Miracle-dz/UniFAR-FTUs huggingface dataset:
The radio properties of optically obscured Spitzer sources

This paper analyses the radio properties of a subsample of optically obscured (R ≥ 25.5) galaxies observed at 24 μm by the Spitzer Space Telescope within the First Look Survey. Ninety-six F 24 μm ≥ 0.35 mJy objects out of 510 are found to have a radio counterpart at 1.4 GHz, 610 MHz or at both frequencies, respectively, down to ∼40 and ∼200 μJy. IRAC photometry sets the majority of them in the redshift interval z ≃ [1-3] and allows for a broad distinction between active galactic nucleus (AGN) dominated galaxies (∼47 per cent of the radio-identified sample) and systems powered by intense star formation (∼13 per cent), the remaining objects being impossible to classify. The percentage of radio identifications is a strong function of 24-μm flux: almost all sources brighter than F 24 μm ∼ 2 mJy are endowed with a radio flux at both 1.4 GHz and 610MHz, while this fraction drastically decreases by lowering the 24-μm flux level. The radio number counts at both radio frequencies suggest that the physical process(es) responsible for radio activity in these objects have a common origin regardless of whether the source shows mid-infrared emission compatible with being an obscured AGN or a star-forming galaxy. We also find that both candidate AGN and star-forming systems follow (although with a large scatter) the relationship between 1.4-GHz and 24-μm fluxes reported by Appleton et al. which identifies sources undergoing intense star formation activity. However, a more scattered relation is observed between 24-μm and 610-MHz fluxes. On the other hand, the inferred radio spectral indices α indicate that a large fraction of objects in our sample (∼60 per cent of all galaxies with estimated α) may belong to the population of ultrasteep spectrum sources, typically 'frustrated' radio-loud AGN. We interpret our findings as a strong indication for concurrent AGN and star-forming activity, whereby the 1.4-GHz flux is of thermal origin, while that at 610 GHz mainly stems from the nuclear source.
A CASE STUDY COMPARISON BETWEEN WEB-BASED AND TRADITIONAL GRADUATE LEVEL ACADEMIC LEADERSHIP INSTRUCTION

Traditional leadership education instructors provide an interactive setting to promote communication skills, team building, and problem-solving skills. Simultaneously, the use of distance technology is also well documented. However, no specific research studies comparing distance education and traditional delivery methods in the area of leadership education were discovered in the course of this inquiry. Therefore, this study was initiated to begin an investigation of the plausibility of delivering leadership education courses using distance education technology. A case study method was employed and results indicated that in most instances students enrolled in both distance and traditional classes had the same perceptions of their leadership competencies and usage. This study found no significant differences between the traditional students and the Web-based students’ scores on any of the five LSI scales, the Leadership Learning Scale, the Leadership Use Scale, or the Leadership Remember Scale. These results suggest that a graduate level academic leadership course can be taught as effectively as a Web-based course.
A Low-Overhead, Fully-Distributed, Guaranteed-Delivery Routing Algorithm for Faulty Network-on-Chips

This paper introduces a new, practical routing algorithm, Maze-routing, to tolerate faults in network-on-chips. The algorithm is the first to provide all of the following properties at the same time: 1) fully-distributed with no centralized component, 2) guaranteed delivery (it guarantees to deliver packets when a path exists between nodes, or otherwise indicate that destination is unreachable, while being deadlock and livelock free), 3) low area cost, 4) low reconfiguration overhead upon a fault. To achieve all these properties, we propose Maze-routing, a new variant of face routing in on-chip networks and make use of deflections in routing. Our evaluations show that Maze-routing has 16X less area overhead than other algorithms that provide guaranteed delivery. Our Maze-routing algorithm is also high performance: for example, when up to 5 links are broken, it provides 50% higher saturation throughput compared to the state-of-the-art.
TOPT: Supporting flash crowd events in hybrid overlay-based live streaming

Recent studies show that an increasing number of over-the-top live streams is delivered over the Internet. For the delivery of those streams, the dynamically changing and potentially large number of users imposes a major challenge. Flash crowds, where the number of users multiplies or significantly drops in a very small time frame, can cause serious degradations in the streaming performance. Due to the missing support for global network-layer multicast, overlay-based approaches have been broadly studied, showing that, with relaxed time constraints, they can scale well with the number of users. Yet, to support flash crowds, scaling has to happen quickly to keep up also with rapidly changing populations. Only a few approaches exist that focus on this aspect by influencing the streaming topology and, so far, it is not clear if and how these mechanisms can be applied to state-of-the-art hybrid streaming systems. Therefore, in this paper, TOPT is proposed, integrating new as well as existing mechanisms in a common framework. The evaluation shows that the streaming topology, indeed, plays a major role during flash crowds. The lightweight and decentralized tree-forming and topology optimization mechanisms of TOPT, combined with tracker extensions to attach new peers in batches, greatly help improving the streaming performance in terms of reduced playback interruptions by more than 60% and slight reduction in communication overhead at an acceptable increase in average startup delays by 24%.
Good Neighbors Make Good Senses: Exploiting Distributional Similarity for Unsupervised WSD

We present an automatic method for senselabeling of text in an unsupervised manner. The method makes use of distributionally similar words to derive an automatically labeled training set, which is then used to train a standard supervised classifier for distinguishing word senses. Experimental results on the Senseval-2 and Senseval-3 datasets show that our approach yields significant improvements over state-of-the-art unsupervised methods, and is competitive with supervised ones, while eliminating the annotation cost.
Understanding IS Education Quality in Developing Countries: Role of Acculturation

Many developing countries (DCs) have limited IS human resources. Hence, their university-level IS classes are often taught by foreign faculty. Despite this arrangement, poor quality of IS education in DCs is a continuing problem. Research indicates cultural differences as an important reason for this problem. Foreign faculty imposing westerncurriculum and teaching practices without adapting to the requirements of native students and native students inability/unwillingness to make the necessary adjustments leads to poor course quality. Therefore, we argue that cultural adaptation of foreign faculty and native students will improve IS course quality. Drawing from the acculturation literature that deals with cultural adaptations we develop a model of acculturation specific to teaching of IS courses by foreign faculty in DCs. In doing so, relevant factors from the IS education literature that influence faculty and students’ adaptation are synthesized. This paper extends acculturation theory and discusses relevant implications for IS education in DCs.
Streptococcus pneumoniae TIGR4 Flavodoxin: Structural and Biophysical Characterization of a Novel Drug Target

Streptococcus pneumoniae (Sp) strain TIGR4 is a virulent, encapsulated serotype that causes bacteremia, otitis media, meningitis and pneumonia. Increased bacterial resistance and limited efficacy of the available vaccine to some serotypes complicate the treatment of diseases associated to this microorganism. Flavodoxins are bacterial proteins involved in several important metabolic pathways. The Sp flavodoxin (Spfld) gene was recently reported to be essential for the establishment of meningitis in a rat model, which makes SpFld a potential drug target. To facilitate future pharmacological studies, we have cloned and expressed SpFld in E. coli and we have performed an extensive structural and biochemical characterization of both the apo form and its active complex with the FMN cofactor. SpFld is a short-chain flavodoxin containing 146 residues. Unlike the well-characterized long-chain apoflavodoxins, the Sp apoprotein displays a simple two-state thermal unfolding equilibrium and binds FMN with moderate affinity. The X-ray structures of the apo and holo forms of SpFld differ at the FMN binding site, where substantial rearrangement of residues at the 91–100 loop occurs to permit cofactor binding. This work will set up the basis for future studies aiming at discovering new potential drugs to treat S. pneumoniae diseases through the inhibition of SpFld.
AVLib: A Simulink R® library for multi-agent systems research

This paper presents a library of Simulink blocks, aimed at facilitating research and education in the area of multi-vehicle systems. The inherent complexity involved in modeling, simulating and experimenting with multi-vehicle systems presents significant challenges for researchers, educators and students. There is a need for re-usable building blocks implementing standard vehicle models and control laws, as well as convenient visualization components and interfaces. Furthermore, well-designed system building blocks make the transition from simulation to actual hardware experiments as seamless as possible. The described library, called AVLib, provides such tools, implemented as a set of Simulink blocks that are easy to use, can be extended with other Simulink blocks, and are scalable to model systems comprised of several hundreds of agents. We present several case studies showing how the AVLib library can facilitate research of multi-vehicle systems.
Disparity and Optical Flow Partitioning Using Extended Potts Priors

This paper addresses the problems of disparity and optical flow partitioning based on the brightness invariance assumption. We investigate new variational approaches to these problems with Potts priors and possibly box constraints. For the optical flow partitioning, our model includes vector-valued data and an adapted Potts regularizer. Using the notion of asymptotically level stable (als) functions, we prove the existence of global minimizers of our functionals. We propose a modified alternating direction method of multipliers. This iterative algorithm requires the computation of global minimizers of classical univariate Potts problems which can be done efficiently by dynamic programming. We prove that the algorithm converges both for the constrained and unconstrained problems. Numerical examples demonstrate the very good performance of our partitioning method.
Does Electronic Trading Improve Market Efficiency? Evidence from Spatial Arbitrage in the Automotive Market

Price disparities across locations can occur when sellers in one location have difficulty matching with buyers in a different location due to the transaction costs of trading across distance. Spatial arbitrageurs exploit these discrepancies by buying goods from locations where prices are low and reselling them at locations where prices are high. Electronic channels should lower the transaction costs of trading across distance, thereby facilitating buyer/seller matching. It follows that electronic trading should reduce spatial arbitrage opportunities, thereby improving market efficiency. We test this hypothesis in the automotive market. The distinguishing feature of our data is that we can identify the distinct buyers, sellers, and vehicles involved in transactions, giving us a detailed look at transaction patterns likely motivated by spatial arbitrage. We conclude that traders are engaging in spatial arbitrage within the market but that spatial arbitrage has become less prevalent over time due to increased electronic trading.
Multi-feature based face detection

In this paper a two-step face detection technique is proposed. The first step uses a conventional skin detection method to extract regions of potential faces from the image database. This skin detection step is based on a Gaussian mixture model in the YCbCr colour space. In the second step faces are detected among the candidate regions by filtering out false positives from the skin colour detection module. The selection process is achieved by applying a learning approach using multiple additional features and a suitable metric in multi-feature space. The metric is derived by learning the underlying parameters using a small set of representative face samples. In this process the parameters are optimized by a multiple objective optimization method based on the Pareto archived evolution strategy. The learned metric in multi-feature space is then applied to a conventional classifier to filter out non faces from the first processing step. Support vector machines and k-nearest neighbours classifiers are used to test the performance of the optimized metric in multi-feature space.
Distributed extended kalman filter based on consensus filter for wireless sensor network

Distributed state estimate is one of the most fundamental problems for wireless sensor network. This paper addresses a type of distributed extended kalman filter that is extended from linear distributed kalman filter. Central extended kalman filter is an effective tool for nonlinear state filter of multisensor network. In this paper central extended kalman filter is decomposed into n micro extended kalman filters with inputs that are provided by consensus filters. When system process model and observation model are nonlinear, it is proved that distributed extended kalman filter can provide an identical state estimate of system state. Two target tracking examples are employed for simulation demonstration. All sensor nodes are able to take a nonlinear observation to moving target, dynamical cluster that is composed of several sensor nodes execute observation and error covariance matrix consensus filter. Each sensor in cluster obtain system estimate through distributed extended kalman filter. Simulation results show the proposed algorithm is effective for nonlinear distributed state estimate.
A closed-loop inverse kinematic scheme for on-line joint-based robot control

SUMMARY A computationally fast inverse kinematic scheme is derived which solves robot's end-effector (EE) trajectories in terms of joint trajectories. The inverse kinematic problem (IKP) is cast as a control problem for a simple dynamic system. The resulting closed-loop algorithms are shown to guarantee satisfactory tracking performance. Differently from previous first-order schemes which only solve for joint positions and velocities, we propose here new second order tracking schemes which allow the on-line generation of joint position + velocity + acceleration (PVA) reference trajectories for any computed torque-like controller in sensor-based robot applications. The algorithms do explicitly solve the IKP for both EE position and orientation. Simulation results for a six-degree-of-freedom PUMA-like geometry demonstrate the effectiveness of the scheme, even near singularities.
A Configurable Graph Data Type for Online Exploration of Web Data

Application domains often include notions that are inherently based on graph structures. In this paper, we propose a comprehensive generic ontology-based datatype for graphs. It focuses on those aspects of graphs that are useful for workflows that require exploration of relevant parts of potentially large graphs by online algorithms. The goal of the ontology is to include as much information as possible supporting the graph exploration process declaratively into the specification of the graph. This allows to separate the (also declarative) specification of the actual exploration process from the maintenance of the graph itself. For concrete applications, the graph specification is given in RDF using this ontology. From the specification, an appropriate instantiation of the abstract datatype is automatically derived which is then used in information workflows.
Methods for Learning Articulated Attractors over Internal Representations

Recurrent attractor networks have many virtues which have prompted their use in a wide variety of connectionist cognit ive models. One of these virtues is the ability of these networks to learnarticulated attractors — meaningful basins of attraction arising from the systematic interaction of explicitly trai ned patterns. Such attractors can improve generalization by enfor cing “well formedness” constraints on representations, massag ing noisy and ill formed patterns of activity into clean and usef l patterns. This paper investigates methods for learning art iculated attractors at the hidden layers of recurrent backpro pagation networks. It has previously been shown that standard connectionist learning techniques fail to form such struct ured attractors over internal representations. To address this problem, this paper presents two unsupervised learning rules th at give rise to componential attractor structures over hidden units. The performance of these learning methods on a simple structured memory task is analyzed.
The Effectiveness of Toondoo and Picture Series to Enhance Students’ Writing Skills in Term of Their Creativity (A Quasi Experimental Study at SMA N 2 Wonogiri)

This research is aimed to verify whether: (1) Toondoo is more effective than picture series in teaching writing; (2) Students having high creativity have better writing skill than those having low one; and (3) There is an interaction between teaching media and the students’ creativity to teach writing.This quasi-experimental study was conducted at the Tenth grade class of SMA Negeri 2 Wonogiri in the Academic Year of 2015/2016. There are 136 students that are divided into four classes. The samples are labeled as experimental class and control class. To get the sample, the researcher used cluster random sampling technique. Each class was divided into three groups according to the level of creativity: High, Medium, and Low. High and low creativity groups are used as the research sample, each consisting of 11 students. Writing test was used as an instrument to collect the data. The data were analyzed by using Multifactor analysis of variance (ANOVA). Before conducting ANOVA test, the researcher needed to conduct normality and homogeneity tests first in order to know the normality and the homogeneity of the data.The research findings are: (1) Toondoo as teaching medium is more effective than picture series to teach writing; (2) students having high creativity have better writing skill than those who have low creativity; and (3) there is an interaction between teaching media and the students’ creativity to teach writing. The results of the research imply that Toondoo is an effective media to teach writing to tenth grade students of SMA Negeri 2 Wonogiri.Keyword: Teaching media, Toondoo, Creativity, Picture Series, Quasi-experiment, Writing Skill.
Evolutionary algorithm based adaptive navigation in information retrieval interfaces

In computer interfaces in general, especially in information retrieval tasks, it is important to be able to quickly find and retrieve information. State of the art approach, used, for example, in search engines, is not effective as it introduces losses of meanings due to context to keywords back and forth translation. Authors argue it increases the time and reduces the accuracy of information retrieval compared to what it could be in the system that employs modern information retrieval and text mining methods while presenting results in an adaptive human- computer interface where system effectively learns what operator needs through iterative interaction. In current work, a combination of adaptive navigational interface and real time collaborative feedback analysis for documents relevance weighting is proposed as an viable alternative to prevailing "telegraphic" approach in information retrieval systems. Adaptive navigation is provided through a dynamic links panel controlled by an evolutionary algorithm. Documents relevance is initially established with standard information retrieval techniques and is further refined in real time through interaction of users with the system. Introduced concepts of multidimensional Knowledge Map and Weighted Point of Interest allow finding relevant documents and users with common interests through a trivial calculation. Browsing search approach, the ability of the algorithm to adapt navigation to users interests, collaborative refinement and the self-organising features of the system are the main factors making such architecture effective in various fields where non-structured knowledge shall be represented to the users.
Parametric Anatomical Modeling: a method for modeling the anatomical layout of neurons and their projections

Computational models of neural networks can be based on a variety of different parameters. These parameters include, for example, the 3d shape of neuron layers, the neurons' spatial projection patterns, spiking dynamics and neurotransmitter systems. While many well-developed approaches are available to model, for example, the spiking dynamics, there is a lack of approaches for modeling the anatomical layout of neurons and their projections. We present a new method, called Parametric Anatomical Modeling (PAM), to fill this gap. PAM can be used to derive network connectivities and conduction delays from anatomical data, such as the position and shape of the neuronal layers and the dendritic and axonal projection patterns. Within the PAM framework, several mapping techniques between layers can account for a large variety of connection properties between pre- and post-synaptic neuron layers. PAM is implemented as a Python tool and integrated in the 3d modeling software Blender. We demonstrate on a 3d model of the hippocampal formation how PAM can help reveal complex properties of the synaptic connectivity and conduction delays, properties that might be relevant to uncover the function of the hippocampus. Based on these analyses, two experimentally testable predictions arose: (i) the number of neurons and the spread of connections is heterogeneously distributed across the main anatomical axes, (ii) the distribution of connection lengths in CA3-CA1 differ qualitatively from those between DG-CA3 and CA3-CA3. Models created by PAM can also serve as an educational tool to visualize the 3d connectivity of brain regions. The low-dimensional, but yet biologically plausible, parameter space renders PAM suitable to analyse allometric and evolutionary factors in networks and to model the complexity of real networks with comparatively little effort.
Combining Local Binary Pattern and Local Phase Quantization for Face Recognition

Combining information contained in image Spatial and frequency domains can provide rich important clues not seen in either individual of these domains. This paper presents a method of face recognition based on local binary pattern(LBP) and local phase quantization(LPQ). The face image is divided into several regions, LBP operator is used to extract LBP feature in the spatial domain and LPQ operator is used to extract LPQ feature in the frequency domain. Then concatenated into an enhanced feature vector to be used as a face descriptor. The simulation experiments illustrate that this method has better recognition rate and more robust than single method on the YALE and AR face database.