent guarantee, by proving a lower bound of $\Omega(d)$ for any deterministic algorithm that has access to both $1^{st}$ and $0^{th}$ oracles. Furthermore, we show that the $0^{th}$ oracle is \textit{essential} to obtain a finite-time convergence guarantee, by showing that any deterministic algorithm with only the $1^{st}$ oracle is not able to find an approximate Goldstein stationary point within a finite number of iterations up to sufficiently small constant parameter and tolerance. Finally, we propose a deterministic smoothing approach under the \textit{arithmetic circuit} model where the resulting smoothness parameter is exponential in a certain parameter $M>0$ (e.g., the number of nodes in the representation of the function), and design a new deterministic first-order algorithm that achieves a dimension-independent complexity bound of $\tilde{O}(M\delta^{-1}\epsilon^{-3})$.
Non-perturbative Unitarity of Gravitational Higgs Mechanism

In this paper we discuss massive gravity in Minkowski space via gravitational Higgs mechanism, which provides a non-perturbative definition thereof. Using this non-perturbative definition, we address the issue of unitarity by studying the full nonlinear Hamiltonian for the relevant metric degrees of freedom. While perturbatively unitarity is not evident, we argue that no negative norm state is present in the full nonlinear theory.
Multi-dataset Training of Transformers for Robust Action Recognition

We study the task of robust feature representations, aiming to generalize well on multiple datasets for action recognition. We build our method on Transformers for its efficacy. Although we have witnessed great progress for video action recognition in the past decade, it remains challenging yet valuable how to train a single model that can perform well across multiple datasets. Here, we propose a novel multi-dataset training paradigm, MultiTrain, with the design of two new loss terms, namely informative loss and projection loss, aiming to learn robust representations for action recognition. In particular, the informative loss maximizes the expressiveness of the feature embedding while the projection loss for each dataset mines the intrinsic relations between classes across datasets. We verify the effectiveness of our method on five challenging datasets, Kinetics-400, Kinetics-700, Moments-in-Time, Activitynet and Something-something-v2 datasets. Extensive experimental results show that our method can consistently improve state-of-the-art performance. Code and models are released.
CryptoGCN: Fast and Scalable Homomorphically Encrypted Graph Convolutional Network Inference

Recently cloud-based graph convolutional network (GCN) has demonstrated great success and potential in many privacy-sensitive applications such as personal healthcare and financial systems. Despite its high inference accuracy and performance on cloud, maintaining data privacy in GCN inference, which is of paramount importance to these practical applications, remains largely unexplored. In this paper, we take an initial attempt towards this and develop $\textit{CryptoGCN}$--a homomorphic encryption (HE) based GCN inference framework. A key to the success of our approach is to reduce the tremendous computational overhead for HE operations, which can be orders of magnitude higher than its counterparts in the plaintext space. To this end, we develop an approach that can effectively take advantage of the sparsity of matrix operations in GCN inference to significantly reduce the computational overhead. Specifically, we propose a novel AMA data formatting method and associated spatial convolution methods, which can exploit the complex graph structure and perform efficient matrix-matrix multiplication in HE computation and thus greatly reduce the HE operations. We also develop a co-optimization framework that can explore the trade offs among the accuracy, security level, and computational overhead by judicious pruning and polynomial approximation of activation module in GCNs. Based on the NTU-XVIEW skeleton joint dataset, i.e., the largest dataset evaluated homomorphically by far as we are aware of, our experimental results demonstrate that $\textit{CryptoGCN}$ outperforms state-of-the-art solutions in terms of the latency and number of homomorphic operations, i.e., achieving as much as a 3.10$\times$ speedup on latency and reduces the total Homomorphic Operation Count by 77.4\% with a small accuracy loss of 1-1.5$\%$.
No-Masking Theorem for Observables and No-Bit Commitment

The no-masking theorem for quantum information proves that it is impossible to encode an input state into a larger bipartite entangled state such that full information is stored in the correlation, but the individual subsystems have no information about the input state. Here, we ask the question: Is it possible to mask an observable such that the information about the observable is available in the joint system, but individual subsystems re-veal nothing about the imprints of observable? This generalises the notion of no-masking theorem to quantum observables. We prove that for qubit systems, an arbitrary observable cannot be masked. Subsequently, we generalise the no-masking theorem for observables in higher dimensions. We also prove a no-hiding theorem for observables which goes beyond the conservation laws under symmetry operations. Furthermore, we prove that the famous unconditional no-bit commitment result follows from the no-masking theorem for observbles. Our results can have important applications in quantum information and quantum communication where we encode information not in states but in observables.
Millimeter Dust Emission and Planetary Dynamics in the HD 106906 System

Debris disks are dusty, optically thin structures around main-sequence stars. HD 106906AB is a short-period stellar binary, host to a wide-separation planet, HD 106906b, and a debris disk. Only a few known systems include a debris disk and a directly imaged planet, and HD 106906 is the only one in which the planet is exterior to the disk. The debris disk is edge-on and highly asymmetric in scattered light. Here we resolve the disk structure at a resolution of 0. ″38 (39 au) with the Atacama Large Millimeter/submillimeter Array (ALMA) at a wavelength of 1.3 mm. We model the disk with both a narrow and broad ring of material, and find that a radially broad, axisymmetric disk between radii of ∼50–100 au is able to capture the structure of the observations without evidence of any asymmetry or eccentricity, other than a tentative stellocentric offset. We place stringent upper limits on both the gas and dust contents of a putative circumplanetary disk. We interpret the ALMA data in concert with scattered-light observations of the inner ring and astrometric constraints on the planet’s orbit, and find that the observations are consistent with a large-separation, low-eccentricity orbit for the planet. A dynamical analysis indicates that the central binary can efficiently stabilize planetesimal orbits interior to ∼100 au, which somewhat relaxes the constraints on the eccentricity and semimajor axis. The observational constraints are consistent with in-situ formation via gravitational instability but cannot rule out a scattering event as the origin for HD 106906b’s current orbit.
Composite Layers for Deep Anomaly Detection on 3D Point Clouds

Deep neural networks require specific layers to process point clouds, as the scattered and irregular location of points prevents us from using convolutional filters. Here we introduce the composite layer, a new convolutional operator for point clouds. The peculiarity of our composite layer is that it extracts and compresses the spatial information from the position of points before combining it with their feature vectors. Compared to well-known point-convolutional layers such as those of ConvPoint and KPConv, our composite layer provides additional regularization and guarantees greater flexibility in terms of design and number of parameters. To demonstrate the design flexibility, we also define an aggregate composite layer that combines spatial information and features in a nonlinear manner, and we use these layers to implement a convolutional and an aggregate CompositeNet. We train our CompositeNets to perform classification and, most remarkably, unsupervised anomaly detection. Our experiments on synthetic and real-world datasets show that, in both tasks, our CompositeNets outperform ConvPoint and achieve similar results as KPConv despite having a much simpler architecture. Moreover, our CompositeNets substantially outperform existing solutions for anomaly detection on point clouds.
THE SEGUE STELLAR PARAMETER PIPELINE. V. ESTIMATION OF ALPHA-ELEMENT ABUNDANCE RATIOS FROM LOW-RESOLUTION SDSS/SEGUE STELLAR SPECTRA

We present a method for the determination of [α/Fe] ratios from low-resolution (R = 2000) SDSS/SEGUE stellar spectra. By means of a star-by-star comparison with degraded spectra from the ELODIE spectral library and with a set of moderately high-resolution (R = 15, 000) and medium-resolution (R = 6000) spectra of SDSS/SEGUE stars, we demonstrate that we are able to measure [α/Fe] from SDSS/SEGUE spectra (with S/N>20/1) to a precision of better than 0.1 dex, for stars with atmospheric parameters in the range Teff = [4500, 7000] K, log g = [1.5, 5.0], and [Fe/H] = [−1.4, +0.3], over the range [α/Fe] = [−0.1, +0.6]. For stars with [Fe/H] <−1.4, our method requires spectra with slightly higher signal-to-noise to achieve this precision (S/N>25/1). Over the full temperature range considered, the lowest metallicity star for which a confident estimate of [α/Fe] can be obtained from our approach is [Fe/H] ∼−2.5; preliminary tests indicate that a metallicity limit as low as [Fe/H] ∼−3.0 may apply to cooler stars. As a further validation of this approach, weighted averages of [α/Fe] obtained for SEGUE spectra of likely member stars of Galactic globular clusters (M15, M13, and M71) and open clusters (NGC 2420, M67, and NGC 6791) exhibit good agreement with the values of [α/Fe] from previous studies. The results of the comparison with NGC 6791 imply that the metallicity range for the method may extend to ∼+0.5.
Unsupervised Model-based Pre-training for Data-efficient Control from Pixels

Controlling artiﬁcial agents from visual sensory data is an arduous task. Reinforcement learning (RL) algorithms can succeed in this but require large amounts of interactions between the agent and the environment. To alleviate the issue, unsupervised RL proposes to employ self-supervised interaction and learning, for adapting faster to future tasks. Yet, whether current unsupervised strategies improve generalization capabilities is still unclear, especially in visual control settings. In this work, we design an effective unsupervised RL strategy for data-efﬁcient visual control. First, we show that world models pre-trained with data collected using unsupervised RL can facilitate adaptation for future tasks. Then, we analyze several design choices to adapt efﬁciently, effectively reusing the agents’ pre-trained components, and learning and planning in imagination, with our hybrid planner, which we dub Dyna-MPC. By combining the ﬁndings of a large-scale empirical study, we establish an approach that strongly improves performance on the Unsupervised RL Benchmark, requiring 20 × less data to match the performance of supervised methods. The approach also demonstrates robust performance on the Real-Word RL benchmark, hinting that the approach generalizes to noisy environments.
Dead or Murdered? Predicting Responsibility Perception in Femicide News Reports

Different linguistic expressions can conceptualize the same event from different viewpoints by emphasizing certain participants over others. Here, we investigate a case where this has social consequences: how do linguistic expressions of gender-based violence (GBV) influence who we perceive as responsible? We build on previous psycholinguistic research in this area and conduct a large-scale perception survey of GBV descriptions automatically extracted from a corpus of Italian newspapers. We then train regression models that predict the salience of GBV participants with respect to different dimensions of perceived responsibility. Our best model (fine-tuned BERT) shows solid overall performance, with large differences between dimensions and participants: salient _focus_ is more predictable than salient _blame_, and perpetrators’ salience is more predictable than victims’ salience. Experiments with ridge regression models using different representations show that features based on linguistic theory similarly to word-based features. Overall, we show that different linguistic choices do trigger different perceptions of responsibility, and that such perceptions can be modelled automatically. This work can be a core instrument to raise awareness of the consequences of different perspectivizations in the general public and in news producers alike.