Miracle-dz/UniFAR-FTUs huggingface dataset:
Determining evolutionary dynamics in complex networks using genetic algorithms

Gene regulatory networks are one of the most important goals in the novel discipline of system biology. These regulatory networks, through the interaction of multiple genes, control and guide the proteic interactions and, in fact, the cellular behaviour. Understanding this regulation is, therefore, essential in the investigation in organogenesis, during the embryonic stages of the organisms, and in the research of genetic diseases. The whole set of simple genetic interactions inside the network converges in complex behaviours, so it is imperative to analyse the problem in the field of network theory and the evolutionary dynamic of complex systems. All this has led us to investigate the evolutionary dynamics in generic networks, this way, the results can be used in experimental researches in the field of system biology. This research aims to decode the evolutionary rules governing the transformation dynamics in a network. To do this, a genetic algorithm has been implemented, in which, starting from initial and ending network stages, it is possible to determine the transformation dynamics between these stages by using simple acting rules. The network description is the following: a) The network nodes states in the initial and ending stages can be active or inactive; b) The network links can act as activators or repressors; c) An evolutionary set of rules is established in order to transform the initial stage into the ending one; d) Due to the low connectivity in gene regulatory networks, each node will hold a maximum of three inputs with no restriction on outputs. The “chromosomes” of the genetic algorithm includes two parts, one related to the nodes links and another related to the evolutionary rules. The implemented evolutionary rules are based in certain genetic interactions behaviour. This set of rules includes the following: 1) Rule of Most: If the target node inputs consist on activators and represors, the node final state is determined by the action of most. 2) Absolute repressor rule: In this case, just one repressor is enough to inhibit the target and deactivate the node. 3) Logic gate AND with activators: The target node will be active only if the input consists on, at least, two activator links. 4) Logic gate AND with repressors: The target node will be inactive only if the input consists on, at least, two repressor links. These rules and their combinations are compound by logic conditions and set the bases to the network motifs formation, which are the building blocks of the network dynamics. The implemented algorithm results discovered satisfactorily appropriate dynamics in complex networks evolution between different stages for several cases.
Integrity Measurement Model Based on Trusted Virtual Platform

A secure and trusted execution environment is a fundamental requirement for distributed network, so that the integrity of service provider platform should be verified prior to services’ distribution in order to assure that the platform is free from a malicious modification and attack. In this paper, we proposed an implementation of trusted execution environment based on the combination of trusted computing and virtualization technology. On the service provider’s platform, from the very beginning of Virtual Machine Monitor (VMM) was booted to the execution environment was set up, all of the platform status changes has been recorded into Platform Configuration Register (PCR) and reported to remote service requester, which make it sure about the trustworthy of the result. Furthermore, the VM image was leveraged to make the execution environment constructed more flexible. Analysis shows our method is efficient and secure.
A one-parameter approach to knot theory

To an oriented knot we associate a trace graph in a thickened torus in such a way that knots are isotopic if and only if their trace graphs can be related by moves of finitely many standard types. For closed braids with a fixed number of strands, we recognize trace graphs up to equivalence excluding one type of moves in polynomial time with respect to the braid length.
Transcriptional regulation of endothelial cell adhesion molecules: NF‐κB and cytokine‐inducible enhancers

Transcription of endothelial‐leukocyte adhesion molecule‐1 (E‐selectin or ELAM‐1), vascular cell adhesion molecule‐1 (VCAM‐1), and intercellular adhesion molecule‐l (ICAM‐1) is induced by the inflammatory cytokines interleukin‐1 β (IL‐lβ) and tumor necrosis faetor‐α (TNFα). The positive regulatory domains required for maximal levels of cytokine induction have been defined in the promoters of all three genes. DNA binding studies reveal a requirement for nuclear factor‐κB (NF‐κB) and a small group of other transcriptional activators. The organization of the cytokine‐inducible element in the E‐se‐ lectin promoter is remarkably similar to that of the virus‐indueible promoter of the human interferon‐β gene in that both promoters require NF‐κB, activating transcription factor‐2 (ATF‐2), and high mobility group protein I(Y) for induction. Based on this structural similarity, a model has been proposed for the cytokine‐induced E‐selectin enhancer that is similar to the stereospecific complex proposed for the inter‐ feron‐β gene promoter. In these models, multiple DNA bending proteins facilitate the assembly of higher order complexes of transcriptional activators that interact as a unit with the basal transcriptional machinery. The assembly of unique enhancer complexes from similar sets of transcriptional factors may provide the specificity required to regulate complex patterns of gene expression and correlate with the distinct patterns of expression of the leukocyte adhe‐sion molecules. —Collins, T. , Read, M. A. , Neisli, A. S. , Whitley, M. Z. , Thanos, D. , Maniatis, T. Transcriptional regulation of endothelial cell adhesion molecules: NF‐κB and cytokine‐indueible enhancers. FASEBJ. 9, 899‐909 (1995)
NN-Based Adaptive Tracking Control of Uncertain Nonlinear Systems Disturbed by Unknown Covariance Noise

A class of uncertain nonlinear systems that are additionally driven by unknown covariance noise is considered. Based on the backstepping technique, adaptive neural control schemes are developed to solve the output tracking control problem of such systems. As it is proven by stability analysis, the proposed controller guarantees that all the error variables are bounded with desired probability in a compact set while the tracking error is mean-square semiglobally uniformly ultimately bounded (M-SGUUB). The tracking performance and the effectiveness of the proposed design are evaluated by simulation results.
Measuring and Privately Building Highly Predictive Blacklisting

Collaborative approaches to network defense are increasingly used to predict attacks as well as to speed up their detection. For instance, with highly predictive blacklisting, one aims to forecast attack sources based on alerts contributed by multiple organizations. While collaboration helps discover groups of correlated attacks targeting similar victims, it also raises important privacy concerns. To address this challenge, we introduce a novel privacy-friendly system whereby organizations are clustered together based on the similarity of their logs, without disclosing them in the clear. Entities in the same cluster only share relevant logs and build more accurate blacklists. At the same time, we investigate how to measure the effect of collaboration on prediction and find that the stateof-the-art (non privacy-preserving) system actually achieves lower accuracy than if organizations predicted based on local alerts only. Our experiments shed light on how to improve the quality of predictions by optimizing information shared across organizations, showing that our privacy-friendly methods markedly outperform non private tools both in terms of precision and recall.
Efficient use of null subcarriers to reduce PAPR in OFDM System

The peak-to-average power ratio (PAPR) of multi-carrier transmission can be reduced by exploiting null subcarriers which are already mandated in most OFDM wireless standards. This new approach requires no channel side information, can be compatible with existing standards, imposes no rate hit, is distortionless, has low computational complexity, and can complement most other PAPR-reduction methods (such as active constellation extension, partial transmit sequences, selective mapping, tone injection, tone reservation). Keywords— Bit error rate (BER), Multicarrier, Orthogonal frequency division multiplexing (OFDM), Peak to average power ratio (PAPR), Null subcarriers.
Commensurate and Incommensurate Phases of a Spin-Peierls System

The interest in the study of spin-Peierls phenomenon has increased with the discovery of copper germanium oxide (CuGeO_3) as an example of spin-Peierls systems. The availability of this inorganic compound as large, high quality single crystals, allow experimental measurements which aid in understanding the spin-Peierls phenomenon. This report details the numerical and analytical work done on the various magnetic phases that occur in a quasi-one-dimensional spin-Peierls system (like that of copper germanium oxide). Taking the system to be finite, the bond displacements, single particle energies, energy eigenfunctions, spin densities and phonon frequencies are calculated numerically for all possible magnetizations, which includes both the commensurate and incommensurate phases, at zero temperature. Solitons of fractional spin are found to be present in the system for appropriate magnetizations. The uniform to dimerized phase transition occurring in this system is also modelled numerically. The single particle and ground-state energies are later obtained theoretically for the uniform and dimerized phases. In addition, the lattice spin displacements are analytically modelled for a particular magnetization of the system.
Z_mirrorChord : A kind of framework for service resources management

According to the deficiency of Chord algorithm supporting single keyword query only, a P2P framework-Z_mirror by combining the Z_mirror and Chord is proposed for managing service resources, which supports DHT-based multi-keyword query and approximate query by means of Z_mirror index to improve resources searching ability. Experiments show that Z_mirrorChord has better efficiency and scalability for managing service resources under the large scale P2P environment with higher density of services.
The Explosive Yields Produced by the First Generation of Core Collapse Supernovae and the Chemical Composition of Extremely Metal Poor Stars

We present a detailed comparison of an extended set of elemental abundances observed in some of the most metal poor stars presently known and the ejecta produced by a generation of primordial core collapse supernovae. At variance with most of the analysis performed up to now (in which mainly the global trends with the overall metallicity are discussed), we think that it is important (and complementary to the other approach) to fit the (available) chemical composition of specific stars. In particular, we first discuss the differences among the five stars that form our initial database and define a "template" ultra-metal-poor star that is then compared to the theoretical predictions. Our main findings are the following: (1) The fit to [Si/Mg] and [Ca/Mg] of these very metal-poor stars seems to favor the presence of a rather large C abundance at the end of the central He burning; in a classical scenario in which the border of the convective core is strictly determined by the Schwarzschild criterion, such a large C abundance would imply a rather low 12C(α,γ)16O reaction rate. (2) A low C abundance left by the central He burning would imply a low [Al/Mg] (<-1.2 dex) independently on the initial mass of the exploding star, while a rather large C abundance would produce such a low [Al/Mg] only for the most massive stellar model. (3) At variance with current beliefs that it is difficult to interpret the observed overabundance of [Co/Fe], we find that a mildly large C abundance in the He exhausted core (well within the present range of uncertainty) easily and naturally allows a very good fit to [Co/Fe]. (4) Our yields allow a reasonable fit to eight of the 11 available elemental abundances. (5) Within the present grid of models it is not possible to find a good match of the remaining three elements, Ti, Cr, and Ni (even for an arbitrary choice of the mass cut). (6) The adoption of other yields available in the literature does not improve the fit. (7) Since no mass in our grid provides a satisfactory fit to these three elements, even an arbitrary choice of the initial mass function would not improve their fit.
Testing Cosmological Models with a Lyα Forest Statistic: The High End of the Optical Depth Distribution

We pay particular attention to the high end of the Lyα optical depth distribution of a quasar spectrum. Based on the flux distribution (Miralda-Escudé et al.), a simple yet seemingly cosmological model-differentiating statistic, Δ—the cumulative probability of a quasar spectrum with Lyα optical depth greater than a high-value τ0—is emphasized. It is shown that two different models—the cold dark matter model with a cosmological constant and the mixed hot and cold dark matter model, both normalized to COBE and local galaxy cluster abundance—yield quite different values of Δτ0: 0.13 for the former versus 0.058 for the latter, for τ0 = 3.0 at z = 3. Moreover, it is argued that Δτ0 may be fairly robust to compute theoretically because it does not seem to depend sensitively on small variations of simulation parameters such as the radiation field, cooling, the feedback process, radiative transfer, the resolution, and the simulation volume within the plausible ranges of the concerned quantities. Furthermore, it is illustrated that Δτ0 can be obtained sufficiently accurately from currently available observed quasar spectra for τ0 ~ 3.0-4.0, when observational noise is properly taken into account. We anticipate that analyses of observations of quasar Lyα absorption spectra over a range of redshift may be able to constrain the redshift evolution of the amplitude of the density fluctuations on small-to-intermediate scales, therefore providing an independent constraint on Ω0, Ω0,HDM, and Λ0.
Storkey Learning Rules for Hopfield Networks

We summarize the Storkey Learning Rules for the Hopfield Model, and evaluate performance relative to other learning rules. Hopfield Models are normally used for auto-association, and Storkey Learning Rules have been found to have good balance between local learning and capacity. In this paper we outline different learning rules and summarise capacity results. Hopfield networks are related to Boltzmann Machines: they are the same as fully visible Boltzmann Machines in the zero temperature limit. Perhaps renewed interest in Boltzmann machines will produce renewed interest in Hopfield learning rules?
SLA-Aware Scheduling of Map-Reduce Applications on Public Clouds

The recent need of processing BigData has led to the development of several Map-Reduce applications for efficient large scale processing. Due to on-demand availability of large computing resources, Public Clouds have become a natural host of these Map-Reduce applications. In this case, users need to decide which resources they need to rent to run their MapReduce cluster other than deployment or scheduling of mapreduce tasks itself. This is not a trivial task particularly when users may have performance constraints such as deadline and have several Cloud product types to choose with intention of not spending much money. Even though there are several existing scheduling systems, however most of them are not developed to manage the scheduling of Map-Reduce applications. That is, they do not consider things like the number of map and reduce tasks and slots per VM. This paper proposes a novel greedy scheduling algorithm (MASA) that considers the users constraints in order to minimize cost of renting Cloud resources while considering the user's budget and deadline constraints. The simulation results show 25-60% reduction cost in comparison to current methods by using our proposed algorithm.
C A ] 1 6 D ec 2 01 1 Linear sparse differential resultant formulas

Let P be a system of n linear nonhomogeneous generic sparse ordinary differential polynomials in n − 1 differential indeterminates. In this paper, differential resultant formulas are presented to compute, whenever it exists, the sparse differential resultant ∂Res(P) introduced by Li, Gao and Yuan in [12], as the determinant of the coefficient matrix of an appropriate set of derivatives of differential polynomials in P.
Interstitial cells of Cajal: is their role in gastrointestinal function in view of therapeutic perspectives underestimated or exaggerated?

This manuscript reviews the current views on morphology and function of the distinct subpopulations of interstitial cells of Cajal (ICC) in the digestive tract and their interrelationships with surrounding cells. Three different functions have been postulated so far, i.e. a pacemaker role, a mediator in enteric excitatory and inhibitory neurotransmission and a mechanosensor. Attention will also be paid to the interstitial cells of Cajal and their possible involvement in pathophysiological conditions. Finally, perspectives for interstitial cells of Cajal as targets for therapeutic intervention will be discussed.
Some Simple Method of Estimation for the Parameters of the Discrete Stable Distribution with the Probability Generating Function

In this article, we develop a method to estimate the two parameters of the discrete stable distribution. By minimizing the quadratic distance between transforms of the empirical and theoretical probability generating functions, we obtain estimators simple to calculate, asymptotically unbiased, and normally distributed. We also derive the expression for their variance–covariance matrix. We simulate several samples of discrete stable distributed datasets with different parameters, to analyze the effect of tuncation on the right tail of the distribution.
Dynamic delay compensation for lane departure warning system

Vision-based active safety and driver assistance systems, such as lane departure warning (LDW) system, have increasingly gained popularity in automotive applications due to their fairly good performance at relatively low cost. Lane detection is a very complicated and time-consuming process and the potential time delay resulting from current image capturing and image processing can potentially impact the performance of the system. The LDW system as discussed in this report may consequently produce a value of time-to-lane-crossing (TLC), which can lead to false alarm or warning at inappropriate time to driver. This paper presents a theoretical method that dynamically compensates the time delay, and thus, enhances the potential the TLC calculation can reduce false alarms. Simulation results show that under the test parameters, the proposed theoretical method can be more effective in computing a TLC by compensating for the excessive time delay. The approach will be further evaluated and tested in the continued work with consideration of a higher-fidelity vehicle dynamics model and a variety of lane models.
Tapped delay multiclass support vector machines for industrial workflow recognition

In this paper, a tapped delay multiclass support vector machine scheme is used for supervised job classification, based on video data taken from Nissan factory. The procedure is based on multiclass SVMs enhanced with the time dimension by incorporating additional information of n-th previous frames and allowing for user feedback when necessary. Such methodology will support the visual supervision of industrial environments by providing essential information to the supervisors and supporting their job.
The deep sub-micron signal integrity challenge

Maintaining signal integrity in digital VLSI systems has become increasingly difficult due to the rising number of analog effects in deep sub-micron design. This paper provides an outline of the tutorial to be presented, as well as essential background material on the principle mechanisms behind signal integrity issues. The goal is to put forth a set of signal integrity challenges for future research and development.
SIP Paging and Tracking Behcet Sarikaya

The paper introduces a new paging technique to track and wake up a mobile node attached to an access point in wireless LAN network after a SIP INVITE message is initiated by a caller. Tracking Agent keeps track of the mobiles’ handoffs between the access points. Paging Agent triggers tracking agent to page the mobile when a SIP INVITE is received for one of its users. Context transfer feature of our paging protocol allows the paging messages to deliver the station context in order to enable faster session reestablishment. Access point then does onlink paging in wireless link. SIP extensions are needed to trigger the paging agent to start paging and mobile nodes to notify their dormant status using an extended SIP REGISTER method. Onlink paging can be implemented using TIM or DTIMs of 802.11. Tracking protocol is analyzed to compare soft-state and hard-state approaches for state inconsistency ratio, message rate and the overall cost. Simulation model we developed enables us to evaluate the traffic introduced by the tracking protocol and the cache (state) size. Paging protocol is analyzed for CPU processing times and the transmission delays in SIP session setup with paging. Simulation of the paging with context transfer is used to show the gains in reauthentication.
BIOINFORMATICS ANALYSES OF ALTERNATIVE SPLICING, EST-BASED AND MACHINE LEARNING-BASED PREDICTION

Alternative splicing is a mechanism for generating different gene transcripts (called isoforms) from the same genomic sequence. Finding alternative splicing events experimentally is both expensive and time consuming. Computational methods in general, and EST analysis and machine learning algorithms in particular, can be used to complement experimental methods in the process of identifying alternative splicing events. In this thesis, I first identify alternative splicing exons by analyzing EST-genome alignment. Next, I explore the predictive power of a rich set of features that have been experimentally shown to affect alternative splicing. I use these features to build support vector machine (SVM) classifiers for distinguishing between alternatively spliced exons and constitutive exons. My results show that simple, linear SVM classifiers built from a rich set of features give results comparable to those of more sophisticated SVM classifiers that use more basic sequence features. Finally, I use feature selection methods to identify computationally the most informative features for the prediction problem considered.
Compact Self-Shielded 2–3 GHz High-Q Coaxial Fixed and Tunable Filters

A novel coaxial stepped-impedance resonator (SIR) filter is proposed based on conventional multi-layer printed-circuit board (PCB) technology. The resonator results in a measured unloaded quality factor (Qu) of 185 at 3.7 GHz, has compact size, is self-shielded and has very low electromagnetic (EM) coupling to nearby RF circuits. The 3-D filter topology easily lends to advanced filter design with multiple transmission zeroes using cross coupling and source-to-load coupling on the top printed-circuit board layer. Two third-order fixed-band bandpass filters, and two fourth-order tunable bandpass filters are demonstrated in this technology. The fixed-band filters have an insertion loss of 1.6 dB with a fractional bandwidth of 6.5% at 3.6 GHz and occupy a size of 9 × 12 mm2. The tunable filter covers 2.1-2.9 GHz with a fractional bandwidth of 3.5%, and an insertion loss of 4.2-9.2 dB, limited by the silicon varactors. Multiple transmission zeroes are used in these designs to significantly improve the filter selectivity. Application areas are in fixed and tunable filters with very compact size and high shielding from nearby circuits for advanced communication systems.