Miracle-dz/UniFAR-FTUs huggingface dataset:
The Second-Order Model of M-interactivity: Exploring the Performance of M-interactivity in Value, Satisfaction, and Loyalty

Despite the significance of mobile interactivity in the success of the business, there have not been many studies in the literature focused on distinguishing mobile interactivity (m-interactivity) from interactivity via the fixed broadband internet (e-interactivity). Accordingly, the current study builds a new conceptual framework for m-interactivity. First, within perception-based interactivity, we hypothesized a positive relationship between system-oriented and user-oriented interactivity (i.e., the second order model of m-interactivity). Second, we looked at the impacts of user-oriented interactivity on the consumer response variables of perceived value, satisfaction, and loyalty, as well as the relationships among consumer response variables.
Scalable liveness verification for communication fabrics

In the realm of multi-core processors and systems-on-chip, communication fabrics constitute a key element. A large number of queues and distributed control are two important aspects of this class of designs. These aspects make decomposition and abstraction techniques difficult to apply. For this class of designs, the application of formal methods is a real challenge. In particular, the verification of liveness properties is often intractable. Communication fabrics can be seen as a set of queues and flops interconnected by combinatorial logic. Based on this simple but powerful observation, we propose a novel method for liveness verification. Our method directly applies to Register Transfer Level designs. The essential aspects of our approach are (1) to abstract away from the details of queue implementations and (2) an efficient encoding of liveness properties in an SMT instance. Experimental results are promising. Designs with hundreds of queues can be analysed for liveness within minutes.
Molecular Nature of Spontaneous Modifications in gacS Which Cause Colony Phase Variation in Pseudomonas sp. Strain PCL1171

ABSTRACT Pseudomonas sp. strain PCL1171 displays colony phase variation between opaque phase I and translucent phase II colonies, thereby regulating the production of secondary metabolites and exoenzymes. Complementation and sequence analysis of 26 phase II mutants and of 13 wild-type phase II sectors growing out of phase I colonies showed that in all these cases the phase II phenotype is caused by spontaneous mutations in gacA or/and gacS. Mutation of gac reduced both the length of the lag phase and the generation time. Isolation and sequencing of the gacS genes from the phase II bacteria revealed one insertion as well as several random point mutations, deletions, and DNA rearrangements. Most phase II colonies reverted with a high frequency, resulting in wild-type gacA and gacS genes and a phase I phenotype. Some phase II bacteria retained the phase II phenotype but changed genotypically as a result of (re)introduction of mutations in either gacA or gacS. The reversion of gacA or gacS to the wild type was not affected by mutation of recA and recB. We conclude that in Pseudomonas sp. strain PCL1171, mutations in gacA and gacS are the basis for phase variation from phase I to phase II colonies and that, since these mutations are efficiently removed, mutations in gac result in dynamic switches between the “wild-type” population and the subpopulations harboring spontaneous mutations in gacA and or gacS, thereby enabling both populations to be maintained.
A Survey of Sequential and Parallel ImplementationTechniques for Functional Programming

This paper surveys sequential and parallel implementation techniques for functional programming languages, as well as optimizations that can improve their performance. Sequential implementations have evolved from simple interpreters to sophisticated super-combinator-based compilers, while most parallel implementations have explored a broad range of techniques. We analyze the purpose and function of each implementation technique and discuss the current state-of-the-art in functional language implementation .
Bounded Arithmetic andPropositional Proof

This is a survey of basic facts about bounded arithmetic and about the relationships between bounded arithmetic and propositional proof complexity. We introduce the theories S i 2 and T i 2 of bounded arithmetic and characterize their proof theoretic strength and their provably total functions in terms of the polynomial time hierarchy. We discuss other axiomatizations of bounded arithmetic, such as minimization axioms. It is shown that the bounded arithmetic hierarchy collapses if and only if bounded arithmetic proves that the polynomial hierarchy collapses. We discuss Frege and extended Frege proof length, and the two translations from bounded arithmetic proofs into propositional proofs. We present some theorems on bounding the lengths of propositional interpolants in terms of cut-free proof length and in terms of the lengths of resolution refutations. We then deene the Razborov-Rudich notion of natural proofs of P 6 = NP and discuss Razborov's theorem that certain fragments of bounded arithmetic cannot prove superpolynomial lower bounds on circuit size, assuming a strong cryptographic conjecture. Finally, a complete presentation of a proof of the theorem of Razborov is given. 1.1 Feasibility This article will be concerned with various \feasible" forms of computability and of provability. For something to be feasibly computable, it must be computable in practice in the real world, not merely eeectively computable in the sense of being recursively computable.
Policies for Reinforcement Learning

Curriculum learning in reinforcement learning is a training methodology that seeks to speed up learning of a difficult target task, by first training on a series of simpler tasks and transferring the knowledge acquired to the target task. Automatically choosing a sequence of such tasks (i.e., a curriculum) is an open problem that has been the subject of much recent work in this area. In this paper, we build upon a recent method for curriculum design, which formulates the curriculum sequencing problem as a Markov Decision Process. We extend this model to handle multiple transfer learning algorithms, and show for the first time that a curriculum policy over this MDP can be learned from experience. We explore various representations that make this possible, and evaluate our approach by learning curriculum policies for multiple agents in two different domains. The results show that our method produces curricula that can train agents to perform on a target task as fast or faster than existing methods.
Adaptive Multi-Level Region Merging for Salient Object Detection

Most existing salient object detection algorithms face the problem of either under or over-segmenting an image. More recent methods address the problem via multi-level segmentation. However, the number of segmentation levels is manually predetermined and only works well on specific class of images. In this paper, a new salient object detection scheme is presented based on adaptive multi-level region merging. A graph based merging scheme is developed to reassemble regions based on their shared contour strength. This merging process is adaptive to complete contours of salient objects that can then be used for global perceptual analysis, e.g., foreground/ground separation. Such contour completion is enhanced by graph-based spectral decomposition. We show that even though simple region saliency measurements are adopted for each region, encouraging performance can be obtained after across-level integration. Experiments by comparing with 13 existing methods on three benchmark datasets including MSRA-1000, SOD and SED show the proposed method results in uniform object enhancement and achieves state-of-the-art performance.
Human memory models and term association

Results of cognitive psychology research are analysed to explain why it is difficult for retrieval system users to bring to mind alternative search terms. A human memory model is modified in such a way that it produces additional search terms instead of human associations. A small experiment shows that such a spreading activation network can find alternative terms with a performance similar to the normally used similarity measures.
A linear programming-driven MCDM approach for multi-objective economic dispatch in smart grids

This paper presents a novel approach to deal with the multi-objective economic dispatch problem in smart grids as a multi-criteria decision making (MCDM) problem, whose decision alternatives are dynamically generated. Four objectives are considered: emissions, energy cost, distance of supply, and load balancing. Objectives are preliminarily preference-ranked through a fuzzy version of the analytic hierarchy process (AHP), and then classified into two categories of importance. The more important objectives form the objective function of a linear programming (LP) problem, whose solution (driving solution) drives the generation of Pareto-optimal alternative configurations of power output of the generators. The technique for order of preference by similarity to ideal solution (TOPSIS) is used to automatically select the most suitable power output configuration, according to initial preferences, derived with fuzzy AHP. The effectiveness of our approach is validated by comparing it to the weighted sum (WS) method, by simulating 40 different operating scenarios on a prototype smart microgrid.
Recovering missing diffusion signal data for reconstructing fiber orientations

Q-ball imaging (QBI) is a recent high angular resolution diffusion imaging technique for studying the orientation of fibers in the brain or heart, but it requires a high number of acquisitions, thus considerably lengthening acquisition time. Reducing diffusion data to be acquired while achieving the same fiber orientation reconstruction quality is then of fundamental importance. In this paper, we propose a new method of fiber orientation reconstruction from much less data acquisitions. The method is based on recovering missing diffusion data. It consists in imposing constraints both in q-space and in spatial space, applying data consistent principle in q-space, and iterating the process until no change is observed in the reconstructed orientation distribution function (ODF). The results show that using diffusion data in 12 directions, the reconstructed ODFs are rather close to those obtained with 42 diffusion directions, which allows shortening considerably acquisition time.
Comprehensive kernel instrumentation via dynamic binary translation

Dynamic binary translation (DBT) is a powerful technique that enables fine-grained monitoring and manipulation of an existing program binary. At the user level, it has been employed extensively to develop various analysis, bug-finding, and security tools. Such tools are currently not available for operating system (OS) binaries since no comprehensive DBT framework exists for the OS kernel. To address this problem, we have developed a DBT framework that runs as a Linux kernel module, based on the user-level DynamoRIO framework. Our approach is unique in that it controls all kernel execution, including interrupt and exception handlers and device drivers, enabling comprehensive instrumentation of the OS without imposing any overhead on user-level code. In this paper, we discuss the key challenges in designing and building an in-kernel DBT framework and how the design differs from user-space. We use our framework to build several sample instrumentations, including simple instruction counting as well as an implementation of shadow memory for the kernel. Using the shadow memory, we build a kernel stack overflow protection tool and a memory addressability checking tool. Qualitatively, the system is fast enough and stable enough to run the normal desktop workload of one of the authors for several weeks.
MIDAS: Mental illness detection and analysis via social media

Mental illnesses rank as some of the most disabling conditions, affecting millions of people, across the globe. In general, the main challenge of mental disorders is that they remain difficult to detect on suffering patients. In an online environment, the challenge extends to the collection of patients data and the implementation of proper algorithms to assist in the detection of such illnesses. In this paper, we propose a novel data collection mechanism and build predictive models that leverage language and behavioral patterns, used particularly on Twitter, to determine whether a user is suffering from a mental disorder. After training the predictive models, they are further pre-trained to serve as the backend for our demonstration, MIDAS. MIDAS offers an analytics web-service to explore several characteristics pertaining to user's linguistic and behavioral patterns on social media, with respect to mental illnesses.
The Digital Divide (DD): A Reconceptualization for Educators.

The author attempts to elaborate upon the popularized notion of the digital divide (DD). Previously, the DD has been defined as a lack of access to information technology for specific groups. This “access DD,” in the opinion of the author, is an incorrect conceptualization for educators. The author then explains why educators must redirect their attention and resources to solve the more nebulous “social DD. ”
On Finite Dimensional Approximation in MIMO

We consider measures of richness in multipath wireless channels. We examine bounds on the dimensionality of spatially constrained wireless signals. We show that the continuous system-model bounds on dimensionality rely on an implicit model for noise. We consider dimensionality bounds where the noise model is made an explicit parameter. We show that the dimensionality of a spatially constrained field may be considered as a parameter for a virtual antenna model. We propose a simple antenna model which incorporates spatial dimensionality and relate this to the current point-like model used in MIMO channel models
High performance encapsulation in Casanova 2

Encapsulation is a programming technique that helps developers keeping code readable and maintainable. However, encapsulation in modern object oriented languages often causes significant runtime overhead. Developers must choose between clean encapsulated code or fast code. In the application domain of computer games, speed of execution is of utmost importance, which means that the choice between clean and fast usually is decided in favor of the latter. In this paper we discuss how encapsulation is embedded in the Casanova 2 game development language, and show how Casanova 2 allows developers to write encapsulated game code which, thanks to extensive optimization, achieves at the same time high levels of performance.
Chronic ammonia exposure does not influence hepatic gene expression in growing pigs

Housed pigs are often exposed to elevated concentrations of atmospheric ammonia. This aerial pollutant is widely considered to be an environmental stressor that also predisposes to reduced growth rates and poor health, although evidence to support this view is limited. Hepatic gene expression is very responsive to stress and metabolic effects. Two batches of growing pigs were therefore exposed to a nominal concentration of atmospheric ammonia of either 5 ppm (low) or 20 ppm (high) from 4 weeks of age for 15 weeks. Growth rates were monitored. Samples of liver were taken after slaughter (at ∼19 weeks of age). Samples from the second batch were analysed for global gene expression using 23 K Affymetrix GeneChip porcine genome arrays. Samples from both batches were subsequently tested for five candidate genes using quantitative real-time PCR (qPCR). The array analysis failed to detect any significant changes in hepatic gene expression following chronic exposure to atmospheric ammonia. Animals clustered into two main groups but this was not related to the experimental treatment. There was also no difference in growth rates between groups. The qPCR analyses validated the array results by showing similar fold changes in gene expression to the arrays. They revealed a significant batch effect in expression of lipin 1 (LPIN1), Chemokine (C-X-C motif) ligand 14 (CXCL14), serine dehydratase (SDS) and hepcidin antimicrobial peptide (HAMP). Only CXCL14, a chemotactic cytokine for monocytes, was significantly down-regulated in response to ammonia. As chronic exposure to atmospheric ammonia did not have a clear influence on hepatic gene expression, this finding implies that 20 ppm of atmospheric ammonia did not pose a significant material risk to the health or metabolism of housed pigs.
Online Evolution of Adaptive Robot Behaviour

The authors propose and evaluate a novel approach to the online synthesis of neural controllers for autonomous robots. The authors combine online evolution of weights and network topology with neuromodulated learning. The authors demonstrate our method through a series of simulation-based experiments in which an e-puck-like robot must perform a dynamic concurrent foraging task. In this task, scattered food items periodically change their nutritive value or become poisonous. The authors demonstrate that the online evolutionary process, both with and without neuromodulation, is capable of generating controllers well adapted to the periodic task changes. The authors show that when neuromodulated learning is combined with evolution, neural controllers are synthesised faster than by evolution alone. An analysis of the evolved solutions reveals that neuromodulation allows for a more effective expression of a given topology's potential due to the active modification of internal dynamics. Neuromodulated networks learn abstractions of the task and different modes of operation that are triggered by external stimulus.
Polarimetric Coherence Optimisation for Multibaseline SAR Data.

This paper analyzes different approaches for polarimetric optimization of multibaseline interferometric coherences. Two general methods are developed which simultaneously optimize coherences for more than two datasets. The first method is based on multiset canonical correlation analysis, and it provides every dataset with a distinguished dominant scattering mechanism. The second optimization method is constrained to the use of an identical scattering mechanism for every dataset. A framework for a multibaseline orthogonal optimal scattering mechanisms decomposition is presented. The both methods are evaluated on real data acquired by DLR’s ESAR sensor at L–band. As experimental results indicate, preferring simultaneous multibaseline coherence optimization to single–baseline optimization improves the estimation of the dominant scattering mechanisms and their interferometric phases.
Vitamin D Receptor Gene BsmI Polymorphism in Polish Patients with Systemic Lupus Erythematosus

The hormonally active form of vitamin D3, 1,25(OH)2D3 (calcitriol), exerts actions through VDR receptor, which acts as a transcriptional factor. Calcitriol is an immunomodulator that affects various immune cells, and several studies link it to many autoimmune diseases. BsmI polymorphism affects the level of VDR gene transcription, transcript stability, and posttranscriptional modifications. It seems to be related to the systemic lupus erythematosus (SLE). Our study examined the characteristics of VDR gene BsmI polymorphism in Polish SLE patients and their relationship with clinical manifestations of the disease. We genotyped 62 patients with SLE and 100 healthy controls using the real-time PCR. There were no differences observed in the frequency of BsmI genotypes in SLE patients and in the control group. There was no significant correlation between BsmI genotypes and clinical symptoms of SLE, but the AA genotype correlates with higher levels of antinuclear antibodies (ANA) in this group (r = 0.438; P = 0.002). A larger study examining BsmI and other VDR gene polymorphisms is needed. It may allow explaining differences in the clinical picture of the disease and choosing a personalized therapy.
SLX4 contributes to telomere preservation and regulated processing of telomeric joint molecule intermediates

SLX4 assembles a toolkit of endonucleases SLX1, MUS81 and XPF, which is recruited to telomeres via direct interaction of SLX4 with TRF2. Telomeres present an inherent obstacle for DNA replication and repair due to their high propensity to form branched DNA intermediates. Here we provide novel insight into the mechanism and regulation of the SLX4 complex in telomere preservation. SLX4 associates with telomeres throughout the cell cycle, peaking in late S phase and under genotoxic stress. Disruption of SLX4's interaction with TRF2 or SLX1 and SLX1's nuclease activity independently causes telomere fragility, suggesting a requirement of the SLX4 complex for nucleolytic resolution of branched intermediates during telomere replication. Indeed, the SLX1–SLX4 complex processes a variety of telomeric joint molecules in vitro. The nucleolytic activity of SLX1-SLX4 is negatively regulated by telomeric DNA-binding proteins TRF1 and TRF2 and is suppressed by the RecQ helicase BLM in vitro. In vivo, in the presence of functional BLM, telomeric circle formation and telomere sister chromatid exchange, both arising out of nucleolytic processing of telomeric homologous recombination intermediates, are suppressed. We propose that the SLX4-toolkit is a telomere accessory complex that, in conjunction with other telomere maintenance proteins, ensures unhindered, but regulated telomere maintenance.
An Approach to Develop Parametric Analogy of Improved H-H Model Using GUI

A simulating panel for neuron modeling is introduced by using graphical user interface (GUI). It is compatible for studying the behavior of neuro transmitter and neuro receiver. It also aids in learning continuous deterministic Hodgkin-Huxley (H-H) complex mathematical equations. Thus it helps user to build a real time module and easy visualization of results in the panel. Therefore, discussion on GUI panel for H-H model in comparison with Kv3.3 model motivates to propose an "improved" H-H model.
Performance analysis of cooperative spatial modulation with multiple-antennas at relay

Spatial modulation (SM) is a novel and promising approach that has been introduced as an alternative to classical multiple-input multiple-output (MIMO) spatial multiplexing techniques. Cooperative communications, on the other hand, provides additional diversity gains and high data rates by improving coverage. Combining the advantages of SM and cooperative communications would then further improve efficiency. Most of the studies in the literature of cooperative SM systems consider the space shift keying (SSK) modulation, which is a special case of SM, and single receive/transmit antenna relay (R) and destination (D). In this work, we investigate the average bit error probability (ABEP) of a decode-and-forward (DF) cooperative SM system where all nodes have multiple transmit and/or receive antennas which has not been studied before. It is shown that the derived analytical expressions for the ABEP are in close match with the computer simulation results. Furthermore, it is demonstrated that the cooperative SM scheme provides 4 dB SNR gain compared to classical M-ary modulated cooperative systems.