Miracle-dz/UniFAR-FTUs huggingface dataset:
Revisited Containment for Graph Patterns

We consider the class of conditional graph patterns (\emph{CGPs}) that allow user to query data graphs with complex patterns that contain negation and predicates. To overcome the prohibitive cost of subgraph isomorphism, we consider matching of \emph{CGPs} under simulation semantics which can be conducted in quadratic time. In emerging applications, one would like to reduce more this matching time, and the static analysis of patterns may allow ensuring part of this reduction. We study the containment problem of \emph{CGPs} that aims to check whether the matches of some pattern $P_1$, over any data graph, are contained in those of another pattern $P_2$ (written $P_1\sqsubseteq P_2$). The optimization process consists to extract matches of $P_1$ only from those of $P_2$ without querying the (possibly large) data graph. We show that the traditional semantics of containment is decidable in quadratic time, but it fails to meet the optimization goal in the presence of negation and predicates. To overcome this limit, we propose a new semantics of containment, called \emph{strong containment}, that is more suitable for \emph{CGPs} and allows to reduce their matching time. We show that \emph{strong containment} can be decided in cubic time by providing such an algorithm. We are planing to use results of this paper to answer \emph{CGPs} using views. This paper is part of an ongoing project that aims to design a caching system for complex graph patterns.
Sources of Interplanetary Dust

The interplanetary dust complex is manifested by the visible-wavelength zodiacal light from small particles near the Earth’s orbit, particles captured by the Earth’s atmosphere (collected by aircraft and observed as meteors), in-situ detections by spacecraft, and broad thermal emission observed from space. Understanding the origin of this dust received a major boost when the Infrared Astronomical Satellite was launched in 1983. For the first time, the strong signature of the zodiacal dust complex in the thermal infrared and parallel ring-like dust bands associated with collisionally produced families of asteroids were revealed. Long cometary dust trails were soon discovered and inferred to be associated with all short-period comets. Trails, in turn, indicated that comets were far ‘rockier’ than previously thought. Other broader trail-like structures (Type II) were detected that possibly originated from very recent asteroid collisions. Reexamination of early Pioneer data traveling beyond Jupiter and Saturn suggested that the dust detected originated from the Kuiper Belt, generated presumably by collisions. Subsequent spacebased infrared satellites have made observations providing deeper insights into the nature of these various phenomena. Recent analyses have identified dust bands arising from the stochastic disruption of small asteroids [1]. Most recently, a survey of comets by Spitzer has both confirmed IRAS-based inference of the commonality of trails, but has shown them to be quite diverse [2]. Spitzer scans of the zodiacal cloud has recovered Type II trails first detected by IRAS, suggesting they extend completely around their orbits [3]. However, the ability to go from sources of dust production to reproducing the zodiacal cloud as a whole has had limited success so far [4]. Now, our understanding of what comprises asteroid and cometary dust may have been turned on its head by the Wild 2 dust samples returned by Stardust [5].
Probabilistic Galois Theory -- The Square Discriminant Case

The paper studies the probability for a Galois group of a random polynomial to be A n . We focus on the so-called large box model, where we choose the coeﬃcients of the polynomial independently and uniformly from {− L, . , L } . The state-of-the-art upper bound is O ( L − 1 ), due to Bhargava. We conjecture a much stronger upper bound L − n/ 2+ ǫ , and that this bound is essentially sharp. We prove strong lower bounds both on this probability and on the related probability of the discriminant being a square.
Generative Extraction of Audio Classifiers for Speaker Identification

It is perhaps no longer surprising that machine learning models, especially deep neural networks, are particularly vulnerable to attacks. One such vulnerability that has been well studied is model extraction: a phenomenon in which the attacker attempts to steal a victim's model by training a surrogate model to mimic the decision boundaries of the victim model. Previous works have demonstrated the effectiveness of such an attack and its devastating consequences, but much of this work has been done primarily for image and text processing tasks. Our work is the first attempt to perform model extraction on {\em audio classification models}. We are motivated by an attacker whose goal is to mimic the behavior of the victim's model trained to identify a speaker. This is particularly problematic in security-sensitive domains such as biometric authentication. We find that prior model extraction techniques, where the attacker \textit{naively} uses a proxy dataset to attack a potential victim's model, fail. We therefore propose the use of a generative model to create a sufficiently large and diverse pool of synthetic attack queries. We find that our approach is able to extract a victim's model trained on \texttt{LibriSpeech} using queries synthesized with a proxy dataset based off of \texttt{VoxCeleb}; we achieve a test accuracy of 84.41\% with a budget of 3 million queries.
Learning Visual Representation from Modality-Shared Contrastive Language-Image Pre-training

Large-scale multi-modal contrastive pre-training has demonstrated great utility to learn transferable features for a range of downstream tasks by mapping multiple modalities into a shared embedding space. Typically, this has employed separate encoders for each modality. However, recent work suggests that transformers can support learning across multiple modalities and allow knowledge sharing. Inspired by this, we investigate a variety of Modality-Shared Contrastive Language-Image Pre-training (MS-CLIP) frameworks. More specifically, we question how many parameters of a transformer model can be shared across modalities during contrastive pre-training, and rigorously examine architectural design choices that position the proportion of parameters shared along a spectrum. In studied conditions, we observe that a mostly unified encoder for vision and language signals outperforms all other variations that separate more parameters. Additionally, we find that light-weight modality-specific parallel modules further improve performance. Experimental results show that the proposed MS-CLIP approach outperforms vanilla CLIP by up to 13\% relative in zero-shot ImageNet classification (pre-trained on YFCC-100M), while simultaneously supporting a reduction of parameters. In addition, our approach outperforms vanilla CLIP by 1.6 points in linear probing on a collection of 24 downstream vision tasks. Furthermore, we discover that sharing parameters leads to semantic concepts from different modalities being encoded more closely in the embedding space, facilitating the transferring of common semantic structure (e.g., attention patterns) from language to vision. Code is available at \href{https://github.com/Hxyou/MSCLIP}{URL}.
Differentially Private Estimation via Statistical Depth

Constructing a differentially private (DP) estimator requires deriving the maximum influence of an observation, which can be difficult in the absence of exogenous bounds on the input data or the estimator, especially in high dimensional settings. This paper shows that standard notions of statistical depth, i.e., halfspace depth and regression depth, are particularly advantageous in this regard, both in the sense that the maximum influence of a single observation is easy to analyze and that this value is typically low. This is used to motivate new approximate DP location and regression estimators using the maximizers of these two notions of statistical depth. A more computationally efficient variant of the approximate DP regression estimator is also provided. Also, to avoid requiring that users specify a priori bounds on the estimates and/or the observations, variants of these DP mechanisms are described that satisfy random differential privacy (RDP), which is a relaxation of differential privacy provided by Hall, Wasserman, and Rinaldo (2013). We also provide simulations of the two DP regression methods proposed here. The proposed estimators appear to perform favorably relative to the existing DP regression methods we consider in these simulations when either the sample size is at least 100-200 or the privacy-loss budget is sufficiently high.
Dean-Kawasaki Equation With Singular Non-local Interactions And Correlated Noise

This paper is concerned with well-posedness of Dean-Kawasaki equation with non-local singular interactions and correlated noise. By imposing Ladyzhenskaya-Prodi-Serrin condition on the interaction kernel, we prove the existence of renormalized kinetic solutions to the Dean-Kawasaki equation, which are weak solutions in the probabilistic sense. The pathwise uniqueness can be achieved under an additional integrability assumption on the divergence of the interaction vector field. With the aid of the pathwise uniqueness, we show the existence of probabilistically strong solutions. The well-posedness result contributes to the study of non-equilibrium fluctuations for the non-local interacting Dean-Kawasaki equation in the small noise regime.
RandProx: Primal-Dual Optimization Algorithms with Randomized Proximal Updates

Proximal splitting algorithms are well suited to solving large-scale nonsmooth optimization problems, in particular those arising in machine learning. We propose a new primal-dual algorithm, in which the dual update is randomized; equivalently, the proximity operator of one of the function in the problem is replaced by a stochastic oracle. For instance, some randomly chosen dual variables, instead of all, are updated at each iteration. Or, the proximity operator of a function is called with some small probability only. A nonsmooth variance-reduction technique is implemented so that the algorithm finds an exact minimizer of the general problem involving smooth and nonsmooth functions, possibly composed with linear operators. We derive linear convergence results in presence of strong convexity; these results are new even in the deterministic case, when our algorithms reverts to the recently proposed Primal-Dual Davis-Yin algorithm. Some randomized algorithms of the literature are also recovered as particular cases (e.g., Point-SAGA). But our randomization technique is general and encompasses many unbiased mechanisms beyond sampling and probabilistic updates, including compression. Since the convergence speed depends on the slowest among the primal and dual contraction mechanisms, the iteration complexity might remain the same when randomness is used. On the other hand, the computation complexity can be significantly reduced. Overall, randomness helps getting faster algorithms. This has long been known for stochastic-gradient-type algorithms, and our work shows that this fully applies in the more general primal-dual setting as well.
Halo Properties and Mass Functions of Groups/Clusters from the DESI Legacy Imaging Surveys DR9

Based on a large group/cluster catalog recently constructed from the DESI Legacy Imaging Surveys DR9 using an extended halo-based group finder, we measure and model the group–galaxy weak-lensing signals for groups/clusters in a few redshift bins within redshift range 0.1 ≤ z < 0.6. Here, the background shear signals are obtained based on the DECaLS survey shape catalog, derived with the Fourier_Quad method. We divide the lens samples into five equispaced redshift bins and seven mass bins, which allow us to probe the redshift and mass dependence of the lensing signals, and hence the resulting halo properties. In addition to these sample selections, we also check the signals around different group centers, e.g., the brightest central galaxy, the luminosity-weighted center, and the number-weighted center. We use a lensing model that includes off-centering to describe the lensing signals that we measure for all mass and redshift bins. The results demonstrate that our model predictions for the halo masses, biases, and concentrations are stable and self-consistent among different samples for different group centers. Taking advantage of the very large and complete sample of groups/clusters, as well as the reliable estimations of their halo masses, we provide measurements of the cumulative halo mass functions up to redshift z = 0.6, with a mass precision at 0.03 ∼ 0.09 dex.
Necessary height of the vertical stiffeners in steel silos on discrete supports

The steel silos are interesting complex facilities. In order to ensure unloading of whole amount of stored product by gravity, the steel silos are often placed on supporting frame structure. Values of stresses in the joints between the thin walled shell and supporting frame elements are very high. It can causes local loss of stability in the shell. To prevent its local buckling, many designers put stiffening elements above the supports. Here the question is how high should be the stiffening elements? The right solution is that they should reach that level till which the values of the meridional normal stresses above the supports and in the middle between them are equalized. Under this level the cylindrical shell will be considered as a ring beam, stiffened by elements above the supports. Above it, the cylinder can be calculated as continuously supported shell. But where is this level? A lot of researchers worked on values and way of distribution of normal meridional stresses above the supports of the cylindrical shells. As a result of their efforts are determined critical height Hcr of the shell and the ideal position HI of intermediate stiffening ring. But these heights are considerably different between each other. To which of them our vertical stiffening elements should achieve?
Missing Values Handling for Machine Learning Portfolios

We characterize the structure and origins of missingness for 159 cross-sectional return predictors and study missing value handling for portfolios constructed using machine learning. Simply imputing with cross-sectional means performs well compared to rigorous expectation-maximization methods. This stems from three facts about predictor data: (1) missingness occurs in large blocks organized by time, (2) cross-sectional correlations are small, and (3) missingness tends to occur in blocks organized by the underlying data source. As a result, observed data provide little information about missing data. Sophisticated imputations introduce estimation noise that can lead to underperformance if machine learning is not carefully applied.
The K2-3 System Revisited: Testing Photoevaporation and Core-powered Mass Loss with Three Small Planets Spanning the Radius Valley

Multiplanet systems orbiting M dwarfs provide valuable tests of theories of small-planet formation and evolution. K2-3 is an early M dwarf hosting three small exoplanets (1.5–2.0 R ⊕) at distances of 0.07–0.20 au. We measure the high-energy spectrum of K2-3 with HST/COS and XMM-Newton and use empirically driven estimates of Lyα and extreme-ultraviolet flux. We use EXOFASTv2 to jointly fit radial velocity, transit, and spectral energy distribution data. This constrains the K2-3 planet radii to 4% uncertainty and the masses of K2-3b and c to 13% and 30%, respectively; K2-3d is not detected in radial velocity measurements. K2-3b and c are consistent with rocky cores surrounded by solar composition envelopes (mass fractions of 0.36−0.11+0.14% and 0.07−0.05+0.09% ), H2O envelopes ( 55−12+14% and 16−10+17% ), or a mixture of both. However, based on the high-energy output and estimated age of K2-3, it is unlikely that K2-3b and c retain solar composition atmospheres. We pass the planet parameters and high-energy stellar spectrum to atmospheric models. Dialing the high-energy spectrum up and down by a factor of 10 produces significant changes in trace molecule abundances, but not at a level detectable with transmission spectroscopy. Though the K2-3 planets span the small-planet radius valley, the observed system architecture cannot be readily explained by photoevaporation or core-powered mass loss. We instead propose that (1) the K2-3 planets are all volatile-rich, with K2-3d having a lower density than typical of super-Earths, and/or (2) the K2-3 planet architecture results from stochastic processes such as planet formation, planet migration, and impact erosion.
Communication-Censored-ADMM for Electric Vehicle Charging in Unbalanced Distribution Grids

We propose an alternating direction method of multipliers (ADMM)-based algorithm for coordinating the charge and discharge of electric vehicles (EVs) to manage grid voltages while minimizing EV time-of-use energy costs. We prove that by including a Communication-Censored strategy, the algorithm maintains its solution integrity, while reducing peer-to-peer communications. By means of a case study on a representative unbalanced two node circuit, we demonstrate that our proposed Communication-Censored-ADMM (CC-ADMM) EV charging strategy reduces peer-to-peer communications by up to 80%, compared to a benchmark ADMM approach.
Static hand gesture recognition for American sign language using neuromorphic hardware

In this paper, we develop four spiking neural network (SNN) models for two static American sign language (ASL) hand gesture classification tasks, i.e., the ASL alphabet and ASL digits. The SNN models are deployed on Intel’s neuromorphic platform, Loihi, and then compared against equivalent deep neural network (DNN) models deployed on an edge computing device, the Intel neural compute stick 2 (NCS2). We perform a comprehensive comparison between the two systems in terms of accuracy, latency, power consumption, and energy. The best DNN model achieves an accuracy of 99.93% on the ASL alphabet dataset, whereas the best performing SNN model has an accuracy of 99.30%. For the ASL-digits dataset, the best DNN model achieves an accuracy of 99.76% accuracy while the SNN achieves 99.03%. Moreover, our obtained experimental results show that the Loihi neuromorphic hardware implementations achieve up to 20.64× and 4.10× reduction in power consumption and energy, respectively, when compared to NCS2.
Inverse cascades of kinetic energy and thermal variance in three-dimensional horizontally extended turbulent convection

Inverse cascades of kinetic energy and thermal variance in the subset of vertically homogeneous modes in spectral space are found to cause a slow aggregation to a pair of convective supergranules that eventually ﬁll the whole horizontally extended, three-dimensional, turbulent Rayleigh-B´enard convection layer when a heat ﬂux is prescribed at the top and bottom. An additional weak rotation of the layer around the vertical axis stops this aggregation at a scale that is smaller than the lateral domain extension and ceases the inverse cascade for the thermal variance. The inverse cascade for the kinetic energy remains intact, even for times at which the root mean square values of temperature and velocity have reached the statistically steady state. This kinetic energy inverse cascade sustains the horizontally extended convection patterns which are best visible in the temperature ﬁeld. The resulting characteristic length of the aggregated convection patterns depends on the thermal driving and linearly on the strength of rotation. Our study demonstrates the importance of inverse energy cascades beyond the two-dimensional turbulence case in a three-dimensional convection ﬂow that is subject to a multi-scale energy injection by thermal plumes and driven by boundary heat ﬂuxes as typically present in natural geo- and astrophysical systems, such as solar convection.
Secure Anonymous Conferencing in Quantum Networks

Federico Grasselli, ∗ Gláucia Murta, † Jarn de Jong, Frederik Hahn, Dagmar Bruß, Hermann Kampermann, and Anna Pappa 4 Institut für Theoretische Physik III, Heinrich-Heine-Universität Düsseldorf, 40225 Düsseldorf, Germany Electrical Engineering and Computer Science Department, Technische Universität Berlin, 10587 Berlin, Germany Dahlem Center for Complex Quantum Systems, Freie Universität Berlin, 14195 Berlin, Germany Fraunhofer Institut for Open Communication Systems FOKUS, 10589 Berlin, Germany
Plug-and-Play Compressed Sensing: Theoretical Guarantees on Exact and Robust Recovery

—In the Plug-and-Play (PnP) framework, regularization is performed by plugging an off-the-shelf denoiser within a proximal algorithm such as ISTA or ADMM. PnP produces state-of-the-art results in many imaging applications, but its theoretical aspects are not well understood. In particular, the present work is motivated by the question that, similar to classical compressed sensing, is it theoretically possible to recover the ground-truth using PnP? More speciﬁcally, under what conditions on the ground-truth, the sensing matrix, and the PnP denoiser is the reconstruction guaranteed to be exact? The foremost hurdle in this regard is the absence of an explicit regularizer–PnP is an algorithmic framework, and it is not apparent if a limit point of the PnP iterations (if one exists) is the minimizer of some objective function. It was recently shown that it is possible to associate a convex regularizer Φ with a class of linear denoisers. For such denoisers, the PnP iterations correspond to solving a convex optimization problem involving Φ . Motivated by this result, we consider the PnP analog of the compressed sensing problem: min Φ( x ) s . t . A x = A ξ , where A ∈ R m × n is a random sensing matrix, Φ is the regularizer associated with a denoiser W from the class mentioned above, and ξ is the ground-truth signal. We prove that if the sensing matrix is Gaussian and ξ ∈ range( W ) , then the minimizer of this problem is almost surely ξ if rank( W ) (cid:54) m , and almost never if rank( W ) > m . In other words, the range of the PnP denoiser plays the role of a signal prior, and its dimension marks a sharp transition from failure to success of exact recovery. We are able to extend the result to subgaussian sensing matrices, except that we can guarantee exact recovery only with high probability (and not almost surely). For noisy measurements of the form b = A ξ + η
On the contribution of cosmic-ray interactions in the circumgalactic gas to the observed high-energy neutrino flux

Cosmic rays escaping the Milky-Way disk interact with circumgalactic gas which fills the virial volume of our Galaxy. These interactions should produce guaranteed fluxes of energetic diffuse neutrinos and photons observable at the Earth. This neutrino flux would be a plausible contribution to the spectrum measured by the IceCube neutrino observatory: the energy emitted in this way is weakly constrained from cascade gamma rays, since the cascades have no time to develop, but the arrival directions of the neutrinos do not point to the Galactic disk, in agreement with observations. However, previous studies reported very different estimates of the corresponding neutrino flux, so it was unclear if this contribution to the observed spectrum is essential. Here we readdress the calculation of this diffuse neutrino flux component under various assumptions about the cosmic-ray spectrum and propagation in the circumgalactic medium. We find that even with these variations, this contribution to the observed neutrino flux remains subleading provided multimessenger constraints are satisfied.