Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving sensing coverage of wireless sensor networks by employing mobile robots

To provide proper coverage of their random deployment regions, wireless sensor networks (WSN) should employ abundant nodes. In this paper we correct such situations by employing some mobile robots as mobile nodes in WSN which can actively move to desired locations for repairing the broken networks. According to the pre-research work we know that the number of nodes employed by WSN is closely relevant to quality of service (QoS) in sensing coverage when the nodes are randomly deployed in the target region. A modified particle swarm optimization (PSO) named particle swarm genetic optimization (PSGO), which imports selection and mutation operators in PSO to overcome the premature fault of classical PSO, is proposed to redeploy the mobile robots according to the node density for repairing the sensing coverage hole after their initial random deployment. It is suggested by the simulated experiment results that the WSN employing the mobile robots can improve the QoS in sensing coverage than the stationary WSN by redeploying mobile robots according to the node density.
Calcium conductance in an identified cholinergic synaptic terminal in the central nervous system of the cockroach

SUMMARY Intracellular microelectrodes were used to study a cholinergic synapse between two identified neurones: the lateral filiform hair sensory neurone (LFHSN) and giant interneurone 3 (GI 3) in the terminal ganglion of the first-instar cockroach Periplaneta americana. The presynaptic cell, LFHSN, was impaled in a region of the axon which forms large numbers of output synapses. The sign and magnitude of the LFHSN spike afterpotential were shown to depend on [Ca 2+ ]o. ljumoir 1 tetrodotoxin (TTX) abolished LFHSN spikes but the addition of 0-1 mmoir 1 4-aminopyridine (4-AP) enabled regenerative depolarizations to be evoked which were followed by large EPSPs in GI3. Addition of 20mmoir
Crossed product C*-algebras by finite group actions with the projection free tracial Rokhlin property

In this paper we introduce an analog of the tracial Rokhlin property, called the {\emph {projection free tracial Rokhlin property}}, for $C^*$-algebras which may not have any nontrivial projections. Using this we show that if $A$ is an infinite dimensional stably finite simple unital $C^*$-algebra with stable rank one, with strict comparison of positive elements, with only finitely many extreme tracial states, and with the property that every 2-quasi-trace is a trace, and if $\alpha$ is an action of a finite group $G$ with the projection free tracial Rokhlin property, then the crossed product $C^*(G, A, \alpha)$ also has stable rank one (Except there is a mistake in Lemma 3.16, so this is no longer proven)
Toward Optimal Ordering of Prediction Tasks

In many practical applications, multiple interrelated tasks must be accomplished sequentially through user interaction. The ordering of the tasks may have a significant impact on the overall utility (or performance) of the systems; hence optimal ordering of tasks is desirable. However, manual specification of optimal ordering is often difficult when task dependencies are complex, and exhaustive search for the optimal order is computationally intractable when the number of tasks is large. We present the first attempt at solving the optimal task ordering problem by learning partial order preferences among tasks based on observed system behavior in context, and by reducing the order optimization problem to a well-known Linear Ordering Problem (LOP). For computational tractability of the LOP solution, we further use link analysis (HITS and PageRank) over a partial-order-preference graph as a heuristic approximation. These strategies allow us to find near-optimal solutions with efficient computation, scalable to large applications. We conducted a comparative evaluation of the proposed approach with two practical applications that involve computer-assisted trouble report generation and IT proposal annotation with heterogeneous classification labels (keywords, collaborators, customers, technical categories, etc.), and obtained highly encouraging results in both applications.
Solving a Special Type of Jigsaw Puzzles: Banknote Reconstruction From a Large Number of Fragments

In this paper, we propose a method to solve a special type of jigsaw puzzles, reconstructing banknotes from a large number of fragments based on fragments' images. Existing jigsaw puzzle assembly algorithms have difficulty solving this problem effectively. A main limitation of these methods is that they do not leverage the following important observations: 1) an intact banknote's image is known and thus can be used as prior information; 2) if two aligned fragments overlap each other, they must not be from a same banknote. Based on these two important observations, a three-step method is proposed to reconstruct banknotes from their fragments. Each fragment is first aligned to its original position on the banknote by a RANSAC method. After evaluating every two aligned fragments' relationships, all fragments are embedded into a lower dimensional space and then clustered into small groups using a modified agglomerative clustering method. Fragments in a same cluster are likely to be from a same banknote. Experiments on both synthetic and real data demonstrate the effectiveness of our proposed method.
Learning arm movements of target reaching for humanoid robot

The autonomous motor skill learning is crucial for the humanoid robot to adapt to various tasks in complex environments and develop human-like behaviors. In this paper a method of autonomously learning arm movements for target reaching is proposed. To model the dynamics of arm joint trajectories during reaching, the dynamical movement primitives (DMP) model is employed. Based on the DMP representation, reinforcement learning based methods are adopted to learn DMP shape and goal parameters, aiming at not only deriving suitable arm joint trajectories satisfying certain constraints such as energy-saving and collision-free, but also enabling the robot to find goal configurations without complicated inverse kinematics calculations. Furthermore, an adaptive exploration strategy is proposed, which accelerates and improves the arm movements learning. The robot experiments demonstrate the effectiveness of the proposed method.
Chapter 10 Bioelectronics for Amperometric Biosensors

The Discrete-to-Integrated Electronics group (D2In), at the University of Barcelona, in partnership with the Bioelectronics and Nanobioengineering Group (SICBIO), is researching Smart Self-Powered Bio-Electronic Systems. Our interest is focused on the development of custom built electronic solutions for bio-electronics applications, from discrete devices to Application-specific integrated circuit (ASIC) solutions.
Query by Browsing: A Visual Query Language Based on the Relational Model and the Desktop User Interface Paradigm

Query by Browsing (QBB) is a visual query language that combines concepts from the relational model and the ubiquitous desktop user interface paradigm. It is a relationally complete method with recursive query capabilities that allows the creation of complex intentional and extensional queries using a direct manipulation interface that resembles a file browser in a modern operating system. In this paper we introduce the QBB paradigm and describe the reasons behind its inception. We also briefly discuss Chiromancer, an implementation of the QBB paradigm on the Palm platform that is underway and which to our knowledge is the first relationally complete visual query interface for a handheld device 1 .
A scalable framework to quantitatively evaluate success factors of mobile learning systems

There has been an enormous increase in the use of mobile learning (m-learning) systems in many fields due to the tremendous advancement in information and communication technologies. Although, there are many frameworks that have been developed for identifying and categorising the different components of m-learning systems, most of them have some limitations, drawbacks, and no support for quantitative assessment for the success factors (global weights) of the system criteria. In this paper, a new scalable hierarchal framework is developed, which identifies and categorises all components that may affect the development and deployments of cost-effective m-learning. Furthermore, due to the hierarchal structure of the framework, any of the analytic hierarchy process techniques can be used to quantitatively estimate the success factors of the system criteria. In order to demonstrate the benefits and flexibility of the new framework, we develop an interactive software tool for computing success factors of the different system criteria. The tool is referred to as SFacts, and it is used to compute success factors for different sets of preferences.
Challenges for using social media for early detection of T2DM

Twitter and other social media data are utilized for a wide variety of applications such as marketing and stock market prediction. Each application and appropriate domain of social media text presents its own challenges and benefits. We discuss methods for detecting obesity, a risk factor for Type II Diabetes Mellitus (T2DM), from the language of food on Twitter on community data, the peculiarities of this data, and the development of individual-level data for this task.
Genetic algorithms: combining evolutionary and 'non'-evolutionary methods in tracking dynamic global optima

The ability to track dynamic functional optima is important in many practical tasks. Recent research in this area has concentrated on modifying evolutionary algorithms (EAs) by triggering changes in control parameters, ensuring population diversity, or remembering past solutions. A set of results are presented that favourably compare hill climbing with a genetic algorithm, and reasons for the results are suggested. A method is then introduced, Evolutionary Random Search (ERS), that combines crossover and hill climbing mutation in a novel manner. It is assessed against the GA and hill climbing tests, and the encouraging results are discussed.
Teaching innovation based on robot Karel auxiliary program design

This article introduces robot Karel programming as an assisted teaching method in program design courses. Robot Karel reduces the threshold of programming to enhance the degree of interest in the course. It allows students to get rid of the complexity of Program language learning, focusing on programming ideas. In teaching, this method has achieved good results through writing a program to solve the problem of Karel, so that the idea of programming can be understood, and C language program teaching can be achieved with high efficiency and high quality provided that the amount of hours needed are definite. Students are able to produce much better design work at the end of the school year.
Some illumination models for industrial applications of photometric stereo

Among the possible sources of error in 3D-reconstruction using the photometric stereo technique, lighting modeling is often neglected, though it can create a dramatic large-scale bias. In this paper, after recalling the physical definition of a primary Lambertian source (isotropic lightings), we show how to derive a lighting model for several real-world scenarios, including directional lightings, nearby sources and extended planar illuminants. Finally, we show how to calibrate general spatially-varying lightings within a plane, in the case where explicitly modeling the lightings would be tedious.
New Constructions of Quadratic Bent Functions in Polynomial Form

New quadratic bent functions in polynomial form are constructed in this paper. The constructions give new Boolean bent, generalized Boolean bent and p-ary bent functions. Based on Z4-valued quadratic forms, a simple method provides several new constructions of generalized Boolean bent functions. From these generalized Boolean bent functions a method is presented to transform them into Boolean bent and semi-bent functions. Moreover, many new p-ary bent functions can also be obtained by applying similar methods.
Small interfering RNA-mediated downregulation of β-catenin inhibits invasion and migration of colon cancer cells in vitro

Summary Background Abnormal regulation of Wnt/β-catenin signaling and subsequently increased β-catenin expression have been found to be involved in the proliferation and growth of colon cancer cells. Whether the down-regulation of β-catenin in colon cancer may result in compromised invasion and migration in vitro still remains to be determined. Material/Methods A human colon cancer cell line (LoVo cells) was transfected with small interfering RNA (siRNA) targeting β-catenin. RT-PCR, Western blot assay, flow cytometry, cell adhesion assay, scratch wound assay, and matrigel invasion assay were performed, and the correlation between cell invasion and migration and β-catenin expressions was analyzed. Results siRNA-mediated down-regulation of β-catenin elevated the E-cadherin expression but reduced the MMP-7 and CD44v6 expressions, which increased the adhesion between LoVo cells but decreased the adhesion of LoVo cells to fibronectin. Significant inhibition of cell invasion and migration was also observed following RNA interference with β-catenin siRNA. Conclusions siRNA-mediated downregulation of β-catenin could be valuable for defining gene expression and functional programs downstream of oncogenic β-catenin signals, which, in turn, may be helpful to isolate novel diagnostic markers, and for designing tumor-specific intervention at downstream targets of oncogenic β-catenin.
Identification and Estimation in Highway Procurement Auctions Under Unobserved Auction Heterogeneity

This paper proposes a semi-parametric method to uncover the distribution of bidders' private information in the market for highway procurement when unobserved auction heterogeneity is present. I derive sufficient conditions under which the model is identified and show that the estimation procedure produces uniformly consistent estimators of the distributions in question. The estimation procedure is applied to data from Michigan highway procurement auctions. I estimate that 75% of the variation in bidders' costs may be attributed to the factors known to all bidders and only 25% may be generated by private information. My results suggest that failing to account for unobserved auction heterogeneity may lead to overestimating uncertainty that bidders face when submitting their bids. As a result both inefficiency of the auction mechanism and mark-ups over the bidders' costs may be overestimated.
Three species of Amphicorina (Annelida, Sabellida, Sabellidae) from Japan, with descriptions of two new species

Abstract We describe two new species and redescribe one in the polychaete genus Amphicorina Claparède, 1864 (Sabellidae) from Hokkaido, Japan. Amphicorina ascidicola sp. n. differs from its 38 congeners chiefly in the reduction of the collar, but also in having three pairs of radioles, one pair of ventral radiolar appendages, a bifurcate ventral lobe on the anterior peristomial ring, six abdominal chaetigers, and a large anterior tooth on the abdominal uncini. Amphicorina ezoensis sp. n. has a crenulated collar, three pairs of radioles, and more than eight (12) abdominal chaetigers; Amphicorina ezoensis shares these character states with Amphicorina anneae (Rouse, 1994), Amphicorina eimeri (Langerhans, 1880), and Amphicorina persinosa (Ben-Eliahu, 1975), but differs from them in having two pairs of ventral radiolar appendages and a non-oblique collar. Amphicorina mobilis (Rouse, 1990) was previously known only from the type locality (New South Wales, Australia), but we identify our Japanese material as conspecific on the basis of morphological and molecular similarity.
The DYRK-family kinase Pom1 phosphorylates the F-BAR protein Cdc15 to prevent division at cell poles

In rod-shaped fission yeast cells, the DYRK-family kinase Pom1, which localizes at cell poles, inhibits the formation of the contractile actomyosin ring at these regions by phosphorylating the key ring component, F-BAR protein Cdc15, allowing the ring to slide toward the middle of the cell.
Joint Diagonalization on the Oblique Manifold for Independent Component Analysis

Several blind source separation algorithms obtain a separating matrix by computing the congruence transformation that "best" diagonalizes a collection of covariance matrices. Recent methods avoid a pre-whitening phase and directly attempt to compute a non-orthogonal diagonalizing congruence. However, since the magnitude of the sources is unknown, there is a fundamental indeterminacy on the norm of the rows of the separating matrix. We show how this indeterminacy can be taken into account by restricting the separating matrix to the oblique manifold. The geometry of this manifold is developed and a trust-region-based algorithm for non-orthogonal joint diagonalization is proposed
Big Data and Intelligence: Applications, Human Capital, and Education

The potential for big data to contribute to the US intelligence mission goes beyond bulk collection, social media and counterterrorism. Applications will speak to a range of issues of major concern to intelligence agencies, from military operations to climate change to cyber security. There are challenges too: procurement lags, data stovepiping, separating signal from noise, sources and methods, a range of normative issues, and central to managing these challenges, human capital. These potential applications and challenges are discussed and a closer look at what data scientists do in the Intelligence Community (IC) is offered. Effectively filling the ranks of the IC’s data science workforce will depend on the provision of well-trained data scientists from the higher education system. Program offerings at America’s top fifty universities will thus be surveyed (just a few years ago there were reportedly no degrees in data science). One Master’s program that has melded data science with intelligence is examined as well as a university big data research center focused on security and intelligence. This discussion goes a long way to clarify the prospective uses of data science in intelligence while probing perhaps the key challenge to optimal application of big data in the IC. Acknowledgements I would like to thank Stephen Coulthart for his generous input while developing this project. I'm also deeply appreciative of the feedback from the two anonymous reviewers. This article is available in Journal of Strategic Security: https://scholarcommons.usf. edu/jss/vol9/iss2/ 6
Evidence for Reionization at z ∼ 6: Detection of a Gunn-Peterson Trough in a z = 6.28 Quasar

We present moderate-resolution Keck spectroscopy of quasars at z = 5.82, 5.99, and 6.28, discovered by the Sloan Digital Sky Survey (SDSS). We find that the Lyα absorption in the spectra of these quasars evolves strongly with redshift. To z ∼ 5.7, the Lyα absorption evolves as expected from an extrapolation from lower redshifts. However, in the highest-redshift object, SDSSp J103027.10+052455.0 (z = 6.28), the average transmitted flux is 0.0038 ± 0.0026 times that of the continuum level over 8450 Å < λ < 8710 Å (5.95 < zabs < 6.16), consistent with zero flux. Thus the flux level drops by a factor of greater than 150 and is consistent with zero flux in the Lyα forest region immediately blueward of the Lyα emission line, compared with a drop by a factor of ∼10 at zabs ∼ 5.3. A similar break is seen at Lyβ; because of the decreased oscillator strength of this transition, this allows us to put a considerably stronger limit, τeff > 20, on the optical depth to Lyα absorption at z = 6. This is a clear detection of a complete Gunn-Peterson trough, caused by neutral hydrogen in the intergalactic medium. Even a small neutral hydrogen fraction in the intergalactic medium would result in an undetectable flux in the Lyα forest region. Therefore, the existence of the Gunn-Peterson trough by itself does not indicate that the quasar is observed prior to the reionization epoch. However, the fast evolution of the mean absorption in these high-redshift quasars suggests that the mean ionizing background along the line of sight to this quasar has declined significantly from z ∼ 5 to 6, and the universe is approaching the reionization epoch at z ∼ 6.
Brain Morphometry Using Diffusion Weighted MRI : Application to Schizophrenia

Loss of cortical gray matter is accompanied by a commensurate increase in the sulcal and intraventricular cerebrospinal fluid (CSF) volume. On diffusion weighted MRI, this would be reflected as a higher apparent diffusion coefficient (ADC) in affected brain regions. We propose voxelwise analysis of ADC maps as an indirect method for assessment of regional brain volume deficits. Here we demonstrate this approach, called ADC-based morphometry (ABM), by applying it to analysis of images from a group of 15 patients with schizophrenia and 15 age-matched healthy controls. ABM revealed widespread regional ADC increases in patients. Affected areas included the bilateral insular cortex, hippocampus, temporal lobe, and occipital areas. These results largely concur with previous findings of cortical volume deficits in schizophrenia.
Searching Annotated Collections : Applications

Despite the great interest in di erent forms of textual annotation (named entity extraction, semantic tagging, syntactic and semantic parsing, etc.), there is still no consensus about which search tasks can be improved with such annotations, and what search algorithms are required to implement e cient engines to solve these tasks. We de ne formally two retrieval tasks in annotated collections: annotated retrieval and element retrieval. We discuss their di erences and describe e cient indexing structures, and how they can be implemented in Lucene and MG4J, two open source retrieval engines. Finally, we give a technical overview of two element retrieval use cases.
Kids, You Make the Choice: Religious and Secular Socialization among Marginal Affiliates and Nonreligious Individuals

What approaches do parents or those who plan to have children employ to socialize their children about religious or secular identification, beliefs, and practices? In what ways do primary and secondary socialization agents interact in this process? How might cultural narratives shape or be shaped by primary and secondary socialization activities? I address these questions by drawing on sixty interviews with two groups of Canadians – those who identify with a Christian group but limit attendance to religious holidays and rites of passage (marginal affiliates), and those who do not identify with any religion and never attend religious services (nonreligious individuals). I found that marginal affiliates did or planned to expose their children to religious belief and practice, while nonreligious individuals were inclined to defer to their children. However, marginal affiliates and nonreligious individuals jointly maintained that children should have choice in this matter. Parents did not or planned not to impose religious or secular views on children. Further, one’s upbringing informed parental socialization strategies and tactics that were largely informed by prevailing Canadian cultural narratives.
An approach for energy optimization in wireless sensor networks

Wireless sensor networks (WSN) are lately an object of significant attention due to their unlimited potential and many areas of implementation. Many sensor network applications require prolonged network lifetime, scalability, and load balancing. Organizing sensor nodes in clusters is an effective technique for satisfying these goals. In this paper we present a study on the optimal number of cluster and the effective radius of the clusters in wireless sensor networks (WSN). Based on the results we investigate the energy dissipation in a single node and the whole network, and propose a scheme, which extends the network lifetime, due its lightweight architecture.