Miracle-dz/UniFAR-FTUs huggingface dataset:
On Refinement of Mobile UML State Machines

We study the semantics and refinement of mobile objects, considering an extension of core UML state machines by primitives that designate the location of objects and their moves within a network. Our contribution is twofold: first, we formalize the semantics of state machines in MTLA, an extension of Lamport’s Temporal Logic of Actions with spatial modalities. Second, we study refinement concepts for state machines that are semantically justified in MTLA.
MINIMALLY BUFFERED ROUTER USING WEIGHTED DEFLECTION ROUTING FOR MESH NETWORK ON CHIP

The scalability, modularity and massive parallelism exhibited by Network on chip(NoC) interconnects make them highly suitable for the inter core communication framework of multiprocessor system-on-chip (MPSoC) designs. Routers play the most vital role in transferring flits through the network, hence efficient microarchitecture and cost effective routing algorithms are highly essential for modern NoC routers. Elimination of buffers and deflection routing help to achieve energy and area efficiency of these routers. The advantages of bufferless and buffered designs can be combined by using a minimum number of side buffers to store a fraction of deflection flits in the router. In this paper, we propose a routing algorithm based on weighted deflection of flits for minimally buffered deflection routers. Evaluations on 4x4 and 8x8 mesh NoC using synthetic workloads as well as benchmark applications demonstrate that deflection rate and average network latency are significantly reduced in comparison with the state of the art NoC routers. Performance analysis of the newly proposed algorithm shows that the network saturation point improves by 26% compared to earlier designs in this domain.
Gas migration regimes and outgassing in particle-rich suspensions

Understanding how gases escape from particle-rich suspensions has important applications in nature and industry. Motivated by applications such as outgassing of crystal-rich magmas, we map gas migration patterns in experiments where we vary (1) particle fractions and liquid viscosity (10 Pa s – 500 Pa s), (2) container shape (horizontal parallel plates and upright cylinders), and (3) methods of bubble generation (single bubble injections, and multiple bubble generation with chemical reactions). We identify two successive changes in gas migration behavior that are determined by the normalized particle fraction (relative to random close packing), and are insensitive to liquid viscosity, bubble growth rate or container shape within the explored ranges. The first occurs at the random loose packing, when gas bubbles begin to deform; the second occurs near the random close packing, and is characterized by gas migration in a fracture-like manner. We suggest that changes in gas migration behavior are caused by dilation of the granular network, which locally resists bubble growth. The resulting bubble deformation increases the likelihood of bubble coalescence, and promotes the development of permeable pathways at low porosities. This behavior may explain the efficient loss of volatiles from viscous slurries such as crystal-rich magmas.
Auditory Nerve Perinodal Dysmyelination in Noise-Induced Hearing Loss

Exposure to loud sound (acoustic overexposure; AOE) induces hearing loss and damages cellular structures at multiple locations in the auditory pathway. Whether AOE can also induce changes in myelin sheaths of the auditory nerve (AN) is an important issue particularly because these changes can be responsible for impaired action potential propagation along the AN. Here we investigate the effects of AOE on morphological and electrophysiological features of the centrally directed part of the rat AN projecting from the cochlear spiral ganglion to brainstem cochlear nuclei. Using electron microscopy and immunocytochemistry, we show that AOE elongates the AN nodes of Ranvier and triggers notable perinodal morphological changes. Compound action potential recordings of the AN coupled to biophysical modeling demonstrated that these nodal and perinodal structural changes were associated with decreased conduction velocity and conduction block. Furthermore, AOE decreased the number of release sites in the cochlear nuclei associated with the reduced amplitudes of EPSCs evoked by AN stimulation. In conclusion, AN dysmyelination may be of fundamental importance in auditory impairment following exposure to loud sound.
Developing IoT applications in the Fog: A Distributed Dataflow approach

In this paper we examine the development of IoT applications from the perspective of the Fog Computing paradigm, where computing infrastructure at the network edge in devices and gateways is leverage for efficiency and timeliness. Due to the intrinsic nature of the IoT: heterogeneous devices/resources, a tightly coupled perception-action cycle and widely distributed devices and processing, application development in the Fog can be challenging. To address these challenges, we propose a Distributed Dataflow (DDF) programming model for the IoT that utilises computing infrastructures across the Fog and the Cloud. We evaluate our proposal by implementing a DDF framework based on Node-RED (Distributed Node-RED or D-NR), a visual programming tool that uses a flow-based model for building IoT applications. Via demonstrations, we show that our approach eases the development process and can be used to build a variety of IoT applications that work efficiently in the Fog.
FLOW CONTROL FOR CONTINUOUS PETRI NET MODELS OF HDS : STABILITY ISSUES

Abstract This paper is about the flow control design of hybrid dynamical systems modelled with continuous Petri nets and described as a set of bilinear state space representations. The marking vector stands for the state space vector, where some places are observable and other are not. The transitions are divided into controllable ones and uncontrollable ones. Gradient-based controllers are investigated in order to adapt the firing speeds of the controllable transitions according to a desired trajectory of the marking. The equilibriums and stability of the controlled system are discussed according to the controller time parameter.
The long-term sucCESs of the neoclassical growth model

In this paper, we seek to re-establish the link between the constant elasticity of substitution (CES) production function and neoclassical Solow growth theory. We do so in three dimensions. First, we review the increasing importance of the CES technology in modern dynamic macroeconomics, in expanding not only theory but also in addressing important policy questions. Second, we argue that the importance of the CES function in growth theory is intimately linked to normalization. Finally, we examine the data congruence between CES functions and recent growth patterns in the USA and the euro-area economies, where we apply a supply-side system incorporating a CES function with factor-augmenting and time-varying technical progress.
A Cropping Robust Geometric Distortion Correcting Algorithm for Watermarked Image Based on SIFT

his paper proposes a cropping robust geometric distortion correcting algorithm for watermarked image using scale invariant feature transform. Centroid of the original watermarked image is calculated, and the features extracted from the triangles composed of the centroid and the scale invariant feature transform feature points are used to estimate the position of the point in attacked image that correspond to the centroid extracted earlier in original watermarked image, then the geometric distortion can be detected and recovered using centroid angle and centroid distance features extracted from the well matched scale invariant feature transform feature points. Simulation results show that the proposed algorithm can correct the geometrical distortion of watermarked image efficiently. Scale invariant feature transform; original image centroid; scale invariant feature transform centroid angle; scale invariant feature transform centroid distance; scale invariant feature transform centroid triangle
Nuclear Ep-ICD Expression Is a Predictor of Poor Prognosis in “Low Risk” Prostate Adenocarcinomas

Introduction Molecular markers for predicting prostate cancer (PCa) that would have poor prognosis are urgently needed for a more personalized treatment for patients. Regulated intramembrane proteolysis of Epithelial cell adhesion molecule results in shedding of the extracellular domain (EpEx) and release of its intracellular domain (Ep-ICD) which triggers oncogenic signaling and might correlate to tumor aggressiveness. This study aimed to explore the potential of Ep-ICD and EpEx to identify PCa that have poor prognosis. Methods Immunohistochemical analysis of Ep-ICD and EpEx was carried out in normal prostate tissues (n = 100), benign prostate hyperplasia (BPH, n = 83), and prostate cancer (n = 249) using domain specific antibodies. The expression of Ep-ICD and EpEx was correlated with clinico- pathological parameters and disease free survival (DFS). Results Reduced expression of nuclear Ep-ICD and membrane EpEx was observed in PCa in comparison with BPH and normal prostate tissues (p = 0.006, p < 0.001 respectively). For patients who had PCa with Gleason Score less than 7, preserved nuclear Ep-ICD emerged as the most significant marker in multivariate analysis for prolonged DFS, where these patients did not have recurrence during follow up of up to 12 years (p = 0.001). Conclusion Reduced expression of nuclear Ep-ICD was associated with shorter disease free survival in patients with a Gleason Score less than 7 and may be useful in identifying patients likely to have aggressive tumors with poor prognosis. Furthermore, nuclear Ep-ICD can differentiate between normal and prostate cancer tissues for ambiguous cases.
Application of Two-Stage Learning on Brain-like System

This paper proposes a two-stage learning strategy constructed by two kinds of neural networks to simulate function of human brain. In the first stage, sequence images from environment input to a HOSM neural network. By unsupervised learning, the weights are fixed which can extract local features like vision's receptive field. In the second stage, an improved HDR neural network is built by supervised learning. This proposed structure has been implemented on a brain-like robot. Experimental results show that the learning strategy is effective
An Empirical Study of the Performance Impacts of Android Code Smells

Android code smells are bad implementation practices within Android applications (or apps) that may lead to poor software quality, in particular in terms of performance. Yet, performance is a main software quality concern in the development of mobile apps. Correcting Android code smells is thus an important activity to increase the performance of mobile apps and to provide the best experience to mobile end-users while considering the limited constraints of mobile devices (e.g., CPU, memory, battery). However, no empirical study has assessed the positive performance impacts of correcting mobile code smells. In this paper, we therefore conduct an empirical study focusing on the individual and combined performance impacts of three Android performance code smells (namely, Internal Getter/Setter, Member Ignoring Method, and HashMap Usage) on two open source Android apps. To perform this study, we use the Paprika toolkit to detect these three code smells in the analyzed apps, and we derive four versions of the apps by correcting each detected smell independently, and all of them. Then, we evaluate the performance of each version on a common user scenario test. In particular, we evaluate the UI and memory performance using the following metrics: frame time, number of delayed frames, memory usage, and number of garbage collection calls. Our results show that correcting these Android code smells effectively improve the UI and memory performance. In particular, we observe an improvement up to 12.4% on UI metrics when correcting Member Ignoring Method and up to 3.6% on memory-related metrics when correcting the three Android code smells. We believe that developers can benefit from these results to guide their refactoring, and thus improve the quality of their mobile apps.
ADVANCING UNDERSTANDINGS : A Linguistic Approach to Tourism Epistemology

Arguing that considerations of knowledge development should reflect a conversational, human-based view of knowledge production, this paper proposes a linguistic approach to understanding tourism epistemology. It then introduces a framework for exploring knowledge progression that includes the components of tourism morphology, or the creation and adjustment of concepts and models; the production and promotion of new interpretations and understandings; and the employment of such interpretations for the purpose of problem solving oriented to the needs of practitioners and policymakers. The paper concludes that scholars should analyze epistemological progress not only to comprehend the development of ideas and interpretations, but also as an exercise in reflectivity regarding the influence of academic forces and trends that govern the process of knowledge production.
Metastable Walking on Stochastically Rough Terrain

Simplified models of limit-cycle walking on flat terrain have provided important insights into the nature of legged locomotion. Real walking robots (and humans), however, do not exhibit true limit cycle dynamics because terrain, even in a carefully designed laboratory setting, is inevitably non-flat. Walking systems on stochastically rough terrain may not satisfy strict conditions for limit-cycle stability but can still demonstrate impressively long-living periods of continuous walking. Here, we examine the dynamics of rimless-wheel and compass-gait walking on randomly generated rough terrain and employ tools from stochastic processes to describe the ‘stochastic stability’ of these gaits. This analysis generalizes our understanding of walking stability and may provide statistical tools for experimental limit cycle analysis on real walking systems.
Phoneme-based word recognition by neural network-a step toward large vocabulary recognition

A neural-network-based word recognition system extendible to large-vocabulary isolated word recognition is presented. The system consists of (1) time-delay neural networks (TDNNs) for phoneme spotting and (2) a higher-level network and a dynamic programming (DP) time alignment procedure for word recognition. TDNN-based phenome-spotting networks are used whose role is to fire when a particular phenome is input. A higher-level network then improves these phenome firing patterns in view of an idealized phoneme sequence. For training of the higher-level network, DP matching is used to determine idealized phoneme firing patterns which are nearest to the actual phoneme firings. During recognition, the system selects the most probable word by applying DP matching to the outputs of the higher-level network. Speaker-dependent and isolated word recognition experiments show that word recognition rates of around 92% can be achieved for medium-size vocabularies
The effect of plot explicit, educational explicit, and implicit inference information and coviewing on children's internal and external cognitive processing

ABSTRACT Parental coviewing – the act of being present when a child is watching television – can influence the child’s cognitive processing and emotional reactions. This study investigated the role coviewing has on the child’s cognitive processing – which is evidenced by the phasic psychophysiological orienting response to three types of information: plot explicit, educational explicit, and implicit inference. An experiment was conducted that measured the heart rate of children (N = 88; mean age = 9.12 years) while watching messages either with or without a parent present in the room. It was predicted, and found, that coviewing leads to greater resource allocation to encoding the message – as indicated by phasic cardiac deceleration, and that information that required internal processing, such as plot explicit or implicit inferential content, leads to greater resources allocated to internal processing – as indicated by phasic cardiac acceleration. Implications for parental mediation strategies and educational television programming are given.
Culturally Relevant Physical Education in Urban Schools

Using a three-part theoretical framework, the cultural relevance cycle—which consists of (a) knowing community dynamics, (b) knowing how community dynamics influence educational processes, and (c) implementing strategies that reflect cultural knowledge of the community—we examined teachers' and students' perspectives on culturally relevant physical education in urban settings. We observed and interviewed 53 physical education teachers and 183 students in urban districts over 4 years. We identified themes of care, respect, language and communication, and curricular content that explained how these teachers enacted the cultural relevance cycle. Within these themes, teachers and students specified global and discipline-specific components of care, the rflattening of social hierarchies among students and between students and teachers, accommodation of English as a second language and urban communication, and relevant curricular content as necessary for achieving cultural relevance. Enacting the cycle of cultural relevance resulted in respectful learning environments in which students were highly engaged; however, very few teachers enacted all three steps of the cycle.
Human Capital Development and Economic Growth: Evidence from Nigeria

This paper examines specifically the impact of human capital development on long-run economic growth in Nigeria, using time serial data for 27 year period: 1981-2007. The Ordinary Least Square multiple regression was used to estimate the necessary models. The gross domestic product per capita growth was adopted as the dependent variable, while the independent variables were primary education enrollment divided by GDP, post-primary education enrollment divided by GDP, tertiary education enrollment divided by GDP, and other variables that promotes economic growth like capital expenditure on education divided by GDP, recurrent expenditure on education divided by GDP, and fixed gross capital formation divided by GDP. The regression result shows that the components of human capital development defined by ratio primary education enrollment, ratio of post-primary education enrollment and ratio of tertiary education enrollment are positive but not significant in promoting economic growth in Nigeria. The paper recommends for the revitalization of Nigerian educational system.
STOCK PRICE AND EXCHANGE RATE DYNAMICS: EVIDENCE FROM THAILAND

This paper analyzes empirically the causal relationship between the stock price and exchange rate using high-frequency data of Thailand. Having established the sta tionary condition of each series using ADF, PP and KPSS unit root tests, the causal linkages were examined using the Granger non-causality test prescribed by Toda and Yamamoto (1995). Among the finding of interest, there exists feedback interaction between exchange rate and stock price before the recent Asian financial crisis. However, during the crisis, the exchange rate leads stock price.
MicroRNA Regulation of Bovine Monocyte Inflammatory and Metabolic Networks in an In Vivo Infection Model

Bovine mastitis is an inflammation-driven disease of the bovine mammary gland that costs the global dairy industry several billion dollars per year. Because disease susceptibility is a multifactorial complex phenotype, an integrative biology approach is required to dissect the molecular networks involved. Here, we report such an approach using next-generation sequencing combined with advanced network and pathway biology methods to simultaneously profile mRNA and miRNA expression at multiple time points (0, 12, 24, 36 and 48 hr) in milk and blood FACS-isolated CD14+ monocytes from animals infected in vivo with Streptococcus uberis. More than 3700 differentially expressed (DE) genes were identified in milk-isolated monocytes (MIMs), a key immune cell recruited to the site of infection during mastitis. Upregulated genes were significantly enriched for inflammatory pathways, whereas downregulated genes were enriched for nonglycolytic metabolic pathways. Monocyte transcriptional changes in the blood, however, were more subtle but highlighted the impact of this infection systemically. Genes upregulated in blood-isolated monocytes (BIMs) showed a significant association with interferon and chemokine signaling. Furthermore, 26 miRNAs were DE in MIMs and three were DE in BIMs. Pathway analysis revealed that predicted targets of downregulated miRNAs were highly enriched for roles in innate immunity (FDR < 3.4E−8), particularly TLR signaling, whereas upregulated miRNAs preferentially targeted genes involved in metabolism. We conclude that during S. uberis infection miRNAs are key amplifiers of monocyte inflammatory response networks and repressors of several metabolic pathways.
Dynamic Bargaining with Transaction Costs

A buyer and seller alternate making offers until an offer is accepted or someone terminates negotiations. The seller's valuation is common knowledge, but the buyer's valuation is known only by the buyer. Impatience to reach an agreement comes from two sources: the traders discount future payoffs and there are transaction costs of bargaining. Equilibrium behavior involves either immediate trade, delayed trade, or immediate termination, depending on the size of the gains from trade and the relative bargaining costs. This contrasts with the pure discounting model where termination never occurs, and the pure transaction cost model where delayed trade never occurs.
Contractual Anonymity

We propose, develop, and implement techniques for achieving contractual anonymity. In contractual anonymity, a user and service provider enter into an anonymity contract. The user is guaranteed anonymity and message unlinkability from the contractual anonymity system unless she breaks the contract. The service provider is guaranteed that it can identify users who break the contract. The significant advantages of our system are that 1) the service provider is not able to take any action toward a particular user (such as revealing her identity or blacklisting her future authentications) unless she violates her contract, 2) our system can enforce a variety of policies, and 3) our system is efficient.
The Effect of Catecholaminergic Depletion Within the Prelimbic and Infralimbic Medial Prefrontal Cortex on Recognition Memory for Recency, Location, and Objects

There is good evidence that the medial prefrontal cortex (mPFC) is involved in different aspects of recognition memory. However, the mPFC is a heterogeneous structure, and the contribution of the prelimbic (PL) and infralimbic (IL) cortices to recognition memory has not been investigated. Similarly, the role of different neuromodulators within the mPFC in these processes is poorly understood. To this end, we tested animals with 6-hydroxydopamine (6-OHDA) lesions of the PL and IL mPFC on three tests of object recognition memory that required judgments about recency, object location, and object identity. In the recency task, lesions to both PL and IL severely impaired animals' ability to differentiate between old (earlier presented) and recently presented familiar objects. Relative to sham and PL animals, the IL lesion also disrupted performance on the object location task. However, both lesions left novel object recognition intact. These data confirm previous reports that the mPFC is not required for discriminations based on the relative familiarity of individual objects. However, these results demonstrate that catecholamines within the PL cortex are crucial for relative recency judgments and suggest a possible role for neural processing within the IL in the integration of information about object location.