Miracle-dz/UniFAR-FTUs huggingface dataset:
Making Complex Network Analysis in System Dynamics

As urban models in system dynamics are extremely complex if an area is subdivided in many dynamic and interacting areas, managing complexity of the urban network interactions is essential. A recently developed interregional model of The Netherlands illustrates the implementation of the spatial dimension in urban dynamics. The model describes 40 self-organizing urban areas and distribution of migrants, firms and commuting between 40 regions simultaneously. Developments in regional labor markets, housing markets and land-use can be explained by internal as well as by external regional conditions. The applied approach gives many opportunities to make large disaggregated models in system dynamics. Spatial or sectional interactions (network models) can be modeled while model structures remain manageable. In general, as vector-based and matrixbased calculations can be implemented in system dynamics easily, many (existing) static models can be applied dynamically. Hence, the usefulness of system dynamics in modeling complex systems broadly is enlarged.
Loop Nest Optimization for SIMD Architectures

In this thesis we investigate the possibility of creating a retargetable compiler that is capable of automatically vectorizing sequentially written code into a form that exploits SIMD extensions found in most modern computer architectures. We describe the efforts made to transform loop nests found in sequential programs into a form suitable for vectorization by using loop transformations. We first try to optimize candidate loop nests by applying loop interchange to increase stride-1 memory access in the inner-loop of a loop nest. Next, we apply a small set of loop transformations that transforms the loop body of a(n) (optimized) loop nest into sets of statements containing consecutive memory access. Partial alignment analysis is done in order to determine whether the expressions in a loop body can be efficiently mapped onto SIMD instructions. An experimental rule-based SIMD back-end is used to map the transformed code onto the SIMD instructions available on the target platform. Our transformation approach is implemented using the CoSy compiler framework and a small compiler is built that is capable of successfully transforming sequential programs into target code that exploits SIMD instructions. A small set of simple, often found, loop kernels are used to measure the impact vectorization has on overall performance. Finally, some indications for future research are provided, with the purpose of further increasing program performance through a combination of additional transformations and analysis.
Usable AI: Experience and Reflections

Adaptive Interfaces: Do People Want Them? Automatic adaptation of user interfaces is a contentious area. Proponents (e.g., [1]) argue that it offers the potential to optimize interactions for a user’s tasks and style while critics (e.g., [4]) maintain that the inherent unpredictability of adaptive interfaces may disorient the user, causing more harm than good. Surprisingly, however, there is very little past research explicitly studying automatic adaptation in graphical user interfaces. The existing research includes both positive and negative examples of adaptation, sometimes reporting contradictory results without analyzing the reasons underlying the discrepancy (e.g., [4] and [13]).
Understanding the Thematic Structure of the Qur’an: An Exploratory Multivariate Approach

In this paper, we develop a methodology for discovering the thematic structure of the Qur'an based on a fundamental idea in data mining and related disciplines: that, with respect to some collection of texts, the lexical frequency profiles of the individual texts are a good indicator of their conceptual content, and thus provide a reliable criterion for their classification relative to one another. This idea is applied to the discovery of thematic interrelationships among the suras (chapters) of the Qur'an by abstracting lexical frequency data from them and then applying hierarchical cluster analysis to that data. The results reported here indicate that the proposed methodology yields usable results in understanding the Qur'an on the basis of its lexical semantics.
DAZ Family Proteins, Key Players for Germ Cell Development

DAZ family proteins are found almost exclusively in germ cells in distant animal species. Deletion or mutations of their encoding genes usually severely impair either oogenesis or spermatogenesis or both. The family includes Boule (or Boll), Dazl (or Dazla) and DAZ genes. Boule and Dazl are situated on autosomes while DAZ, exclusive of higher primates, is located on the Y chromosome. Deletion of DAZ gene is the most common causes of infertility in humans. These genes, encoding for RNA binding proteins, contain a highly conserved RNA recognition motif and at least one DAZ repeat encoding for a 24 amino acids sequence able to bind other mRNA binding proteins. Basically, Daz family proteins function as adaptors for target mRNA transport and activators of their translation. In some invertebrate species, BOULE protein play a pivotal role in germline specification and a conserved regulatory role in meiosis. Depending on the species, DAZL is expressed in primordial germ cells (PGCs) and/or pre-meiotic and meiotic germ cells of both sexes. Daz is found in fetal gonocytes, spermatogonia and spermatocytes of adult testes. Here we discuss DAZ family genes in a phylogenic perspective, focusing on the common and distinct features of these genes, and their pivotal roles during gametogenesis evolved during evolution.
Differentiating Type of Muscle Movement via AR Modeling and Neural Network Classification

The aim of this study is to classify electromyogram (EMG) signals for controlling multifunction proshetic devices. An articial neural network (ANN) implementation was used for this purpose. Autoregressive (AR) parameters of a1;a2;a3;a4 and their signal power obtained from dierent arm muscle motions were applied to the input of ANN, which is a multilayer perceptron. At the output layer, for 5000 iterations, six movements were distinguished at a high accuracy of 97.6%.
On (cosmological) singularity avoidance in loop quantum gravity

Loop quantum cosmology (LQC), mainly due to Bojowald, is not the cosmological sector of loop quantum gravity (LQG). Rather, LQC consists of a truncation of the phase space of classical general relativity to spatially homogeneous situations which is then quantized by the methods of LQG. Thus, LQC is a quantum-mechanical toy model (finite number of degrees of freedom) for LQG (a genuine QFT with an infinite number of degrees of freedom) which provides important consistency checks. However, it is a non-trivial question whether the predictions of LQC are robust after switching on the inhomogeneous fluctuations present in full LQG. Two of the most spectacular findings of LQC are that: (1) the inverse scale factor is bounded from above on zero-volume eigenstates which hints at the avoidance of the local curvature singularity and (2) the quantum Einstein equations are non-singular which hints at the avoidance of the global initial singularity. This rests on (1) a key technique developed for LQG and (2) the fact that there are no inhomogeneous excitations. We display the result of a calculation for LQG which proves that the (analogon of the) inverse scale factor, while densely defined, is not bounded from above on zero-volume eigenstates. Thus, in full LQG, if curvature singularity avoidance is realized, then not in this simple way. In fact, it turns out that the boundedness of the inverse scale factor is neither necessary nor sufficient for the curvature singularity avoidance and that non-singular evolution equations are neither necessary nor sufficient for initial singularity avoidance because none of these criteria are formulated in terms of observable quantities. After outlining what would be required, we present the results of a calculation for LQG which could be a first indication that our criteria at least for curvature singularity avoidance are satisfied in LQG.
Integrated Modeling Program, Applied Chemical Theory (IMPACT)

We provide an overview of the IMPACT molecular mechanics program with an emphasis on recent developments and a description of its current functionality. With respect to core molecular mechanics technologies we include a status report for the fixed charge and polarizable force fields that can be used with the program and illustrate how the force fields, when used together with new atom typing and parameter assignment modules, have greatly expanded the coverage of organic compounds and medicinally relevant ligands. As we discuss in this review, explicit solvent simulations have been used to guide our design of implicit solvent models based on the generalized Born framework and a novel nonpolar estimator that have recently been incorporated into the program. With IMPACT it is possible to use several different advanced conformational sampling algorithms based on combining features of molecular dynamics and Monte Carlo simulations. The program includes two specialized molecular mechanics modules: Glide, a high‐throughput docking program, and QSite, a mixed quantum mechanics/molecular mechanics module. These modules employ the IMPACT infrastructure as a starting point for the construction of the protein model and assignment of molecular mechanics parameters, but have then been developed to meet specialized objectives with respect to sampling and the energy function. © 2005 Wiley Periodicals, Inc. J Comput Chem 26: 1752–1780, 2005
Vehicular networks for reduction of fuel consumption and CO2 emission

With recent advances in the development of wireless communication networks, vehicular networks have been receiving considerable research interest. One of the major applications of vehicular networks is Intelligent Transportation Systems (ITS). To exchange and distribute messages, geocast routing protocols have been proposed for ITS applications. Almost all of these protocols evaluate network-centric performance measures, instead of evaluating the impact of the protocol on the vehicular system. Nowadays, rising fuel costs and the harmful effects of air pollutants have been the subject of considerable public debate. Therefore, it is desirable to create new economical and environmentally friendly geocast (EEFG) protocols, which focus on minimizing vehicle fuel consumption and emissions. The main goals of this paper are to motivate communications researchers to design EEFG protocols, demonstrate the ability to integrate fuel and emission models with vehicular networks, and illustrate the benefit of transmitting the traffic light signal information to vehicles for fuel consumption and emission reduction. By means of an example, we show how vehicular networks can be used to reduce fuel consumption and carbon dioxide (CO2) emission in a city environment. Simulation results demonstrate that vehicle fuel consumption and CO2 emission will be reduced if such an EEFG protocol is used.
Characterization of the dnaG Locus inMycobacterium smegmatis Reveals Linkage of DNA Replication and Cell Division

ABSTRACT We have isolated a UV-induced temperature-sensitive mutant ofMycobacterium smegmatis that fails to grow at 42°C and exhibits a filamentous phenotype following incubation at the nonpermissive temperature, reminiscent of a defect in cell division. Complementation of this mutant with an M. smegmatis genomic library and subsequent subcloning reveal that the defect lies within the M. smegmatis dnaG gene encoding DNA primase. Sequence analysis of the mutant dnaG allele reveals a substitution of proline for alanine at position 496. Thus, dnaG is an essential gene in M. smegmatis, and DNA replication and cell division are coupled processes in this species. Characterization of the sequences flanking the M. smegmatis dnaG gene shows that it is not part of the highly conserved macromolecular synthesis operon present in other eubacterial species but is part of an operon with a dgt gene encoding dGTPase. The organization of this operon is conserved in Mycobacterium tuberculosis andMycobacterium leprae, suggesting that regulation of DNA replication, transcription, and translation may be coordinated differently in the mycobacteria than in other bacteria.
ChaLearn Looking at People and Faces of the World: Face AnalysisWorkshop and Challenge 2016

We present the 2016 ChaLearn Looking at People and Faces of the World Challenge and Workshop, which ran three competitions on the common theme of face analysis from still images. The first one, Looking at People, addressed age estimation, while the second and third competitions, Faces of the World, addressed accessory classification and smile and gender classification, respectively. We present two crowd-sourcing methodologies used to collect manual annotations. A custom-build application was used to collect and label data about the apparent age of people (as opposed to the real age). For the Faces of the World data, the citizen-science Zooniverse platform was used. This paper summarizes the three challenges and the data used, as well as the results achieved by the participants of the competitions. Details of the ChaLearn LAP FotW competitions can be found at http://gesture.chalearn. org.
A Novel Parameters Estimation Scheme for Multivariate Symmetric Alpha Stable Noise in High-Order Modulation System

The impulsive interferences are not negligible in many communication channels, such as power line channels. During the initialization of Turbo iterative decode algorithm, e.g., message passing algorithm for Low-Density Parity-Check code's decoder, it is necessary to estimate parameters of channel to retrieve the prior information from the received signals. Performances of the detector/estimator based on Gaussian optimization are deteriorated sharply due to impulsive noise. It does make sense to restudy how to get the prior information in impulsive channel. We introduce symmetric alpha-stable (SocS) noise into a statistical model of impulsive noise, and consider that interference in the receiver is a mixture of multivariate SocS noise. A novel parameters estimation scheme for impulsive channel in high-order modulation scenarios is proposed, which is based on equivalent characteristic function. It proves to be effective and low complexity for both MPSK and MQAM, as we can see in the simulations.
Gender Classification with Voice Intensity

In this paper, a new approach to classify the two types of gender, male and female, is proposed and implemented. The research is based on the idea that male and female can be classified on the basis of the intensity of their utterances. To implement the idea, the Simpson's rule of numerical integration is used to calculate the voice intensity by measuring area under the normalized curve. The curve is obtained after multiplying a factor with the polynomial of degree three (fitted through the peaks), where peaks are found from each frame of speech utterance when it is divided into frames of length of 20 milliseconds. A certain level of amplitude is considered while finding the peaks. To make the decision about the type of gender an adaptively adjusted threshold is used. If the area of an utterance is above the threshold the gender type is a female otherwise male. Experiments show that result is promising that is 96.44%.
Psychologically Motivated Text Mining

Natural language processing techniques are increasingly applied to identify social trends and predict behavior based on large text collections. Existing methods typically rely on surface lexical and syntactic information. Yet, research in psychology shows that patterns of human conceptualisation, such as metaphorical framing, are reliable predictors of human expectations and decisions. In this paper, we present a method to learn patterns of metaphorical framing from large text collections, using statistical techniques. We apply the method to data in three different languages and evaluate the identified patterns, demonstrating their psychological validity.
Deformable Generic Elastic Models from a single 2D image for facial expression and large pose face together synthesis and recognition

In this paper, we propose an efficient method to reconstruct the 3D models of a human face from a single 2D face image robustness under a variety facial expressions using the Deformable Generic Elastic Model (D-GEM). We extended the Generic Elastic Model (GEM) approach and combined it with statistical information of the human face and deformed generic depth models by computing the distance around face lips. Particularly, we demonstrate that D-GEM can approximate the 3D shape of the input face image more accurately, achieving a better and higher quality of 3D face modeling and reconstruction robustness under a variety of facial expressions compared to the original GEM and Gender and Ethnicity-GEM (GE-GEM) approach. It has been tested on an available 2D face database and new synthesized facial expression and large pose changes together from gallery images. We acquire promising results for handling pose and expression changes based on the proposed method compared to the GEM and GE-GEM.
Extending Black Domain Name List by Using Co-occurrence Relation between DNS Queries

Botnet threats, such as server attacks or sending of spam e-mail, have been increasing. Therefore, infected hosts must be found and their malicious activities mitigated. An effective method for finding infected hosts is to use a blacklist of domain names. When a bot receives attack commands from a Command and Control (CC therefore a blacklist cannot cover all black domain names. We thus present a method for finding unknown black domain names by using DNS query data and an existing blacklist of known black domain names. To achieve this, we focus on DNS queries sent by infected hosts. One bot sends several queries on black domain names due to C&C server redundancy. We use the co-occurrence relation of two different domain names to find unknown black domain names and extend the blacklist. If a domain name frequently co-occurs with a known black name, we assume that the domain name is also black. A cross-validation evaluation of the proposed method showed that 91.2% of domain names that are on the validation list scored in the top 1%.
Aggregation management design for user-defined network infrastructure

Recently, OpenFlow and FlowVisor are the most promising architecture components of the software-defined networks (SDNs). The evolution of these components brought the revolution of utilizing network elements for the shared and virtual network infrastructure, however from the aspects of production networks, it still lacks of some key component. For example, policy-based services provisioning for user-defined requirement is an indispensable part. Hence, innovative aggregation management design for virtual network management system framework must be developed and tested. This paper describes an initial design to user-defined network infrastructure in order to make it capable of running policy-based engineering related experiments. We developed the framework regarding the real use cases upon the shared and virtual network in KREONET infrastructure. Moreover, we also presented perflow-user-defined specifications which include max rate limiter and dynamic priority assignment strategy to enhance the validation performance of our system for production networks.
Enabling Large-scale Network Simulations: A Selective Abstraction Approach

ions Abstraction is possible because most protocol architectures are layered such that one protocol makes use of another. In design or evaluation of a level-n protocol, we need information provided by level n-1 and below, but not necessarily (depending on the research questions) all the details of the lower level protocols. For instance, a multicast transport protocol may need multicast routing tables in order to forward multicast packets. The detailed exchange of messages to generate those routing tables is often not important. If we abstract away unnecessary details, the memory and time consumption that would have been used by these details can be conserved and used to simulate larger scale scenarios. Based on this reasoning, we proposed centralized computation and end-to-end packet delivery. These two techniques are aimed to abstract away, respectively, network layer and hop-by-hop transmission details that higher layer protocol studies may or may not require. Protocol complexity is only one dimension of the scaling problems. To be able to simulate very large networks and substantial amounts of traffic, we need to enhance those elements that construct network connectivity or generate traffic in the simulations. Currently, in most network simulators, the cost to construct network connectivity is proportional to the cost of creating nodes O(N), links O(L) and routing table O(N). For large topologies (N > 1000), routing table cost is the most significant. Hierarchical routing has the
#FailedRevolutions: Using Twitter to study the antecedents of ISIS support

Lately, the Islamic State of Iraq and Syria (ISIS) has managed to control large parts of Syria and Iraq. To better understand the roots of support for ISIS, we present a study using Twitter data. We collected a large number of Arabic tweets referring to ISIS and classified them as pro-ISIS or anti-ISIS. We then analyzed the historical timelines of both user groups and looked at their pre-ISIS period to gain insights into the antecedents of support. Also, we built a classifier to ‘predict’, in retrospect, who will support or oppose the group. We show that ISIS supporters largely differ from ISIS opposition in that the former referred a lot more to Arab Spring uprisings that failed than the latter.
Astrocytic Actions on Extrasynaptic Neuronal Currents

In the last few decades, knowledge about astrocytic functions has significantly increased. It was demonstrated that astrocytes are not passive elements of the central nervous system (CNS), but active partners of neurons. There is a growing body of knowledge about the calcium excitability of astrocytes, the actions of different gliotransmitters and their release mechanisms, as well as the participation of astrocytes in the regulation of synaptic functions and their contribution to synaptic plasticity. However, astrocytic functions are even more complex than being a partner of the “tripartite synapse,” as they can influence extrasynaptic neuronal currents either by releasing substances or regulating ambient neurotransmitter levels. Several types of currents or changes of membrane potential with different kinetics and via different mechanisms can be elicited by astrocytic activity. Astrocyte-dependent phasic or tonic, inward or outward currents were described in several brain areas. Such currents, together with the synaptic actions of astrocytes, can contribute to neuromodulatory mechanisms, neurosensory and -secretory processes, cortical oscillatory activity, memory, and learning or overall neuronal excitability. This mini-review is an attempt to give a brief summary of astrocyte-dependent extrasynaptic neuronal currents and their possible functional significance.