Miracle-dz/UniFAR-FTUs huggingface dataset:
Antimicrobial Activity of Marjoram (Origanum Majorana) Essential Oil Against the Multidrug‐Resistant Salmonella Enterica Serovar Schwarzengrund Inoculated in Vegetables from Organic Farming

Antimicrobial efficacy of the marjoram essential oil (MEO) on organic vegetables inoculated with 4 log/cfu Salmonella enterica serovar Schwarzengrund was evaluated. Previously inoculated samples of smooth lettuce (Lactuva sativa), kale (Brassica oleracea) and arugula (Eruca sativa) were immersed, separately, in three MEO solutions (0.1, 0.3 and 0.5%) for 1 and 2 min. Control groups with sterile phosphate-buffered saline and sodium hypochlorite (250 ppm) solution were also used. The enumeration of Salmonella was performed for 3 consecutive days, and the essential oil yielded an average reduction in microbial load, ranging from 2.12–3.31 log/cfu, in all samples, against 1.86 − 2.81 log/cfu obtained by the traditional disinfection method with hypochlorite solution. In lettuce samples, the 0.5% MEO concentration showed similar efficacy to the hypochlorite control. The same concentration of essential oil tested in kale and arugula showed significant antimicrobial activity when compared to the same control. Sensory and affective tests showed good acceptability of the MEO flavor on lettuce leaves. According to this experimental model, this oil has promising antimicrobial activity against Salmonella enterica serovar Schwarzengrund and may be used to reduce or replace sodium hypochlorite during the disinfection process of vegetables. Practical Applications The marjoram essential oil at 0.5% concentration showed potential application in the disinfection process of vegetables for human consumption. Its disinfection capacity against a high concentration of multidrug-resistant Salmonella enterica serovar Schwarzengrund was similar or even better than the sodium hypochlorite solution commonly used to remove or reduce the microbial load of fresh vegetables or similar foods.
Interaction potentials, electric moments, polarizabilities, and chemical reactions of YbCu, YbAg, and YbAu molecules

Ultracold YbAg molecules have been recently proposed as promising candidates for electron electric dipole moment searches Verma et al (2020 Phys. Rev. Lett. 125 153201). Here, we calculate potential energy curves, permanent electric dipole and quadrupole moments, and static electric dipole polarizabilities for the YbCu, YbAg, and YbAu molecules in their ground electronic states. We use the coupled cluster method restricted to single, double, and noniterative triple excitations with large Gaussian basis sets, while the scalar relativistic effects are included within the small-core energy-consistent pseudopotentials. We find that the studied molecules are relatively strongly bound with the well depths of 5708 cm−1, 5253 cm−1, and 13349 cm−1 and equilibrium distances of 5.50 bohr, 5.79 bohr, and 5.55 bohr for YbCu, YbAg, and YbAu, respectively. They have large permanent electric dipole moments of 3.2D, 3.3D, and 5.3D at equilibrium distances, respectively. We also calculate equilibrium geometries and energies of corresponding trimers. The studied molecules are chemically reactive unless they are segregated in an optical lattice or shielded with external fields. The investigated molecules may find application in ultracold controlled chemistry, dipolar many-body physics, or precision measurement experiments.
Crucial Factors for Lyα Transmission in the Reionizing Intergalactic Medium: Infall Motion, H ii Bubble Size, and Self-shielded Systems

Using the CoDa II simulation, we study the Lyα transmissivity of the intergalactic medium (IGM) during reionization. At z > 6, a typical galaxy without an active galactic nucleus fails to form a proximity zone around itself due to the overdensity of the surrounding IGM. The gravitational infall motion in the IGM makes the resonance absorption extend to the red side of Lyα, suppressing the transmission up to roughly the circular velocity of the galaxy. In some sight lines, an optically thin blob generated by a supernova in a neighboring galaxy results in a peak feature, which can be mistaken for a blue peak. Redward of the resonance absorption, the damping-wing opacity correlates with the global IGM neutral fraction and the UV magnitude of the source galaxy. Brighter galaxies tend to suffer lower opacity because they tend to reside in larger H ii regions, and the surrounding IGM transmits redder photons, which are less susceptible to attenuation, owing to stronger infall velocity. The H ii regions are highly nonspherical, causing both sight-line-to-sight-line and galaxy-to-galaxy variation in opacity. Also, self-shielded systems within H ii regions strongly attenuate the emission for certain sight lines. All these factors add to the transmissivity variation, requiring a large sample size to constrain the average transmission. The variation is largest for fainter galaxies at higher redshift. The 68% range of the transmissivity is similar to or greater than the median for galaxies with M UV ≥ −21 at z ≥ 7, implying that more than a hundred galaxies would be needed to measure the transmission to 10% accuracy.
Ingrained: An Automated Framework for Fusing Atomic-Scale Image Simulations into Experiments.

To fully leverage the power of image simulation to corroborate and explain patterns and structures in atomic resolution microscopy, an initial correspondence between the simulation and experimental image must be established at the outset of further high accuracy simulations or calculations. Furthermore, if simulation is to be used in context of highly automated processes or high-throughput optimization, the process of finding this correspondence itself must be automated. In this work, "ingrained," an open-source automation framework which solves for this correspondence and fuses atomic resolution image simulations into the experimental images to which they correspond, is introduced. Herein, the overall "ingrained" workflow, focusing on its application to interface structure approximations, and the development of an experimentally rationalized forward model for scanning tunneling microscopy simulation are described.
An Efficient Network Solver for Dynamic Simulation of Power Systems Based on Hierarchical Inverse Computation and Modification

In power system dynamic simulation, up to 90% of the computational time is devoted to solve the network equations, i.e., a set of linear equations. Traditional approaches are based on sparse LU factorization, which is inherently sequential. In this paper, an inverse-based network solution is proposed by a hierarchical method for computing and store the approximate inverse of the conductance matrix in electromagnetic transient (EMT) simulations. The proposed method can also efficiently update the inverse by modifying only local sub-matrices to reflect changes in the network, e.g., loss of a line. Experiments on a series of simplified 179-bus Western Interconnection demonstrate the advantages of the proposed methods.
The surroundings of the Milky Way globular cluster NGC 6809

We study the outer regions of the Milky Way globular cluster NGC 6809 based on Dark Energy Camera (DECam) observations, which reach nearly 6 mag below the cluster main sequence (MS) turnoff. In order to unveil its fainter outermost structure, we built stellar density maps using cluster MS stars, once the contamination of field stars was removed from the cluster color-magnitude diagram. We found that only the resulting stellar density map for the lightest stars exhibits some excesses of stars at opposite sides from the cluster centre that diminish soon thereafter at ∼ 0.32◦. Studied globular clusters with apogalactic distances smaller than that of NGC 6809 (5.5 kpc) do not have observed tidal tails. The lack of detection of tidal tails in the studied inner globular cluster sample could be due to the reduced diffusion time of tidal tails by the kinematically chaotic nature of the orbits of these globular clusters, thus shortening the time interval during which the tidal tails can be detected. Further investigations with an enlarged cluster sample are needed to confirm whether chaotic and non-chaotic orbits are responsible for the existence of globular clusters with tidal tails and those with extra-tidal features that are different from tidal tails or without any signatures of extended stellar density profiles.
3ARVisualizer: Debugging 3D Models using Augmented Reality

Often neglected in traditional education, spatial thinking has proved to play a critical role in achievement in science, technology, engineering, and mathematics (STEM) education. Spatial thinking skills can be enhanced by training, life experience, and practice. One approach to train these skills is through 3D modeling (also known as Computer-Aided Design or CAD). Although 3D modeling tools have shown promising results in training and enhancing spatial thinking skills in undergraduate engineering students, when it comes to novices especially middleand high-school students, they are not sufficient in providing rich 3D experience since the 3D models created in CAD are isolated from the actual 3D physical world. Resulting in novice students finding it difficult to create error-free 3D models that would 3D print successfully. This leads to student frustration where students are not motivated to create 3D models themselves instead, they prefer to download them from online repositories. To address this problem, researchers are focusing on integrating 3D models and displays into the physical world with the help of technologies like Augmented Reality (AR). In this demo, we present an AR application – 3DARVisualizer that helps us explore the role of AR as a 3D model debugger including enhancing 3D modeling abilities and spatial thinking skills of middleand high-school students. Tools, Skills and Materials • Tools➝3D Printing • Tools➝Augmented Reality • Skills➝3D Modeling, Spatial Thinking
Molecular line ratio diagnostics and gas kinematics in the AGN host Seyfert galaxy NGC 5033

Multiple molecular lines are useful for studying the physical properties of multiphase starforming gas in different types of galaxies. We probe the molecular gas throughout the disc of the spiral galaxy NGC 5033, hosting an active galactic nucleus (AGN), using multiple low-J CO lines [CO(1–0, 2–1, 3–2 and CO(1–0, 2–1)] and dense gas tracers [HCN(1–0) and HCO(1–0)]. Firstly, we determine the ratios of the integrated intensity maps and the ratio of intensities in position velocity diagrams. Secondly, we obtain the ratios of CO lines and highdensity tracers at the centre; and thirdly, we model these line ratios using a radiative transfer code. Line ratio diagnostics reveal that the south of the gaseous disc contains cooler gas than the northern part, and the centre hosts warmer and less tenuous gas with a similar dense gas fraction compared to most galaxies of similar type. Our model results mostly agree with the empirical ones in the sense that the central region of NGC 5033 harbours warmer gas than that in the centres of normal spirals and lenticulars without showing AGN activity. Finally, the beam-averaged total molecular gas mass and gas surface density along the galaxy’s major axis show a radial gradient, i.e. increasing from the outskirts up to the central region of size 1 kpc where there is a depression in both gas mass and surface density.
Fast Crack Detection Using Convolutional Neural Network

To improve the efficiency and reduce the labour cost of the renovation process, this study presents a lightweight Convolutional Neural Network (CNN)-based architecture to extract crack-like features, such as cracks and joints. Moreover, Transfer Learning (TF) method was used to save training time while offering comparable prediction results. For three different objectives: 1) Detection of the concrete cracks; 2) Detection of natural stone cracks; 3) Differentiation between joints and cracks in natural stone; We built a natural stone dataset with joints and cracks information as complementary for the concrete benchmark dataset. As the results show, our model is demonstrated as an effective tool for industry use.
An Automated and Comprehensive Framework for IoT Botnet Detection and Analysis (IoT-BDA)

The proliferation of insecure Internet-connected devices gave rise to the IoT botnets which can grow very large rapidly and may perform high-impact cyber-attacks. The related studies for tackling IoT botnets are concerned with either capturing or analyzing IoT botnet samples, using honeypots and sandboxes, respectively. The lack of integration between the two implies that the samples captured by the honeypots must be manually submitted for analysis in sandboxes, introducing a delay during which a botnet may change its operation. Furthermore, the effectiveness of the proposed sandboxes is limited by the potential use of anti-analysis techniques and the inability to identify features for effective detection and identification of IoT botnets. In this paper, we propose and evaluate a novel framework, the IoT-BDA framework, for automated capturing, analysis, identification, and reporting of IoT botnets. The framework consists of honeypots integrated with a novel sandbox that supports a wider range of hardware and software configurations, and can identify indicators of compromise and attack, along with anti-analysis, persistence, and anti-forensics techniques. These features can make botnet detection and analysis, and infection remedy more effective. The framework reports the findings to a blacklist and abuse service to facilitate botnet suspension. The paper also describes the discovered anti-honeypot techniques and the measures applied to reduce the risk of honeypot detection. Over the period of seven months, the framework captured, analyzed, and reported 4077 unique IoT botnet samples. The analysis results show that some IoT botnets used anti-analysis, persistence, and anti-forensics techniques typically seen in traditional botnets.
Asynchronous wreath product and cascade decompositions for concurrent behaviours

We develop new algebraic tools to reason about concurrent behaviours modelled as languages of Mazurkiewicz traces and asynchronous automata. These tools reflect the distributed nature of traces and the underlying causality and concurrency between events, and can be said to support true concurrency. They generalize the tools that have been so efficient in understanding, classifying and reasoning about word languages. In particular, we introduce an asynchronous version of the wreath product operation and we describe the trace languages recognized by such products (the so-called asynchronous wreath product principle). We then propose a decomposition result for recognizable trace languages, analogous to the Krohn-Rhodes theorem, and we prove this decomposition result in the special case of acyclic architectures. Finally, we introduce and analyze two distributed automata-theoretic operations. One, the local cascade product, is a direct implementation of the asynchronous wreath product operation. The other, global cascade sequences, although conceptually and operationally similar to the local cascade product, translates to a more complex asynchronous implementation which uses the gossip automaton of Mukund and Sohoni. This leads to interesting applications to the characterization of trace languages definable in first-order logic: they are accepted by a restricted local cascade product of the gossip automaton and 2-state asynchronous reset automata, and also by a global cascade sequence of 2-state asynchronous reset automata. Over distributed alphabets for which the asynchronous Krohn-Rhodes theorem holds, a local cascade product of such automata is sufficient and this, in turn, leads to the identification of a simple temporal logic which is expressively complete for such alphabets.
Regularization Can Help Mitigate Poisoning Attacks... with the Right Hyperparameters

Machine learning algorithms are vulnerable to poisoning attacks, where a fraction of the training data is manipulated to degrade the algorithms' performance. We show that current approaches, which typically assume that regularization hyperparameters remain constant, lead to an overly pessimistic view of the algorithms' robustness and of the impact of regularization. We propose a novel optimal attack formulation that considers the effect of the attack on the hyperparameters, modelling the attack as a \emph{minimax bilevel optimization problem}. This allows to formulate optimal attacks, select hyperparameters and evaluate robustness under worst case conditions. We apply this formulation to logistic regression using $L_2$ regularization, empirically show the limitations of previous strategies and evidence the benefits of using $L_2$ regularization to dampen the effect of poisoning attacks.
Human-AI Collaboration with Bandit Feedback

Human-machine complementarity is important when neither the algorithm nor the human yield dominant performance across all instances in a given domain. Most research on algorithmic decision-making solely centers on the algorithm's performance, while recent work that explores human-machine collaboration has framed the decision-making problems as classification tasks. In this paper, we first propose and then develop a solution for a novel human-machine collaboration problem in a bandit feedback setting. Our solution aims to exploit the human-machine complementarity to maximize decision rewards. We then extend our approach to settings with multiple human decision makers. We demonstrate the effectiveness of our proposed methods using both synthetic and real human responses, and find that our methods outperform both the algorithm and the human when they each make decisions on their own. We also show how personalized routing in the presence of multiple human decision-makers can further improve the human-machine team performance.
Principled Design of Translation, Scale, and Rotation Invariant Variation Operators for Metaheuristics

In the past three decades, a large number of metaheuristics have been proposed and shown high performance in solving complex optimization problems. While most variation operators in existing metaheuristics are empirically designed, this paper aims to design new operators automatically, which are expected to be search space independent and thus exhibit robust performance on different problems. For this purpose, this work first investigates the influence of translation invariance, scale invariance, and rotation invariance on the search behavior and performance of some representative operators. Then, we deduce the generic form of translation, scale, and rotation invariant operators. Afterwards, a principled approach is proposed for the automated design of operators, which searches for high-performance operators based on the deduced generic form. The experimental results demonstrate that the operators generated by the proposed approach outperform state-of-the-art ones on a variety of problems with complex landscapes and up to 1000 decision variables.
DepressionNet: Learning Multi-modalities with User Post Summarization for Depression Detection on Social Media

Twitter is currently a popular online social media platform which allows users to share their user-generated content. This publicly-generated user data is also crucial to healthcare technologies because the discovered patterns would hugely benefit them in several ways. One of the applications is in automatically discovering mental health problems, e.g., depression. Previous studies to automatically detect a depressed user on online social media have largely relied upon the user behaviour and their linguistic patterns including user's social interactions. The downside is that these models are trained on several irrelevant content which might not be crucial towards detecting a depressed user. Besides, these content have a negative impact on the overall efficiency and effectiveness of the model. To overcome the shortcomings in the existing automatic depression detection methods, we propose a novel computational framework for automatic depression detection that initially selects relevant content through a hybrid extractive and abstractive summarization strategy on the sequence of all user tweets leading to a more fine-grained and relevant content. The content then goes to our novel deep learning framework comprising of a unified learning machinery comprising of Convolutional Neural Network (CNN) coupled with attention-enhanced Gated Recurrent Units (GRU) models leading to better empirical performance than existing strong baselines.
A Lower Bound for the n-queens Problem

The $n$-queens puzzle is to place $n$ mutually non-attacking queens on an $n \times n$ chessboard. We present a simple two stage randomized algorithm to construct such configurations. In the first stage, a random greedy algorithm constructs an approximate \textit{toroidal} $n$-queens configuration. In this well-known variant the diagonals wrap around the board from left to right and from top to bottom. We show that with high probability this algorithm succeeds in placing $(1-o(1))n$ queens on the board. In the second stage, the method of absorbers is used to obtain a complete solution to the non-toroidal problem. By counting the number of choices available at each step of the random greedy algorithm we conclude that there are more than $\left( \left( 1 - o(1) \right) n e^{-3} \right)^n$ solutions to the $n$-queens problem. This proves a conjecture of Rivin, Vardi, and Zimmerman in a strong form.
Real-Time People Tracking and Identification From Sparse mm-Wave Radar Point-Clouds

Mm-wave radars have recently gathered significant attention as a means to track human movement and identify subjects from their gait characteristics. A widely adopted method to perform the identification is the extraction of the micro-Doppler signature of the targets, which is computationally demanding in case of co-existing multiple targets within the monitored physical space. Such computational complexity is the main problem of state-of-the-art approaches, and makes them inapt for real-time use. In this work, we present an end-to-end, low-complexity but highly accurate method to track and identify multiple subjects in real-time using the sparse point-cloud sequences obtained from a low-cost mm-wave radar. Our proposed system features an extended object tracking Kalman filter, used to estimate the position, shape and extension of the subjects, which is integrated with a novel deep learning classifier, specifically tailored for effective feature extraction and fast inference on radar point-clouds. The proposed method is thoroughly evaluated on an edge-computing platform from NVIDIA (Jetson series), obtaining greatly reduced execution times (reduced complexity) against the best approaches from the literature. Specifically, it achieves accuracies as high as 91.62%, operating at 15 frames per seconds, in identifying three subjects that concurrently and freely move in an unseen indoor environment, among a group of eight.
The Gauge Argument: A Noether Reason

Why is gauge symmetry so important in modern physics, given that one must eliminate it when interpreting what the theory represents? In this paper we discuss the sense in which gauge symmetry can be fruitfully applied to constrain the space of possible dynamical models in such a way that forces and charges are appropriately coupled. We review the most well-known application of this kind, known as the ‘gauge argument’ or ‘gauge principle’, discuss its diﬃculties, and then reconstruct the gauge argument as a valid theorem in quantum theory. We then present what we take to be a better and more general gauge argument, based on Noether’s second theorem in classical Lagrangian ﬁeld theory, and argue that this provides a more appropriate framework for understanding how gauge symmetry helps to constrain the dynamics of physical theories.