nils-herrmann/clean_scirepeval huggingface dataset:
Text Relatedness Based on a Word Thesaurus

The computation of relatedness between two fragments of text in an automated manner requires taking into account a wide range of factors pertaining to the meaning the two fragments convey, and the pairwise relations between their words. Without doubt, a measure of relatedness between text segments must take into account both the lexical and the semantic relatedness between words. Such a measure that captures well both aspects of text relatedness may help in many tasks, such as text retrieval, classification and clustering. In this paper we present a new approach for measuring the semantic relatedness between words based on their implicit semantic links. The approach exploits only a word thesaurus in order to devise implicit semantic links between words. Based on this approach, we introduce Omiotis, a new measure of semantic relatedness between texts which capitalizes on the word-to-word semantic relatedness measure (SR) and extends it to measure the relatedness between texts. We gradually validate our method: we first evaluate the performance of the semantic relatedness measure between individual words, covering word-to-word similarity and relatedness, synonym identification and word analogy; then, we proceed with evaluating the performance of our method in measuring text-to-text semantic relatedness in two tasks, namely sentence-to-sentence similarity and paraphrase recognition. Experimental evaluation shows that the proposed method outperforms every lexicon-based method of semantic relatedness in the selected tasks and the used data sets, and competes well against corpus-based and hybrid approaches.
The inverted multi-index

A new data structure for efficient similarity search in very large dataseis of high-dimensional vectors is introduced. This structure called the inverted multi-index generalizes the inverted index idea by replacing the standard quantization within inverted indices with product quantization. For very similar retrieval complexity and preprocessing time, inverted multi-indices achieve a much denser subdivision of the search space compared to inverted indices, while retaining their memory efficiency. Our experiments with large dataseis of SIFT and GIST vectors demonstrate that because of the denser subdivision, inverted multi-indices are able to return much shorter candidate lists with higher recall. Augmented with a suitable reranking procedure, multi-indices were able to improve the speed of approximate nearest neighbor search on the dataset of 1 billion SIFT vectors by an order of magnitude compared to the best previously published systems, while achieving better recall and incurring only few percent of memory overhead.
Involvement of the V2 Vasopressin Receptor in Adaptation to Limited Water Supply

Mammals adapted to a great variety of habitats with different accessibility to water. In addition to changes in kidney morphology, e.g. the length of the loops of Henle, several hormone systems are involved in adaptation to limited water supply, among them the renal-neurohypophysial vasopressin/vasopressin receptor system. Comparison of over 80 mammalian V2 vasopressin receptor (V2R) orthologs revealed high structural and functional conservation of this key component involved in renal water reabsorption. Although many mammalian species have unlimited access to water there is no evidence for complete loss of V2R function indicating an essential role of V2R activity for survival even of those species. In contrast, several marsupial V2R orthologs show a significant increase in basal receptor activity. An increased vasopressin-independent V2R activity can be interpreted as a shift in the set point of the renal-neurohypophysial hormone circuit to realize sufficient water reabsorption already at low hormone levels. As found in other desert mammals arid-adapted marsupials show high urine osmolalities. The gain of basal V2R function in several marsupials may contribute to the increased urine concentration abilities and, therefore, provide an advantage to maintain water and electrolyte homeostasis under limited water supply conditions.
A Fast Two Dimensional Deblocking Filter for H.264/AVC Video Coding

H.264/AVC as the most recent video coding standard delivers significantly better performance compared to previous standards, supporting higher video quality over lower bit rate channels. The H.264 in-loop deblocking filter is one of the several complex techniques that have realized this superior coding quality. The deblocking filter is a computationally and data intensive tool resulting in increased execution time of both the encoding and decoding processes. In this paper and in order to reduce the deblocking complexity, we propose a new 2D deblocking filtering algorithm based on the existing 1D method of the H.264/AVC standard. Simulation results indicate that the proposed technique achieves a 40% speed improvement compared to the existing 1D H.264/AVC deblocking filter, while affecting the SNR by 0.15% in average
AUTOMATIC MUSIC TRANSCRIPTION

Music transcription is the process of creating a written score of music from an audio recording. Musicians and musicologists use transcription to better understand music that may not have a written form, from improvised jazz solos to traditional folk music. Automatic music transcription introduces signal-processing algorithms to extract pitch and rhythm information from recordings. This speeds up and automates the process of music transcription, which requires musical training and is very time consuming even for experts. This thesis explores the still unsolved problem of automatic music transcription through an in-depth analysis of the problem itself and an overview of different techniques to solve the hardest subtask of music transcription, multiple pitch estimation. It concludes with a close study of a typical multiple pitch estimation algorithm and highlights the challenges that remain unsolved.
Activation of the mammalian target of rapamycin (mTOR) is essential for oligodendrocyte differentiation.

Although both extrinsic and intrinsic factors have been identified that orchestrate the differentiation and maturation of oligodendrocytes, less is known about the intracellular signaling pathways that control the overall commitment to differentiate. Here, we provide evidence that activation of the mammalian target of rapamycin (mTOR) is essential for oligodendrocyte differentiation. Specifically, mTOR regulates oligodendrocyte differentiation at the late progenitor to immature oligodendrocyte transition as assessed by the expression of stage specific antigens and myelin proteins including MBP and PLP. Furthermore, phosphorylation of mTOR on Ser 2448 correlates with myelination in the subcortical white matter of the developing brain. We demonstrate that mTOR exerts its effects on oligodendrocyte differentiation through two distinct signaling complexes, mTORC1 and mTORC2, defined by the presence of the adaptor proteins raptor and rictor, respectively. Disrupting mTOR complex formation via siRNA mediated knockdown of raptor or rictor significantly reduced myelin protein expression in vitro. However, mTORC2 alone controlled myelin gene expression at the mRNA level, whereas mTORC1 influenced MBP expression via an alternative mechanism. In addition, investigation of mTORC1 and mTORC2 targets revealed differential phosphorylation during oligodendrocyte differentiation. In OPC-DRG cocultures, inhibiting mTOR potently abrogated oligodendrocyte differentiation and reduced numbers of myelin segments. These data support the hypothesis that mTOR regulates commitment to oligodendrocyte differentiation before myelination.
Finding a team of experts in social networks

Given a task T, a pool of individuals X with different skills, and a social network G that captures the compatibility among these individuals, we study the problem of finding X, a subset of X, to perform the task. We call this the TEAM FORMATION problem. We require that members of X' not only meet the skill requirements of the task, but can also work effectively together as a team. We measure effectiveness using the communication cost incurred by the subgraph in G that only involves X'. We study two variants of the problem for two different communication-cost functions, and show that both variants are NP-hard. We explore their connections with existing combinatorial problems and give novel algorithms for their solution. To the best of our knowledge, this is the first work to consider the TEAM FORMATION problem in the presence of a social network of individuals. Experiments on the DBLP dataset show that our framework works well in practice and gives useful and intuitive results.
Retrieval characteristics of the Bard Denali and Argon Option inferior vena cava filters.

The purpose of this study was to compare the retrieval characteristics of the Option Elite (Argon Medical, Plano, Tex) and Denali (Bard, Tempe, Ariz) retrievable inferior vena cava filters (IVCFs), two filters that share a similar conical design.



A single-center, retrospective study reviewed all Option and Denali IVCF removals during a 36-month period. Attempted retrievals were classified as advanced if the routine "snare and sheath" technique was initially unsuccessful despite multiple attempts or an alternative endovascular maneuver or access site was used. Patient and filter characteristics were documented.



In our study, 63 Option and 45 Denali IVCFs were retrieved, with an average dwell time of 128.73 and 99.3 days, respectively. Significantly higher median fluoroscopy times were experienced in retrieving the Option filter compared with the Denali filter (12.18 vs 6.85 minutes; P = .046). Use of adjunctive techniques was also higher in comparing the Option filter with the Denali filter (19.0% vs 8.7%; P = .079). No significant difference was noted between these groups in regard to gender, age, or history of malignant disease.



Option IVCF retrieval procedures required significantly longer retrieval fluoroscopy time compared with Denali IVCFs. Although procedure time was not analyzed in this study, as a surrogate, the increased fluoroscopy time may also have an impact on procedural direct costs and throughput.
Retrieval of clothing images based on relevance feedback with focus on collar designs

The content-based image retrieval methods are developed to help people find what they desire based on preferred images instead of linguistic information. This paper focuses on capturing the image features representing details of the collar designs, which is important for people to choose clothing. The quality of the feature extraction methods is important for the queries. This paper presents several new methods for the collar-design feature extraction. A prototype of clothing image retrieval system based on relevance feedback approach and optimum-path forest algorithm is also developed to improve the query results and allows users to find clothing image of more preferred design. A series of experiments are conducted to test the qualities of the feature extraction methods and validate the effectiveness and efficiency of the RF-OPF prototype from multiple aspects. The evaluation scores of initial query results are used to test the qualities of the feature extraction methods. The average scores of all RF steps, the average numbers of RF iterations taken before achieving desired results and the score transition of RF iterations are used to validate the effectiveness and efficiency of the proposed RF-OPF prototype.
MicroPlay: a networking framework for local multiplayer games

Smartphones are an ideal platform for local multiplayer games, thanks to their computational and networking capabilities as well as their popularity and portability. However, existing game engines do not exploit the locality of players to improve game latency. In this paper, we propose MicroPlay, a complete networking framework for local multiplayer mobile games. To the best of our knowledge, this is the first framework that exploits local connections between smartphones, and in particular, the broadcast nature of the wireless medium, to provide smooth, accurate rendering of all players with two desired properties. First, it performs direct-input rendering (i.e., without any inter- or extrapolation of game state) for all players; second, it provides very low game latency. We implement a MicroPlay prototype on Android phones, as well as an example multiplayer car racing game, called Racer, in order to demonstrate MicroPlay's capabilities. Our experiments show that cars can be rendered smoothly, without any prediction of state, and with only 20-30 ms game latency.
Geo-semantic segmentation

The availability of GIS (Geographical Information System) databases for many urban areas, provides a valuable source of information for improving the performance of many computer vision tasks. In this paper, we propose a method which leverages information acquired from GIS databases to perform semantic segmentation of the image alongside with geo-referencing each semantic segment with its address and geo-location. First, the image is segmented into a set of initial super-pixels. Then, by projecting the information from GIS databases, a set of priors are obtained about the approximate location of the semantic entities such as buildings and streets in the image plane. However, there are significant inaccuracies (misalignments) in the projections, mainly due to inaccurate GPS-tags and camera parameters. In order to address this misalignment issue, we perform data fusion such that it improves the segmentation and GIS projections accuracy simultaneously with an iterative approach. At each iteration, the projections are evaluated and weighted in terms of reliability, and then fused with the super-pixel segmentations. First segmentation is performed using random walks, based on the GIS projections. Then the global transformation which best aligns the projections to their corresponding semantic entities is computed and applied to the projections to further align them to the content of the image. The iterative approach continues until the projections and segments are well aligned.
Robust Manhattan Frame estimation from a single RGB-D image

This paper proposes a new framework for estimating the Manhattan Frame (MF) of an indoor scene from a single RGB-D image. Our technique formulates this problem as the estimation of a rotation matrix that best aligns the normals of the captured scene to a canonical world axes. By introducing sparsity constraints, our method can simultaneously estimate the scene MF, the surfaces in the scene that are best aligned to one of three coordinate axes, and the outlier surfaces that do not align with any of the axes. To test our approach, we contribute a new set of annotations to determine ground truth MFs in each image of the popular NYUv2 dataset. We use this new benchmark to experimentally demonstrate that our method is more accurate, faster, more reliable and more robust than the methods used in the literature. We further motivate our technique by showing how it can be used to address the RGB-D SLAM problem in indoor scenes by incorporating it into and improving the performance of a popular RGB-D SLAM method.
Phylogeographical patterns shed light on evolutionary process in South America.

The South American continent is composed of several biogeographical regions harbouring the highest biodiversity on the globe, encompassing five of the world's biodiversity 'hot spots'. Nonetheless, the patterns and processes responsible for shaping its astonishing species diversity are largely unknown. Here, we present a review of current South American phylogeographical knowledge based on published articles on this topic. An appraisal of the literature reveals emerging phylogeographical patterns in the biota of South America. The striking phylogeographical divergence observed among organism lineages in South American studies is suggestive of high levels of undocumented species diversity. The interplay between Pleistocene climatic oscillations and Pliocene/Miocene orogenic events has contributed to shaping the current diversity and distribution of modern lineages in both the tropical and temperate regions of South America. Although older divergence times were observed for a range of species, most herpetofauna underwent an intraspecific lineage split much earlier than other organisms. The geographical ranges of species associated with forest habitats were reduced mainly during glacial cycles, whereas species associated with open vegetation domains have shown variable responses to climatic oscillations. The results suggest a highly complex mosaic of phylogeographical patterns in South America. We suggest future research directions to promote a better understanding of the origin and maintenance of the South American biota.
Odd Graceful Labeling of the Revised Friendship Graphs

R. B. Gnanajothi, Topics in graph theory, Ph. D. thesis, Madurai Kamaraj University, India, 1991. E. M. Badr, On the Odd Gracefulness of Cyclic Snakes With Pendant Edges, International journal on applications of graph theory in wireless ad hoc networks and sensor networks (GRAPH-HOC) Vol. 4, No. 4, December 2012. E. M. Badr, M. I. Moussa & K. Kathiresan (2011): Crown graphs and subdivision of ladders are odd graceful, International Journal of Computer Mathematics, 88:17, 3570-3576. A. Rosa, On certain valuation of the vertices of a graph, Theory of Graphs (International Symposium, Rome, July 1966), Gordon and Breach, New York and Dunod Paris (1967) 349-355. A. Solairaju & P. Muruganantham, Even Vertex Gracefulness of Fan Graph,
Deep Unsupervised Clustering Using Mixture of Autoencoders

Unsupervised clustering is one of the most fundamental challenges in machine learning. A popular hypothesis is that data are generated from a union of low-dimensional nonlinear manifolds; thus an approach to clustering is identifying and separating these manifolds. In this paper, we present a novel approach to solve this problem by using a mixture of autoencoders. Our model consists of two parts: 1) a collection of autoencoders where each autoencoder learns the underlying manifold of a group of similar objects, and 2) a mixture assignment neural network, which takes the concatenated latent vectors from the autoencoders as input and infers the distribution over clusters. By jointly optimizing the two parts, we simultaneously assign data to clusters and learn the underlying manifolds of each cluster.
Q-methodology as a research and design tool for HCI

A "discount" version of Q-methodology for HCI, called "HCI-Q", can be used in iterative design cycles to explore, from the point of view of users and other stakeholders, what makes technologies personally significant. Initially, designers critically reflect on their own assumptions about how a design may affect social and individual behavior. Then, designers use these assumptions as stimuli to elicit other people's points of view. This process of critical self-reflection and evaluation helps the designer to assess the fit between a design and its intended social context of use. To demonstrate the utility of HCI-Q for research and design, we use HCI-Q to explore stakeholders' responses to a prototype Alternative and Augmentative Communication (AAC) application called Vid2Speech. We show that our adaptation of Q-methodology is useful for revealing the structure of consensus and conflict among stakeholder perspectives, helping to situate design within the context of relevant value tensions and norms.
On Bistatic Inverse Synthetic Aperture Radar

The use of multiple radar configurations can overcome some of the geometrical limitations that exist when obtaining radar images of a target using inverse synthetic aperture radar (ISAR) techniques. It is shown here how a particular bistatic configuration can produce three view angles and three ISAR images simultaneously. A new ISAR signal model is proposed and the applicability of employing existing monostatic ISAR techniques to bistatic configurations is analytically demonstrated. An analysis of the distortion introduced by the bistatic geometry to the ISAR image point spread function (PSF) is then carried out and the limits of the applicability of ISAR techniques (without the introduction of additional signal processing) are found and discussed. Simulations and proof of concept experimental data are also provided that support the theory.
Suppression of Metal Artifacts in CT Using a Reconstruction Procedure That Combines MAP and Projection Completion

Metal implants such as hip prostheses and dental fillings produce streak and star artifacts in the reconstructed computed tomography (CT) images. Due to these artifacts, the CT image may not be diagnostically usable. A new reconstruction procedure is proposed that reduces the streak artifacts and that might improve the diagnostic value of the CT images. The procedure starts with a maximum a posteriori (MAP) reconstruction using an iterative reconstruction algorithm and a multimodal prior. This produces an artifact-free constrained image. This constrained image is the basis for an image-based projection completion procedure. The algorithm was validated on simulations, phantom and patient data, and compared with other metal artifact reduction algorithms.
An in-depth characterisation of Bots and Humans on Twitter

Recent research has shown a substantial active presence of bots in online social networks (OSNs). In this paper we utilise our past work on studying bots (Stweeler) to comparatively analyse the usage and impact of bots and humans on Twitter, one of the largest OSNs in the world. We collect a large-scale Twitter dataset and define various metrics based on tweet metadata. We divide and filter the dataset in four popularity groups in terms of number of followers. Using a human annotation task we assign ‘bot’ and ‘human’ ground-truth labels to the dataset, and compare the annotations against an online bot detection tool for evaluation. We then ask a series of questions to discern important behavioural bot and human characteristics using metrics within and among four popularity groups. From the comparative analysis we draw important differences as well as surprising similarities between the two entities, thus paving the way for reliable classification of automated political infiltration, advertisement campaigns, and general bot detection.
Advanced Modulation Schemes for Short-Range Optical Communications

The performance of advanced modulation schemes for spectrally efficient data transmission is reviewed, targeting short-range intensity-modulated optical channels with direct detection. Hereby, the focus lies on the performance of multilevel pulse-amplitude modulation combined with electronic equalization and, as an alternative modulation scheme, discrete multitone. A comprehensive statistical analysis of clipping noise is presented and exact expressions for the performance of symmetrically clipped discrete multitone are derived. It is shown that the clipping noise is impulsive and obeys a generalized Laplace distribution. The bit-error probability due to clipping is studied in detail, and it is found that the impact of clipping noise is reduced for an increasing number of subchannels. Finally, the optical link margins of multilevel pulse-amplitude modulation in combination with electronic equalization and that of discrete multitone in combination with margin-adaptive bit loading are compared. It is found that even symmetrically clipped discrete multitone suffers from its large crest factor in the peak-power-limited channel and that, in many instances, pulse-amplitude modulation provides higher link margins for the same target bit-error probability.