nils-herrmann/clean_scirepeval huggingface dataset:
Progressive EM for Latent Tree Models and Hierarchical Topic Detection
Venue: AAAI

Hierarchical latent tree analysis (HLTA) is recently proposed as a new method for topic detection. It differs fundamentally from the LDA based methods in terms of topic definition, topic document relationship, and learning method. It has been shown to discover significantly more coherent topics and better topic hierarchies. However, HLTA relies on the Expectation Maximization (EM) algorithm for parameter estimation and hence is not efficient enough to deal with large datasets. In this paper, we propose a method to drastically speed up HLTA using a technique inspired by recent advances in the moments method. Empirical experiments show that our method greatly improves the efficiency of HLTA. It is as efficient as the state of the art LDA based method for hierarchical topic detection and finds substantially better topics and topic hierarchies.
Clinical profile, treatment and survival outcomes of peadiatric germ cell tumours: A Pakistani perspective.
Venue: JPMA. The Journal of the Pakistan Medical Association

Germ Cell Tumours (GCTs) are rare tumours. Generally 80% are benign and 20% malignant with a bimodal age distribution. The retrospective study was conducted at Shaukat Khanum Cancer Hospital, Lahore, Pakistan, and comprised all paediatric patients below 18 years of age who received treatment for histology proven GCT from 2006 to 2014. Of the 207 patients, 98(42.3% were males and 109(52.7% were females. The most common GCT was yolk sac tumour in 90(43.5% children followed by mixed GCT in 40(19.3% and dysgerminoma in 34(16.4% Gonads were most commonly involved in 165(79.7% patients with metastasis in 24(11.6% at presentation and recurrence in 26(12.5% patients. Overall, 133(64.3% patients are well and followed up at regular intervals and 55(26.5% have been lost to follow up with an expected overall 5 year median survival of 45% Despite the distinct clinical profile of paediatric GCT, survival can be improved by early diagnosis, regimented treatment according to set guidelines, protocols and by improving follow up.
Choosing a monetary value of greenhouse gases in assessment tools: A comprehensive review

There is a societal need for using monetary estimates of social impacts of CO2 and other greenhouse gases in different assessment tools, such as cost benefit analysis and life cycle assessment. A number of estimates are available in the literature. Since these differ by several orders of magnitude, there is ambiguity and confusion about which to use. This review aims to give some guidance on this issue. The variation in carbon value estimates depends on several uncertain aspects which will remain uncertain including climate sensitivity, assumptions about future emissions, and decision makers' ethical standpoints. Hence, there is no single correct monetary value for CO2: it will depend on the ethical standpoint of the user. Due to this, estimates of social costs of CO2 emissions cannot be used for calculating an optimal emission level, although they can inform such assessments. It is suggested that marginal abatement cost values are used for emissions capped by binding targets in short term assessments, and that social cost of carbon values should be used for all other emissions. Benchmark principles for choosing a monetary carbon value are suggested along with associated estimates. Depending on the choices made with regard to ethical standpoints and assumptions about future emissions and climate sensitivity, estimates can be significantly higher than the ones typically used in assessment tools today. The estimates need continuous updating, and there is need for better understanding and communication around the limitations and uncertainties involved.
Automated Segmentation of Kidneys from MR Images in Patients with Autosomal Dominant Polycystic Kidney Disease.
Venue: Clinical journal of the American Society of Nephrology CJASN

Our study developed a fully automated method for segmentation and volumetric measurements of kidneys from magnetic resonance images in patients with autosomal dominant polycystic kidney disease and assessed the performance of the automated method with the reference manual segmentation method.  Study patients were selected from the Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease. At the enrollment of the Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease Study in 2000, patients with autosomal dominant polycystic kidney disease were between 15 and 46 years of age with relatively preserved GFRs. Our fully automated segmentation method was on the basis of a spatial prior probability map of the location of kidneys in abdominal magnetic resonance images and regional mapping with total variation regularization and propagated shape constraints that were formulated into a level set framework. T2 weighted magnetic resonance image sets of 120 kidneys were selected from 60 patients with autosomal dominant polycystic kidney disease and divided into the training and test datasets. The performance of the automated method in reference to the manual method was assessed by means of two metrics: Dice similarity coefficient and intraclass correlation coefficient of segmented kidney volume. The training and test sets were swapped for crossvalidation and reanalyzed.  Successful segmentation of kidneys was performed with the automated method in all test patients. The segmented kidney volumes ranged from 177.2 to 2634 ml (mean, 885.4+ 569.7 ml) The mean Dice similarity coefficient SD between the automated and manual methods was 0.88+ 0.08. The mean correlation coefficient between the two segmentation methods for the segmented volume measurements was 0.97 (P<0.001 for each crossvalidation set) The results from the crossvalidation sets were highly comparable.  We have developed a fully automated method for segmentation of kidneys from abdominal magnetic resonance images in patients with autosomal dominant polycystic kidney disease with varying kidney volumes. The performance of the automated method was in good agreement with that of manual method.
Automated segmentation of liver and liver cysts from bounded abdominal MR images in patients with autosomal dominant polycystic kidney disease.
Venue: Physics in medicine and biology

Liver and liver cyst volume measurements are important quantitative imaging biomarkers for assessment of disease progression in autosomal dominant polycystic kidney disease (ADPKD) and polycystic liver disease (PLD) To date, no study has presented automated segmentation and volumetric computation of liver and liver cysts in these populations. In this paper, we proposed an automated segmentation framework for liver and liver cysts from bounded abdominal MR images in patients with ADPKD. To model the shape and variations in ADPKD livers, the spatial prior probability map (SPPM) of liver location and the tissue prior probability maps (TPPMs) of liver parenchymal tissue intensity and cyst morphology were generated. Formulated within a three dimensional level set framework, the TPPMs successfully captured liver parenchymal tissues and cysts, while the SPPM globally constrained the initial surfaces of the liver into the desired boundary. Liver cysts were extracted by combined operations of the TPPMs, thresholding, and false positive reduction based on spatial prior knowledge of kidney cysts and distance map. With cross validation for the liver segmentation, the agreement between the radiology expert and the proposed method was 84% for shape congruence and 91% for volume measurement assessed by the intra class correlation coefficient (ICC) For the liver cyst segmentation, the agreement between the reference method and the proposed method was ICC 0.91 for cyst volumes and ICC 0.94 for cyst to liver volume.
A novel intuitionistic fuzzy set approach for segmentation of kidney MR images
Venue: 2016 IEEE Signal Processing in Medicine and Biology Symposium (SPMB)

This paper presents a novel algorithm, which uses intuitionistic fuzzy sets and rough set theory to segment the renal components in kidney MR images. A new membership function is proposed and then is used to obtain an intuitionistic fuzzy model of the image to compensate the inherent heterogeneity present among the different renal tissue classes. In addition, a new method, which uses Hamming distance is proposed to calculate the histon. The histon is then used to compute intuitionistic fuzzy roughness measure which yields optimum valley points for image segmentation. The proposed algorithm segments the kidney MR images into medulla, cortex, and blood vessels. The quantitative performance evaluation indicates better performance of the proposed algorithm over a competing technique.
Sketching Use Case Scenarios Based on Use Case Goals and Patterns
Venue: KKIO Software Engineering Conference

Use cases are a scenario based technique used to express functional requirements from the user perspective. They are often elicited using a top down approach by first identifying their goals and later documenting their scenarios. In the paper, we investigate the possibility of supporting analysts in progressing from definitions of use case goals to full documentation of their scenarios. We propose a semi automatic approach to generate use case scenarios based on use case patterns. The proposed approach is a result of an empirical analysis of 217 use cases from 12 projects. The analysis revealed that a notion of use case transaction could be used to organize use case patterns into a catalog of patterns. We have implemented a prototype tool called UC Sketch to illustrate the proposed idea. The acceptance of the proposed approach by its potential users was assessed through the use of Technology Acceptance Model.
Stakeholders perspectives on paper based and electronic clinical decision support systems in Malawi Africa

The objective of this paper is to explore the perceptions of key stakeholders involved and/or affected by existing paper based decision support guidelines (known as Community Case Management (CCM) and a proposed digitised mobile clinical decision support system (CDSS) of CCM in rural settings of Malawi, Africa. Data was collected using field notes and semi structured interviews with 17 key stakeholders (i.e. clinical, technical, development aid support (NGO) government and community health workers both in Malawi, Europe and USA) Stakeholders provide a rich insight into the variety of both perceived benefits and challenges of the existing guidelines and the proposed electronic CDSS. It was found that all stakeholders believe that the CDSS will improve adherence to guidelines and subsequently result in better care for children. It is further envisioned that the time needed for administration with the current paper based approach could be reduced using electronic, as opposed to manual, collation and sending of records. This paper acts to underpin the rationale and motivation for the development and rollout of an electronic CDSS to support community health workers in their assessment, classification and treatment of young children in rural settings in Malawi, Africa.
Comparison of ALIF vs. XLIF for L4/5 interbody fusion: pros, cons, and literature review.
Venue: Journal of spine surgery

The incidence of lumbar fusion for the treatment of various degenerative lumbar spine diseases has increased dramatically over the last twenty years. Many lumbar fusion techniques have been developed and popularized, each with its own advantages and disadvantages. Anterior lumbar interbody fusion (ALIF) initially introduced in the 1930's, has become a common and widely accepted technique for lumbar fusions over the last decade offering several advantages over standard posterior lumbar interbody fusion (PLIF) or transforaminal lumbar interbody fusion (TLIF) More recently, the lateral trans psoas approach termed extreme, direct or lateral lumbar interbody fusion (XLIF, DLIF, LLIF) is gaining widespread popularity. The aim of this paper is to compare the approaches, advantages and disadvantages of ALIF and XLIF for L4/5 interbody fusion based on relevant literature.
Assessment of the nutritional composition of quinoa (Chenopodium quinoa Willd.
Venue: Food chemistry

Quinoa (Chenopodium quinoa Willd. is an ancient crop which can play an important role for worldwide food security. The current review aimed at evaluating existing compositional data which were compiled according to international standards. A limited number of data were found that met the dataset quality criteria. In general, high variations in nutrient contents of quinoa were observed per 100g edible portion on fresh weight basis, for example: protein (9.1 15.7g) total fat (4.0 7.6g) and dietary fiber (8.8 14.1g) The variations of nutrient values among different varieties and among different data sources were considerable. The results show the nutritional potential of quinoa but they also demonstrate that more high quality analytical data of quinoa are needed, especially for minerals and vitamins.
La Literatura Filipina en Espanol Durante la era Barroca (Philippine Literature in Spanish during the Baroque Age)

This paper presents a comprehensive description of the Literature written in Spanish in the Philippine Archipelago from the 16th century to mid 18th century. Philippine literature has been divided in the 20th century by a political historiography that distinguishes between what is considered national from non national literature. Literary Criticism has not been able to build a holistic picture of Philippine Literature, as it has tended till now to create independent islands and fragmented traditions. The literature written in the Philippines during the Baroque period, together with its aesthetics, constitutes one of the most neglected of these traditions. This paper aims to enrich Philippine corpora recovering a huge heritage, composed of relaciones, poemas, loas, pasiones, sermones, doctrinas, and the first novels and narratives in the country. Being the product of an international society, the Baroque period exposes the genesis of the Philippines as exceptional culture within the global world. This is the first general study undertaken about this topic.  Historiography, Criticism, Baroque, Cronicas, Loas, Blancas de San Jose, Gaspar de San Agustin, Vicente Alemany
Re doped SnO2 oxides for efficient UV Vis to infrared photon conversion application to solar cells

Spectral conversion using lanthanide doped materials with excellent performances is a great challenging topic and of particular interest for photovoltaic. This work aims at functionalizing transparent conductive oxide materials with rare earth elements for photons conversion purpose without affecting transparency and transport properties of the TCO. The spectral conversion targeted in this thesis is of type "down" in other words, we aim at converting high energy UV photons into low energy visible or NIR photons useful to solar cells. For this purpose we investigated the doping process of SnO2 as a host material with different rare earths such as Nd, Tb, Pr, and Yb. To understand the insertion process and the optical activation of the rare earth, RE doped SnO2 nanoparticles (powders) have been synthesised by two chemical methods: co precipitation and sol gel. The results have shown an efficient insertion of the RE into the SnO2 structure with excellent emission properties. In view of application of RE doped SnOx thin films to solar cells, studies concerning NIR emitting RE have been conducted (Nd, Yb, and co doping with Yb and Nd) using sputtering. Several deposition parameters and post deposition treatments have been done in order to find the best chemical environment favourable to the RE emission. We have precisely identified the region of the UV light converted into NIR photons and proposed several energy transfer mechanisms occurring between the host SnOx and the REs. In case of co doping, a second spectral conversion process has been identified; visible photons can be efficiently converted into NIR photons through energy transfer from Nd3+ to Yb3+ ions. Finally, application of these conversion layers to solar cells such as CIGS and Si based have shown an improvement of the cells characteristics, among others the Field factor, the cell efficiency and the increase of the spectral response of the cell in the UV region, thanks to the conversion of the UV photons into NIR photons. The good electrical properties of the RE doped SnOx layers have been highlighted as well. We believe that these conversion layers will provide a step ahead towards better solar cells performances.
Near infrared downconversion in Y(1 x)YbxVO4 for sensitization of c Si solar cells

The material YVO4 provide suitable environment as host lattice for Yb3+ ion. The materials Y(1 x)YbxVO4were successfully synthesized by co precipitation method varying the concentration of Yb3+ ions from 0 mol% to 2.0 mol% and annealed at high temperature. It was characterize by powder X ray powder diffraction (XRD) and surface morphology was studied by scanning electronic microscope (SEM) The photoluminescence (PL) properties were studied by spectrophotometers in near infra red (NIR) and ultra violet visible (UV vis) region. The Yb3+ ion doped YVO4 phosphors that can convert a photon of UV region (318 nm) into two photons of NIR region (992 nm) Hence this phosphor could be used as a downconversion (DC) luminescent convertor in front of crystalline silicon solar cell (c Si) panels to reduce thermalization loss of the solar cell. The theoretical value of quantum efficiency (QE) was calculated from steady time decay measurement and the maximum efficiency approached up to 162.53% The Y(1 x)YbxVO4 can be potentiality used for betterment of photovoltaic (PV) technology. This result further indicates its potential application as a luminescence converter layer for enhancing solar cells performance.
Deep sparse rectifier neural networks for speech denoising
Venue: 2016 IEEE International Workshop on Acoustic Signal Enhancement (IWAENC)

Deep neural networks (DNNs) have been widely applied in speech recognition and enhancement. In this paper we present some experiments using deep rectifier neural networks for speech denoising. Rectified linear units (ReLUs) can make a sparse connection between hidden layers. We analyze the usage of regularization coefficient during training to encourage more sparseness. This method further improves the generalization ability of the DNN regression model in unseen noisy conditions. After pruning and retraining the sparse network, the computation and storage load can be largely reduced without degradation in performance, making it easier to deploy speech denoising DNNs on portable devices.
Facial Expression Recognition Using ROI KNN Deep Convolutional Neural Networks

Deep neural networks have been proved to be able to mine distributed representation of data including image, speech and text. By building two models of deep convolutional neural networks and deep sparse rectifier neural networks on facial expression dataset, we make contrastive evaluations in facial expression recognition system with deep neural networks. Additionally, combining region of interest (ROI) and K nearest neighbors (KNN) we propose a fast and simple improved method called "ROI KNN" for facial expression classification, which relieves the poor generalization of deep neural networks due to lacking of data and decreases the testing error rate apparently and generally. The proposed method also improves the robustness of deep learning in facial expression classification.
Build It, Break It, Fix It: Contesting Secure Development
Venue: CCS

Typical security contests focus on breaking or mitigating the impact of buggy systems. We present the Build it, Break it, Fix it (BIBIFI) contest, which aims to assess the ability to securely build software, not just break it. In BIBIFI, teams build specified software with the goal of maximizing correctness, performance, and security. The latter is tested when teams attempt to break other teams' submissions. Winners are chosen from among the best builders and the best breakers. BIBIFI was designed to be open ended teams can use any language, tool, process, etc. that they like. As such, contest outcomes shed light on factors that correlate with successfully building secure software and breaking insecure software. During 2015, we ran three contests involving a total of 116 teams and two different programming problems. Quantitative analysis from these contests found that the most efficient build it submissions used C/C+ but submissions coded in other statically typed languages were less likely to have a security flaw; build it teams with diverse programming language knowledge also produced more secure code. Shorter programs correlated with better scores. Break it teams that were also successful build it teams were significantly better at finding security bugs.
Cortical Bone Porosity: What Is It, Why Is It Important, and How Can We Detect It?
Venue: Current Osteoporosis Reports

There is growing recognition of the role of micro architecture in osteoporotic bone loss and fragility. This trend has been driven by advances in imaging technology, which have enabled a transition from measures of mass to micro architecture. Imaging trabecular bone has been a key research focus, but advances in resolution have also enabled the detection of cortical bone micro architecture, particularly the network of vascular canals, commonly referred to as 'cortical porosity. This review aims to provide an overview of what this level of porosity is, why it is important, and how it can be characterized by imaging. Moving beyond a 'trabeculocentric' view of bone loss holds the potential to improve diagnosis and monitoring of interventions. Furthermore, cortical porosity is intimately linked to the remodeling process, which underpins bone loss, and thus a larger potential exists to improve our fundamental understanding of bone health through imaging of both humans and animal models.
[The lateral dorsal transposition flap for closure of a palmar soft tissue defect of the proximal phalanx on the little finger after limited fasciectomy in recurrent Dupuytren's contracture]
Venue: Operative Orthopadie und Traumatologie

Closure of a palmar soft tissue defect of the proximal phalanx after limited fasciectomy in recurrent Dupuytren's contracture.  A palmar soft tissue defect between the distal flexion crease of the palm and the flexion crease of the proximal interphalangeal joint (PIP) after limited fasciectomy in Dupuytren's contracture. CONTRAINDICATIONS Scars at the lateral dorsal portion of the proximal phalanx (e.g. after burns) SURGICAL TECHNIQUE Modified incision after Bruner "mini Bruner" Removal of the involved fascial cord. If necessary, arthrolysis of the PIP. Raising the lateral dorsal transposition flap from distal to proximal and rotating it into the palmar soft tissue defect of the proximal phalanx. Closure of the donor site with a skin transplant.  Dorsal plaster of Paris with extended fingers and compressive dressing in the palm for 2 days. Afterwards static dorsal splint and daily physiotherapy.  Between 2002 and 2007, a total of 32 lateral dorsal transposition flaps in 30 patients with recurrent Dupuytren's disease of the little finger underwent surgery. In a retrospective study, 19 patients with 20 flaps were available for follow up evaluation after a mean of 6 years. All flaps had healed. The median flexion contracture of the metacarpophalangeal joint was 0deg (preoperatively, 20deg) and of the PIP 20deg (preoperatively, 85deg) according to Tubiana stage 1 (preoperatively, Tubiana stage 3) The median grip strength of both the operated and the contralateral hand was 39 kg. The DASH score averaged 11 points. Overall, 11 patients were very satisfied, 6 patients were satisfied, 1 patient was less satisfied, and 1 patient was unsatisfied.