Miracle-dz/UniFAR-FTUs huggingface dataset:
Focusing on the hybrid quantum computing - Tabu search algorithm: new results on the Asymmetric Salesman Problem

Quantum Computing is an emerging paradigm which is gathering a lot of popularity in the current scientific and technological community. Widely conceived as the next frontier of computation, Quantum Computing is still at the dawn of its development. Thus, current solving systems suffer from significant limitations in terms of performance and capabilities. Some interesting approaches have been devised by researchers and practitioners in order to overcome these barriers, being quantum-classical hybrid algorithms one of the most often used solving schemes. The main goal of this paper is to extend the results and findings of the recently proposed hybrid Quantum Computing - Tabu Search Algorithm for partitioning problems. To do that, we focus our research on the adaptation of this method to the Asymmetric Traveling Salesman Problem. In overall, we have employed six well-known instances belonging to TSPLIB to assess the performance of Quantum Computing - Tabu Search Algorithm in comparison to QBSolv. Furthermore, as an additional contribution, this work also supposes the first solving of the Asymmetric Traveling Salesman Problem using a Quantum Computing based method. Aiming to boost whole community's research in QC, we have released the project's repository as open source code for further application and improvements.
On high-dimensional wavelet eigenanalysis

In this paper, we characterize the asymptotic and large scale behavior of the eigenvalues of wavelet random matrices in high dimensions. We assume that possibly non-Gaussian, finite-variance $p$-variate measurements are made of a low-dimensional $r$-variate ($r \ll p$) fractional stochastic process with non-canonical scaling coordinates and in the presence of additive high-dimensional noise. The measurements are correlated both time-wise and between rows. We show that the $r$ largest eigenvalues of the wavelet random matrices, when appropriately rescaled, converge in probability to scale-invariant functions in the high-dimensional limit. By contrast, the remaining $p-r$ eigenvalues remain bounded in probability. Under additional assumptions, we show that the $r$ largest log-eigenvalues of wavelet random matrices exhibit asymptotically Gaussian distributions. The results have direct consequences for statistical inference.
HyperPocket: Generative Point Cloud Completion

Scanning real-life scenes with modern registration devices typically give incomplete point cloud representations, mostly due to the limitations of the scanning process and 3D occlusions. Therefore, completing such partial representations remains a fundamental challenge of many computer vision applications. Most of the existing approaches aim to solve this problem by learning to reconstruct individual 3D objects in a synthetic setup of an uncluttered environment, which is far from a real-life scenario. In this work, we reformulate the problem of point cloud completion into an objects hallucination task. Thus, we introduce a novel autoencoder-based architecture called HyperPocket that disentangles latent representations and, as a result, enables the generation of multiple variants of the completed 3D point clouds. Furthermore, we split point cloud processing into two disjoint data streams and leverage a hypernetwork paradigm to fill the spaces, dubbed pockets, that are left by the missing object parts. As a result, the generated point clouds are smooth, plausible, and geometrically consistent with the scene. Moreover, our method offers competitive performances to the other state-of-the-art models, enabling a plethora of novel applications.
ParaVS: A Simple, Fast, Efficient and Flexible Graph Neural Network Framework for Structure-Based Virtual Screening

Structure-based virtual screening (SBVS) is a promising in silico technique that integrates computational methods into drug design. An extensively used method in SBVS is molecular docking. However, the docking process can hardly be computationally efficient and accurate simultaneously because classic mechanics scoring function is used to approximate, but hardly reach, the quantum mechanics precision in this method. In order to reduce the computational cost of the protein-ligand scoring process and use data driven approach to boost the scoring function accuracy, we introduce a docking-based SBVS method and, furthermore, a deep learning non-docking-based method that is able to avoid the computational cost of the docking process. Then, we try to integrate these two methods into an easy-to-use framework, ParaVS, that provides both choices for researchers. Graph neural network (GNN) is employed in ParaVS, and we explained how our in-house GNN works and how to model ligands and molecular targets. To verify our approaches, cross validation experiments are done on two datasets, an open dataset Directory of Useful Decoys: Enhanced (DUD.E) and an in-house proprietary dataset without computational generated artificial decoys (NoDecoy). On DUD.E we achieved a state-of-the-art AUC of 0.981 and a state-of-the-art enrichment factor at 2% of 36.2; on NoDecoy we achieved an AUC of 0.974. We further finish inference of an open database, Enamine REAL Database (RDB), that comprises over 1.36 billion molecules in 4050 core-hours using our ParaVS non-docking method (ParaVS-ND). The inference speed of ParaVS-ND is about 3.6e5 molecule / core-hour, while this number of a conventional docking-based method is around 20, which is about 16000 times faster. The experiments indicate that ParaVS is accurate, computationally efficient and can be generalized to different molecular.
b/Surf: Interactive Bézier Splines on Surfaces

B\'ezier curves provide the basic building blocks of graphic design in 2D. In this paper, we port B\'ezier curves to manifolds. We support the interactive drawing and editing of B\'ezier splines on manifold meshes with millions of triangles, by relying on just repeated manifold averages. We show that direct extensions of the De Casteljau and Bernstein evaluation algorithms to the manifold setting are fragile, and prone to discontinuities when control polygons become large. Conversely, approaches based on subdivision are robust and can be implemented efficiently. We define B\'ezier curves on manifolds, by extending both the recursive De Casteljau bisection and a new open-uniform Lane-Riesenfeld subdivision scheme, which provide curves with different degrees of smoothness. For both schemes, we present algorithms for curve tracing, point evaluation, and point insertion. We test our algorithms for robustness and performance on all watertight, manifold, models from the Thingi10k repository, without any pre-processing and with random control points. For interactive editing, we port all the basic user interface interactions found in 2D tools directly to the mesh. We also support mapping complex SVG drawings to the mesh and their interactive editing.
The Barrier of meaning in archaeological data science

Archaeologists, like other scientists, are experiencing a dataflood in their discipline, fueled by a surge in computing power and devices that enable the creation, collection, storage and transfer of an increasingly complex (and large) amount of data, such as remotely sensed imagery from a multitude of sources. In this paper, we pose the preliminary question if this increasing availability of information actually needs new computerized techniques, and Artificial Intelligence methods, to make new and deeper understanding into archaeological problems. Simply said, while it is a fact that Deep Learning (DL) has become prevalent as a type of machine learning design inspired by the way humans learn, and utilized to perform automatic actions people might describe as intelligent, we want to anticipate, here, a discussion around the subject whether machines, trained following this procedure, can extrapolate, from archaeological data, concepts and meaning in the same way that humans would do. Even prior to getting to technical results, we will start our reflection with a very basic concept: Is a collection of satellite images with notable archaeological sites informative enough to instruct a DL machine to discover new archaeological sites, as well as other potential locations of interest? Further, what if similar results could be reached with less intelligent machines that learn by having people manually program them with rules? Finally: If with barrier of meaning we refer to the extent to which humanlike understanding can be achieved by a machine, where should be posed that barrier in the archaeological data science?
Meta-Thompson Sampling

Efficient exploration in bandits is a fundamental online learning problem. We propose a variant of Thompson sampling that learns to explore better as it interacts with bandit instances drawn from an unknown prior. The algorithm meta-learns the prior and thus we call it MetaTS. We propose several efficient implementations of MetaTS and analyze it in Gaussian bandits. Our analysis shows the benefit of meta-learning and is of a broader interest, because we derive a novel prior-dependent Bayes regret bound for Thompson sampling. Our theory is complemented by empirical evaluation, which shows that MetaTS quickly adapts to the unknown prior.
Sequential Change-Point Detection for Mutually Exciting Point Processes

Abstract We present a new CUSUM procedure for sequential change-point detection in self- and mutually-exciting point processes (specifically, Hawkes networks) using discrete events data. Hawkes networks have become a popular model in statistics and machine learning, primarily due to their capability in modeling irregularly observed data where the timing between events carries a lot of information. The problem of detecting abrupt changes in Hawkes networks arises from various applications, including neuroengineering, sensor networks, and social network monitoring. Despite this, there has not been an efficient online algorithm for detecting such changes from sequential data. To this end, we propose an online recursive implementation of the CUSUM statistic for Hawkes processes, which is computationally and memory-efficient and can be decentralized for distributed computing. We first prove theoretical properties of this new CUSUM procedure, then show the improved performance of this approach over existing methods, including the Shewhart procedure based on count data, the generalized likelihood ratio statistic, and the standard score statistic. This is demonstrated via simulation studies and an application to population code change-detection in neuroengineering.
Frame Difference-Based Temporal Loss for Video Stylization

Neural style transfer models have been used to stylize an ordinary video to specific styles. To ensure temporal inconsistency between the frames of the stylized video, a common approach is to estimate the optic flow of the pixels in the original video and make the generated pixels match the estimated optical flow. This is achieved by minimizing an optical flow-based (OFB) loss during model training. However, optical flow estimation is itself a challenging task, particularly in complex scenes. In addition, it incurs a high computational cost. We propose a much simpler temporal loss called the frame difference-based (FDB) loss to solve the temporal inconsistency problem. It is defined as the distance between the difference between the stylized frames and the difference between the original frames. The differences between the two frames are measured in both the pixel space and the feature space specified by the convolutional neural networks. A set of human behavior experiments involving 62 subjects with 25,600 votes showed that the performance of the proposed FDB loss matched that of the OFB loss. The performance was measured by subjective evaluation of stability and stylization quality of the generated videos on two typical video stylization models. The results suggest that the proposed FDB loss is a strong alternative to the commonly used OFB loss for video stylization.
Radio signatures from encounters between neutron stars and QCD-axion minihalos around primordial black holes

Probing the QCD axion dark matter (DM) hypothesis is extremely challenging as the axion interacts very weakly with Standard Model particles. We propose a new avenue to test the QCD axion DM via transient radio signatures coming from encounters between neutron stars (NSs) and axion minihalos around primordial black holes (PBHs). We consider a general QCD axion scenario in which the PQ symmetry breaking occurs before (or during) inflation coexisting with a small fraction of DM in the form of PBHs. The PBHs will unavoidably acquire around them axion minihalos with the typical length scale of parsecs. The axion density in the minihalos may be much higher than the local DM density, and the presence of these compact objects in the Milky Way today provides a novel chance for testing the axion DM hypothesis. We study the evolution of the minihalo mass distribution in the Galaxy accounting for tidal forces and estimate the encounter rate between NSs and the dressed PBHs. We find that the encounters give rise to transient line-like emission of radio frequency photons produced by the resonant axion-photon conversion in the NS magnetosphere and the characteristic signal could be detectable with the sensitivity of current and prospective radio telescopes. It would be important to investigate in detail search strategies for such signals which would provide a novel pathway for QCD axion detection.
Sparse Graph Based Sketching for Fast Numerical Linear Algebra

In recent years, a variety of randomized constructions of sketching matrices have been devised, that have been used in fast algorithms for numerical linear algebra problems, such as least squares regression, low-rank approximation, and the approximation of leverage scores. A key property of sketching matrices is that of subspace embedding. In this paper, we study sketching matrices that are obtained from bipartite graphs that are sparse, i.e., have left degree s that is small. In particular, we explore two popular classes of sparse graphs, namely, expander graphs and magical graphs. For a given subspace ${\mathcal{U}} \subseteq {{\mathbb{R}}^n}$ of dimension k, we show that the magical graph with left degree s = 2 yields a (1 ± ϵ) ℓ2-subspace embedding for ${\mathcal{U}}$, if the number of right vertices (the sketch size) $m = {\mathcal{O}}\left( {{k^2}/{\varepsilon ^2}} \right)$. The expander graph with $s = {\mathcal{O}}(\log k/\varepsilon )$ yields a subspace embedding for $m = {\mathcal{O}}\left( {k\log k/{\varepsilon ^2}} \right)$. We also discuss the construction of sparse sketching matrices with reduced randomness using expanders based on error-correcting codes. Empirical results on various synthetic and real datasets show that these sparse graph sketching matrices work very well in practice.
Massive MIMO under Double Scattering Channels: Power Minimization and Congestion Controls

This paper considers a massive MIMO system under the double scattering channels. We derive a closed-form expression of the uplink ergodic spectral efficiency (SE) by exploiting the maximum-ratio combining technique with imperfect channel state information. We then formulate and solve a total uplink data power optimization problem that aims at simultaneously satisfying the required SEs from all the users with limited power resources. We further propose algorithms to cope with the congestion issue appearing when at least one user is served by lower SE than requested. Numerical results illustrate the effectiveness of our proposed power optimization. More importantly, our proposed congestion-handling algorithms can guarantee the required SEs to many users under congestion, even when the SE requirement is high.
Uncertainty Propagation in Convolutional Neural Networks: Technical Report

In this technical report we study the problem of propagation of uncertainty (in terms of variances of given uni-variate normal random variables) through typical building blocks of a Convolutional Neural Network (CNN). These include layers that perform linear operations, such as 2D convolutions, fully-connected, and average pooling layers, as well as layers that act non-linearly on their input, such as the Rectified Linear Unit (ReLU). Finally, we discuss the sigmoid function, for which we give approximations of its first- and second-order moments, as well as the binary cross-entropy loss function, for which we approximate its expected value under normal random inputs.
Auctus: A Dataset Search Engine for Data Discovery and Augmentation

The large volumes of structured data currently available, from Web tables to open-data portals and enterprise data, open up new opportunities for progress in answering many important scientific, societal, and business questions. However, finding relevant data is difficult. While search engines have addressed this problem for Web documents, there are many new challenges involved in supporting the discovery of structured data. We demonstrate how the Auctus dataset search engine addresses some of these challenges. We describe the system architecture and how users can explore datasets through a rich set of queries. We also present case studies which show how Auctus supports data augmentation to improve machine learning models as well as to enrich analytics.
TESS Hunt for Young and Maturing Exoplanets (THYME). V. A Sub-Neptune Transiting a Young Star in a Newly Discovered 250 Myr Association

The detection and characterization of young planetary systems offer a direct path to study the processes that shape planet evolution. We report on the discovery of a sub-Neptune-sized planet orbiting the young star HD 110082 (TOI-1098). Transit events we initially detected during TESS Cycle 1 are validated with time-series photometry from Spitzer. High-contrast imaging and high-resolution, optical spectra are also obtained to characterize the stellar host and confirm the planetary nature of the transits. The host star is a late-F dwarf (M ⋆ = 1.2M ⊙) with a low-mass, M dwarf binary companion (M ⋆ = 0.26M ⊙) separated by nearly one arcminute (∼6200 au). Based on its rapid rotation and Lithium absorption, HD 110082 is young, but is not a member of any known group of young stars (despite proximity to the Octans association). To measure the age of the system, we search for coeval, phase-space neighbors and compile a sample of candidate siblings to compare with the empirical sequences of young clusters and to apply quantitative age-dating techniques. In doing so, we find that HD 110082 resides in a new young stellar association we designate MELANGE-1, with an age of Myr. Jointly modeling the TESS and Spitzer light curves, we measure a planetary orbital period of 10.1827 days and radius of R p = 3.2 ± 0.1R ⊕. HD 110082 b’s radius falls in the largest 12% of field-age systems with similar host-star mass and orbital period. This finding supports previous studies indicating that young planets have larger radii than their field-age counterparts.
Spectropolarimetry of low redshift quasars: origin of the polarization and implications for black hole mass estimates

We present the results of high signal-to-noise ratio VLT spectropolarimetry of a representative sample of 25 bright type 1 AGN at z<0.37, of which nine are radio-loud. The sample covers uniformly the 5100 A optical luminosity at $L_{5100}\sim 10^{44}-10^{46}$ erg s$^{-1}$, and H$\alpha$ width at FWHM$\sim 1000-10,000$~ km/s. We derive the continuum and the H$\alpha$ polarization amplitude, polarization angle, and angle swing across the line, together with the radio properties. We find the following: 1. The broad line region (BLR) and continuum polarization are both produced by a single scattering medium. 2. The scattering medium is equatorial, and at right angle to the system axis. 3. The scattering medium is located at or just outside the BLR. The continuum polarization and the H$\alpha$ polarization angle swing, can both serve as an inclination indicator. The observed line width is found to be affected by inclination, which can lead to an underestimate of the black hole mass by a factor of $\sim 5$ for a close-to face-on view. The line width measured in the polarized flux overcomes the inclination bias, and provides a close-to equatorial view of the BLR in all AGN, which allows to reduce the inclination bias in the BLR based black hole mass estimates.
Adversarial Poisoning Attacks and Defense for General Multi-Class Models Based On Synthetic Reduced Nearest Neighbors

State-of-the-art machine learning models are vulnerable to data poisoning attacks whose purpose is to undermine the integrity of the model. However, the current literature on data poisoning attacks is mainly focused on ad hoc techniques that are only applicable to specific machine learning models. Additionally, the existing data poisoning attacks in the literature are limited to either binary classifiers or to gradient-based algorithms. To address these limitations, this paper first proposes a novel model-free label-flipping attack based on the multi-modality of the data, in which the adversary targets the clusters of classes while constrained by a label-flipping budget. The complexity of our proposed attack algorithm is linear in time over the size of the dataset. Also, the proposed attack can increase the error up to two times for the same attack budget. Second, a novel defense technique based on the Synthetic Reduced Nearest Neighbor (SRNN) model is proposed. The defense technique can detect and exclude flipped samples on the fly during the training procedure. Through extensive experimental analysis, we demonstrate that (i) the proposed attack technique can deteriorate the accuracy of several models drastically, and (ii) under the proposed attack, the proposed defense technique significantly outperforms other conventional machine learning models in recovering the accuracy of the targeted model.
Impact of photochemical hazes and gases on exoplanet atmospheric thermal structure

We investigate the impact of photochemical hazes and disequilibrium gases on the thermal structure of hot-Jupiters, using a detailed 1-D radiative-convective model. We find that the inclusion of photochemical hazes results in major heating of the upper and cooling of the lower atmosphere. Sulphur containing species, such as SH, S2 and S3 provide significant opacity in the middle atmosphere and lead to local heating near 1 mbar, while OH, CH, NH, and CN radicals produced by the photochemistry affect the thermal structure near 1 μbar. Furthermore we show that the modifications on the thermal structure from photochemical gases and hazes can have important ramifications for the interpretation of transit observations. Specifically, our study for the hazy HD 189733 b shows that the hotter upper atmosphere resulting from the inclusion of photochemical haze opacity imposes an expansion of the atmosphere, thus a steeper transit signature in the UV-Visible part of the spectrum. In addition, the temperature changes in the photosphere also affect the secondary eclipse spectrum. For HD 209458 b we find that a small haze opacity could be present in this atmosphere, at pressures below 1 mbar, which could be a result of both photochemical hazes and condensates. Our results motivate the inclusion of radiative feedback from photochemical hazes in general circulation models for a proper evaluation of atmospheric dynamics.