Miracle-dz/UniFAR-FTUs huggingface dataset:
DROCC: Deep Robust One-Class Classification

Classical approaches for one-class problems such as one-class SVM and isolation forest require careful feature engineering when applied to structured domains like images. State-of-the-art methods aim to leverage deep learning to learn appropriate features via two main approaches. The first approach based on predicting transformations (Golan & El-Yaniv, 2018; Hendrycks et al., 2019a) while successful in some domains, crucially depends on an appropriate domain-specific set of transformations that are hard to obtain in general. The second approach of minimizing a classical one-class loss on the learned final layer representations, e.g., DeepSVDD (Ruff et al., 2018) suffers from the fundamental drawback of representation collapse. In this work, we propose Deep Robust One-Class Classification (DROCC) that is both applicable to most standard domains without requiring any side-information and robust to representation collapse. DROCC is based on the assumption that the points from the class of interest lie on a well-sampled, locally linear low dimensional manifold. Empirical evaluation demonstrates that DROCC is highly effective in two different one-class problem settings and on a range of real-world datasets across different domains: tabular data, images (CIFAR and ImageNet), audio, and time-series, offering up to 20% increase in accuracy over the state-of-the-art in anomaly detection. Code is available at this https URL.
Possible Phason-Polaron Effect on Purely One-Dimensional Charge Order of 
Mo6Se6
 Nanowires

In one-dimensional (1D) metallic systems, the diverging electron susceptibility and electron-phonon coupling collaboratively drive the electrons into a charge density wave (CDW) state. However, strictly 1D system is unstable against perturbations, whose effect on CDW order requires clarification ideally with altered coupling to surroundings. Here, we fabricate such a system with nanowires of Mo6Se6 bundles, which are either attached to edges of monolayer MoSe2 or isolated freely, by post-annealing the preformed MoSe2. Using scanning tunneling microscopy (STM), we visualized charge modulations and CDW gaps with prominent coherent peaks in the edge-attached nanowires. Astonishingly, the CDW order becomes suppressed in the isolated nanowires, showing CDW correlation gaps without coherent peaks. The contrasting behavior, as revealed with theoretical modeling, is interpreted as the effect of phason-polarons on the 1D CDW state. Our work elucidates a possibly unprecedented many body effect that may be generic to strictly 1D system but undermined in quasi-1D system.
On Biased Compression for Distributed Learning

In the last few years, various communication compression techniques have emerged as an indispensable tool helping to alleviate the communication bottleneck in distributed learning. However, despite the fact {\em biased} compressors often show superior performance in practice when compared to the much more studied and understood {\em unbiased} compressors, very little is known about them. In this work we study three classes of biased compression operators, two of which are new, and their performance when applied to (stochastic) gradient descent and distributed (stochastic) gradient descent. We show for the first time that biased compressors can lead to linear convergence rates both in the single node and distributed settings. Our {\em distributed} SGD method enjoys the ergodic rate $\mathcal{O}\left(\frac{\delta L \exp(-K) }{\mu} + \frac{(C + D)}{K\mu}\right)$, where $\delta$ is a compression parameter which grows when more compression is applied, $L$ and $\mu$ are the smoothness and strong convexity constants, $C$ captures stochastic gradient noise ($C=0$ if full gradients are computed on each node) and $D$ captures the variance of the gradients at the optimum ($D=0$ for over-parameterized models). Further, via a theoretical study of several synthetic and empirical distributions of communicated gradients, we shed light on why and by how much biased compressors outperform their unbiased variants. Finally, we propose a new highly performing biased compressor---combination of Top-$k$ and natural dithering---which in our experiments outperforms all other compression techniques.
Advances in centerline estimation for autonomous lateral control

The ability of autonomous vehicles to maintain an accurate trajectory within their road lane is crucial for safe operation. This requires detecting the road lines and estimating the car relative pose within its lane. Lateral lines are usually retrieved from camera images. Still, most of the works on line detection are limited to image mask retrieval and do not provide a usable representation in world coordinates. What we propose in this paper is a complete perception pipeline based on monocular vision and able to retrieve all the information required by a vehicle lateral control system: road lines equation, centerline, vehicle heading and lateral displacement. We evaluate our system by acquiring data with accurate geometric ground truth. To act as a benchmark for further research, we make this new dataset publicly available at http://airlab.deib. polimi.it/datasets/.
Rotaviruses, Reoviruses, Coltiviruses, and Orbiviruses

Rotavirus is a genus in the family Reoviridae. Rotaviruses have three important antigenic specificities: group, subgroup, and serotype. Group A rotaviruses are major pathogens in humans and animals. Ten serotypes of human group A rotaviruses are defined by neutralization of one (VP7) of the two outer capsid proteins. Of the nongroup A rotaviruses (groups B through G), only groups B and C have been detected in humans; they are not an important cause of disease in infants and young children.
DGST : Discriminator Guided Scene Text detector

Scene text detection task has attracted considerable attention in computer vision because of its wide application. In recent years, many researchers have introduced methods of semantic segmentation into the task of scene text detection, and achieved promising results. This paper proposes a detector framework based on the conditional generative adversarial networks to improve the segmentation effect of scene text detection, called DGST (Discriminator Guided Scene Text detector). Instead of binary text score maps generated by some existing semantic segmentation based methods, we generate a multi-scale soft text score map with more information to represent the text position more reasonably, and solve the problem of text pixel adhesion in the process of text extraction. Experiments on standard datasets demonstrate that the proposed DGST brings noticeable gain and outperforms state-of-the-art methods. Specifically, it achieves an F-measure of 87% on ICDAR 2015 dataset.
Infrared and 3D Skeleton Feature Fusion for RGB-D Action Recognition

For skeleton-based action recognition from depth cameras, distinguishing object-related actions with similar motions is a difficult task. The other available video streams (RGB, infrared, depth) may provide additional clues, given an appropriate feature fusion strategy. We propose a modular network combining skeleton and infrared data. A pre-trained 2D convolutional neural network (CNN) is used as a pose module to extract features from skeleton data. A pre-trained 3D CNN is used as an infrared module to extract visual features from videos. Both feature vectors are then fused and exploited jointly using a multilayer perceptron (MLP). The 2D skeleton coordinates are used to crop a region of interest around the subjects for the infrared videos. Infrared is favored over RGB, as it is less affected by illumination conditions and usable in the dark. We are the first to combine infrared and skeleton data. We evaluate our method on the NTU RGB+D dataset, the largest dataset for human action recognition from depth cameras. We perform extensive ablation studies. In particular, we show the strong contributions of our cropping strategy and pre-training on action classification accuracy. We also test various feature fusion schemes. Feature sum on an element-wise level yields the best results. Our method achieves state-of-the-art performances on NTU RBG+D.
IoT Connectivity Technologies and Applications: A Survey

The Internet of Things (IoT) is rapidly becoming an integral part of our life and also multiple industries. We expect to see the number of IoT connected devices explosively grows and will reach hundreds of billions during the next few years. To support such a massive connectivity, various wireless technologies are investigated. In this survey, we provide a broad view of the existing wireless IoT connectivity technologies and discuss several new emerging technologies and solutions that can be effectively used to enable massive connectivity for IoT. In particular, we categorize the existing wireless IoT connectivity technologies based on coverage range and review diverse types of connectivity technologies with different specifications. We also point out key technical challenges of the existing connectivity technologies for enabling massive IoT connectivity. To address the challenges, we further review and discuss some examples of promising technologies such as compressive sensing (CS) random access, non-orthogonal multiple access (NOMA), and massive multiple input multiple output (mMIMO) based random access that could be employed in future standards for supporting IoT connectivity. Finally, a classification of IoT applications is considered in terms of various service requirements. For each group of classified applications, we outline its suitable IoT connectivity options.
Sub-Goal Trees - a Framework for Goal-Based Reinforcement Learning

Many AI problems, in robotics and other domains, are goal-based, essentially seeking trajectories leading to various goal states. Reinforcement learning (RL), building on Bellman's optimality equation, naturally optimizes for a single goal, yet can be made multi-goal by augmenting the state with the goal. Instead, we propose a new RL framework, derived from a dynamic programming equation for the all pairs shortest path (APSP) problem, which naturally solves multi-goal queries. We show that this approach has computational benefits for both standard and approximate dynamic programming. Interestingly, our formulation prescribes a novel protocol for computing a trajectory: instead of predicting the next state given its predecessor, as in standard RL, a goal-conditioned trajectory is constructed by first predicting an intermediate state between start and goal, partitioning the trajectory into two. Then, recursively, predicting intermediate points on each sub-segment, until a complete trajectory is obtained. We call this trajectory structure a sub-goal tree. Building on it, we additionally extend the policy gradient methodology to recursively predict sub-goals, resulting in novel goal-based algorithms. Finally, we apply our method to neural motion planning, where we demonstrate significant improvements compared to standard RL on navigating a 7-DoF robot arm between obstacles.
Improved Image Coding Autoencoder With Deep Learning

In this paper, we build autoencoder based pipelines for extreme end-to-end image compression based on Ball\'e's approach, which is the state-of-the-art open source implementation in image compression using deep learning. We deepened the network by adding one more hidden layer before each strided convolutional layer with exactly the same number of down-samplings and up-samplings. Our approach outperformed Ball\'e's approach, and achieved around 4.0% reduction in bits per pixel (bpp), 0.03% increase in multi-scale structural similarity (MS-SSIM), and only 0.47% decrease in peak signal-to-noise ratio (PSNR), It also outperforms all traditional image compression methods including JPEG2000 and HEIC by at least 20% in terms of compression efficiency at similar reconstruction image quality. Regarding encoding and decoding time, our approach takes similar amount of time compared with traditional methods with the support of GPU, which means it's almost ready for industrial applications.
Behavior of Cross Sections for Large Numbers of Particles

It has been suggested that scattering cross sections at very high energies for producing large numbers of Higgs particles may exhibit factorial growth, and that curing this growth might be relevant to other questions in the Standard Model. We point out, first, that the question is inherently non-perturbative; low orders in the formal perturbative expansion do not give a good approximation to the scattering amplitude for sufficiently large N for any fixed, small value of the coupling. Focusing on $\lambda \phi^{4}$ theory, we argue that there may be a systematic approximation scheme for processes where N particles near threshold scatter to produce N particles, and discuss the leading contributions to the scattering amplitude and cross sections in this limit. Scattering amplitudes do not grow as rapidly as in perturbation theory. Additionally, partial and total cross sections do not show factorial growth. In the case of cross sections for $2 \to N$ particles, there is no systematic large N approximation available. That said, we provide evidence that non-perturbatively, there is no factorial growth in partial or total cross sections.
Structure and kinematics of the Virgo cluster of galaxies

Aims. This work considers the Virgo cluster of galaxies, focusing on its structure, kinematics, and morphological landscape. Our principal aim is to estimate the virial mass of the cluster. For this purpose, we present a sample of 1537 galaxies with radial velocities $V_{LG} < 2600$~km~s$^{-1}$ situated within a region of $\Delta{}SGL = 30^\circ$ and $\Delta{}SGB = 20^\circ$ around M87. About half of the galaxies have distance estimates. Methods. We selected 398 galaxies with distances in a $(17\pm5)$~Mpc range. Based on their 1D and 2D number-density profiles and their radial velocity dispersions, we made an estimate for the virial mass of the Virgo cluster. Results. We identify the infall of galaxies towards the Virgo cluster core along the Virgo Southern Extension filament. From a 1D profile of the cluster, we obtain the virial mass estimate of $(6.3\pm0.9) \times 10^{14} M_\odot, $ which is in tight agreement with its mass estimate via the external infall pattern of galaxies. Conclusions. We conclude that the Virgo cluster outskirts between the virial radius and the zero-velocity radius do not contain significant amounts of dark matter beyond the virial radius.
Hyperbolic Knot Theory

This book is an introduction to hyperbolic geometry in dimension three, and its applications to knot theory and to geometric problems arising in knot theory. It has three parts. The first part covers basic tools in hyperbolic geometry and geometric structures on 3-manifolds. The second part focuses on families of knots and links that have been amenable to study via hyperbolic geometry, particularly twist knots, 2-bridge knots, and alternating knots. It also develops geometric techniques used to study these families, such as angle structures and normal surfaces. The third part gives more detail on three important knot invariants that come directly from hyperbolic geometry, namely volume, canonical polyhedra, and the A-polynomial.
Detecting Patch Adversarial Attacks with Image Residuals

We introduce an adversarial sample detection algorithm based on image residuals, specifically designed to guard against patch-based attacks. The image residual is obtained as the difference between an input image and a denoised version of it, and a discriminator is trained to distinguish between clean and adversarial samples. More precisely, we use a wavelet domain algorithm for denoising images and demonstrate that the obtained residuals act as a digital fingerprint for adversarial attacks. To emulate the limitations of a physical adversary, we evaluate the performance of our approach against localized (patch-based) adversarial attacks, including in settings where the adversary has complete knowledge about the detection scheme. Our results show that the proposed detection method generalizes to previously unseen, stronger attacks and that it is able to reduce the success rate (conversely, increase the computational effort) of an adaptive attacker.
Certification of Semantic Perturbations via Randomized Smoothing

We introduce a novel certification method for parametrized perturbations by generalizing randomized smoothing. Using this method, we construct a provable classifier that can establish state-of-the-art robustness against semantic perturbations including geometric transformations (e.g., rotation, translation), for different types of interpolation, and, for the first time, volume changes on audio data. Our experimental results indicate that the method is practically effective: for ResNet-50 on ImageNet, it achieves rotational robustness provable up to $\pm 30^\circ$ for 28% of images.
Physically based alternative to the PE criterion for meteoroids

Meteoroids impacting the Earth atmosphere are commonly classified using the PE criterion. This criterion was introduced to support the identification of the fireball type by empirically linking its orbital origin and composition characteristics. Additionally, it is used as an indicator of the meteoroid tensile strength and its ability to penetrate the atmosphere. However, the level of classification accuracy of the PE criterion depends on the ability to constrain the value of the input data, retrieved from the fireball observation, required to derive the PE value. To overcome these uncertainties and achieve a greater classification detail we propose a new formulation using scaling laws and dimensionless variables that groups all the input variables into two parameters that are directly obtained from the fireball observations. These two parameters, ${\alpha}$ and ${\beta}$, represent the drag and the mass loss rates along the luminous part of the trajectory, respectively, and are linked to the shape, strength, ablation efficiency, mineralogical nature of the projectile, and duration of the fireball. Thus, the new formulation relies on a physical basis. This work shows the mathematical equivalence between the PE criterion and the logarithm of $2{\alpha}{\beta}$ under the same PE-criterion assumptions. We demonstrate that $log(2{\alpha}{\beta})$ offers a more general formulation which does not require any preliminary constraint on the meteor flight scenario and discuss the suitability of the new formulation for expanding the classification beyond fully disintegrating fireballs to larger impactors including meteorite-dropping fireballs. The reliability of the new formulation is validated using the Prairie Network meteor observations.
Learning Multivariate Hawkes Processes at Scale

Multivariate Hawkes Processes (MHPs) are an important class of temporal point processes that have enabled key advances in understanding and predicting social information systems. However, due to their complex modeling of temporal dependencies, MHPs have proven to be notoriously difficult to scale, what has limited their applications to relatively small domains. In this work, we propose a novel model and computational approach to overcome this important limitation. By exploiting a characteristic sparsity pattern in real-world diffusion processes, we show that our approach allows to compute the exact likelihood and gradients of an MHP -- independently of the ambient dimensions of the underlying network. We show on synthetic and real-world datasets that our model does not only achieve state-of-the-art predictive results, but also improves runtime performance by multiple orders of magnitude compared to standard methods on sparse event sequences. In combination with easily interpretable latent variables and influence structures, this allows us to analyze diffusion processes at previously unattainable scale.
Describing Physics For Physical Reasoning: Force-Based Sequential Manipulation Planning

Physical reasoning is a core aspect of intelligence in animals and humans. A central question is what model should be used as a basis for reasoning. Existing work considered models ranging from intuitive physics and physical simulators to contact dynamics models used in robotic manipulation and locomotion. In this work we propose descriptions of physics which directly allow us to leverage optimization methods for physical reasoning and sequential manipulation planning. The proposed multi-physics formulation enables the solver to mix various levels of abstraction and simplifications for different objects and phases of the solution. As an essential ingredient, we propose a specific parameterization of wrench exchange between object surfaces in a path optimization framework, introducing the point-of-attack as decision variable. We demonstrate the approach on various robot manipulation planning problems, such as grasping a stick in order to push or lift another object to a target, shifting and grasping a book from a shelve, and throwing an object to bounce towards a target.
MNN: A Universal and Efficient Inference Engine

Deploying deep learning models on mobile devices draws more and more attention recently. However, designing an efficient inference engine on devices is under the great challenges of model compatibility, device diversity, and resource limitation. To deal with these challenges, we propose Mobile Neural Network (MNN), a universal and efficient inference engine tailored to mobile applications. In this paper, the contributions of MNN include: (1) presenting a mechanism called pre-inference that manages to conduct runtime optimization; (2)deliveringthorough kernel optimization on operators to achieve optimal computation performance; (3) introducing backend abstraction module which enables hybrid scheduling and keeps the engine lightweight. Extensive benchmark experiments demonstrate that MNN performs favorably against other popular lightweight deep learning frameworks. MNN is available to public at: this https URL.
Tycho: Realistically Simulating Exoplanets within Stellar Clusters. I. Improving the Monte Carlo Approach

To fully understand the diverse population of exoplanets, we must study their early lives within open clusters, the birthplace of most stars with masses >0.5M⊙ (including those currently in the field). Indeed, when we observe planets within clustered environments, we notice highly eccentric and odd systems that suggest the importance of dynamical pathways created by interactions with additional bodies (as in the case of HD 285507b). However, it has proven difficult to investigate these effects, as many current numerical solvers for the multi-scale N-body problem are simplified and limited in scope. To remedy this, we aim to create a physically complete computational solution to explore the role of stellar close encounters and interplanetary interactions in producing the observed exoplanet populations for both open cluster stars and field stars. We present a new code, Tycho, which employs a variety of different computational techniques, including multiple N-body integration methods, close encounter handling, modified Monte Carlo scattering experiments, and a variety of empirically informed initial conditions. We discuss the methodology in detail, and its implementation within the AMUSE software framework. Approximately 1% of our systems are promptly disrupted by star-star encounters contributing to the rogue planets occurrence rate. Additionally, we find that close encounters which that perturb long-period planets lead to 38.3% of solar-system-like planetary systems becoming long-term unstable.