Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards a mathematical theory of communication for the human connectome

Modeling communication dynamics in the brain is a key challenge in network neuroscience. We present here a framework that combines two measurements for any system where different communication processes are taking place on top of a fixed structural topology: Path Processing Score (PPS) estimates how much the brain signal has changed or has been transformed between any two brain regions (source and target); Path Broadcasting Strength (PBS) estimates the propagation of the signal through edges adjacent to the path being assessed. We use PPS and PBS to explore communication dynamics in large-scale brain networks. We show that brain communication dynamics can be divided into three main communication regimes of information transfer: absent communication (no communication happening); relay communication (information is being transferred almost intact); transducted communication (the information is being transformed). We use PBS to categorize brain regions based on the way they broadcast information Subcortical regions are mainly direct broadcasters to multiple receivers; Temporal and frontal nodes mainly operate as broadcast relay brain stations; Visual and somato-motor cortices act as multi-channel transducted broadcasters. This work paves the way towards the field of brain network information theory by providing a principled methodology to explore communication dynamics in large-scale brain networks.
SIMMC: Situated Interactive Multi-Modal Conversational Data Collection And Evaluation Platform

As digital virtual assistants become ubiquitous, it becomes increasingly important to understand the situated behaviour of users as they interact with these assistants. To this end, we introduce SIMMC, an extension to ParlAI for multi-modal conversational data collection and system evaluation. SIMMC simulates an immersive setup, where crowd workers are able to interact with environments constructed in AI Habitat or Unity while engaging in a conversation. The assistant in SIMMC can be a crowd worker or Artificial Intelligent (AI) agent. This enables both (i) a multi-player / Wizard of Oz setting for data collection, or (ii) a single player mode for model / system evaluation. We plan to open-source a situated conversational data-set collected on this platform for the Conversational AI research community.
The Effect of Teachers' Reflection and Experience on Their Creativity: A Case Study of Language Institutes in Eghlid and Abadeh Cities

The study of creativity and reflectivity has been of great interest to educationalists in general and language teaching practitioners in particular. The main purpose of this study was to investigate the relationship between teacher's reflection and experience on their creativity in language institutes. To this end, 80 teachers were selected randomly from different language institutes based on some criteria: level of teaching, degree, gender. The participants were asked to complete two sets of standard questionnaires during the study. Reflectivity questionnaire was developed by Kurt and Atamturk (2012) in the form of 5 point Likert scale, the reliability of which was found to be . 84. Creativity questionnaire was developed by Khani and Boghayeri (2014). Data analysis and statistical calculations revealed that there was a significant relationship between the teachers’ reflectivity and creativity. Also, there was a significant relationship between reflectivity and creativity as well as teachers’ years of experience.
Federated Learning With Differential Privacy: Algorithms and Performance Analysis

Federated learning (FL), as a type of distributed machine learning, is capable of significantly preserving clients’ private data from being exposed to adversaries. Nevertheless, private information can still be divulged by analyzing uploaded parameters from clients, e.g., weights trained in deep neural networks. In this paper, to effectively prevent information leakage, we propose a novel framework based on the concept of differential privacy (DP), in which artificial noise is added to parameters at the clients’ side before aggregating, namely, noising before model aggregation FL (NbAFL). First, we prove that the NbAFL can satisfy DP under distinct protection levels by properly adapting different variances of artificial noise. Then we develop a theoretical convergence bound on the loss function of the trained FL model in the NbAFL. Specifically, the theoretical bound reveals the following three key properties: 1) there is a tradeoff between convergence performance and privacy protection levels, i.e., better convergence performance leads to a lower protection level; 2) given a fixed privacy protection level, increasing the number <inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> of overall clients participating in FL can improve the convergence performance; and 3) there is an optimal number aggregation times (communication rounds) in terms of convergence performance for a given protection level. Furthermore, we propose a <inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula>-client random scheduling strategy, where <inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> (<inline-formula> <tex-math notation="LaTeX">$1\leq K< N$ </tex-math></inline-formula>) clients are randomly selected from the <inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> overall clients to participate in each aggregation. We also develop a corresponding convergence bound for the loss function in this case and the <inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula>-client random scheduling strategy also retains the above three properties. Moreover, we find that there is an optimal <inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> that achieves the best convergence performance at a fixed privacy level. Evaluations demonstrate that our theoretical results are consistent with simulations, thereby facilitating the design of various privacy-preserving FL algorithms with different tradeoff requirements on convergence performance and privacy levels.
Relationship between host plant diversity and gall-inducing insect’s richness in the Brazilian Cerrado

Several hypotheses have been proposed to explain the diversity patterns of gall-inducing insect species. Some of them take into consideration the influence of host plants on these patterns, mainly in relation to plant community richness and composition. In the present study, we intended to answer three questions: (i) Is gall richness dependent of the host plant family size? , (ii) This relationship also occurs for the plant genus size? , and (iii) Does the composition of host taxa influence gall richness? We carried out inventories of gall-inducing insect diversity in different areas of the Cerrado region in the state of Goias, in the Midwestern Region of Brazil. Gall richness was positively correlated to host plant family size in number of species. Larger families presented higher richness of gall morphotypes, such as Fabaceae, a family that exhibits the highest number of plants in the Cerrado area. Also, plant community composition influenced gall richness and the presence of some super-host taxa incremented gall diversity. Our results indicate that plant community composition may be as determinant of gall-inducing insect species diversity as host plant richness. Key words: floristic composition, galls, host plant richness, super-hosts.
Wind Power Plant Voltage Stability Evaluation: Preprint

Voltage stability refers to the ability of a power system to maintain steady voltages at all buses in the system after being subjected to a disturbance from a given initial operating condition. Voltage stability depends on a power system's ability to maintain and/or restore equilibrium between load demand and supply. Instability that may result occurs in the form of a progressive fall or rise of voltages of some buses. Possible outcomes of voltage instability are the loss of load in an area or tripped transmission lines and other elements by their protective systems, which may lead to cascading outages. The loss of synchronism of some generators may result from these outages or from operating conditions that violate a synchronous generator's field current limit, or in the case of variable speed wind turbine generator, the current limits of power switches. This paper investigates the impact of wind power plants on power system voltage stability by using synchrophasor measurements.
Persistent Homology as Stopping-Criterion for Natural Neighbor Interpolation

In this study the method of natural neighbours is used to interpolate data that has been drawn from a topological space with higher homology groups on its filtration. A particular difficulty with this algorithm are the boundary points. Its core is based on the Voronoi diagram, which induces a natural dual map to the Delaunay triangulation. Advantage is taken from this fact and the persistent homology is therefore calculated after each iteration to capture the changing topology of the data. The Bottleneck and Wasserstein distance serve as a measure of quality between the original data and the interpolation. If the norm of two distances exceeds a heuristically determined threshold, the algorithm terminates. The theoretical basis for this procedure is given and the validity of this approach is justified with numerical experiments.
Theoretical Guarantees for Model Auditing with Finite Adversaries

Privacy concerns have led to the development of privacy-preserving approaches for learning models from sensitive data. Yet, in practice, even models learned with privacy guarantees can inadvertently memorize unique training examples or leak sensitive features. To identify such privacy violations, existing model auditing techniques use finite adversaries defined as machine learning models with (a) access to some finite side information (e.g., a small auditing dataset), and (b) finite capacity (e.g., a fixed neural network architecture). Our work investigates the requirements under which an unsuccessful attempt to identify privacy violations by a finite adversary implies that no stronger adversary can succeed at such a task. We do so via parameters that quantify the capabilities of the finite adversary, including the size of the neural network employed by such an adversary and the amount of side information it has access to as well as the regularity of the (perhaps privacy-guaranteeing) audited model.
Mining Bursting Communities in Temporal Graphs

Temporal graphs are ubiquitous. Mining communities that are bursting in a period of time is essential to seek emergency events in temporal graphs. Unfortunately, most previous studies for community mining in temporal networks ignore the bursting patterns of communities. In this paper, we are the first to study a problem of seeking bursting communities in a temporal graph. We propose a novel model, called (l, {\delta})-maximal dense core, to represent a bursting community in a temporal graph. Specifically, an (l, {\delta})-maximal dense core is a temporal subgraph in which each node has average degree no less than {\delta} in a time segment with length no less than l. To compute the (l, {\delta})-maximal dense core, we first develop a novel dynamic programming algorithm which can calculate the segment density efficiently. Then, we propose an improved algorithm with several novel pruning techniques to further improve the efficiency. In addition, we also develop an efficient algorithm to enumerate all (l, {\delta})-maximal dense cores that are not dominated by the others in terms of the parameters l and {\delta}. The results of extensive experiments on 9 real-life datasets demonstrate the effectiveness, efficiency and scalability of our algorithms.
Detecting and Tracking Small Moving Objects in Wide Area Motion Imagery (WAMI) Using Convolutional Neural Networks (CNNs)

This paper proposes an approach to detect moving objects in Wide Area Motion Imagery (WAMI), in which the objects are both small and well separated. Identifying the objects only using foreground appearance is difficult since a 100–pixel vehicle is hard to distinguish from objects comprising the background. Our approach is based on background subtraction as an efficient and unsupervised method that is able to output the shape of objects. In order to reliably detect low contrast and small objects, we configure the background subtraction to extract foreground regions that might be objects of interest. While this dramatically increases the number of false alarms, a Convolutional Neural Network (CNN) considering both spatial and temporal information is then trained to reject the false alarms. In areas with heavy traffic, the background subtraction yields merged detections. To reduce the complexity of multi-target tracker needed, we train another CNN to predict the positions of multiple moving objects in an area. Our approach shows competitive detection performance on smaller objects relative to the state-of-the-art. We adopt a GM-PHD filter to associate detections over time and analyse the resulting performance.
Accurate Vision-based Manipulation through Contact Reasoning

Planning contact interactions is one of the core challenges of many robotic tasks. Optimizing contact locations while taking dynamics into account is computationally costly and, in environments that are only partially observable, executing contact-based tasks often suffers from low accuracy. We present an approach that addresses these two challenges for the problem of vision-based manipulation. First, we propose to disentangle contact from motion optimization. Thereby, we improve planning efficiency by focusing computation on promising contact locations. Second, we use a hybrid approach for perception and state estimation that combines neural networks with a physically meaningful state representation. In simulation and real-world experiments on the task of planar pushing, we show that our method is more efficient and achieves a higher manipulation accuracy than previous vision-based approaches.
Observed Fractions of Core-Collapse Supernova Types and Initial Masses of their Single and Binary Progenitor Stars

We analyse the observed fractions of core-collapse supernova (SN) types from the Lick Observatory Supernova Search (LOSS), and we discuss the corresponding implications for massive star evolution. For a standard initial mass function, observed fractions of SN types cannot be reconciled with the expectations of single-star evolution. The mass range of Wolf–Rayet (WR) stars that shed their hydrogen envelopes via their own mass-loss accounts for less than half of the observed fraction of Type Ibc supernovae (SNe Ibc). The true progenitors of SNe Ibc must extend to a much lower range of initial masses than classical WR stars, and we argue that most SN Ibc and SN IIb progenitors must arise from binary Roche lobe overflow. In this scenario, SNe Ic would still trace higher initial mass and metallicity, because line-driven winds in the WR stage remove the helium layer and propel the transition from SN Ib to Ic. Less massive progenitors of SNe Ib and IIb may not be classical WR stars; they may be underluminous with weak winds, possibly hidden by overluminous mass-gainer companions that could appear as B[e] supergiants or related objects having aspherical circumstellar material. The remaining SN types (II-P, II-L and IIn) need to be redistributed across the full range of initial masses, so that even some very massive single stars retain H envelopes until explosion. We consider the possibility of direct collapse to black holes without visible SNe, but find this hypothesis difficult to accommodate in most scenarios. Major areas of remaining uncertainty are (1) the detailed influence of binary separation, rotation and metallicity; (2) mass differences in progenitors of SNe IIn compared to SNe II-L and II-P; and (3) the fraction of SNe Ic arising from single stars with the help of eruptive mass-loss, how this depends on metallicity and how it relates to diversity within the SN Ic subclass. Continued studies of progenitor stars and their environments in nearby galaxies, accounting for SN types, may eventually test these ideas.
A Solution for Controlling and Managing User Profiles based on Data Privacy for IoT Applications

IoT is an emerging area in which we expect to have billions of devices connected to the Internet by 2020. IoT applications can offer many benefits to environments, society and the economy through the interconnection and cooperation of smart objects. However, there are many privacy challenges, such as authentication, authorization, and confidentiality of personal data. With this in mind, we have developed a solution for managing user profiles based on privacy and evolution. For that, we define the criteria and characteristics for each environment. The contribution of the work also applies to the evolution of the UbiPri Middleware, with a module implemented and tested according to the rules of environments so that they can modify the user's profile over time according to their frequency in the environment. The modified profile can be raised, reduced and blocked. This implementation has been validated using scripts that perform probabilistic simulation and user authentication. From the rules assigned to the environments, it was possible to perceive the high adaptability of the implementation, and it can be easily adjusted to any IoT environment that wants to treat the authentication and privacy of environments and users. The results obtained were satisfactory and could be useful for both the evolution of the UbiPri Middleware and other related works.
Boundary conditions for the effective-medium description of subwavelength multilayered structures

Nanostructures with one-dimensional periodicity, such as multilayered structures, are currently in the focus of active research in the context of hyperbolic metamaterials and photonic topological structures. An efficient way to describe the materials with subwavelength periodicity is based on the concept of effective material parameters, which can be rigorously derived incorporating both local and nonlocal responses. However, to provide any predictions relevant for applications, effective material parameters have to be supplemented by appropriate boundary conditions. In this work, we provide a comprehensive treatment of spatially dispersive bulk properties of multilayered metamaterials as well as derive boundary conditions for the averaged fields. We demonstrate that local bianisotropic model does not capture all the features related to second-order nonlocal effects in the bulk of metamaterial. As we prove, while the bulk response of multilayers does not depend on the unit cell choice, effective boundary conditions are strongly sensitive to the sequence of layers and multilayer termination. The developed theory provides a clear interpretation of the recent experiments on the reflectance of all-dielectric deeply subwavelength multilayers suggesting further avenues to experimentally probe electromagnetic nonlocality in metamaterials.
Global generalized solutions to a forager–exploiter model with superlinear degradation and their eventual regularity properties

In this article we consider a cascaded taxis model for two proliferating and degrading species which thrive on the same nutrient but orient their movement according to different schemes. In particular, we assume the first group, the foragers, to orient their movement directly along an increasing gradient of the food density, while the second group, the exploiters, instead track higher densities of the forager group. Specifically, we will investigate an initial boundary-value problem for a prototypical forager-exploiter model of the form \begin{align*} \left\{ \begin{array}{r@{\,}c@{\,}l@{\quad }l@{\quad}l@{\quad}l@{\,}c} u_{t}&=&\Delta u-\nabla\cdot\big(u\nabla w\big)+f(u),\ &x\in\Omega,& t>0,\\ v_{t}&=&\Delta v-\nabla\cdot\big(v\nabla u\big)+g(v),\ &x\in\Omega,& t>0,\\ w_{t}&=&\Delta w-(u+v)w-\mu w+r(x,t),\ &x\in\Omega,& t>0, \end{array}\right. \end{align*}%} in a smoothly bounded domain $\Omega\subset\mathbb{R}^2$, where $\mu\geq 0$, $r\in\ C^1(\bar{\Omega}\times[0,\infty))\cap L^\infty\! \left(\Omega\times(0,\infty)\right)$ is nonnegative and the functions $f,g\in C^1\! \left([0,\infty)\right)$ are assumed to satisfy $f(0)\geq0$, $g(0)\geq0$ as well as $$-k_f s^{\alpha} -l_f\leq f(s)\leq -K_f s^\alpha+L_f\ \text{ and }\ -k_g s^\beta -l_g\leq g(s)\leq -K_g s^\beta+L_g\quad\text{for }s\geq0,$$ respectively, with constants $\alpha,\beta>1$, $k_f,K_f,k_g,K_g>0$ and $l_f,L_f,l_g,L_g\geq0$ and $\alpha,\beta>1$. Assuming that $\alpha>1+\sqrt{2}$, $\min\{\alpha,\beta\}>\frac{\alpha+1}{\alpha-1}$ and that $r$ satisfies certain structural conditions we establish the global solvability of this system with respect to a suitable generalized solution concept and then, for the more restrictive case of $\alpha,\beta>1+\sqrt{2}$ and $\mu>0$, investigate an eventual regularity effect driven by the decay of the nutrient density $w$.
Faster Least Squares Optimization

We investigate randomized methods for solving overdetermined linear least-squares problems, where the Hessian is approximated based on a random projection of the data matrix. We consider a random subspace embedding which is either drawn at the beginning and then fixed, or, refreshed at each iteration. We provide an exact finite-time analysis of the refreshed embeddings method for a broad class of random matrices, an exact asymptotic analysis of the fixed embedding method with a Gaussian matrix, and a non-asymptotic analysis of the fixed embedding method for Gaussian and SRHT matrices, with and without momentum acceleration. Surprisingly, we show that, for Gaussian matrices, the refreshed sketching method with no momentum yields the same asymptotic rate of convergence as the fixed embedding method accelerated with momentum. Furthermore, we characterize optimal step sizes and prove that, for a broad class of random matrices including the Gaussian ensemble, momentum does not accelerate the refreshed embeddings method. Hence, among the class of randomized algorithms we consider, a fixed subspace embedding with momentum yields the fastest rate of convergence, along with the lowest computational complexity. Then, picking the accelerated, fixed embedding method as the algorithm of choice, we obtain a faster algorithm by optimizing over the choice of the sketching dimension. Our choice of the sketch size yields an algorithm, for solving overdetermined least-squares problem, with a lower computational complexity compared to current state-of-the-art least-squares iterative methods based on randomized pre-conditioners. In particular, given the sketched data matrix, as the sample size grows, the resulting computational complexity becomes sub-linear in the problem dimensions. We validate numerically our guarantees on large sample datasets, both for Gaussian and SRHT embeddings.
Genetic markers and their application in livestock breeding in South Africa: a review: review article

The development of molecular biological techniques has created new possibilities for the selection and genetic improvement of livestock. The discovery of the PCR had a major impact on the research of eukaryotic genomes and contributed to the development and application of various DNA markers. DNA markers have already found wide application in parentage verification, individual identification and control of genetic disorders. The ultimate use of DNA markers would be to identify quantitative trait loci (QTL) in order to practice genotypic selection. This paper reviews DNA markers (RAPD, DFP, RFLP AFLP, minisatellites, microsatellites, SNP) and provides a brief overview of the current application of these markers in animal breeding.