Miracle-dz/UniFAR-FTUs huggingface dataset:
Experimenting with Convolutional Neural Network Architectures for the automatic characterization of Solitary Pulmonary Nodules' malignancy rating

Lung Cancer is the most common cause of cancer-related death worldwide. Early and automatic diagnosis of Solitary Pulmonary Nodules (SPN) in Computer Tomography (CT) chest scans can provide early treatment as well as doctor liberation from time-consuming procedures. Deep Learning has been proven as a popular and influential method in many medical imaging diagnosis areas. In this study, we consider the problem of diagnostic classification between benign and malignant lung nodules in CT images derived from a PET/CT scanner. More specifically, we intend to develop experimental Convolutional Neural Network (CNN) architectures and conduct experiments, by tuning their parameters, to investigate their behavior, and to define the optimal setup for the accurate classification. For the experiments, we utilize PET/CT images obtained from the Laboratory of Nuclear Medicine of the University of Patras, and the publically available database called Lung Image Database Consortium Image Collection (LIDC-IDRI). Furthermore, we apply simple data augmentation to generate new instances and to inspect the performance of the developed networks. Classification accuracy of 91% and 93% on the PET/CT dataset and on a selection of nodule images form the LIDC-IDRI dataset, is achieved accordingly. The results demonstrate that CNNs are a trustworth method for nodule classification. Also, the experiment confirms that data augmentation enhances the robustness of the CNNs.
The Elliptical Processes: a New Family of Flexible Stochastic Processes

We present the elliptical processes-a new family of stochastic processes that subsumes the Gaussian process and the Student-t process. This generalization retains computational tractability while substantially increasing the range of tail behaviors that can be modeled. We base the elliptical processes on a representation of elliptical distributions as mixtures of Gaussian distributions and derive closed-form expressions for the marginal and conditional distributions. We perform an in-depth study of a particular elliptical process, where the mixture distribution is piecewise constant, and show some of its advantages over the Gaussian process through a number of experiments on robust regression. Looking forward, we believe there are several settings, e.g. when the likelihood is not Gaussian or when accurate tail modeling is critical, where the elliptical processes could become the stochastic processes of choice.
Mitigating decoherence in hot electron interferometry

Due to their high energy, hot electrons in quantum Hall edge (QHE) states can be considered as single particles that have the potential to be used for quantum optics-like experiments. Unlike photons, however, electrons typically undergo scattering processes in transport, which results in a loss of coherence and limits their ability to show quantum-coherent behaviour. Here we study theoretically the decoherence mechanisms of hot electrons in a Mach–Zehnder interferometer (MZI), and highlight the role played by both acoustic and optical phonon emission. We discuss optimal choices of experimental parameters and show that high visibilities of ≳ 85% are achievable in hot-electron devices over relatively long distances of 10 μm. We also discuss energy filtration techniques to remove decoherent electrons and show that this can increase visibilities to over 95%. This represents an improvement over Fermi-level electron quantum optics, and suggests hot-electron charge pumps as a platform for realising quantum-coherent nanoelectronic devices.
Cosmological signatures of torsion and how to distinguish torsion from the dark sector

Torsion is a non-Riemannian geometrical extension of general relativity that allows including the spin of matter and the twisting of spacetime. Cosmological models with torsion have been considered in the literature to solve problems of either the very early (high redshift $z$) or the present-day Universe. This paper focuses on distinguishable observational signatures of torsion that could not be otherwise explained with a scalar field in pseudo-Riemannian geometry. We show that when torsion is present, the cosmic duality relation between the angular diameter distance, $D_{\mathrm A}$, and the luminosity distance, $D_{\mathrm L}$, is broken. We show how the deviation described by the parameter $\eta = D_{\mathrm L}/[ D_{\mathrm A}(1+z)^2] -1 $ is linked to torsion and how different forms of torsion lead to special-case parametrisations of $\eta$, including $\eta_0 z$, $\eta_0 z/(1+z)$, and $\eta_0 \ln (1+z)$. We also show that the effects of torsion could be visible in low-redshift data, inducing biases in supernovae-based $H_0$ measurements. We also show that torsion can impact the Clarkson-Bassett-Lu (CBL) function ${\cal C}(z) = 1 + H^2 (D D'' - D'^2) + H H' D D'$, where $D$ is the transverse comoving distance. If $D$ is inferred from the luminosity distance, then, in general non-zero torsion models, ${\cal C}(z) \ne 0$. For pseudo-Riemannian geometry, the Friedmann-Lemaitre-Robertson-Walker (FLRW) metric has ${\cal C}(z) \equiv 0$; thus, measurement of the CBL function could provide another diagnostic of torsion.
Identifying Individual Dogs in Social Media Images

We present the results of an initial study focused on developing a visual AI solution able to recognize individual dogs in unconstrained (wild) images occurring on social media. The work described here is part of joint project done with Pet2Net, a social network focused on pets and their owners. In order to detect and recognize individual dogs we combine transfer learning and object detection approaches on Inception v3 and SSD Inception v2 architectures respectively and evaluate the proposed pipeline using a new data set containing real data that the users uploaded to Pet2Net platform. We show that it can achieve 94.59% accuracy in identifying individual dogs. Our approach has been designed with simplicity in mind and the goal of easy deployment on all the images uploaded to Pet2Net platform. A purely visual approach to identifying dogs in images, will enhance Pet2Net features aimed at finding lost dogs, as well as form the basis of future work focused on identifying social relationships between dogs, which cannot be inferred from other data collected by the platform.
Compositional Conformance Checking of Nested Petri Nets and Event Logs of Multi-Agent Systems

This paper presents a compositional conformance checking approach between nested Petri nets and event logs of multi-agent systems. By projecting an event log onto model components, one can perform conformance checking between each projected log and the corresponding component. We formally demonstrate the validity of our approach proving that, to check fitness of a nested Petri net is equivalent to check fitness of each of its components. Leveraging the multi-agent system structure of nested Petri nets, this approach may provide specific conformance diagnostics for each system component as well as to avoid to compute artificial boundaries when decomposing a model for conformance checking.
Magnetar formation through a convective dynamo in protoneutron stars

Strong field dynamos can explain magnetar formation consistently with the millisecond magnetar model for extreme explosions. The release of spin-down energy by a magnetar is a promising scenario to power several classes of extreme explosive transients. However, it lacks a firm basis because magnetar formation still represents a theoretical challenge. Using the first three-dimensional simulations of a convective dynamo based on a protoneutron star interior model, we demonstrate that the required dipolar magnetic field can be consistently generated for sufficiently fast rotation rates. The dynamo instability saturates in the magnetostrophic regime with the magnetic energy exceeding the kinetic energy by a factor of up to 10. Our results are compatible with the observational constraints on galactic magnetar field strength and provide strong theoretical support for millisecond protomagnetar models of gamma-ray burst and superluminous supernova central engines.
Joint COCO and Mapillary Workshop at ICCV 2019 Keypoint Detection Challenge Track Technical Report: Distribution-Aware Coordinate Representation for Human Pose Estimation

In this paper, we focus on the coordinate representation in human pose estimation. While being the standard choice, heatmap based representation has not been systematically investigated. We found that the process of coordinate decoding (i.e. transforming the predicted heatmaps to the coordinates) is surprisingly significant for human pose estimation performance, which nevertheless was not recognised before. In light of the discovered importance, we further probe the design limitations of the standard coordinate decoding method and propose a principled distribution-aware decoding method. Meanwhile, we improve the standard coordinate encoding process (i.e. transforming ground-truth coordinates to heatmaps) by generating accurate heatmap distributions for unbiased model training. Taking them together, we formulate a novel Distribution-Aware coordinate Representation for Keypoint (DARK) method. Serving as a model-agnostic plug-in, DARK significantly improves the performance of a variety of state-of-the-art human pose estimation models. Extensive experiments show that DARK yields the best results on COCO keypoint detection challenge, validating the usefulness and effectiveness of our novel coordinate representation idea. The project page containing more details is at this https URL
Holographic angular streaking of electrons and the Wigner time delay

For a circularly polarized single-color field at a central frequency of $2\omega$ the final electron momentum distribution upon strong field ionization does not carry any information about the phase of the initial momentum distribution. Adding a weak, co-rotating, circularly polarized field at a central frequency of $\omega$ gives rise to a sub-cycle interference pattern (holographic angular streaking of electrons (HASE)). This interference pattern allows for the retrieval of the derivative of the phase of the initial momentum distribution after tunneling $\phi^{\prime}_{\mathrm{off}}(p_i)$. A trajectory-based semi-classical model (HASE model) is introduced which links the experimentally accessible quantities to $\phi^{\prime}_{\mathrm{off}}(p_i)$. It is shown that a change in $\phi^{\prime}_{\mathrm{off}}$ is equivalent to a displacement in position space $\Delta x$ of the initial wave packet after tunneling. This offset in position space allows for an intuitive interpretation of the Wigner time delay $\Delta \tau_W$ in strong field ionization for circularly polarized single-color fields. The influence of Coulomb interaction after tunneling is investigated quantitatively.
On $\BCI$-groups and $\CI$-groups

Let $G$ be a finite group and $S$ be a subset of $G. $ A bi-Cayley graph $\BCay(G,S)$ is a simple and an undirected graph with vertex-set $G\times\{1,2\}$ and edge-set $\{\{(g,1),(sg,2)\}\mid g\in G, s\in S\}$. A bi-Cayley graph $\BCay(G,S)$ is called a $\BCI$-graph if for any bi-Cayley graph $\BCay(G,T)$, whenever $\BCay(G,S)\cong\BCay(G,T)$ we have $T=gS^\sigma$ for some $g\in G$ and $\sigma\in\Aut(G). $ A group $G$ is called a $\BCI$-group if every bi-Cayley graph of $G$ is a $\BCI$-graph. In this paper, we showed that every $\BCI$-group is a $\CI$-group, which gives a positive answer to a conjecture proposed by Arezoomand and Taeri in \cite{arezoomand1}. Also we proved that there is no any non-Abelian $4$-$\BCI$-simple group. In addition all $\BCI$-groups of order $2p$, $p$ a prime, are characterized.
On the optical theorem and optimal extinction, scattering and absorption in lossy media

This paper reformulates and extends some recent analytical results concerning a new optical theorem and the associated physical bounds on absorption in lossy media. The analysis is valid for any linear scatterer (such as an antenna), consisting of arbitrary materials (bianisotropic, etc.) and arbitrary geometries, as long as the scatterer is circumscribed by a spherical volume embedded in a lossy background medium. The corresponding formulas are here reformulated and extended to encompass magnetic as well as dielectric background media. Explicit derivations, formulas and discussions are also given for the corresponding bounds on scattering and extinction. A numerical example concerning the optimal microwave absorption and scattering in atmospheric oxygen in the 60 GHz communication band is included to illustrate the theory.
GraphTCN: Spatio-Temporal Interaction Modeling for Human Trajectory Prediction

Predicting the future paths of an agent’s neighbors accurately and in a timely manner is central to the autonomous applications for collision avoidance. Conventional approaches, e.g., LSTM-based models, take considerable computational costs in the prediction, especially for the long sequence prediction. To support more efficient and accurate trajectory predictions, we propose a novel CNN-based spatial-temporal graph framework GraphTCN, which models the spatial interactions as social graphs and captures the spatio-temporal interactions with a modified temporal convolutional network. In contrast to conventional models, both the spatial and temporal modeling of our model are computed within each local time window. Therefore, it can be executed in parallel for much higher efficiency, and meanwhile with accuracy comparable to best-performing approaches. Experimental results confirm that our model achieves better performance in terms of both efficiency and accuracy as compared with state-of-the-art models on various trajectory prediction benchmark datasets.
How Fast Can We Insert? A Performance Study of Apache Kafka

Message brokers see widespread adoption in modern IT landscapes, with Apache Kafka being one of the most employed platforms. These systems feature well-defined APIs for use and configuration and present flexible solutions for various data storage scenarios. Their ability to scale horizontally enables users to adapt to growing data volumes and changing environments. However, one of the main challenges concerning message brokers is the danger of them becoming a bottleneck within an IT architecture. To prevent this, knowledge about the amount of data a message broker using a specific configuration can handle needs to be available. In this paper, we propose a monitoring architecture for message brokers and similar Java Virtual Machine-based systems. We present a comprehensive performance analysis of the popular Apache Kafka platform using our approach. As part of the benchmark, we study selected data ingestion scenarios with respect to their maximum data ingestion rates. The results show that we can achieve an ingestion rate of about 420,000 messages/second on the used commodity hardware and with the developed data sender tool.
Towards a Resilient Machine Learning Classifier - a Case Study of Ransomware Detection

The damage caused by crypto-ransomware, due to encryption, is difficult to revert and cause data losses. In this paper, a machine learning (ML) classifier was built to early detect ransomware (called crypto-ransomware) that uses cryptography by program behavior. If a signature-based detection was missed, a behavior-based detector can be the last line of defense to detect and contain the damages. We find that input/output activities of ransomware and the file-content entropy are unique traits to detect crypto-ransomware. A deep-learning (DL) classifier can detect ransomware with a high accuracy and a low false positive rate. We conduct an adversarial research against the models generated. We use simulated ransomware programs to launch a gray-box analysis to probe the weakness of ML classifiers and to improve model robustness. In addition to accuracy and resiliency, trustworthiness is the other key criteria for a quality detector. Making sure that the correct information was used for inference is important for a security application. The Integrated Gradient method was used to explain the deep learning model and also to reveal why false negatives evade the detection. The approaches to build and to evaluate a real-world detector were demonstrated and discussed.
Robot Path Planning by Traveling Salesman Problem with Circle Neighborhood: modeling, algorithm, and applications

This study investigates the problem of traveling salesman problem with circular neighborhood (TSPCN). Instead of cities there are circles and each point on circle can be a potential visiting node. The problem is to find the minimum length Hamiltonian cycle connecting the circles. Among the various real life applications of the problem, this paper concentrates on robot path planning for the laser welding robot, and data collection in a wireless sensor network by unmanned aerial vehicles (UAVs). The TSPCN is formulated as a nonlinear model, the objective function is linearized, and as a solution procedure decomposed into a two-phase model. The Models are coded in Cplex and Knitro and solved for small and medium sized instances.
In search of infall motion in molecular clumps II: HCO+ (1–0) and HCN (1–0) observations toward a sub-sample of infall candidates

Gravitational accretion accumulates the original mass. This process is crucial for us to understand the initial phases of star formation. Using the specific infall profiles in optically thick and thin lines, we searched the clumps with infall motion from the Milky Way Imaging Scroll Painting (MWISP) CO data in previous work. In this study, we selected 133 sources as a sub-sample for further research and identification. The excitation temperatures of these sources are between 7.0 and 38.5 K, while the H2 column densities are between 1021 and 1023 cm−2. We have observed optically thick lines HCO+ (1–0) and HCN (1–0) using the DLH 13.7-m telescope, and found 56 sources with a blue profile and no red profile in these two lines, which are likely to have infall motions, with a detection rate of 42%. This suggests that using CO data to restrict the sample can effectively improve the infall detection rate. Among these confirmed infall sources are 43 associated with Class 0/I young stellar objects (YSOs), and 13 which are not. These 13 sources are probably associated with the sources in the earlier evolutionary stage. In comparison, the confirmed sources that are associated with Class 0/I YSOs have higher excitation temperatures and column densities, while the other sources are colder and have lower column densities. Most infall velocities of the sources that we confirmed are between 10−1 to 100 km s−1, which is consistent with previous studies.
Absorption-line Abundances in the SMC-like Galaxy UGC 5282: Evidence of ISM Dilution from Inflows on Kiloparsec Scales

We present a Hubble Space Telescope Cosmic Origins Spectrograph spectrum of the QSO SDSS J095109.12+330745.8 ( ) whose sightline passes through the SMC-like dwarf galaxy UGC 5282 ( , cz = 1577 km s−1), 1.2 kpc in projection from the central H ii region of the galaxy. Damped Lyα (DLA) absorption is detected at the redshift of UGC 5282 with log [N(H i) cm . Analysis of the accompanying S ii, P ii, and O i metal lines yields a neutral gas metallicity, , of [S/H] ≃ [P/H] = −0.80 ± 0.24. The metallicity of ionized gas from the central H ii region measured from its emission lines is [O/H] = −0.37 ± 0.10, a difference of +0.43 ± 0.26 from . This difference δ is consistent with that seen toward H ii regions in other star-forming galaxies and supports the idea that ionized gas near star-forming regions shows systematically higher metallicities than exist in the rest of a galaxy’s neutral interstellar medium (ISM). The positive values of δ found in UGC 5282 (and the other star-forming galaxies) is likely due to infalling low-metallicity gas from the intergalactic medium that mixes with the galaxy’s ISM on kiloparsec scales. This model is also consistent with broad Lyα emission detected at the bottom of the DLA absorption, offset by ∼125 km s−1 from the absorption velocity. Models of galaxy evolution that attempt to replicate population characteristics, such as the mass–metallicity relation, may need to start with a galaxy metallicity represented by rather than that measured traditionally from .
Symplectic Eisenstein Series

We compute explicit formulae for the constant terms and Fourier coefficients for Eisenstein series on $\operatorname{Sp}(4,\mathbb{R})$, in terms of zeta functions and Whittaker functions. We also develop a generalisation of Ramanujan sums to $\operatorname{Sp}(4,\mathbb{Z})$, which appears as coefficients in the Fourier coefficients for the minimal Eisenstein series.
Grammars for Document Spanenrs

A new grammar-based language for deﬁning information-extractors from textual content based on the document spanners framework of Fagin et al. is proposed. While studied languages for document spanners are mainly built upon regex formulas, which are regular expressions extended with variables, this new language is based on context-free grammars. The expressiveness of these grammars is compared with previously studied classes of spanners and the complexity of their evaluation is discussed. An enumeration algorithm that outputs the results with constant delay after cubic preprocessing in the input document is presented.
An Automatic Attribute-Based Access Control Policy Extraction From Access Logs

With the rapid advances in computing and information technologies, traditional access control models have become inadequate in terms of capturing fine-grained, and expressive security requirements of newly emerging applications. An attribute-based access control (ABAC) model provides a more flexible approach to addressing the authorization needs of complex and dynamic systems. While organizations are interested in employing newer authorization models, migrating to such models pose as a significant challenge. Many large-scale businesses need to grant authorizations to their user populations that are potentially distributed across disparate and heterogeneous computing environments. Each of these computing environments may have its own access control model. The manual development of a single policy framework for an entire organization is tedious, costly, and error-prone. In this article, we present a methodology for automatically learning ABAC policy rules from access logs of a system to simplify the policy development process. The proposed approach employs an unsupervised learning-based algorithm for detecting patterns in access logs and extracting ABAC authorization rules from these patterns. In addition, we present two policy improvement algorithms, including rule pruning and policy refinement algorithms to generate a higher quality mined policy. Finally, we implement a prototype of the proposed approach to demonstrate its feasibility.
End-to-End Velocity Estimation for Autonomous Racing

Velocity estimation plays a central role in driverless vehicles, but standard, and affordable methods struggle to cope with extreme scenarios like aggressive maneuvers due to the presence of high sideslip. To solve this, autonomous race cars are usually equipped with expensive external velocity sensors. In this letter, we present an end-to-end recurrent neural network that takes available raw sensors as input (IMU, wheel odometry, and motor currents), and outputs velocity estimates. The results are compared to two state-of-the-art Kalman filters, which respectively include, and exclude expensive velocity sensors. All methods have been extensively tested on a formula student driverless race car with very high sideslip (10<inline-formula><tex-math notation="LaTeX">$^\circ$</tex-math></inline-formula> at the rear axle), and slip ratio (<inline-formula><tex-math notation="LaTeX">$\approx\! 20$</tex-math></inline-formula>%), operating close to the limits of handling. The proposed network is able to estimate lateral velocity up to 15x better than the Kalman filter with the equivalent sensor input, and matches (0.06 m/s RMSE) the Kalman filter with the expensive velocity sensor setup.