Miracle-dz/UniFAR-FTUs huggingface dataset:
The effect of compliant joint and caudal fin in thrust generation for robotic fish

Fish generates large thrust through an oscillating motion with a fin. It is assumed that the flexibility of a fin affects the thrust generated by the fish. However, detailed investigation on the relationship between the flexibility of the fin and thrust generation is lacking. In this paper, the driving mechanism of a robotic fish is implemented using a compliant joint and caudal fin that is adapted from fish. The effect of the passive mechanism on the thrust generation with changes in the stiffness and frequency is investigated with this driving mechanism. The present research is carried out to understand the relationship among the compliance of the joint and caudal fin, the frequency of oscillating motion and the thrust which is generated by oscillating motion. The thrust is measured using a force transducer while varying the frequency and compliance. The bending angles between the compliant joint and caudal fin are also compared with the changes of the thrust in one cycle. The results show the appropriate stiffness of the compliant mechanism can be found in order to generate maximum thrust under the condition that the other parameters are not varied.
Collective Intelligence, Data Routing and Braess' Paradox

We consider the problem of designing the the utility functions of the utility-maximizing agents in a multi-agent system so that they work synergistically to maximize a global utility. The particular problem domain we explore is the control of network routing by placing agents on all the routers in the network. Conventional approaches to this task have the agents all use the Ideal Shortest Path routing Algorithm (ISPA). We demonstrate that in many cases, due to the side-effects of one agent's actions on another agent's performance, having agents use ISPA's is suboptimal as far as global aggregate cost is concerned, even when they are only used to route infinitesimally small amounts of traffic. The utility functions of the individual agents are not ``aligned'' with the global utility, intuitively speaking. As a particular example of this we present an instance of Braess' paradox in which adding new links to a network whose agents all use the ISPA results in a decrease in overall throughput. We also demonstrate that load-balancing, in which the agents' decisions are collectively made to optimize the global cost incurred by all traffic currently being routed, is suboptimal as far as global cost averaged across time is concerned. This is also due to `side-effects', in this case of current routing decision on future traffic. The mathematics of Collective Intelligence (COIN) is concerned precisely with the issue of avoiding such deleterious side-effects in multi-agent systems, both over time and space. We present key concepts from that mathematics and use them to derive an algorithm whose ideal version should have better performance than that of having all agents use the ISPA, even in the infinitesimal limit. We present experiments verifying this, and also showing that a machine-learning-based version of this COIN algorithm in which costs are only imprecisely estimated via empirical means (a version potentially applicable in the real world) also outperforms the ISPA, despite having access to less information than does the ISPA. In particular, this COIN algorithm almost always avoids Braess' paradox.
Towards Guidelines for Conducting Software Process Simulation in Industry

Background: Since the 1950s explicit software process models have been used for planning, executing and controlling software development activities. To overcome the limitation of static models at capturing the inherent dynamism in software development, Software Process Simulation Modelling (SPSM) was introduced in the late 1970s. SPSM has been used to address various challenges, e.g. estimation, planning and process assessment. The simulation models developed over the years have varied in their scope, purpose, approach and the application domain. However, there is a need to aggregate the evidence regarding the usefulness of SPSM for achieving its intended purposes. Objective: This thesis aims to facilitate adoption of SPSM in industrial practice by exploring two directions. Firstly it aims to establish the usefulness of SPSM for its intended purposes, e.g. for planning, training and as an alternative to study the real world software (industrial and open source) development. Secondly to define and evaluate a process for conducting SPSM studies in industry. Method: Two systematic literature reviews (SLR), a literature review, a case study and an action research study were conducted. A literature review of existing SLRs was done to identify the strategies for selecting studies. The resulting process for study selection was utilized in an SLR to capture and aggregate evidence regarding the usefulness of SPSM. Another SLR was used to identify existing process descriptions of how to conduct an SPSM study. The consolidated process and associated guidelines identified in this review were used in an action research study to develop a simulation model of the testing process in a large telecommunication vendor. The action research was preceded by a case study to understand the testing process at the company. Results: A study selection process based on the strategies identified from literature was proposed. It was found to systemize selection and to support inclusiveness with reasonable additional effort in an SLR of the SPSM literature. The SPSM studies identified in literature scored poorly on the rigor and relevance criteria and lacked evaluation of SPSM for the intended purposes. Lastly, based on literature, a six-step process to conduct an SPSM study was used to develop a System Dynamics model of the testing process for training purposes in the company. Conclusion: The findings identify two potential directions for facilitating SPSM adoption. First, by learning from other disciplines having done simulation for a longer time. It was evident how similar the consolidated process for conducting an SPSM study was to the process used in simulation in general. Second the existing work on SPSM can at best be classified as strong ``proof-of-concept’’ that SPSM can be useful in the real world software development. Thus, there is a need to evaluate and report the usefulness of SPSM for the intended purposes with scientific rigor.
Texturing and Deforming Meshes with Casual Images

Using (casual) images to texture 3D models is a common way to create realistic 3D models, which is a very important task in computer graphics. However, if the shape of the casual image does not look like the target model or the target mapping area, the textured model will become strange since the image will be distorted very much. In this paper, we present a novel texturing and deforming approach for mapping the pattern and shape of a casual image to a 3D model at the same time based on an alternating least-square approach. Through a photogrammetric method, we project the target model onto the source image according to the estimated camera model. Then, the target model is deformed according to the shape of the source image using a surface-based deformation method while minimizing the image distortion simultaneously. The processes are performed iteratively until convergence. Hence, our method can achieve texture mapping, shape deformation, and detail-preserving at once, and can obtain more reasonable texture mapped results than traditional methods.
Activation of the adult mode of ovine growth hormone receptor gene expression by cortisol during late fetal development

The developmental and tissue‐specific regulation of growth hormone receptor (GHR) mRNA expression is complex and involves alternate leader exon usage. The transcript composition of hepatic GHR mRNA has therefore been determined in fetal sheep during late gestation and after experimental manipulation of fetal plasma cortisol levels by fetal adrenalectomy and exogenous cortisol infusion, using RNase protection assays and a riboprobe containing exons 1A, 2, and 3 of the ovine GHR gene. Expression of the adult liver‐specific GHR mRNA transcript containing exon 1A was not detected earlier than 138 days of gestation (term 145 ±2 days). Thereafter, expression of this leader exon increased and accounted for 25–30% of the total GHR mRNA in the fetal liver at term. Hepatic GHR mRNA derived from leader exons other than 1A was detectable at 97 days and increased in abundance toward term in parallel with the normal prepartum rise in fetal plasma cortisol. Abolition of this cortisol surge by fetal adrenalectomy prevented both the activation of exon 1A expression and the prepartum rise in GHR mRNA derived from the other leader exons in fetal ovine liver. Conversely, raising cortisol levels by exogenous infusion earlier in gestation prematurely activated exon 1A expression and enhanced the abundance of GHR mRNA transcripts derived from the other leader exons. Cortisol therefore appears to activate the adult mode of GHR gene expression in fetal ovine liver during late gestation. These observations have important implications for the maturation of the somatotrophic axis and for the onset of GH‐dependent growth after birth. —Li, J., Gilmour, R. S. , Saunders, J. C. , Dauncey, M. J., Fowden, A. L. Activation of the adult mode of ovine growth hormone receptor (GHR) gene expression by cortisol during late fetal development. FASEB J. 13, 545–552 (1999)
Competition- and Identity-based Roots of Anti-immigration Prejudice among Individuals with and without an Immigrant Background

Most social psychological research on anti-immigration prejudice has examined the point of view of the national ingroup—generally defined as the citizens of the country under consideration—toward immigrant outgroups. Threat perceptions related to immigration as well as national identification have been shown to underlie negative attitudes. Whether these two factors also explain attitudes among individuals sharing characteristics with the immigrants remains largely unstudied. To fill this gap, the present research examines perceived threat, national identification, and different facets of anti-immigration prejudice among Swiss residents with and without an immigrant background. Results of a path model conducted on data from the International Social Survey Programme 2013 (N = 1,198) showed that, as expected, natives reported higher levels of prejudice than both citizens of foreign descent and immigrants. While group differences in prejudice were in part explained by differences in threat perceptions, the role of national identification was more nuanced. Altogether, these findings suggest that social psychological research should go beyond simplistic “national ingroup vs. immigrant outgroup” conceptualisations when examining anti-immigration prejudice.
High Speed Partial Run-Time Reconfiguration Using Enhanced ICAP Hard Macro

Achieving high speed run-time reconfiguration is important for the adaptation of partial reconfiguration in many applications. The reconfiguration speed that is currently available today is somehow artificially limited by the FPGA vendors, while the fabrication process technologies used for building the latest devices today are capable of achieving much higher reconfiguration speed. In this paper we will present a new design and implementation method for achieving high speed partial run-time reconfiguration that exceeds the specified reconfiguration speed of today's FPGAs. By adding custom logic around the Internal Configuration Access Port (ICAP) to implement an enhanced ICAP hard macro, we will investigate the partial run-time reconfiguration speed and explore the limits of the ICAP interface. This is done by using over clocking of the ICAP. Compared with previously work on high-speed reconfiguration, using the enhanced ICAP hard macro will significantly increase the reconfiguration speed.
New Designs of a Two-Phase E-Core Switched Reluctance Machine by Optimizing the Magnetic Structure for a Specific Application: Concept, Design, and Analysis

Three new magnetic structures for an E-core switched reluctance machine (SRM) comprising two segmented stator cores or a monolithic stator core are proposed for good manufacturability, mechanically robustness, ease of assembly, and electromagnetic-performance improvement. The E-core stator has four small poles with phase windings and two or four large poles (hereinafter referred to as common poles) in between. The common poles do not have copper windings or permanent magnets. The common poles are shared by both phases for positive-torque generation during the entire operation. The air gap around the common pole has constant and minimum reluctance, irrespective of rotor position, by its unique design, and the two small stator poles experience variable reluctance with respect to rotor position. The segmented stator can be constructed with two independent and physically separate E-shaped cores with L- or I-shaped yokes so that the straight back iron in the stator rather than the curved back iron is suitable for assembly with other parts like end bells or brackets. The monolithic stator also has an L-shaped yoke for the same reason, and the new E-core SRMs are designed by means of pole-arc optimization. The novel E-core SRMs are compared to a conventional two-phase SRM. The comparison includes cost savings, torque, copper and core losses, efficiency, and radial force in order to validate the distinct features of the E-core SRMs. For experimental verification, laboratory testing of a segmented E-core SRM is conducted, and the simulated and measured phase currents and voltages correlate well.
Roads Belong in Databases

The popularity of location-based services and the need to perform real-time processing on them has led to an interest in queries on road networks, such as ﬁnding shortest paths and ﬁnding nearest neighbors. The challenge here is that the efﬁcient execution of operations usually involves the computation of distance along a spatial network instead of “as the crow ﬂies,” which is not simple. This requires the precomputation of the shortest paths and network distance between every pair of points (i.e., vertices) with as little space as possible rather than having to store the n 2 shortest paths and distances between all pairs. This problem is related to a ‘holy grail’ problem in databases of how to incorporate road networks into relational databases. A data structure called a road network oracle is introduced that resides in a database and enables the processing of many operations on road networks with just the aid of relational operators. Two implementations of road network oracles are presented.
User Oriented Approach to Website Navigation Concept Using Mathematical Model

Web sites are most effective when they meet both the contents and usability needs of their users. Due to tremendous growth of web application, it increases the complexity of web applications and web navigation. Website design is easy task but, to navigate user efficiently is big challenge, one of the reason is user behavior is keep changing and web developer or designer not think according to user's behavior. Designing well-structured websites to facilitate effective user navigation patterns has long been a challenge in web usage mining with various applications like navigation prediction and improvement of website management. Different methods have been proposed to reorganize Web Pages to improve users navigation using users navigation data but it still causes disorienting users and increases user's cognitive load. There is a need to improve the efficiency and the performance of a website for effective user navigation. Through this Paper we will propose Mathematical programming model to improve user navigation while minimizing alteration to current structure. Optimal solution can be very quickly obtained. We are minimizing unnecessary changes to the present structure of a Web site and hence can be applied for Web site maintenance on a regular basis.
Equalization of fiber impairments using high-speed digital signal processing

We present modulation-transparent digital signal processing algorithms for the equalization of linear and nonlinear fiber impairments. A three-dimensional Stokes space algorithm is shown to provide universal polarization demultiplexing, by employing a best fitting plane strategy. Intra-channel nonlinear impairments are digitally counteracted by a simplified Volterra series nonlinear equalizer, based on parallel array of one-dimensional frequency-domain filters. Finally, we show hardware implementation results of a time-domain chromatic dispersion equalizer using a Virtex-6 FPGA.
Demand-and Supply-Driven Externalities in OECD Countries : A Dynamic Panel Approach

This paper provides empirical evidence of the relevance of supplyand demand-driven externalities in determining sectoral employment growth for 18 industrial sectors on a set of OECD countries. The paper improves over the existing literature in two ways. First, we use dynamic panel estimation technique. This allows us to utilise lagged variables as natural instruments and to take account of possible dynamic externalities. Second, we look for evidence of externalities by relating activity in sector i to a weighted sums of downstream and upstream activity – thanks to input-output tables. This allows us to distinguish between demandand supply-driven externalities.
Querying complex data

Database technology has advanced to support increasingly complex data – from relations to semi-structured data and unstructured documents. More recently, graph databases have regained attention following demands from applications like social networks and recommendation systems. Graph analysis, usually associated with the Complex Networks field, has become a central tool in areas such as biology, physics and linguistics. Database management systems should improve support to these data and applications beyond the data model level tackled by current graph databases, including more flexible querying models and management mechanisms. In this paper, we define the characteristics of the highly interconnected data that underlies many of these modern applications. We adopt the term complex data as a reference to the field of complex networks. A database management system for complex data requires a flexible query model that explores the topology of the relationships, taking into account their eventual uncertainty. Efficient query processing becomes a challenge, requiring new mechanisms for relationship-based query optimizations. To meet the new requirements, our solution models complex data as property graphs with weighted relationships. We propose a new query language that allows ranking of elements based on properties of the topology of the graph. The queries are evaluated based on a variation of the spreading activation model, which is the core of the query processor and the main target for query optimization strategies. Experiments with real data show the practicability of our approach and support our analysis of several query optimization and approximation mechanisms.
Grouping Priors and the Bayesian Elastic Net Luke

1 In the literature surrounding Bayesian penalized regression, the two primary choices of prior distribution on the regression coefficients are zero-mean Gaussian and Laplace. While both have been compared numerically and theoretically, there remains little guidance on which to use in real-life situations. We propose two viable solutions to this problem in the form of prior distributions which combine and compromise between Laplace and Gaussian priors, respectively. Through cross-validation the prior which optimizes prediction performance is automatically selected. We then demonstrate the improved performance of these new prior distributions relative to Laplace and Gaussian priors in both a simulated and experimental environment. Revivification of work presented at the CMS-MITACS Joint Conference, May 31 to June 3, 2007 ([2]). Since this time, considerable effort has been made on these and related models (i.e. [7], [3], and [6]). Our goal in producing this technical report is simply to make more readily available (in comparison to the poster format of the original) our initial contribution ([2]). 1
ESBIR : Extended Semantic Based Information Retrieval Algorithm using Synonyms and Antonyms

Information Retrieval can be defined as the activity of obtaining information or data relevant to information needs from a collection of information resources. In general, resources are available in huge, vast and unstructured data formats. From the available resources, only a part or some is wanted by the users. Documents that satisfy the given query in the judgment of the user are said to be relevant. The documents that are not belonging to the given topic are said to be non-relevant. An IR engine uses the query to classify the documents in a given collection and returns a subset of documents that satisfy some classification criterion to the user. Sometimes the relevant documents may not contain the keywords specified in the query. The lack of the given term in a document does not necessarily mean that the document is not relevant because more than one term can be semantically similar although they are lexicographically different. In this paper a new algorithm, Extended Semantic Based Boolean Information Retrieval using Synonyms and Antonyms (ESBIR), is proposed to retrieve the documents with semantically similar terms to enhance the performance of Information Retrieval System by improving the recall and precision.
When Terror Hits Home: Identity Fused Americans Who Saw Boston Bombing Victims as “Family” Provided Aid

When tragedy strikes a group, only some group members characteristically rush to the aid of the victims. What motivates the altruism of these exceptional individuals? Here, we provide one set of answers based on data collected before and shortly after the 15 April 2013, Boston Marathon bombings. The results of three studies indicated that Americans who were strongly “fused” with their country were especially inclined to provide various forms of support to the bombing victims. Moreover, the degree to which participants reported perceiving fellow Americans as psychological kin statistically mediated links between fusion and pro-group outcomes. Together, these findings shed new light on relationships between personal and group identity, cognitive representations of group members, and personally costly, pro-group actions.
Optimal Monetary Policy Under Discretion with a Zero Bound on Nominal Interest Rates

We determine optimal discretionary monetary policy in a New Keynesian model when nominal interest rates are bounded below by zero. Nominal interest rates should be lowered faster in response to adverse shocks than in the case without bound. Such ‘pre-emptive easing’ is optimal because expectations of a possibly binding bound in the future amplify the effects of adverse shocks. Calibrating the model to the US economy we find the easing effect to be quantitatively important. Moreover, the lower bound binds rather frequently and imposes significant welfare losses. Losses increase further when inflation is partly determined by lagged inflation in the Phillips curve. Targeting positive inflation rates reduces the frequency of a binding lower bound, but tends to reduce welfare compared to a target rate of zero. The welfare gains from policy commitment, however, appear significant and are much larger than in the case without lower bound.