Miracle-dz/UniFAR-FTUs huggingface dataset:
An overview of the phylogeny of the families Lafoeidae and Hebellidae (Hydrozoa : Leptothecata): their composition and classification

A cladistic analysis of the genera of the family ‘Lafoeidae’ was performed in order to investigate suprageneric classifications and the boundaries of the family, as well as to organise the available morphological data and discuss the possible evolution of some morphological characters. Our results suggest that the former ‘Lafoeidae’ must be separated into two families: the Hebellidae and the Lafoeidae (including the subfamilies Lafoeinae and Zygophylacinae).
Influence of charges on the behavior of polyelectrolyte microgels confined to oil-water interfaces.

The role of electrostatics on the interfacial properties of polyelectrolyte microgels has been discussed controversially in the literature. It is not yet clear if, or how, Coulomb interactions affect their behavior under interfacial confinement. In this work, we combine compression isotherms, atomic force microscopy imaging, and computer simulations to further investigate the behavior of pH-responsive microgels at oil-water interfaces. At low compression, charged microgels can be compressed more than uncharged microgels. The in-plane effective area of charged microgels is found to be smaller in comparison to uncharged ones. Thus, the compressibility is governed by in-plane interactions of the microgels with the interface. At high compression, however, charged microgels are less compressible than uncharged microgels. Microgel fractions located in the aqueous phase interact earlier for charged than for uncharged microgels because of their different swelling perpendicular to the interface. Therefore, the compressibility at high compression is controlled by out-of-plane interactions. In addition, the size of the investigated microgels plays a pivotal role. The charge-dependent difference in compressibility at low compression is only observed for small but not for large microgels, while the behavior at high compression does not depend on the size. Our results highlight the complex nature of soft polymer microgels as compared to rigid colloidal particles. We clearly demonstrate that electrostatic interactions affect the interfacial properties of polyelectrolyte microgels.
Cyberattacks on Miniature Brain Implants to Disrupt Spontaneous Neural Signaling

Brain-Computer Interfaces (BCI) arose as systems that merge computing systems with the human brain to facilitate recording, stimulation, and inhibition of neural activity. Over the years, the development of BCI technologies has shifted towards miniaturization of devices that can be seamlessly embedded into the brain and can target single neuron or small population sensing and control. We present a motivating example highlighting vulnerabilities of two promising micron-scale BCI technologies, demonstrating the lack of security and privacy principles in existing solutions. This situation opens the door to a novel family of cyberattacks, called neuronal cyberattacks, affecting neuronal signaling. This article defines the first two neural cyberattacks, Neuronal Flooding (FLO) and Neuronal Scanning (SCA), where each threat can affect the natural activity of neurons. This work implements these attacks in a neuronal simulator to determine their impact over the spontaneous neuronal behavior, defining three metrics: number of spikes, percentage of shifts, and dispersion of spikes. Several experiments demonstrate that both cyberattacks produce a reduction of spikes compared to spontaneous behavior, generating a rise in temporal shifts and a dispersion increase. Mainly, SCA presents a higher impact than FLO in the metrics focused on the number of spikes and dispersion, where FLO is slightly more damaging, considering the percentage of shifts. Nevertheless, the intrinsic behavior of each attack generates a differentiation on how they alter neuronal signaling. FLO is adequate to generate an immediate impact on the neuronal activity, whereas SCA presents higher effectiveness for damages to the neural signaling in the long-term.
Probing noncommutative gravity with gravitational wave and binary pulsar observations

Noncommutative gravity is a natural method of quantizing spacetime by promoting the spacetime coordinates themselves to operators which do not commute. This approach is motivated, for example, from a quantum gravity perspective, among others. Noncommutative gravity has been tested against the binary black hole merger event GW150914. Here, we extend and improve upon such a previous analysis by (i) relaxing an assumption made on the preferred direction due to noncommutativity, (ii) using posterior samples produced by the LIGO/Virgo Collaborations, (iii) consider other gravitational wave events, namely GW151226, GW170608, GW170814 and GW170817, and (iv) consider binary pulsar observations. Using Kepler's law that contains the noncommutative effect at second post-Newtonian order, we derive corrections to the gravitational waveform phase and the pericenter precession. Using the gravitational wave and double pulsar binary observations, we find bounds on a space-time noncommutative tensor $\theta^{0i}$ in terms of the preferred frame direction with respect to the orientation of each binary. We find that the gravitational wave bounds are stronger than the binary pulsar one by an order of magnitude and the noncommutative tensor normalized by the Planck length and time is constrained to be of order unity.
The Histopathological Changes after Neoadjuvant Theraphy for Rectal Carcinoma

cases performed, present tumour neoadjuvant and staging systems There are several classification systems Abstract Colorectal carcinomas are the third most common carcinomas in men and the second most common carcinomas in women. It has also been reported that deaths due to colorectal cancers account for approximately 9% of all cancer mortalities. Because chemotherapy, radiotherapy and/or neoadjuvant chemoradiotherapy are potentially beneficial during the preoperative period in terms of local control and sphincter-preserving surgery, they are particularly recommended for advanced stage carcinomas. With changing oncological treatments, care should be taken to ensure that the pathological staging of these cases are diligently and correctly performed, and pathological changes that may be present in the tumour after neoadjuvant therapy and staging systems have to be known.
Electronic and fluctuation dynamics following a quench to the superconducting phase

We investigate the dynamics of superconducting fluctuations in the attractive three-dimensional Hubbard model after a quench from the disordered phase to the ordered regime. While the long-time evolution is well understood in terms of dissipative time-dependent Ginzburg-Landau models with unstable potentials, early times are more demanding due to the inseparable dynamics of the pairing fluctuations and the electronic quasiparticles. Our simulation using the time-dependent fluctuation exchange approximation treats both degrees of freedom on the same footing and reveals a nonthermal electronic regime causing a nonmonotonous growth of the fluctuations. This feature is not directly captured by the Ginzburg-Landau theory but nevertheless remains observable beyond the thermalization time of the electrons. We further explore how the growth of the order parameter fluctuations leads to an opening of a pseudogap in the electronic spectrum and identify Andreev reflections as the dominant mechanism behind the gap opening.
On Learned Sketches for Randomized Numerical Linear Algebra

We study "learning-based" sketching approaches for diverse tasks in numerical linear algebra: least-squares regression, $\ell_p$ regression, Huber regression, low-rank approximation (LRA), and $k$-means clustering. Sketching methods are used to quickly and approximately compute properties of large matrices. Linear maps called "sketches" are applied to compress data, and these concise representations are used to compute the desired properties. Specifically, we consider sparse sketches (such as CountSketch). Recent works have dealt with optimizing sketches for data distributions to perform better than their random counterparts. We extend this theme to several important and ubiquitous tasks, each of which requires a new analysis and novel practical methods. Specifically, our contributions are: 1) For all tasks, we introduce fast algorithms using learned sketches with worst-case guarantees. We give a simple task-agnostic method for retaining the worst-case guarantees of randomized sketching, which yields time-optimal algorithms for LRA and least-squares regression. Also, for $k$-means clustering, we give a faster alternative for retaining worst-case guarantees. 2) We show empirically that learned sketches are reliable in improving approximation accuracy, with comparison against "non-learned" sketching baselines. 3) We introduce a greedy algorithm for optimizing the location of the nonzero entries of a sparse sketch and prove guarantees for certain distributions on the LRA task. Previous work only looked at optimizing the values rather than the locations. Also, we show empirically that it further improves learned sketch performance.
Fairwashing Explanations with Off-Manifold Detergent

Explanation methods promise to make black-box classifiers more transparent. As a result, it is hoped that they can act as proof for a sensible, fair and trustworthy decision-making process of the algorithm and thereby increase its acceptance by the end-users. In this paper, we show both theoretically and experimentally that these hopes are presently unfounded. Specifically, we show that, for any classifier $g$, one can always construct another classifier $\tilde{g}$ which has the same behavior on the data (same train, validation, and test error) but has arbitrarily manipulated explanation maps. We derive this statement theoretically using differential geometry and demonstrate it experimentally for various explanation methods, architectures, and datasets. Motivated by our theoretical insights, we then propose a modification of existing explanation methods which makes them significantly more robust.
Learning High-Level Policies for Model Predictive Control

The combination of policy search and deep neural networks holds the promise of automating a variety of decision-making tasks. Model Predictive Control (MPC) provides robust solutions to robot control tasks by making use of a dynamical model of the system and solving an optimization problem online over a short planning horizon. In this work, we leverage probabilistic decision-making approaches and the generalization capability of artificial neural networks to the powerful online optimization by learning a deep high-level policy for the MPC (High-MPC). Conditioning on robot’s local observations, the trained neural network policy is capable of adaptively selecting high-level decision variables for the low-level MPC controller, which then generates optimal control commands for the robot. First, we formulate the search of high-level decision variables for MPC as a policy search problem, specifically, a probabilistic inference problem. The problem can be solved in a closed-form solution. Second, we propose a self-supervised learning algorithm for learning a neural network high-level policy, which is useful for online hyperparameter adaptations in highly dynamic environments. We demonstrate the importance of incorporating the online adaption into autonomous robots by using the proposed method to solve a challenging control problem, where the task is to control a simulated quadrotor to fly through a swinging gate. We show that our approach can handle situations that are difficult for standard MPC.
Nengo and Low-Power AI Hardware for Robust, Embedded Neurorobotics

In this paper we demonstrate how the Nengo neural modeling and simulation libraries enable users to quickly develop robotic perception and action neural networks for simulation on neuromorphic hardware using tools they are already familiar with, such as Keras and Python. We identify four primary challenges in building robust, embedded neurorobotic systems, including: (1) developing infrastructure for interfacing with the environment and sensors; (2) processing task specific sensory signals; (3) generating robust, explainable control signals; and (4) compiling neural networks to run on target hardware. Nengo helps to address these challenges by: (1) providing the NengoInterfaces library, which defines a simple but powerful API for users to interact with simulations and hardware; (2) providing the NengoDL library, which lets users use the Keras and TensorFlow API to develop Nengo models; (3) implementing the Neural Engineering Framework, which provides white-box methods for implementing known functions and circuits; and (4) providing multiple backend libraries, such as NengoLoihi, that enable users to compile the same model to different hardware. We present two examples using Nengo to develop neural networks that run on CPUs and GPUs as well as Intel's neuromorphic chip, Loihi, to demonstrate two variations on this workflow. The first example is an implementation of an end-to-end spiking neural network in Nengo that controls a rover simulated in Mujoco. The network integrates a deep convolutional network that processes visual input from cameras mounted on the rover to track a target, and a control system implementing steering and drive functions in connection weights to guide the rover to the target. The second example uses Nengo as a smaller component in a system that has addressed some but not all of those challenges. Specifically it is used to augment a force-based operational space controller with neural adaptive control to improve performance during a reaching task using a real-world Kinova Jaco2 robotic arm. The code and implementation details are provided1, with the intent of enabling other researchers to build and run their own neurorobotic systems.
Smart Network Slicing for Vehicular Fog-RANs

Modern transportation systems are facing a sharp alteration since the Internet of Vehicles (IoV) has activated intense information exchange among vehicles, infrastructure, and pedestrians. Existing approaches fail in efficiently handling the heterogeneous network traffic because of the complicated network environment and dynamic vehicle density. Recently, the fog-radio access network with network slicing has emerged as a promising solution to fulfill the demands of the maldistributed network traffic. However, available fog resources as well as network traffic are all dynamic and unpredictable due to high mobility of vehicles, which results in weak resource utilization. To address this problem, we propose a smart slice scheduling scheme in vehicular fog radio access networks. This scheduling scheme is formed as a Markov decision process. Accordingly, an intelligent algorithm for network slices is proposed based on the Monte Carlo tree search in terms of a new metric cross entropy, which is able to allocate the resource allocation for the match of traffic load in the time-space domain. This slice scheduling algorithm does not require any prior knowledge of the network traffic. Furthermore, this paper first reveals the relationship between road traffic and the IoV resource based on the metric perception-reaction time. A collaborative scheduling scheme is proposed to tune the road traffic speed to further release available IoV resource under the heavy traffic load. Simulation results indicate that the proposed algorithm outperforms several baselines in terms of throughput and delay with low complexity.
Functional aspects of phosphorus uptake and carbon translocation in incompatible ectomycorrhizal associations between Pinus sylvestris and Suillus grevillei and Boletinus cauipes

summary Pinus sylvestris L. and Larix eurolepis Henry seedlings were grown together in non–sterile laboratory microcosms containing functional mycelial networks of one of three different ectomyeorrhizal Fungi. The fungi used were Suillus bovinus (Fr.) O. Kuntze, and two Fungi which are commonly considered to be specific to Larix spp. , Suillus grevillei (Klotzsch) Sing, and Boletirms cavipes (Opat.) Kalchbr. Patterns of mycorrhizal infection, transport of labelled assimilate and phosphorus uptake within the microcosms were studied in order to evaluate tine functional attributes of associations formed between P. sylvestris and the two‘larch specific’fungi. Fungal colonization of lateral roots and accumulation of labelled assimilate from adjacent plants fed with 14CO2 was significantly greater in the roots of Pinus plants when the ectomycorrhizal fungus was S. bovinu, and significantly greater in Larix roots when the fungus was V. grevillei or li. cavipes. Kctomycorrhizas formed between P. sylvestris and Ii, cavipes were fully functional in terms of transfer of labelled assimilate to the fungal mycelium. In mixed communities infected by B. caripes, myeclial uptake and translocation of 32P-labelled orthophosphate was significantly greater to Larix plants than to Pinus plants. Formation of sheathed lateral roots on the Pinus plants was low. There was some autoradiographic evidence of phosphorus translocation from mycorrhizal pine roots to stem and leaf tissue hut levels of activity in the shoots were low, indicating limited translocation across the host-fungus interface.
A method for computing the Perron root for primitive matrices

Following the Perron theorem, the spectral radius of a primitive matrix is a simple eigenvalue. It is shown that for a primitive matrix A, there is a positive rank one matrix X such that B = A ∘ X, where ∘ denotes the Hadamard product of matrices, and such that the row (column) sums of matrix B are the same and equal to the Perron root. An iterative algorithm is presented to obtain matrix B without an explicit knowledge of X. The convergence rate of this algorithm is similar to that of the power method but it uses less computational load. A byproduct of the proposed algorithm is a new method for calculating the first eigenvector.
Inter-Homines: Distance-Based Risk Estimation for Human Safety

In this document, we report our proposal for modeling the risk of possible contagiousity in a given area monitored by RGB cameras where people freely move and interact. Our system, called Inter-Homines, evaluates in real-time the contagion risk in a monitored area by analyzing video streams: it is able to locate people in 3D space, calculate interpersonal distances and predict risk levels by building dynamic maps of the monitored area. Inter-Homines works both indoor and outdoor, in public and private crowded areas. The software is applicable to already installed cameras or low-cost cameras on industrial PCs, equipped with an additional embedded edge-AI system for temporary measurements. From the AI-side, we exploit a robust pipeline for real-time people detection and localization in the ground plane by homographic transformation based on state-of-the-art computer vision algorithms; it is a combination of a people detector and a pose estimator. From the risk modeling side, we propose a parametric model for a spatio-temporal dynamic risk estimation, that, validated by epidemiologists, could be useful for safety monitoring the acceptance of social distancing prevention measures by predicting the risk level of the scene.
Easy Computation of the Bayes Factor to Fully Quantify Occam’s Razor: Least-squares Fitting and Policy Decisions

The Bayes factor is the gold-standard figure of merit for comparing fits of models to data, for hypothesis selection and parameter estimation. However, it is little-used because it has been considered to be subjective, and to be computationally very intensive. A simple computational method has been known for at least 30 years, but has been dismissed as an approximation. We show here that all three criticisms are misplaced. The method should be used with all least-squares fitting, because it can give very different, better outcomes than classical methods. It can discriminate between models with equal numbers of parameters and equally good fits to data. It quantifies the Occam’s Razor injunction against over-fitting, and it demands that physically-meaningful parameters rejected by classical significance testing be included in the fitting, to avoid spurious precision and incorrect values for the other parameters. It strongly discourages the use of physically-meaningless parameters, thereby satisfying the Occam’s Razor injunction to use existing entities for explanation rather than multiplying new ones. More generally, being a relative probability, the Bayes factor combines naturally with other quantitative information to guide action in the absence of certain knowledge.
Agent-Based Software Testing: A Definition and Systematic Mapping Study

The emergence of new technologies in software testing has increased the automation and flexibility of the testing process. In this context, the adoption of agents in software testing remains an active research area in which various agent methodologies, architectures, and tools are employed to improve different test problems. Even though research that investigates agents in software testing has been growing, these agent-based techniques should be considered in a broader perspective. In order to provide a comprehensive overview of this research area, which we define as agent-based software testing (ABST), a systematic mapping study has been conducted. This mapping study aims to identify the topics studied within ABST, as well as examine the adopted research methodologies, identify the gaps in the current research and point to directions for future ABST research. Our results suggest that there is an interest in ABST after 1999 that resulted in the development of solutions using reactive, BDI, deliberative and cooperate agent architectures for software testing. In addition, most of the ABST approaches are designed using the JADE framework, have targeted the Java programming language, and are used at system-level testing for functional, non-functional and white-box testing. In regards to regression testing, our results indicate a research gap that could be addressed in future studies.