nils-herrmann/clean_scirepeval huggingface dataset:
A novel fast search method to find disparity vectors in multiview video coding
Venue: Multim. Tools Appl.
Year: 2021

In multiview/3D video, the amount of data to be transmitted to the decoder increases proportionally with the number of cameras. One way to efficiently compress such video is to use Multiview Video Coding (MVC) which simultaneously reduces temporal and spatial redundancy within the same view and among multiple views. But, existing disparity estimation methods used to extract the redundancy among views are typically too computationally complex. In this paper, we propose a method for fast disparity estimation in multiview/3D video. First, for each block of the predicted frame, the view dependent geometry and the depth information are used to find the corresponding block in the reference views. Then, a fast and greedy search method is proposed to search the surrounding areas to find the most similar block. Simulation results show that our proposed method achieves better bitrate and quality with much lower computational complexity compared to state of the art methods.
Dynamic 3D facial expression modeling using Laplacian smooth and multi scale mesh matching
Venue: The Visual Computer
Year: 2014

We propose a novel algorithm for the high resolution modeling of dynamic 3D facial expressions from a sequence of unstructured face point clouds captured at video rate. The algorithm can reconstruct not only the global facial deformations caused by muscular movements, but also the expressional details generated by local skin deformations. Our algorithm consists of two parts: Extraction of expressional details and Reconstruction of expressions. In the extraction part, we extract the subtle expressional details such as wrinkles and folds from each point cloud with Laplacian smooth operator. In the reconstruction part, we use a multi scale deformable mesh model to match each point cloud to reconstruct time varying expressions. In each matching, we first use the low scale mesh to match the global deformations of point cloud obtained after filtering out the expressional details, and then use the high scale mesh to match the extracted expressional details. Comparing to many existing non rigid ICP based algorithms that match directly the mesh model to the entire point cloud, our algorithm overcomes the probable large errors occurred where the local sharp deformations are matched since it extracts the expressional details for separate matching, therefore, our algorithm can produce a high resolution dynamic model reflecting time varying expressions. Additionally, utilization of multi scale mesh model makes our algorithm achieve high speed because it decreases iterative optimizations in matching. Experiments demonstrate the efficiency of our algorithm.
Cyanobacterial metabolites as promising drug leads against the Mpro and PLpro of SARS CoV 2: an in silico analysis
Venue: Journal of biomolecular structure dynamics
Year: 2020

A novel severe acute respiratory syndrome coronavirus (SARS CoV 2) has emerged as the causative agent behind the coronavirus disease 2019 (COVID 19) pandemic. Treatment efforts have been severely impeded due to the lack of specific effective antiviral drugs for the treatment of COVID associated pathologies. In the present research endeavour the inhibitory prospects of cyanobacterial metabolites were assessed at the active binding pockets of the two vital SARS CoV 2 proteases namely, main protease (Mpro) and the papain like protease (PLpro) that proteolytically process viral polyproteins and facilitate viral replication, employing an in silico molecular interaction based approach. It was evident from our analysis based on the binding energy scores that the metabolites cylindrospermopsin, deoxycylindrospermopsin, carrageenan, cryptophycin 52, eucapsitrione, tjipanazole, tolyporphin and apratoxin A exhibited promising inhibitory potential against the SARS CoV 2 Mpro. The compounds cryptophycin 1, cryptophycin 52 and deoxycylindrospermopsin were observed to display encouraging binding energy scores with the PLpro of SARS CoV 2. Subsequent estimation of physicochemical properties and potential toxicity of the metabolites followed by robust molecular dynamics simulations and analysis of MM PBSA energy scoring function established deoxycylindrospermopsin as the most promising inhibitory candidate against both SARS CoV 2 proteases. Present research findings bestow ample scopes to further exploit the potential of deoxycylindrospermopsin as a successful inhibitor of SARS CoV 2 in vitro and in vivo and pave the foundation for the development of novel effective therapeutics against COVID 19. Communicated by Ramaswamy H. Sarma
Posture Evaluation of the Automotive Maintenance Workers: A Case Study
Year: 2020

Automotive maintenance workers or commonly known as mechanics are exposed to numerous work stressors while performing their works. Maintenance activities required worker to be in different type of working posture, which mostly awkward posture and in high risk to developed musculoskeletal disorder. Statistics data disclosed that musculoskeletal disorder among maintenance workers is quite high compared to the other types of job sectors. Therefore, this study aims to examine the working posture amongst the mechanics based on their job activities. By using an ergonomic assessment sheet for posture known as Rapid Entire Body Assessment (REBA) ten common vehicle maintenance activities with ten mechanic in the maintenance jobs have been evaluated based on their feedback. The findings showed that, nine out of ten activities are in the high risk and necessary action need to be taken soon. Awkward posture such as excessive bending and twisting for several body parts with heavy parts as well as poor coupling design on the automotive parts or tools are the factors that influence the risk of injury in this maintenance activities. This study is very useful for automotive industry to evaluate their worker's condition in maintenance activities. This should be an employer and employee essential knowledge to understand that vehicle maintenance work will include awkward posture. Self awareness regarding the high risk in developing musculoskeletal disorder will reduce the worker chance to be in discomfort while working.
Detection of Breast Tumour Tissue Regions in Histopathological Images using Convolutional Neural Networks
Venue: 2018 IEEE International Conference on Image Processing, Applications and Systems (IPAS)
Year: 2018

Ductal carcinoma in situ (DCIS) is considered a pre invasive breast cancer and sometimes it can develop into an invasive ductal carcinoma. The analysis of histopathological images to detect tumour border of DCIS could provide important information for better diagnosis of patients. We present a deep learning based system to automatically identify DCIS in histopathological images. Specifically, a convolutional neural network (CNN) is first trained to predict labels of small patches cropped out of a histopathological whole slide image. Next, a sliding window method is used to produce a probability map of DCIS. Finally, given the probability map, a tumor border of DCIS is produced and delineated with the method of Marching Cubes to facilitate pathologists' review and assessment. Evaluation of cross validation demonstrates that the CNN model of GoogleNet performs well in histology image patch classification with an overall accuracy of (98.46+ 0.40) and identifies the DCIS tissue patches with a F1 score of (97.40+ 1.18) (mean+ variance) Moreover, around 95.6% tumour tissue within the enclosed tumour regions can be identified by our developed method. Finally, the goal of tumor border detection can be well achieved with a few post processing steps.
On Figures of Comparisons by Animal Words in Macbeth and Their Translation
Year: 2014

As the common images,animals are widely used in Shakespeare's dramas through the figures of comparisons such as simile and metaphor. As a result,they have drawn great attention from researchers on Shakespeare's dramas. Macbeth is an epitome of Shakespeare's dramas in which animals play an important part in constructing stories. The differences in English and Chinese cultural traditions and cultural mentality give rise to such a phenomenon: the same animal word in English and Chinese culture stands for different meanings. We should pay more attention to animal words when translating them from English to Chinese.
Cancer Immunotherapy Based on Mutation Specific CD4+ T Cells in a Patient with Epithelial Cancer
Venue: Science
Year: 2014

T Cells for Epithelial Tumors Malignant tumors harbor genetic alterations. Recently, adoptive T cell therapies have taken advantage of this: T cells specific for mutations in tumors are infused into patients to generate an antitumor immune response. Although therapeutic benefit has been seen for melanomas, effectiveness against more common epithelial tumors is unclear. Using whole exome sequencing, Tran et al. (p. 641) identified tumor infiltrating CD4+ T cells specific for a mutated antigen expressed by a tumor from a patient with metastatic cholangiocarcinoma. Infusion of this patient with an expanded population, mutation specific T cell resulted in tumor regression and stabilization of disease. T cells specific for a mutation expressed by tumor cells show antitumor activity in a patient with an epithelial cancer. Limited evidence exists that humans mount a mutation specific T cell response to epithelial cancers. We used a whole exomic sequencing based approach to demonstrate that tumor infiltrating lymphocytes (TIL) from a patient with metastatic cholangiocarcinoma contained CD4+ T helper 1 (TH1) cells recognizing a mutation in erbb2 interacting protein (ERBB2IP) expressed by the cancer. After adoptive transfer of TIL containing about 25% mutation specific polyfunctional TH1 cells, the patient achieved a decrease in target lesions with prolonged stabilization of disease. Upon disease progression, the patient was retreated with a >95% pure population of mutation reactive TH1 cells and again experienced tumor regression. These results provide evidence that a CD4+ T cell response against a mutated antigen can be harnessed to mediate regression of a metastatic epithelial cancer.
Electronic structure of dense solid oxygen from insulator to metal investigated with X ray Raman scattering
Venue: Proceedings of the National Academy of Sciences
Year: 2019

Oxygen diatomic molecules have lone pair electrons and magnetic moments. A high pressure phase called epsilon oxygen is considered stable in a wide pressure range. This material exhibits the transition to metal at ~100 GPa (1,000,000x atmospheric pressure) The change in the electronic structure involved in the transition under pressure is difficult to measure using conventional methods. In this study, the electronic structures of oxygen have been successfully measured with oxygen K edge X ray Raman scattering spectroscopy. We found a change in the spectra related to the metallization of oxygen. Another change in the electronic structure was also observed at ~40 GPa. This is likely related to the semimetallic transition. Electronic structures of dense solid oxygen have been investigated up to 140 GPa with oxygen K edge X ray Raman scattering spectroscopy with the help of ab initio calculations based on density functional theory with semilocal metageneralized gradient approximation and nonlocal van der Waals density functionals. The present study demonstrates that the transition energies (Pi* Sigma* and the continuum) increase with compression, and the slopes of the pressure dependences then change at 94 GPa. The change in the slopes indicates that the electronic structure changes at the metallic transition. The change in the Pi* and Sigma* bands implies metallic characteristics of dense solid oxygen not only in the crystal a b plane but also parallel to the c axis. The pressure evolution of the spectra also changes at ~40 GPa. The experimental results are qualitatively reproduced in the calculations, indicating that dense solid oxygen transforms from insulator to metal via the semimetallic transition.
Well Calibrated Regression Uncertainty in Medical Imaging with Deep Learning
Venue: MIDL
Year: 2020

The consideration of predictive uncertainty in medical imaging with deep learning is of utmost importance. We apply estimation of predictive uncertainty by variational Bayesian inference with Monte Carlo dropout to regression tasks and show why predictive uncertainty is systematically underestimated. We suggest to use s scaling with a single scalar value; a simple, yet effective calibration method for both aleatoric and epistemic uncertainty. The performance of our approach is evaluated on a variety of common medical regression data sets using different state of the art convolutional network architectures. In all experiments, s scaling is able to reliably recalibrate predictive uncertainty, surpassing more complex calibration methods. It is easy to implement and maintains the accuracy. Well calibrated uncertainty in regression allows robust rejection of unreliable predictions or detection of out of distribution samples. Our source code is available at: github.com/link withhelduntil acceptance
Sparse Polynomial Chaos Expansions: Literature Survey and Benchmark
Venue: SIAM/ASA J. Uncertain. Quantification
Year: 2021

Sparse polynomial chaos expansions are a popular surrogate modelling method that takes advantage of the properties of polynomial chaos expansions (PCE) the sparsity of effects principle, and powerful sparse regression solvers to approximate computer models with many input parameters, relying on only few model evaluations.Within the last decade, a large number of algorithms for the computation of sparse PCE have been published in the applied math and engineering literature. We present an extensive review of the existing methods and develop a framework to classify the algorithms. Furthermore, we conduct a benchmark on a selection of methods to identify which methods work best in practical applications. Comparing their accuracy on several benchmark models of varying dimension and complexity, we find that the choice of sparse regression solver and sampling scheme for the computation of a sparse PCE surrogate can make a significant difference, of up to several orders of magnitude in the resulting mean square error. Different methods seem to be superior in different regimes of model dimensionality and experimental design size.
What comes next? Practical theology, faithful presence, and prophetic witness
Year: 2020

The paper is a reflection on some aspects of and developments within practical theology within the UK and Ireland. It highlights some of the possibilities for Practical Theology alongside of the potential dangers. It recognises the diversity of practical theology but argues that this is a strength for the discipline. The paper develops the image of practical theology as a diamond that is shaped and formed as it encounters various pressures. It argues that amidst the anxieties and concerns of Post Brexit Britain and Ireland, Practical Theology needs to develop its identity as a prophetic witness called to the vocation of truth telling and critical thinking, public theology, hospitality, and interdisciplinary friendship. BIAPT has the potential to be central to facilitating this prophetic process. It is a place where we are shaped and formed and where the diamond of Practical Theology finds cohesion and clarity.
Bottom Up and Top Down Attention
Venue: The Neuroscientist a review journal bringing neurobiology, neurology and psychiatry
Year: 2014

The brain is limited in its capacity to process all sensory stimuli present in the physical world at any point in time and relies instead on the cognitive process of attention to focus neural resources according to the contingencies of the moment. Attention can be categorized into two distinct functions: bottom up attention, referring to attentional guidance purely by externally driven factors to stimuli that are salient because of their inherent properties relative to the background; and top down attention, referring to internal guidance of attention based on prior knowledge, willful plans, and current goals. Over the past few years, insights on the neural circuits and mechanisms of bottom up and top down attention have been gained through neurophysiological experiments. Attention affects the mean neuronal firing rate as well as its variability and correlation across neurons. Although distinct processes mediate the guidance of attention based on bottom up and top down factors, a common neural apparatus, the frontoparietal network, is essential in both types of attentional processes.
Double moire with a twist: super moire in encapsulated graphene.
Venue: Nano letters
Year: 2020

A periodic spatial modulation, as created by a moire pattern, has been extensively studied with the view to engineer and tune the properties of graphene. Graphene encapsulated by hexagonal boron nitride (hBN) when slightly misaligned with the top and bottom hBN layers experiences two interfering moire patterns, resulting in a so called super moire (SM) This leads to a lattice and electronic spectrum reconstruction. A geometrical construction of the non relaxed SM patterns allows us to indicate qualitatively the induced changes in the electronic properties and to locate the SM features in the density of states and in the conductivity. To emphasize the effect of lattice relaxation, we report band gaps at all Dirac like points in the hole doped part of the reconstructed spectrum, which are expected to be enhanced when including interaction effects. Our result is able to distinguish effects due to lattice relaxation and due to the interfering SM and provides a clear picture on the origin of recently experimentally observed effects in such trilayer heterostuctures.
Introduction History of Dairy Farming
Year: 2011

Commercial dairy farms evolved at the end of the Industrial Revolution. Growth was spurred by the demand for cheese and butter in urban areas. Early dairy farms were small in terms of cow numbers and utilized family labor for milking, feeding, and feed harvesting. Mechanization, led by the development of the milking machine, was the first type of innovation that led to increased farm size. Improvements in transportation, rural electrification, which enabled on farm cooling of milk, and pasteurization increased the demand for milk and dairy products prior to 1950. The sciences of genetics, nutrition, engineering, medicine, and physiology allowed cows to be managed in groups and expanded the geographic distribution of dairy farming. Computers and information technology enhanced the ability of dairy farm managers to make informed decisions on cow health and productivity, feeding practices, and herd replacements. Automation shows promise as the next wave of innovation for improving the care and welfare of cows as illustrated by the use of robots replacing human labor for milk harvesting. Science and technology have made dramatic improvements in the production capacity of the cow. Going forward, innovation at the dairy farm will continue to improve the efficiency of milk production in an environmentally friendly manner and by expanding the geographic distribution of dairy farms into Asia and Africa.
Liquid love? Dating apps, sex, relationships and the digital transformation of intimacy
Year: 2017

In Liquid Love Zygmunt Bauman argued that the solidity and security once provided by life long partnerships has been 'liquefied' by rampant individualisation and technological change. He believes internet dating is symptomatic of social and technological change that transforms modern courtship into a type of commodified game. This article explores the experiences of users of digital dating and hook up applications (or 'apps' in order to assess the extent to which a digital transformation of intimacy might be under way. It examines the different affordances provided by dating apps, and whether users feel the technology has influenced their sexual practices and views on long term relationships, monogamy and other romantic ideals. This study shows that dating apps are intermediaries through which individuals engage in strategic performances in pursuit of love, sex and intimacy. Ultimately, this article contends that some accounts of dating apps and modern romantic practices are too pessimistic, and downplay the positives of 'networked intimacy'
Meteorin like (Metrnl) adipomyokine improves glucose tolerance in type 2 diabetes via AMPK pathway
Year: 2018

Meteorin like (metrnl) is a recently identified adipomyokine that has beneficial effects on glucose metabolism. However, its underlying mechanism of action is not completely understood. In this study, we have shown that a level of metrnl increase in vitro under electrical pulse stimulation (EPS) and in vivo in exercise mice, suggesting that metrnl is an exercise induced myokine. In addition, metrnl increases glucose uptake through the calcium dependent AMPK pathway. Metrnl also increases the phosphorylation of HDAC5, a transcriptional repressor of GLUT4, in an AMPK dependent manner. Phosphorylated HDAC5 interacts with 14 3 3 proteins and sequesters them in the cytoplasm, resulting in the activation of GLUT4 transcription. The intraperitoneal injection of recombinant metrnl improves glucose tolerance in mice with high fat induced obesity or type 2 diabetes (db/db) but this is not seen in AMPK b1b2 muscle specific null mice (AMPK b1b2 MKO) In conclusion, we have demonstrated that metrnl induces beneficial effects on glucose metabolism via AMPK and is a promising therapeutic candidate for glucose related diseases such as type 2 diabetes.
Learning Graph Topology Representation with Attention Networks
Venue: 2020 IEEE International Conference on Visual Communications and Image Processing (VCIP)
Year: 2020

Contextualized neural language models have gained much attention in Information Retrieval (IR) with its ability to achieve better word understanding by capturing contextual structure on sentence level. However, to understand a document better, it is necessary to involve contextual structure from document level. Moreover, some words contributes more information to delivering the meaning of a document. Motivated by this, in this paper, we take the advantages of Graph Convolutional Networks (GCN) and Graph Attention Networks (GAN) to model global word relation structure of a document with attention mechanism to improve context aware document ranking. We propose to build a graph for a document to model the global contextual structure. The nodes and edges of the graph are constructed from contextual embeddings. We first apply graph convolution on the graph and then use attention networks to explore the influence of more informative words to obtain a new representation. This representation covers both local contextual and global structure information. The experimental results show that our method outperforms the state of the art contextual language models, which demonstrate that incorporating contextual structure is useful for improving document ranking.