Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-peak Electronic Density of States in CrBr3 Revealed by Scanning Tunneling Microscopy

CrBr3 is a layered van der Waals material with magnetic ordering down to the 2D limit. So far, the electronic properties of CrBr3 are relatively unexplored. Here, we present the scanning tunneling microscopy and spectroscopy (STM/S) results of the CrBr3 thin flakes exfoliated onto a Highly Ordered Pyrolytic Graphite (HOPG) surfaces. Atomic resolution topography images show the crystal structure and the dI/dV spectra exhibit multiple peak features measured at 77 K. The conduction band - valence band peak pairs in the multi-peak dI/dV spectrum agrees very well with all the reported optical transitions in literature. The density of states (DOS) calculated by the density functional theory (DFT) agrees qualitatively with the experimental dI/dV spectrum, except two small peaks near the Fermi energy. The flake degradation is found at the edges of the mono- and bi-layer flakes. The results shown here pave the way toward the fundamental understanding of the CrBr3.
HTMLPhish: Enabling Accurate Phishing Web Page Detection by Applying Deep Learning Techniques on HTML Analysis

Recently, the development and implementation of phishing attacks require little technical skills and costs. This uprising has led to an ever-growing number of phishing attacks on the World Wide Web daily. Consequently, proactive techniques to fight phishing attacks have become extremely necessary. In this paper, we propose a deep learning model HTMLPhish based on the HTML analysis of a web page for accurate phishing attack detection. By using our proposed HTMLPhish, the experimental results on a dataset of over 300,000 web pages yielded 97.2% accuracy, which significantly outperforms the traditional machine learning methods such as Support Vector Machine, Random Forest and Logistics Regression. We also show the advantage of HTMLPhish in the aspect of the temporal stability and robustness by testing our proposed model on a dataset collected after two months when the model was trained. In addition, HTMLPhish is a completely language-independent and client-side strategy which can, therefore, conduct web page phishing detection regardless of the textual language.
Topology and interactions: using photonic systems as quantum simulators

The latest advances in the field of photonics have enabled the simulation of an increasing number of quantum models in photonic systems, turning them into an important tool for realizing exotic quantum phenomena. In this paper we suggest different ways in which these systems can be used to study the interplay between flat band dynamics, topology and interactions in a well-known quasi-1D topological insulator: the Creutz ladder. Firstly, a simple experimental protocol is proposed to observe the Aharonov-Bohm localization in the noninteracting system, and the different experimental setups that might be used for this are reviewed. We then consider the inclusion of a repulsive Hubbard-type interaction term, which can give rise to repulsively bound pairs termed doublons. The dynamics of these quasiparticles are studied for different points of the phase diagram, including a regime in which pairs are localized and particles are free to move. Finally, a scheme for the photonic implementation of a two-particle bosonic Creutz-Hubbard model is presented.
Evolutionary reinforcement learning of dynamical large deviations

We show how to bound and calculate the likelihood of dynamical large deviations using evolutionary reinforcement learning. An agent, a stochastic model, propagates a continuous-time Monte Carlo trajectory and receives a reward conditioned upon the values of certain path-extensive quantities. Evolution produces progressively fitter agents, potentially allowing the calculation of a piece of a large-deviation rate function for a particular model and path-extensive quantity. For models with small state spaces, the evolutionary process acts directly on rates, and for models with large state spaces, the process acts on the weights of a neural network that parameterizes the model's rates. This approach shows how path-extensive physics problems can be considered within a framework widely used in machine learning.
Selfie: User-defined Sensitive Memory Protection and Recovery

Different users always have different requirement for sensitive memory definition. It is not flexible for aborting program execution once detecting memory corruption. Because the users may loose some sensitive data. We presented Selfie, a hybrid solution to provide one flexible solution to protect the sensitive memory according to users' requirements in runtime. Finally, Selfie can provide one solution to decide whether execution needs to be recovered. If the memory corruption doesn't belong sensitive memory, Selfie provides symbolic solver that can help figure out whether the memory corruption can affect the sensitive memory in future.
Pluggable Social Artificial Intelligence for Enabling Human-Agent Teaming

As intelligent systems are increasingly capable of performing their tasks without the need for continuous human input, direction, or supervision, new human-machine interaction concepts are needed. A promising approach to this end is human-agent teaming, which envisions a novel interaction form where humans and machines behave as equal team partners. This paper presents an overview of the current state of the art in human-agent teaming, including the analysis of human-agent teams on five dimensions; a framework describing important teaming functionalities; a technical architecture, called SAIL, supporting social human-agent teaming through the modular implementation of the human-agent teaming functionalities; a technical implementation of the architecture; and a proof-of-concept prototype created with the framework and architecture. We conclude this paper with a reflection on where we stand and a glance into the future showing the way forward.
How Reasonable are Common-Sense Reasoning Tasks: A Case-Study on the Winograd Schema Challenge and SWAG

Recent studies have significantly improved the state-of-the-art on common-sense reasoning (CSR) benchmarks like the Winograd Schema Challenge (WSC) and SWAG. The question we ask in this paper is whether improved performance on these benchmarks represents genuine progress towards common-sense-enabled systems. We make case studies of both benchmarks and design protocols that clarify and qualify the results of previous work by analyzing threats to the validity of previous experimental designs. Our protocols account for several properties prevalent in common-sense benchmarks including size limitations, structural regularities, and variable instance difficulty.
Patient trajectory prediction in the Mimic-III dataset, challenges and pitfalls

Automated medical prognosis has gained interest as artificial intelligence evolves and the potential for computer-aided medicine becomes evident. Nevertheless, it is challenging to design an effective system that, given a patient's medical history, is able to predict probable future conditions. Previous works, mostly carried out over private datasets, have tackled the problem by using artificial neural network architectures that cannot deal with low-cardinality datasets, or by means of non-generalizable inference approaches. We introduce a Deep Learning architecture whose design results from an intensive experimental process. The final architecture is based on two parallel Minimal Gated Recurrent Unit networks working in bi-directional manner, which was extensively tested with the open-access Mimic-III dataset. Our results demonstrate significant improvements in automated medical prognosis, as measured with Recall@k. We summarize our experience as a set of relevant insights for the design of Deep Learning architectures. Our work improves the performance of computer-aided medicine and can serve as a guide in designing artificial neural networks used in prediction tasks.
LIC-Fusion: LiDAR-Inertial-Camera Odometry

This paper presents a tightly-coupled multi-sensor fusion algorithm termed LiDAR-inertial-camera fusion (LIC-Fusion), which efficiently fuses IMU measurements, sparse visual features, and extracted LiDAR points. In particular, the proposed LIC-Fusion performs online spatial and temporal sensor calibration between all three asynchronous sensors, in order to compensate for possible calibration variations. The key contribution is the optimal (up to linearization errors) multi-modal sensor fusion of detected and tracked sparse edge/surf feature points from LiDAR scans within an efficient MSCKF-based framework, alongside sparse visual feature observations and IMU readings. We perform extensive experiments in both indoor and outdoor environments, showing that the proposed LIC-Fusion outperforms the state-of-the-art visual-inertial odometry (VIO) and LiDAR odometry methods in terms of estimation accuracy and robustness to aggressive motions.
Convex Controller Synthesis for Contact

—Controlling contacts is truly challenging, and this has been a major hurdle to deploying industrial robots into unstructured/human-centric environments. More speciﬁcally, the main challenges are: (i) how to ensure stability at all times; (ii) how to satisfy task-speciﬁc performance requirements; (iii) how to achieve (i) and (ii) under environment uncertainty, robot parameters uncertainty, sensor and actuator time delays, external perturbations, etc. Here, we propose a new approach – Convex Controller Synthesis (CCS) – to tackle the above challenges based on robust control theory and convex optimization. In two physical interaction tasks – robot hand guiding and sliding on surfaces with different and unknown stiffnesses – we show that CCS controllers outperform their classical counterparts in an essential way.
Compressed sensing with signal averaging for improved sensitivity and motion artifact reduction in fluorine‐19 MRI

Fluorine‐19 (19F) MRI of injected perfluorocarbon emulsions (PFCs) allows for the non‐invasive quantification of inflammation and cell tracking, but suffers from a low signal‐to‐noise ratio and extended scan time. To address this limitation, we tested the hypotheses that a 19F MRI pulse sequence that combines a specific undersampling regime with signal averaging has both increased sensitivity and robustness against motion artifacts compared with a non‐averaged fully sampled pulse sequence, when both datasets are reconstructed with compressed sensing. As a proof of principle, numerical simulations and phantom experiments were performed on selected variable ranges to characterize the point spread function of undersampling patterns, as well as the vulnerability to noise of undersampling and reconstruction parameters with paired numbers of x signal averages and acceleration factor x (NAx‐AFx). The numerical simulations demonstrated that a probability density function that uses 25% of the samples to fully sample the k‐space central area allowed for an optimal balance between limited blurring and artifact incoherence. At all investigated noise levels, the Dice similarity coefficient (DSC) strongly depended on the regularization parameters and acceleration factor. In phantoms, the motion robustness of an NA8‐AF8 undersampling pattern versus NA1‐AF1 was evaluated with simulated and real motion patterns. Differences were assessed with the DSC, which was consistently higher for the NA8‐AF8 compared with the NA1‐AF1 strategy, for both simulated and real cyclic motion patterns (P < 0.001). Both strategies were validated in vivo in mice (n = 2) injected with perfluoropolyether. Here, the images displayed a sharper delineation of the liver with the NA8‐AF8 strategy than with the NA1‐AF1 strategy. In conclusion, we validated the hypotheses that in 19F MRI the combination of undersampling and averaging improves both the sensitivity and the robustness against motion artifacts.
Hypergraph Partitioning With Embeddings

Problems in scientific computing, such as distributing large sparse matrix operations, have analogous formulations as hypergraph partitioning problems. A hypergraph is a generalization of a traditional graph wherein “hyperedges” may connect any number of nodes. As a result, hypergraph partitioning is an NP-Hard problem to both solve or approximate. State-of-the-art algorithms that solve this problem follow the multilevel paradigm, which begins by iteratively “coarsening” the input hypergraph to smaller problem instances that share key structural features. Once identifying an approximate problem that is small enough to be solved directly, that solution can be interpolated and refined to the original problem. While this strategy represents an excellent trade off between quality and running time, it is sensitive to coarsening strategy. In this work we propose using graph embeddings of the initial hypergraph in order to ensure that coarsened problem instances retrain key structural features. Our approach prioritizes coarsening within self-similar regions within the input graph, and leads to significantly improved solution quality across a range of considered hypergraphs. Reproducibility: All source code, plots and experimental data are available at https://sybrandt.com/2019/partition.
Flooding and diameter in general weighted random graphs

We study in this paper, the first passage percolation on a random graph model, the configuration model. We first introduce, the notions of weighted diameter, which is the maximum of the weighted lengths of all optimal paths between any two vertices in the graph, and the flooding time, which represents the time (weighted length) needed to reach all the vertices in the graph starting from a uniformly chosen vertex. Our result consists of describing the asymptotic behavior of the diameter and the flooding time, as the number of vertices n tends to infinity, in the case where the weight distribution G has an exponential tail behavior, and proving that this category of distributions is the largest possible for which the asymptotic behavior holds.
No further evidence for a transiting inner companion to the hot Jupiter HATS‐50b

Most hot Jupiter exoplanets do not have a nearby planetary companion in their planetary system. One remarkable exception is the system of WASP-47 with an inner and outer nearby companion to a hot Jupiter, providing detailed constrains on its formation history. In this work, we follow-up on a tentative photometric signal of a transiting inner companion to the hot Jupiter HATS-50b. If confirmed, it would be the third case of a hot Jupiter with an inner companion. 63 hours of new ground-based photometry were employed to rule out this signal to about 96% confidence. The injection of artificial transit signals showed the data to be of sufficient quality to reveal the potential photometric feature at high significance. However, no transit signal was found. The discrete pattern of observing blocks leaves a slight chance that the transit was missed.
PMD: A New User Distance for Recommender Systems

Collaborative filtering, a widely-used recommendation technique, predicts a user's preference by aggregating the ratings from similar users. As a result, these measures cannot fully utilize the rating information and are not suitable for real world sparse data. To solve these issues, we propose a novel user distance measure named Preference Mover's Distance (PMD) which makes full use of all ratings made by each user. Our proposed PMD can properly measure the distance between a pair of users even if they have no co-rated items. We show that this measure can be cast as an instance of the Earth Mover's Distance, a well-studied transportation problem for which several highly efficient solvers have been developed. Experimental results show that PMD can help achieve superior recommendation accuracy than state-of-the-art methods, especially when training data is very sparse.
A stellar relic filament in the Orion star-forming region

We report the discovery of the oldest stellar substructure in the Orion star-forming region (OSFR), the Orion relic filament. The relic filament is physically associated with the OSFR, as demonstrated by Gaia DR2 photometry and astrometry, as well as by targeted radial velocity follow-up observations of a bright subsample of proper motion-selected candidate members. Gaia DR2 parallaxes place the Orion relic filament in the more distant part of the OSFR, ∼430 pc from the Sun. Given its age, velocity dispersion, spatial extent, and shape, it is not possible for the Orion relic filament to have formed as a single stellar cluster, even taking into account residual gas expulsion. The relic filament is also too young to be a tidal stream, because Galactic tides act on much longer time-scales, of the order of 100 Myr. It therefore appears likely that the structure formed from a molecular cloud filament similar to Orion A in the OSFR and retained its morphology despite decoupling from its natal gas. Hence, the Orion relic filament bears witness to the short-lived evolutionary phase between gas removal and dispersion owing to shears and tides, and provides crucial new insights into how stars are formed in molecular clouds.
MultiObjective Evolutionary Approach to Grey-Box Identification of Buck Converter

The present study proposes a simple grey-box identification approach to model a real DC-DC buck converter operating in continuous conduction mode. The problem associated with the information void in the observed dynamical data, which is often obtained over a relatively narrow input range, is alleviated by exploiting the known static behavior of buck converter as a priori knowledge. A simple method is developed based on the concept of term clusters to determine the static response of the candidate models. The error in the static behavior is then directly embedded into the multi-objective framework for structure selection. In essence, the proposed approach casts grey-box identification problem into a multi-objective framework to balance bias-variance dilemma of model building while explicitly integrating a priori knowledge into the structure selection process. The results of the investigation, considering the case of practical buck converter, demonstrate that it is possible to identify parsimonious models which can capture both the dynamic and static behavior of the system over a wide input range.
OCR4all - An Open-Source Tool Providing a (Semi-)Automatic OCR Workflow for Historical Printings

Optical Character Recognition (OCR) on historical printings is a challenging task mainly due to the complexity of the layout and the highly variant typography. Nevertheless, in the last few years, great progress has been made in the area of historical OCR, resulting in several powerful open-source tools for preprocessing, layout analysis and segmentation, character recognition, and post-processing. The drawback of these tools often is their limited applicability by non-technical users like humanist scholars and in particular the combined use of several tools in a workflow. In this paper, we present an open-source OCR software called OCR4all, which combines state-of-the-art OCR components and continuous model training into a comprehensive workflow. While a variety of materials can already be processed fully automatically, books with more complex layouts require manual intervention by the users. This is mostly due to the fact that the required ground truth for training stronger mixed models (for segmentation, as well as text recognition) is not available, yet, neither in the desired quantity nor quality. To deal with this issue in the short run, OCR4all offers a comfortable GUI that allows error corrections not only in the final output, but already in early stages to minimize error propagations. In the long run, this constant manual correction produces large quantities of valuable, high quality training material, which can be used to improve fully automatic approaches. Further on, extensive configuration capabilities are provided to set the degree of automation of the workflow and to make adaptations to the carefully selected default parameters for specific printings, if necessary. During experiments, the fully automated application on 19th Century novels showed that OCR4all can considerably outperform the commercial state-of-the-art tool ABBYY Finereader on moderate layouts if suitably pretrained mixed OCR models are available. Furthermore, on very complex early printed books, even users with minimal or no experience were able to capture the text with manageable effort and great quality, achieving excellent Character Error Rates (CERs) below 0.5%. The architecture of OCR4all allows the easy integration (or substitution) of newly developed tools for its main components by standardized interfaces like PageXML, thus aiming at continual higher automation for historical printings.
Automatic detection of estuarine dolphin whistles in spectrogram images

An algorithm for detecting tonal vocalizations from estuarine dolphin (Sotalia guianensis) specimens without interference of a human operator is developed. The raw audio data collected from a passive monitoring sensor in the Cananeia underwater soundscape is converted to spectrogram images, containing the desired acoustic event (whistle) as a linear pattern in the images. Detection is a four-step method: first, ridge maps are obtained from the spectrogram images; second, a probabilistic Hough transform algorithm is applied to detect roughly linear ridges, which are adjusted to the true corresponding shape of the whistles via an active contour algorithm; third, feature vectors are built from the geometry of each detected curve; and fourth, the detections are fed to a random forest classifier to parse out false positives. We develop a system capable of reliably classifying roughly 97% of the characteristic patterns detected as Sotalia guianensis whistles or random empty detections.
A Filtering Approach for Resiliency of Distributed Observers against Smart Spoofers

For a Linear Time-Invariant (LTI) system, a network of observers is considered where through asynchronous (with bounded delay) communications, they all obtain the state variables of the LTI system. In such setting, a new type of adversarial nodes might affect the observation process by impersonating the identity of the regular nodes, which is a violation against communication authenticity. These adversaries also inherit the capabilities of Byzantine nodes making them more powerful threats called smart spoofers. We show how asynchronous networks are vulnerable to smart spoofing attack. In the estimation scheme considered in this paper, the estimated state variables are flowed from the sets of source nodes, which can detect a portion of the state variables each, to the other follower nodes. The regular nodes, to avoid getting misguided by the impersonation threats, distributively filter the extreme values received from the nodes in their neighborhood. A topological condition based on graph strong robustness is obtained to guarantee the convergence. Two simulation scenarios are provided to verify the results.
Differentiable Mask Pruning for Neural Networks

Pruning of neural networks is one of the well-known and promising model simpliﬁcation techniques. Most neural network models are large and require expensive computations to predict new instances. It is imperative to compress the network to deploy models on low resource devices. Most compression techniques, especially pruning have been focusing on computer vision and convolution neural networks. Existing techniques are complex and require multi-stage optimization and ﬁne-tuning to recover the state-of-the-art accuracy. We introduce a Differentiable Mask Pruning (DMP), that simpliﬁes the network while training, and can be used to induce sparsity on weight, ﬁlter, node or sub-network. Our method achieves competitive results on standard vision and NLP benchmarks, and is easy to integrate within the deep learning toolbox. DMP bridges the gap between neural model compression and differentiable neural architecture search.