Miracle-dz/UniFAR-FTUs huggingface dataset:
Silicon nanowires as enhancement-mode Schottky barrier field-effect transistors

Silicon nanowire field-effect transistors (SiNWFETs) have been fabricated with a highly simplified integration scheme to function as Schottky barrier transistors with excellent enhancement-mode characteristics and a high on/off current ratio ∼107. SiNWFETs show significant improvement in the thermal emission leakage (∼6 × 10−13 A µm−1) compared to reference FETs with a larger channel width (∼7 × 10−10 A µm−1). The drain current level depends substantially on the contact metal work function as determined by examining devices with different source/drain contacts of Ti (≈4.33 eV) and Cr (≈4.50 eV). The different conduction mechanisms for accumulation- and inversion-mode operation are discussed and compared with two-dimensional numerical simulation results.
Axiomatic analysis of aggregation methods for collective annotation

Crowdsourcing is an important tool, e.g., in computational linguistics and computer vision, to efficiently label large amounts of data using nonexpert annotators. The individual annotations collected need to be aggregated into a single collective annotation. The hope is that the quality of this collective annotation will be comparable to that of a traditionally sourced expert annotation. In practice, most scientists working with crowdsourcing methods use simple majority voting to aggregate their data, although some have also used probabilistic models and treated aggregation as a problem of maximum likelihood estimation. The observation that the aggregation step in a collective annotation exercise may be considered a problem of social choice has only been made very recently. Following up on this observation, we show that the axiomatic method, as practiced in social choice theory, can make a contribution to this important domain and we develop an axiomatic framework for collective annotation, focusing amongst other things on the notion of an annotator's bias. We complement our theoretical study with a discussion of a crowdsourcing experiment using data from dialogue modelling in computational linguistics.
Electrically Conductive Carbon Nanofiber Composites with High-Density Polyethylene and Glass Fibers

Carbon nanofibers are being investigated for incorporation into composites to improve mechanical, thermal, and electrical properties. The difficulties in making such composites are issues of dispersion of the nanofiber and wetting of the nanofibers by the matrix. The processing methods developed to date tend to be complex, involving multiple steps. This paper reports on a study to make electrically conductive composites with small volume fraction of vapor-grown carbon nanofibers (VGCF). The matrix is a high-density polyethylene (HDPE); the effect of adding glass fibers to this composite is also studied. Certain types of the VGCF fibers did not produce conductive composites with standard mixing techniques; however, VGCF nanofibers heat treated with a post-processing surface treatment produced conductive composites without extensive or vigorous dispersion techniques. The results indicate that surface treatments and dispersion methods are important factors in producing conductive composites. It is demonstrated that small volume fractions of nanofiber can be used to produce conductive composites without extensive processing steps.
Robust Adaptive Classifier Grids for Object Detection from Static Cameras

In this work we present a robust object detection system for static cameras, which is suitable for real-time applications. Thus, the system has to cope with changes of environmental conditions, which is realized by adaptive on-line learning a scene specific classifier. In particular, we apply the ideas of grid-based classification, where each image patch corresponds to one classifier. Thus, the com- plexity of the detection task is reduced and a more compact and thus more efficient representation can be applied. The main contribution of this paper is to introduce three learn- ing strategies to improve the performance of grid-based de- tectors: (a) pre-selecting features to assure a more efficient representation, (b) pre-training the positive representation, and (c) combining off-line and on-line learning. The exper- imental results on person and car detection show that these strategies significantly improve the overall performance of the detection system. In addition, a long-term experiment demonstrates that the proposed system is stable over time and can thus be applied for real-world tasks.
The Essential Role of Pin1 via NF-κB Signaling in Vascular Inflammation and Atherosclerosis in ApoE−/− Mice

Atherosclerosis, as a chronic inflammatory disease, is the major underlying cause of death worldwide. However, the mechanisms that underlie the inflammatory process are not completely understood. Prolyl-isomerase-1 (Pin1), as a unique peptidyl-prolyl isomerase, plays an important role in inflammation and endothelial dysfunction. Herein, we investigate whether Pin1 regulates vascular inflammation and atherosclerosis, and clarify its mechanisms in these processes. ApoE−/− mice were randomly given either juglone (0.3, 1 mg/kg, two times per week) or vehicle i.p. for 4 weeks. Compared with ApoE−/− mice, treatment by juglone resulted not only in markedly attenuated macrophage infiltration and atherosclerotic lesion area in a lipid-independent manner, but also in decreased expression of Pin1, vascular cell adhesion molecule-1 (VCAM-1), monocyte chemoattractant protein-1 (MCP-1), and NF-κB activity in aorta. Then, EA.hy926 cells were pretreated with juglone (6 μmol/L), Pin1 siRNA, NF-κB inhibitor, or vehicle prior to exposure to ox-LDL (50 μg/mL). It was observed that treatment with juglone or Pin1 siRNA suppressed expression of VCAM-1 in oxLDL-incubated EA.hy926 cells and decreased THP-1 cell adhesion to oxLDL-stimulated endothelial cells through the NF-κB signal pathway. Our findings indicate that Pin1 plays a vital role on the development of vascular inflammation and atherosclerosis.
Joint Data Detection and Channel Sounding for TDD Systems with Antenna Selection

Antenna subset selection is a cost-effective way of exploiting the spatial dimension by only using a subset of the available antennas and thereby reducing the number of RF chains. A simple but effective way of precoding signals in multiantenna OFDM systems using antenna subset selection is per-tone antenna selection, which can be applied to the chosen subset. In a TDD system, estimates of the strength of the antennas can easily be obtained from the uplink and then used for the downlink precoding. However if only a subset of the antennas is active, no information is available for the remaining antennas; this may result in poor downlink performance. In this paper we propose a simple modification to the uplink reception which allows channel sounding of inactive antennas; this will aid the per-tone selection process in the downlink. With simulations we show that this scheme outperforms other alternatives, such as switch-and-stay.
Fuzzy region competition-based auto-color-theme design for textile images

We propose a model to recolor textile images by different color themes. The model contains three phases. The first phase is to partition an input textile image into several homogeneous regions. The CIELab color mean of each region and a bias-field function are obtained from the segmentation results. The combination of the color mean values of all regions is considered as the color theme of the input image. The second phase is to retrieve the relevant color themes from a given dataset. The retrieved color themes preserve the color mood of the input image in the sense of the similarity measurement defined in the color mood space. In the third phase, we reconstruct new images with different appearances from the input image by using the retrieved color themes. The proposed method provides a powerful tool for designers to generate and search for all relevant color combinations related to a given theme. Numerical results indicate that our recolorization model performs well on complex textile design patterns.
A modified Unsharp Masking method using Particle Swarm Optimization

Unsharp Masking is an effective and simple method of contrast enhancement. This paper explores the Unsharp Mask using the Laplacian Operator. The proposed method optimizes the Laplacian coefficient, α, and the Unsharp Mask's gain by maximizing the information content. The optimization process used was Multi-objective Particle Swarm Optimization. The proposed method was applied on both a global and local scale. The results show that the proposed method outperforms the classic Unsharp Masking method. The method is intended for gray-level images but can be applied to color images in general.
Bringing Paxos Consensus in Multi-agent Systems

Reaching consensus has long been regarded as one of the most important problems in distributed systems. Being able to do so under failures is addressed by the Paxos family of algorithms which is able to guarantee safety, while probabilistically satisfying progress. The role-based approach of Paxos makes it an ideal candidate for implementation using autonomous agents which can dynamically assume their part(s). This paper aims to bring the basic Paxos fault-tolerant algorithm into the multi-agent world by describing a system architecture and implementation under the Jade platform, and presents valuable experimental results for convergence in unfavorable cases.
Protein Ligands to Hur Modulate Its Interaction with Target Mrnas in Vivo

AU-rich elements (AREs) present in the 3′ untranslated regions of many protooncogene, cytokine, and lymphokine messages target them for rapid degradation. HuR, a ubiquitously expressed member of the ELAV (embryonic lethal abnormal vision) family of RNA binding proteins, selectively binds AREs and stabilizes ARE-containing mRNAs in transiently transfected cells. Here, we identify four mammalian proteins that bind regions of HuR known to be essential for its ability to shuttle between the nucleus and the cytoplasm and to stabilize mRNA: SETα, SETβ, pp32, and acidic protein rich in leucine (APRIL). Three have been reported to be protein phosphatase 2A inhibitors. All four ligands contain long, acidic COOH-terminal tails, while pp32 and APRIL share a second motif: rev-like leucine-rich repeats in their NH2-terminal regions. We show that pp32 and APRIL are nucleocytoplasmic shuttling proteins that interact with the nuclear export factor CRM1 (chromosomal region maintenance protein 1). The inhibition of CRM1 by leptomycin B leads to the nuclear retention of pp32 and APRIL, their increased association with HuR, and an increase in HuR's association with nuclear poly(A)+ RNA. Furthermore, transcripts from the ARE-containing c-fos gene are selectively retained in the nucleus, while the cytoplasmic distribution of total poly(A)+ RNA is not altered. These data provide evidence that interaction of its ligands with HuR modulate HuR's ability to bind its target mRNAs in vivo and suggest that CRM1 is instrumental in the export of at least some cellular mRNAs under certain conditions. We discuss the possible role of these ligands upstream of HuR in pathways that govern the stability of ARE-containing mRNAs.
Transversal positioned (TRAP) 2D laser range finder configuration

This paper propose a new position configuration for 2D laser range finders (LRF) on mobile robots for simultaneous localization and mapping (SLAM) algorithms. In this study, a pair of 2D LRFs with 270°scanning range are replaced with exterior corresponding angles to the front left/right and rear right/left of the vehicle. This configuration is named as TRAnsversal Positioned (TRAP) and offers a 360°angle of field of view for the mobile robot without any blind region. This configuration ensures the AGV to identify anything inside the scanning area instantaneously. This study compares the TRAP configuration with the traditional configuration that the 2D LRF is positioned centred on the front and the rear for "G-Mapping" SLAM method. Comparison tests are achieved under ROS (Robot Operating System) environment and results shows that TRAP configuration achieves better performance over the traditional configuration and these results are given for benchmarking.
ImageCLEF 2017: Supervoxels and Co-occurrence for Tuberculosis CT Image Classification

The paper presents image description and classification methods which were used by United Institute of Informatics Problems (UIIP) group for tuberculosis image classification task. A method based on cooccurrence of adjacent supervoxels in 3D computed tomography (CT) images was used for subtask #1 which was dedicated to image-based recognition of multi-drug resistant tuberculosis. For subtask #2 which is dedicated to automated categorization of tuberculosis patients into one of five types of tuberculosis, extended multidimensional multi-sort co-occurrence matrices were used for describing the CT scans. Both two submitted runs were ranked 7th in both subtasks.
Type-Based Data Structure Verification Author information hidden for double-blind review

We present a refinement type-based approach for the static verification of complex data structure invariants. Our approach is based on the observation that complex data structures are often fashioned from two elements: recursion (e.g., lists and trees), and maps (e.g., arrays and hash tables). We introduce two novel type-based mechanisms targeted towards these elements: recursive refinements and polymorphic refinements. These mechanisms automate the challenging work of generalizing and instantiating rich universal invariants by piggybacking simple refinement predicates on top of types, and carefully dividing the labor of analysis between the type system and an SMT solver [6]. Further, the mechanisms permit the use of the abstract interpretation framework of liquid type inference [18] to automatically synthesize complex invariants from simple logical qualifiers, thereby almost completely automating the verification. We have implemented our approach in DSOLVE, which uses liquid types to verify OCAML programs. We present experiments that show that our type-based approach reduces the manual annotations required to verify complex properties like sortedness, balancedness, binary-search-ordering, and acyclicity by more than an order of magnitude.
Shape Descriptors for Maximally Stable Extremal Regions

This paper introduces an affine invariant shape descriptor for maximally stable extremal regions (MSER). Affine invariant feature descriptors are normally computed by sampling the original grey-scale image in an invariant frame defined from each detected feature, but we instead use only the shape of the detected MSER itself. This has the advantage that features can be reliably matched regardless of the appearance of the surroundings of the actual region. The descriptor is computed using the scale invariant feature transform (SIFT), with the resampled MSER binary mask as input. We also show that the original MSER detector can be modified to achieve better scale invariance by detecting MSERs in a scale pyramid. We make extensive comparisons of the proposed feature against a SIFT descriptor computed on grey-scale patches, and also explore the possibility of grouping the shape descriptors into pairs to incorporate more context. While the descriptor does not perform as well on planar scenes, we demonstrate various categories of full 3D scenes where it outperforms the SIFT descriptor computed on grey-scale patches. The shape descriptor is also shown to be more robust to changes in illumination. We show that a system can achieve the best performance under a range of imaging conditions by matching both the texture and shape descriptors.
Applicability of semi-supervised learning assumptions for gene ontology terms prediction

Gene Ontology (GO) is one of the most important resources in bioinformatics, aiming to provide a unified framework for the biological annotation of genes and proteins across all species. Predicting GO terms is an essential task for bioinformatics, but the number of available labelled proteins is in several cases insufficient for training reliable machine learning classifiers. Semi-supervised learning methods arise as a powerful solution that explodes the information contained in unlabelled data in order to improve the estimations of traditional supervised approaches. However, semi-supervised learning methods have to make strong assumptions about the nature of the training data and thus, the performance of the predictor is highly dependent on these assumptions. This paper presents an analysis of the applicability of semi-supervised learning assumptions over the specific task of GO terms prediction, focused on providing judgment elements that allow choosing the most suitable tools for specific GO terms. The results show that semi-supervised approaches significantly outperform the traditional supervised methods and that the highest performances are reached when applying the cluster assumption. Besides, it is experimentally demonstrated that cluster and manifold assumptions are complimentary to each other and an analysis of which GO terms can be more prone to be correctly predicted with each assumption, is provided.
Specificity and Topology of the Escherichia coli Xanthosine Permease, a Representative of the NHS Subfamily of the Major Facilitator Superfamily

ABSTRACT The specificity of XapB permease was compared with that of the known nucleoside transporters NupG and NupC. XapB-mediated xanthosine uptake is abolished by 2,4-dinitrophenol and exhibits saturation kinetics with an apparent Km of 136 μM. A 12-transmembrane-segment model was confirmed by translational fusions to alkaline phosphatase and the α fragment of β-galactosidase.
An Analysis of Machine Learning Intelligence

Deep neural networks (DNNs) have set state of the art results in many machine learning and NLP tasks. However, we do not have a strong understanding of what DNN models learn. In this paper, we examine learning in DNNs through analysis of their outputs. We compare DNN performance directly to a human population, and use characteristics of individual data points such as difficulty to see how well models perform on easy and hard examples. We investigate how training size and the incorporation of noise affect a DNN's ability to generalize and learn. Our experiments show that unlike traditional machine learning models (e.g., Naive Bayes, Decision Trees), DNNs exhibit human-like learning properties. As they are trained with more data, they are more able to distinguish between easy and difficult items, and performance on easy items improves at a higher rate than difficult items. We find that different DNN models exhibit different strengths in learning and are robust to noise in training data.
Deciphering Drift Time Measurements from Travelling Wave Ion Mobility Spectrometry-Mass Spectrometry Studies

Detailed knowledge of the tertiary and quaternary structure of proteins and protein complexes is of immense importance in understanding their functionality. Similarly, variations in the conformational states of proteins form the underlying mechanisms behind many biomolecular processes, numerous of which are disease-related. Thus, the availability of reliable and accurate biophysical techniques that can provide detailed information concerning these issues is of paramount importance. Ion mobility spectrometry (IMS) coupled to mass spectrometry (MS) offers a unique opportunity to separate multi-component biomolecular entities and to measure the molecular mass and collision cross-section of individual components in a single, rapid (≤ 2 min) experiment, providing 3D-architectural information directly. Here we report a method of calibrating a commercially available electrospray ionisation (ESI)-travelling wave ion mobility spectrometry (TWIMS)–mass spectrometer using known cross-sectional areas determined for a range of biomolecules by conventional IMS-MS. Using this method of calibration, we have analysed a range of proteins of differing mass and 3D architecture in their native conformations by ESI-TWIMS-MS and found that the cross-sectional areas measured in this way compare extremely favourably with cross-sectional areas calculated using an in-house computing method based on Protein Data Bank NMR-derived co-ordinates. This not only provides a high degree of confidence in the calibration method, but also suggests that the gas phase ESI-TWIMS-MS measurements relate well to solution-based measurements derived from other biophysical techniques. In order to determine which instrumental parameters affect the ESI-TWIMS-MS cross-sectional area calibration, a systematic study of the parameters used to optimise TWIMS drift time separations has been carried out, observing the effect each parameter has on drift times and IMS resolution. Finally, the ESI-TWIMS-MS cross-sectional area calibration has been applied to the analysis of the amyloidogenic protein β2-microglobulin and measurements for three co-populated conformational families, present under denaturing conditions, have been made: the folded, partially unfolded and unfolded states.
A Comparison of MT Methods for Closely Related Languages: a Case Study on Czech - Slovak Language Pair

This paper describes an experiment comparing results of machine translation between two closely related languages, Czech and Slovak. The comparison is performed by means of two MT systems, one representing rule-based approach, the other one representing statistical approach to the task. Both sets of results are manually evaluated by native speakers of the target language. The results are discussed both from the linguistic and quantitative points of view.
Estimation of Nonlinear Site Effects of Soil Profiles in Korea

In a nonlinear site response analysis which is performed in time domain, small strain damping is modeled as viscous damping through use of various forms of Rayleigh damping formulations. Small strain damping of soil is known to be independent of the loading frequency, but the viscous damping is greatly influenced by the loading frequency. The type of Rayleigh damping formulation has a pronounced influence on the dependence. This paper performs a series of nonlinear analyses to evaluate the degree of influence of the viscous damping formulation on Korean soil profiles. Analyses highlight the strong influence of the viscous damping formulation for soil profiles exceeding 30 m in thickness, commonly used in simplified Rayleigh damping formulation overestimating energy dissipation at high frequencies due to artificially introduced damping. When using the full Rayleigh damping formulation and carefully selecting the optimum modes, the artificial damping is greatly reduced. Results are further compared to equivalent linear analyses. The equivalent linear analyses can overestimate the peak ground acceleration even for shallow profiles less than 20 m in thickness.
Phosphorus segregation in 2.25Cr–1Mo steel

Abstract Phosphorus segregation to grain boundaries in a commercial grade 2.25Cr–1Mo steel subjected to a variety of heat treatments has been examined using X-ray spectroscopy in a field emission gun scanning transmission electron microscope. A number of points have been established. Segregation of phosphorus to grain boundaries is present after austenitising and quenching. Tempering for 2 h at 650°C enhances phosphorus segregation during the following aging processes at 520°C. A higher austenitisation temperature (larger grain size) makes subsequent segregation more intense. For specimens austenitised at 1100°C, the concentration of phosphorus segregated to grain boundaries steadily increases with aging time after tempering (from 0 to 210 h). For specimens austenitised at 900°C, the phosphorus concentration at grain boundaries first increases, reaches a maximum at an aging time of ∼ 210 h and then decreases with aging time (480 h).