Miracle-dz/UniFAR-FTUs huggingface dataset:
Multiband Ballistic Transport and Anisotropic Commensurability Magnetoresistance in Antidot Lattices of AB-stacked Trilayer Graphene

Ballistic transport was studied in a multiple-band system consisting of an antidot lattice of AB-stacked trilayer graphene. The low temperature magnetoresistance showed commensurability peaks arising from matching of the antidot lattice period and radius of cyclotron orbits for each mono- and bilayer-like band in AB stacked trilayer graphene. The commensurability peak of the monolayer-like band appeared at a lower magnetic field than that of the bilayer-like band, which reflects the fact that the Fermi surface of the bilayer-like band is larger than that of monolayer-like band. Rotation of the antidot lattice relative to the crystallographic axes of graphene resulted in anisotropic magnetoresistance, which reflects the trigonally warped Fermi surface of the bilayer-like band. Numerical simulations of magnetoresistance that assumed ballistic transport in the mono- and bilayer-like bands approximately reproduced the observed magnetoresistance features. It was found that the monolayer-like band significantly contributes to the conductivity even though its carrier density is an order smaller than that of the bilayer-like band. These results indicate that ballistic transport experiments could be used for studying the anisotropic band structure of multiple-band systems.
Homological perturbation theory with curvature

We prove a general version of the homological perturbation lemma which works in the presence of curvature, and without the restriction to strong deformation retracts, building on work of Markl. A key observation is that the notion of strong homotopy equivalence of complexes (or objects in an abstract dg category) has a natural expression in the language of curved twisted complexes.
A Distributed Forward–Backward Algorithm for Stochastic Generalized Nash Equilibrium Seeking

We consider the stochastic generalized Nash equilibrium problem (SGNEP) with expected-value cost functions. Inspired by Yi and Pavel (2019), we propose a distributed generalized Nash equilibrium seeking algorithm based on the preconditioned forward–backward operator splitting for SGNEPs, where, at each iteration, the expected value of the pseudogradient is approximated via a number of random samples. Our main contribution is to show almost sure convergence of the proposed algorithm if the pseudogradient mapping is restricted (monotone and) cocoercive.
A viral propagation model with Holling type-II response function and free boundaries

In this paper we put forward a viral propagation model with Holling type-II response function and free boundaries and investigate the dynamical properties. This model is composed of two ordinary differential equations and one partial differential equation, in which the spatial range of the first equation is the whole space $\R$, and the last two equations have free boundaries. As a new mathematical model, we prove the existence, uniqueness and uniform estimates of global solution, and provide the criteria for spreading and vanishing, and long time behavior of the solution components $u,v,w$. Comparing with the corresponding ordinary differential systems, the {\it Basic Reproduction Number} ${\cal R}_0$ plays a different role. We find that when ${\cal R}_0\le 1$, the virus cannot spread successfully; when ${\cal R}_0>1$, the successful spread of virus depends on the initial value and varying parameters.
Bundle Adjustment Revisited

3D reconstruction has been developing all these two decades, from moderate to medium size and to large scale. It's well known that bundle adjustment plays an important role in 3D reconstruction, mainly in Structure from Motion(SfM) and Simultaneously Localization and Mapping(SLAM). While bundle adjustment optimizes camera parameters and 3D points as a non-negligible final step, it suffers from memory and efficiency requirements in very large scale reconstruction. In this paper, we study the development of bundle adjustment elaborately in both conventional and distributed approaches. The detailed derivation and pseudo code are also given in this paper.
Complexity Growth for Topological Black Holes with a Probe String

We consider the growth of the action for black hole spacetime with a fundamental string. According to "complexity - action" conjecture it is expected to be equal to complexity which describes the quantum states of black holes. We consider black holes with three different horizon topologies. These have positive, negative and zero curvatures. We are interested in the complexity growth of these system with a fundamental string. We find that for the case where the black holes have the toroidal horizon structure the probe string behaves very differently from the other two cases.
Data-Driven Linear Quadratic Optimization for Controller Synthesis With Structural Constraints

For various typical cases and situations where the formulation results in an optimal control problem, the linear quadratic regulator (LQR) approach and its variants continue to be highly attractive. In certain scenarios, it can happen that some prescribed structural constraints on the gain matrix would arise. Consequently then, the algebraic Riccati equation (ARE) is no longer applicable in a straightforward way to obtain the optimal solution. This work presents a rather effective alternative optimization approach based on gradient projection. The utilized gradient is obtained through a data-driven methodology, and then projected onto applicable constrained hyperplanes. Essentially, this projection gradient determines a direction of progression and computation for the gain matrix update with a decreasing functional cost; and then the gain matrix is further refined in an iterative framework. With this formulation, a data-driven optimization algorithm is summarized for controller synthesis with structural constraints. This data-driven approach has the key advantage that it avoids the necessity of precise modeling which is always required in the classical model-based counterpart; and thus the approach can additionally accommodate various model uncertainties. Illustrative examples are also provided in the work to validate the theoretical results.
Variational Autoencoder Trajectory Primitives with Continuous and Discrete Latent Codes

Imitation learning is an intuitive approach for teaching motion to robotic systems. Although previous studies have proposed various methods to model demonstrated movement primitives, one of the limitations of existing methods is that it is not trivial to modify their planned trajectory once the model is learned. The trajectory of a robotic manipulator is often high-dimensional, and it is not easy to tune the shape of the planned trajectory in an intuitive manner. We address this problem by learning the latent space of the robot trajectory. If the latent variable of the trajectories can be learned, it can be used to tune the trajectory in an intuitive manner even when the user is an expert. We propose a framework for modeling demonstrated trajectories with a neural network that learns the low-dimensional latent space. Our neural network structure is built on the variational autoencoder (VAE) with discrete and continuous latent variables. We extend the structure of the existing VAE to obtain the decoder that is conditioned on the goal position of the trajectory for generalization to different goal positions. To cope with requirement of the massive training data, we use a trajectory augmentation technique inspired by the data augmentation commonly used in the computer vision community. In the proposed framework, the latent variables that encodes the multiple types of trajectories are learned in an unsupervised manner. The learned decoder can be used as a motion planner in which the user can specify the goal position and the trajectory types by setting the latent variables. The experimental results show that our neural network can be trained using a limited number of demonstrated trajectories and that the interpretable latent representations can be learned.
Transformer Based Reinforcement Learning For Games

Recent times have witnessed sharp improvements in reinforcement learning tasks using deep reinforcement learning techniques like Deep Q Networks, Policy Gradients, Actor Critic methods which are based on deep learning based models and back-propagation of gradients to train such models. An active area of research in reinforcement learning is about training agents to play complex video games, which so far has been something accomplished only by human intelligence. Some state of the art performances in video game playing using deep reinforcement learning are obtained by processing the sequence of frames from video games, passing them through a convolutional network to obtain features and then using recurrent neural networks to figure out the action leading to optimal rewards. The recurrent neural network will learn to extract the meaningful signal out of the sequence of such features. In this work, we propose a method utilizing a transformer network which have recently replaced RNNs in Natural Language Processing (NLP), and perform experiments to compare with existing methods.
Lossless Compression for 3DCNNs Based on Tensor Train Decomposition

Three dimensional convolutional neural networks (3DCNNs) have been applied in many tasks of video or 3D point cloud recognition. However, due to the higher dimension of convolutional kernels, the space complexity of 3DCNNs is generally larger than that of traditional two dimensional convolutional neural networks (2DCNNs). To miniaturize 3DCNNs for the deployment in confining environments such as embedded devices, neural network compression is a promising approach. In this work, we adopt the tensor train (TT) decomposition, the most compact and simplest \emph{in situ} training compression method, to shrink the 3DCNN models. We give the tensorizing for 3D convolutional kernels in TT format and investigate how to select appropriate ranks for the tensor in TT format. In the light of multiple contrast experiments based on VIVA challenge and UCF11 datasets, we conclude that the TT decomposition can compress redundant 3DCNNs in a ratio up to 121\(\times\) with little accuracy improvement. Besides, we achieve a state-of-the-art result of TT-3DCNN on VIVA challenge dataset (81.83\%).
An interpretable probabilistic machine learning method for heterogeneous longitudinal studies

Identifying risk factors from longitudinal data requires statistical tools that are not restricted to linear models, yet provide interpretable associations between different types of covariates and a response variable. Here, we present a widely applicable and interpretable probabilistic machine learning method for nonparametric longitudinal data analysis using additive Gaussian process regression. We demonstrate that it outperforms previous longitudinal modeling approaches and provides useful novel features, including the ability to account for uncertainty in disease effect times as well as heterogeneity in their effects.
Hardening Random Forest Cyber Detectors Against Adversarial Attacks

Machine learning algorithms are effective in several applications, but they are not as much successful when applied to intrusion detection in cyber security. Due to the high sensitivity to their training data, cyber detectors based on machine learning are vulnerable to targeted adversarial attacks that involve the perturbation of initial samples. Existing defenses assume unrealistic scenarios; their results are underwhelming in non-adversarial settings; or they can be applied only to machine learning algorithms that perform poorly for cyber security. We present an original methodology for countering adversarial perturbations targeting intrusion detection systems based on random forests. As a practical application, we integrate the proposed defense method in a cyber detector analyzing network traffic. The experimental results on millions of labelled network flows show that the new detector has a twofold value: it outperforms state-of-the-art detectors that are subject to adversarial attacks; it exhibits robust results both in adversarial and non-adversarial scenarios.
The Development and Validation of a Dysphagia-Specific Quality-of-Life Questionnaire for Patients With Head and Neck Cancer

Results: The internal consistency reliability of the MDADI was calculated using the Cronbach a coefficient. The Cronbach a coefficients of the MDADI subscales ranged from 0.85 to 0.93. Test-retest reliability coefficients of the subscales ranged from 0.69 to 0.88. Spearman correlation coefficients between the MDADI subscales and the SF-36 subscales demonstrated construct validity. Patients with primary tumors of the oral cavity and oropharynx had significantly greater swallowing disability with an adverse impact on their QOL compared with patients with primary tumors of the larynx and hypopharynx (P,.001). Patients with a malignant lesion also had significantly greater disability than patients with a benign lesion (P,.001). Conclusions: The MDADI is the first validated and reliable self-administered questionnaire designed specifically for evaluating the impact of dysphagia on the QOL of patients with head and neck cancer. Standardized questionnaires that measure patients’ QOL offer a means for demonstrating treatment impact and improving medical care. The development and validation of the MDADI and its use in prospective clinical trials allow for better understanding of the impact of treatment of head and neck cancer on swallowing and of swallowing difficulty on patients’ QOL. Arch Otolaryngol Head Neck Surg. 2001;127:870-876
4-[2-(Cyclohexa-1,4-dien-1-yl)ethoxy]benzene-1,2-dicarbonitrile

In the title compound, C16H14N2O, the dihedral angle between the aromatic rings is 70.23 (6)°. The linking chain has a zigzag conformation. In the crystal, molecules are linked by weak intermolecular C—H⋯N hydrogen bonds, forming a zigzag chain along the c axis.
Robust network function virtualization

Network function virtualization (NFV) enables on‐demand network function (NF) deployment providing agile and dynamic network services. Through an evaluation metric that quantifies the minimal reliability among all NFs for all demands, service providers and operators may better facilitate flexible NF service recovery and migration, thus offering higher service reliability. In this paper, we present evaluation metrics on NFV reliability and solution approaches to solve robust NFV under random NF‐enabled node failure(s). We demonstrate how to construct an auxiliary NF‐enabled network and its mapping onto the physical substrate network. With the constructed NF‐enabled network, we develop pseudo‐polynomial algorithms to solve the robust NF and SFC s − t path problems: subproblems of robust NFV. We also present approximation algorithms for robust NFV with the SFC‐Fork as the NF forwarding graph. Furthermore, we propose exact solution approaches via mixed‐integer linear programming under the general setting. Computational results show that our proposed solution approaches are capable of managing robust NFV in a large‐size network.
Towards vestibular implants: effective pulse modulation in human patients and electrically evoked compound action potentials in animal models

We use our five senses in everyday life to interact with our environment. A sixth sense – the sense of balance – helps us navigate and orient and the vestibular system is the main contributor to this sense. Its sensory organs are located in the inner ear. Bilateral loss of vestibular sensation (BVL) is severely debilitating and significantly reduces quality of life. Affected patients have currently no effective treatment option and a vestibular implant (VI) might be the most promising option. The field of vestibular implant research is entering an exciting period. More than a decade of animal work is now being translated to human patients and studies with patients are surging forward. With several collaborators within the EU-US project CLONS, we made further inroads towards a commercial VI. In the first part of this thesis, we developed a real-time research platform to investigate pulse rate and pulse amplitude modulation. These two modulation paradigms were then tested in four BVL patients which were instrumented with modified cochlear implants for vestibular stimulation. Our results strongly support pulse amplitude modulation as stimulation paradigm for implant activation because it evoked significantly stronger eye movement responses. Furthermore, a model of the eye movement reflex pathway provided insights into how differently the two modulation paradigms engage vestibular afferents. Future experiments should investigate how the performance of these modulation paradigms changes with continuous stimulation as animal research suggests improvements in eye movement response over time. In the second part of the thesis, we lay the groundwork to pursue the goal of a closed-loop VI that could boost implant performance. We adapted artifact reduction techniques to record vestibular electrically evoked compound action potentials with a custom electrode array. We
Some Observations on the Smallest Adjacency Eigenvalue of a Graph

Abstract In this paper, we discuss various connections between the smallest eigenvalue of the adjacency matrix of a graph and its structure. There are several techniques for obtaining upper bounds on the smallest eigenvalue, and some of them are based on Rayleigh quotients, Cauchy interlacing using induced subgraphs, and Haemers interlacing with vertex partitions and quotient matrices. In this paper, we are interested in obtaining lower bounds for the smallest eigenvalue. Motivated by results on line graphs and generalized line graphs, we show how graph decompositions can be used to obtain such lower bounds.
Temporal Wasserstein non-negative matrix factorization for non-rigid motion segmentation and spatiotemporal deconvolution

Motion segmentation for natural images commonly relies on dense optic flow to yield point trajectories which can be grouped into clusters through various means including spectral clustering or minimum cost multicuts. However, in biological imaging scenarios, such as fluorescence microscopy or calcium imaging, where the signal to noise ratio is compromised and intensity fluctuations occur, optical flow may be difficult to approximate. To this end, we propose an alternative paradigm for motion segmentation based on optimal transport which models the video frames as time-varying mass represented as histograms. Thus, we cast motion segmentation as a temporal non-linear matrix factorization problem with Wasserstein metric loss. The dictionary elements of this factorization yield segmentation of motion into coherent objects while the loading coefficients allow for time-varying intensity signal of the moving objects to be captured. We demonstrate the use of the proposed paradigm on a simulated multielectrode drift scenario, as well as calcium indicating fluorescence microscopy videos of the nematode Caenorhabditis elegans (C. elegans). The latter application has the added utility of extracting neural activity of the animal in freely conducted behavior.
Isolated facial palsy in Iranian multiple sclerosis patients

According to the World Health Organization, the prevalence of multiple sclerosis (MS) in Iran is 4 in 100,000. One of MS manifestations is peripheral facial palsy. There has not been any study of the prevalence of facial palsy secondary to MS in Iran. Therefore we conducted a retrospective descriptive analytical cross sectional study in which we reviewed the medical records of all patients diagnosed with MS who visited the neurology clinic between years 1991 and 2007. One thousand and sixty nine patients were studied and among them 53 patients (5%) had isolated facial palsy. In 22 patients (2.1%), isolated facial palsy occurred as the first MS clinical manifestation. In these patients, the interval to the second neurological symptom was 52 months. We compared the occurrence of other neurologic manifestations in patients with and without facial palsy. Facial numbness, internuclear ophthalmoplegia, gustatory disturbance and pyramidal disorders were significantly more prevalent in patients with facial palsy. In conclusion, isolated facial palsy occurs in about 5% of MS patients in Iran. It may rarely be the presenting feature of MS.
The effect of group discussion and telephone counselling on perceived stress of women as caregivers of patient with Alzheimer disease

Background and aims: Caring for patients with dementia especially Alzheimer, is the most hardship, damaging and challenging of cares that caregivers expose to it. The lack of education about it increases the problems. The aim of this study was to evaluate the effect of group discussion and telephone counselling on perceived stress of women as caregivers of patient with Alzheimer disease. Methods: In this study, 150 women who had 18-60 years old as a close relative to elderly patient with Alzheimer and at least being direct caregivers for six months, with convenience method were selected and by blocking randomization were allocated to control and experimental groups. The experimental group was participated in the group discussion and telephone counselling. Post-test was carried out six weeks after the program. The instruments for data gathering were demographic items and perceived stress Questionnaire. Independent t-test, paired t-test, Chi-square and ANOVA test were used. Results: The study findings showed that before group discussion and telephone counselling 50.7% of samples in intervention group and 57.3% of samples in control group had high level of stress. Our results demonstrated that after the intervention, the mean of perceived stress in experimental group was decreased from 28.38±8.62 to 22.45±5.55 (p≤0.001) which shows decrease of stress. In the control group mean was increased from 29.88±8.66 to 31.92± 6.31 (p=0.009) which shows increase of stress. There were significant relationship between perceived stress and the variables of education (p= 0.03), and economic status (p= 0.01). Conclusion: Caregiver women of patient elders with Alzheimer disease as the main resource of informal caregivers, experience high levels of stress that this problem treats health of women and their elderly. Group discussion and telephone counselling are effective to decrease perceived stress of women as caregivers of patient with Alzheimer disease. Thus, this method of education is suggested.
4-[(2-Hydroxy-5-nitrobenzylidene)amino]benzenesulfonamide

The title Schiff base compound, C13H11N3O5S, exists in an E configuration with respect to the C=N double bond. The benzene rings are almost coplanar, making a dihedral angle of 2.82 (6). The sulfonamide group is twisted away from the attached phenyl ring with an N—S—C—C torsion angle of 64.84 (11)°. An intramolecular O—H⋯N hydrogen bond stabilizes the molecule, generating an S(6) ring motif. In the crystal, intermolecular N—H⋯O and C—H⋯O hydrogen bonds link the molecules into a three-dimensional network.
The Grandmother's Role in Breastfeeding

1. Profesora Titular, Universidad Rey Juan Carlos, Madrid, España. marta.losa@urjc.es 2. Especialista obstétrico-ginecológica. Unidad Docente de La Paz, Universidad Rey Juan Carlos, Madrid, España. rrodriguezvazquez@alumnos.urjc.es 3. Profesor Titular, Universidad Complutense de Madrid, España. ribebeva@enf.ucm. es Recibido: 12 de marzo de 2012 Enviado a pares: 14 de mayo de 2012 Aceptado por pares: 15 de julio de 2013 Aprobado: 17 de julio de 2013