Miracle-dz/UniFAR-FTUs huggingface dataset:
Choice Overload and Height Ranking of Menus in Partially-Ordered Sets

When agents face incomplete information and their knowledge about the objects of choice is vague and imprecise, they tend to consider fewer choices and to process a smaller portion of the available information regarding their choices. This phenomenon is well-known as choice overload and is strictly related to the existence of a considerable amount of option-pairs that are not easily comparable. Thus, we use a finite partially-ordered set (poset) to model the subset of easily-comparable pairs within a set of options/items. The height ranking, a new ranking rule for menus, namely subposets of a finite poset, is then introduced and characterized. The height ranking rule ranks subsets of options in terms of the size of the longest chain that they include and is therefore meant to assess menus of available options in terms of the maximum number of distinct and easily-comparable alternative options that they offer.
Platelet-rich plasma in otolaryngology.

BACKGROUND Platelet-rich plasma is a novel material that is being used more frequently in many surgical specialties. METHODS A literature review on the current and potential uses of platelet-rich plasma in otolaryngology was performed. RESULTS There is limited evidence on the use of platelet-rich plasma in otolaryngology compared with other specialties: only 11 studies on various subspecialties (otology, rhinology and laryngology) were included in the final review. CONCLUSION Based on the limited number of studies, we cannot draw safe conclusions about the value of platelet-rich plasma in otolaryngology. Nevertheless, the available literature suggests that platelet-rich plasma holds promise for future research and may have a number of clinical applications.
Constellation Queries over Big Data

A geometrical pattern is a set of points with all pairwise distances (or, more generally, relative distances) specified. Finding matches to such patterns has applications to spatial data in seismic, astronomical, and transportation contexts. Finding geometric patterns is a challenging problem as the potential number of sets of elements that compose shapes is exponentially large in the size of the dataset and the pattern. In this paper, we propose algorithms to find patterns in large data applications. Our methods combine quadtrees, matrix multiplication, and bucket join processing to discover sets of points that match a geometric pattern within some additive factor on the pairwise distances. Our distributed experiments show that the choice of composition algorithm (matrix multiplication or nested loops) depends on the freedom introduced in the query geometry through the distance additive factor. Three clearly identified blocks of threshold values guide the choice of the best composition algorithm.
Single-Stage Isolated Electrolytic Capacitor-Less EV Onboard Charger With Power Decoupling

In this paper, a new single-stage single-phase isolated AC-DC converter derived from a differential boost AC-DC converter is proposed. This converter eliminates the need to use the bulky electrolytic capacitor from the system and at the same time provides DC charging by employing the AC Power Decoupling waveform control method, effectively addressing the power density and reliability related issues commonly associated with the bulky electrolytic capacitor. As half of the switches of this converter act as synchronous rectifier during half grid cycle they are inherently ZVS Turned On while the remaining switches achieve ZVS Turn On as they act as synchronous rectifier during other half grid cycle. However, all diodes at the secondary side achieve ZCS Turn Off during the entire line cycle. A conventional controller is implemented for output voltage regulation and PFC control whereas a power decoupling controller is added to compensate second harmonic ripple power. Besides, an interleaving technique is applied to provide high-frequency links for transformers' connection while at the same time increasing the power range and effectively reducing the size of the input filter. Finally, the operating principle of the converter is validated through simulation, and the experimental results are provided.
A New Approach for the Identification of Processed Pseudogenes

Processed pseudogenes are DNA sequences generated through reverse transcription (RT) and retrotransposition of mature mRNAs. These sequences are usually considered junk DNA, since in most cases they lack a suitable promoter and are no longer transcribed. Nonetheless, due to their origin, they represent a valuable source of information on the transcriptome, which becomes particularly interesting for organisms lacking large EST collections. Here, we describe REtrotransposed Gene EXPlorer (REGEXP), a new method for the systematic identification of retrotransposition events that, unlike existing approaches, does not rely on a priori knowledge of mRNA sequences. Using our pipeline, we were able to identify 2288 processed pseudogenes in the human genome, showing a good overlap with the ENSEMBL, VEGA, and pseudogene.org datasets. These pseudogenes could be traced back to 987 genes, mostly corresponding to already known genes. In many cases, we recovered the signature of additional exons, likely due to alternative splicing. Interestingly, some of our predictions did not match previously known or predicted genes, and we were able to validate most of them by RT-polymerase chain reaction (PCR). Similar results were obtained with the mouse genome. Our data show that the REGEXP method is capable of identifying processed pseudogenes and to predict most of the corresponding genes with high specificity. Therefore, it may represent a valuable integration to the current genome annotation pipelines.
The Weighted Averages Algorithm Revisited

The classic weighted averages (WA) algorithm for the evaluation of Sommerfeld-like integrals is reviewed and reappraised. As a result, a new version of the WA algorithm, called generalized WA, is introduced. The new version can be considered as a generalization of the well established Hölder and Cèsaro means, used to sum divergent series. Generalized WA exhibits a more compact formulation, devoid of iterative and recursive steps, and a wider range of applications. It is more robust, as it provides a unique formulation, valid for monotonic and oscillating functions. The implementation of the new version is easier and more economical in terms of basic operations. Preliminary numerical examples show that generalized WA also outperforms in terms of accuracy the classic WA algorithm, which is currently recognized as the most competitive algorithm to evaluate Sommerfeld integral tails.
Establishment of reference values for endocrine tests--part V: acromegaly.

BACKGROUND Plasma insulin-like growth factor 1 (IGF-1) and the response of growth hormone (GH) to oral glucose are frequently used in the evaluation of patients suspected of acromegaly. Because of the implementation of new assay methodology for GH and IGF-1, we have established the reference values for these tests, as well as for urinary GH excretion. METHODS From the general population, 50 subjects were recruited, equally distributed according to sex and age between 20 and 70 years. Two consecutive 24-hour urine samples were collected to determine urinary GH. Plasma IGF-1 was measured as well as the GH response during an oral glucose tolerance test (OGTT) with 100 g glucose. Basal plasma IGF-1 was also measured in 250 subjects recruited likewise from the general population who had participated in previous studies on reference values. RESULTS The following reference ranges were established: urinary GH < 5-46 microU/24 h; nadir GH after OGTT < or =1.5 mU/l for males and < or =2.0 mU/l for females. IGF-1 was divided into age groups: 20-30 years 8-61 nmol/l; 31-40 years 8-41 nmol/; 41-50 years 7-36 nmol/l; 51-60 years 5-37 nmol/l; and 61-70 years 7-27 nmol/l. CONCLUSION We have established reference values with state-of-the-art assay methodology for the diagnostic tests frequently used in the evaluation of patients suspected of acromegaly.
Who are More Influential Spreaders? Users with Broader Social Circle or with More Friends?

Social network plays an important role in information diffusion. Identifying the most influential spreaders can contribute to the prediction of the speed and scale of information dissemination, which is very valuable to business such as advertising delivering. In contrast to common belief that users who are highly connected are most important spreaders, this paper shows that user's social circle play a more important role in information diffusion. In this paper, we firstly define the diversity of a user's social circle, which means the dispersion extent of the user's friend distribution in the network. A method for measuring diversity and broadness of a user's social circle in network is also presented. Preliminary experiment demonstrates that user's social circle does matter in information diffusion. Moreover, a social circle weighted PageRank algorithm which takes both popularity (in-degree) and social circle broadness into account is proposed. Experiments on Susceptible-Infectious-Recovered (SIR) model show that our method in terms of identification of influential spreaders outperforms other methods such as in-degree and PageRank on two real social network datasets.
A novel method of topic detection and tracking for BBS

Topic Detection and Tracking (TDT) has been studied for years, but most existing research is oriented to news web pages. Compared to news web pages, texts in Bulletin Board System (BBS) are more complicated and filled with user participation. In this paper, we propose a novel method of TDT for BBS, which mainly includes: a representation posts selection procedure based on post quality ranking and an efficient topic clustering algorithm based on candidate topic set. Experiment results demonstrate that our method significantly improves the performance of TDT in BBS environment on both accuracy and time complexity.
Toward Better Mapping between Regulations and Operational Details of Enterprises Using Vocabularies and Semantic Similarity

Industry governance, risk, and compliance (GRC) solutions stand to gain from various analyses offered by formal compliance checking approaches. Such adoption is made difficult by the fact that most formal approaches assume that a mapping between concepts of regulations and models of operational specifics exists. We propose to use Semantics of Business Vocabularies and Rules along with similarity measures to create an explicit mapping between concepts of regulations and models of operational specifics of enterprises. We believe that this proposal takes a step toward adapting and leveraging formal compliance checking approaches in industry GRC solutions.
Energy-efficient Geographic Multicast Routing for Error-prone Wireless Sensor Networks

Most usage scenarios for ad hoc and wireless sensor networks (WSN) require some degree of one-to-many or many-to-many interactions. In particular, for the case of WSN there is a number of scenarios in which a node has to send the same data to multiple destinations. Given that sensor networks have very limited resources, multicasting is a very interesting approach to deliver the same data packet to multiple destinations while reducing the amount of bandwidth and power consumption. Furthermore, recent studies have shown that it is of paramount importance to take into account the error prune nature of the wireless links when designing energy-efficient routing protocols. In this paper, we extend our previously proposed protocol LEMA (localized energy-efficient multicast algorithm), to deal with the problems of the error prone WSN. Our simulation results show that for networks with enough density the protocol is able to outperform even well-known centralized heuristics such as minimum incremental power (MIP) and shortest path tree (SPT) based on energy
Diversity of Purple Phototrophic Bacteria, Inferred from pufM Gene, within Epilithic Biofilm in Tama River, Japan

The diversity of purple phototrophic bacteria in algae-dominated biofilm of a streambed in Tama River, Japan was investigated. Clone library analysis of the pufM gene encoding a subunit of the photochemical reaction center of purple bacteria detected 18 operational taxonomic units (OTUs) in several classes of Proteobacteria. Most of the OTUs showed less than 85% identity to the PufM amino acid sequences of known phototrophic bacteria. These results suggest that phylogenetically divergent and unknown purple phototrophic bacteria are present in the epilithic biofilm of the river.
Trace Inference, Curvature Consistency, and Curve Detection

An approach is described for curve inference that is based on curvature information. The inference procedure is divided into two stages: a trace inference stage, which is the subject of the present work, and a curve synthesis stage. It is shown that recovery of the trace of a curve requires estimating local models for the curve at the same time, and that tangent and curvature information are sufficient. These make it possible to specify powerful constraints between estimated tangents to a curve, in terms of a neighborhood relationship called cocircularity, and between curvature estimates, in terms of a curvature consistency relation. Because all curve information is quantized, special care must be taken to obtain accurate estimates of trace points, tangents, and curvatures. This issue is addressed specifically to the introduction of a smoothness constraint and a maximum curvature constraint. The procedure is applied to two types of images: artificial images designed to evaluate curvature and noise sensitivity, and natural images. >
A comprehensive study of batch construction strategies for recurrent neural networks in MXNet

In this work we compare different batch construction methods for mini-batch training of recurrent neural networks. While popular implementations like TensorFlow and MXNet suggest a bucketing approach to improve the parallelization capabilities of the recurrent training process, we propose a simple ordering strategy that arranges the training sequences in a stochastic alternatingly sorted way. We compare our method to sequence bucketing as well as various other batch construction strategies on the CHiME-4 noisy speech recognition corpus. The experiments show that our alternated sorting approach is able to compete both in training time and recognition performance while being conceptually simpler to implement.
Wireless communications in Olympic Games: R&I into the frame of the updated gatekeeping model

This paper explores the influence of digital technologies on media networks and in particular how they affect the traditional gatekeeping model. The wireless communications are the hot point of all digital technologies and their applications for the transmission of Olympic Games is the time milestone of the global creative industries every two and four years, in winder and summer Olympic Games. The authors argue that the R&I industries' role involved with media industries need to be reconsidered into the frame of the updated media gatekeeping model. To investigate this research question results are reported from a case study regarding the gatekeeping processes in the 2008 Olympic Games in Beijing and the next Olympics up to 2016. Results show the need for a new gatekeeping model that takes into consideration the impact of digital technologies, especially the wireless communications. Additionally, the factor of R&I into this new model could prevail new decision models about in vesting into innovation in global media industry, as the last has un impact on the media gatekeeping model itself.
A distributed proximal gradient descent method for tensor completion

In this paper, we propose a novel proximal method for CANDECOMP/PARAFAC (CP) decomposition to deal with the tensor completion problem. This approach is based on solving local optimization problems, rather than confronting the entire optimization problem at once. In addition, we propose two distributed algorithms for dealing with data of high dimensionality that scale up to tensors of dimension 108×108×108. We show that our proximal method outperforms the Stochastic Gradient Descent (SGD) for CP decomposition in terms of convergence accuracy by a factor of 2.8 and it can efficiently and effectively reconstruct a color image by observing only 10% of its entries. Experimental results show that our distributed methods are scalable in terms of dimensionality, factorization rank, number of machines and perform efficiently in both dense and sparse settings. The proposed distributed proximal approach outperforms existing distributed methods in terms of speed of convergence by a factor of two. Moreover, it can successfully recover a hyperspectral image, by observing 10% of its values.
Extra and Intracellular Synthesis of Nickel Oxide Nanoparticles Mediated by Dead Fungal Biomass

The use of dead biomass of the fungus Hypocrea lixii as a biological system is a new, effective and environmentally friendly bioprocess for the production and uptake of nickel oxide nanoparticles (NPs), which has become a promising field in nanobiotechnology. Dead biomass of the fungus was successfully used to convert nickel ions into nickel oxide NPs in aqueous solution. These NPs accumulated intracellularly and extracellularly on the cell wall surface through biosorption. The average size, morphology and location of the NPs were characterized by transmission electron microscopy, high-resolution transmission electron microscopy, scanning electron microscopy, and energy dispersive X-ray spectroscopy. The NPs were mainly spherical and extra and intracellular NPs had an average size of 3.8 nm and 1.25 nm, respectively. X-ray photoelectron spectroscopy analysis confirmed the formation of nickel oxide NPs. Infrared spectroscopy detected the presence of functional amide groups, which are probable involved in particle binding to the biomass. The production of the NPs by dead biomass was analyzed by determining physicochemical parameters and equilibrium concentrations. The present study opens new perspectives for the biosynthesis of nanomaterials, which could become a potential biosorbent for the removal of toxic metals from polluted sites.
Attack Classification Based on Data Mining Technique and Its Application for Reliable Medical Sensor Communication

Detecting network intrusion has been not only important but also difficult in the network security research area. In Medical Sensor Network(MSN), network intrusion is critical because the data delivered through network is directly related to patients’ lives. Traditional supervised learning techniques are not appropriate to detect anomalous behaviors and new attacks because of temporal changes in network intrusion patterns and characteristics in MSN. Therefore, unsupervised learning techniques such as SOM (Self-Organizing Map) are more appropriate for anomaly detection. In this paper, we propose a real-time intrusion detection system based on SOM that groups similar data and visualize their clusters. Our system labels the map produced by SOM using correlations between features. We experiments our system with KDD Cup 1999 data set because MSN data is not available yet. Our system yields the reasonable misclassification rates and takes 0.5 seconds to decide whether a behavior is normal or attack.
Evaluation of 3D registration reliability and speed - A comparison of ICP and NDT

To advance robotic science it is important to perform experiments that can be replicated by other researchers to compare different methods. However, these comparisons tend to be biased, since re-implementations of reference methods often lack thoroughness and do not include the hands-on experience obtained during the original development process. This paper presents a thorough comparison of 3D scan registration algorithms based on a 3D mapping field experiment, carried out by two research groups that are leading in the field of 3D robotic mapping. The iterative closest points algorithm (ICP) is compared to the normal distributions transform (NDT). We also present an improved version of NDT with a substantially larger valley of convergence than previously published versions.
Massive MIMO and Millimeter Wave for 5G Wireless HetNet: Potentials and Challenges

There have been active research activities worldwide in developing the next-generation 5G wireless network. The 5G network is expected to support significantly large amount of mobile data traffic and huge number of wireless connections, achieve better cost- and energy-efficiency as well as quality of service (QoS) in terms of communication delay, reliability and security. To this end, the 5G wireless network should exploit potential gains in different network dimensions including super dense and heterogeneous deployment of cells and massive antenna arrays (i.e., massive multiple input multiple output (MIMO) technologies) and utilization of higher frequencies, in particular millimeter wave (mmWave) frequencies. This article discusses potentials and challenges of the 5G heterogeneous wireless network (HetNet) which incorporates massive MIMO and mmWave technologies. We will first provide the typical requirements of the 5G wireless network. Then, the significance of massive MIMO and mmWave in engineering the future 5G HetNet is discussed in detail. Potential challenges associated with the design of such 5G HetNet are discussed. Finally, we provide some case studies, which illustrate the potential benefits of the considered technologies.
Phosphorylated olig1 localizes to the cytosol of oligodendrocytes and promotes membrane expansion and maturation

Oligodendroglial cells undergo rapid transcriptional and dynamic morphological transformation in order to effectively myelinate neuronal axons. Olig1, a basic helix‐loop‐helix transcription factor, functions to promote the transcription of myelin‐specific genes and promotes differentiation and (re)myelination. While the role for nuclear Olig1 is well established, the function for cytoplasmic Olig1 remains uncertain. We observe that translocation of Olig1 into the cytosol highly correlates with differentiation of oligodendrocytes both invivo and invitro. By enforcing expression of a nuclear‐specific form of Olig1 into OPCs in a null‐Olig1 background, we demonstrate that nuclear Olig1 is sufficient to facilitate MBP expression, but with greatly diminished membrane volume and area. We demonstrate that serine 138 in the helix‐loop‐helix domain of Olig1 is phosphorylated and that this form resides in the cytosol. Mutating serine 138 to alanine restricts Olig1 to the nucleus, facilitating MBP expression but limiting membrane expansion. However, a serine to aspartic acid mutation results in the cytoplasmic localization of Olig1 enhancing membrane expansion. Our results suggest a novel role for a phosphorylated cytosolic Olig1 in membrane expansion and maturation of oligodendrocytes. © 2012 Wiley Periodicals, Inc.
QUICKBIRD IMAGERY PROCESSING FOR ARCHAEOLOGICAL APPLICATIONS : PERFORMANCE EVALUATION FROM DATA FUSION ALGORITHMS

The application of data fusion techniques to very high resolution (VHR) satellite data can fruitfully improve the enhancement of archaeological marks and facilitate their detection. Nevertheless, the quantitative evaluation of the quality of the fused images is one the most crucial aspects in the context of data fusion. This issue is particularly relevant in the case of the identification of archaeological marks, because (i) data fusion application is a rather recent topic approached in the field of remote sensing of archaeology (Lasaponara and Masini, 2006, 20007); (ii) the criteria generally adopted for the data fusion evaluation can not fit the needs of remote sensing archaeology. This paper deals with the quantitative evaluation of data fusion algorithms in sharpening archaeological marks focusing on data fusion capability in (i) preserving spectral fidelity and (ii) sharpening spatial and textural