nils-herrmann/clean_scirepeval huggingface dataset:
PAC-optimal, Non-parametric Algorithms and Bounds for Exploration in Concurrent MDPs with Delayed Updates

PAC-optimal, Non-parametric Algorithms and Bounds for Exploration in Concurrent MDPs with Delayed Updates by Jason Pazis Department of Computer Science Duke University Date: Approved: Ronald Parr, Supervisor Vincent Conitzer George Konidaris Mauro Maggioni Peter Stone An abstract of a dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Computer Science in the Graduate School of Duke University 2015
Conversion rate optimization through evolutionary computation

Conversion optimization means designing a web interface so that as many users as possible take a desired action on it, such as register or purchase. Such design is usually done by hand, testing one change at a time through A/B testing, or a limited number of combinations through multivariate testing, making it possible to evaluate only a small fraction of designs in a vast design space. This paper describes Sentient Ascend, an automatic conversion optimization system that uses evolutionary optimization to create effective web interface designs. Ascend makes it possible to discover and utilize interactions between the design elements that are difficult to identify otherwise. Moreover, evaluation of design candidates is done in parallel online, i.e. with a large number of real users interacting with the system. A case study on an existing media site shows that significant improvements (i.e. over 43%) are possible beyond human design. Ascend can therefore be seen as an approach to massively multivariate conversion optimization, based on a massively parallel interactive evolution.
Granger Causality: Basic Theory and Application to Neuroscience

Multi-electrode neurophysiological recordings produce massive quantities of data. Multivariate time series analysis provides the basic framework for analyzing the patterns of neural interactions in these data. It has long been recognized that neural interactions are directional. Being able to assess the directionality of neuronal interactions is thus a highly desired capability for understanding the cooperative nature of neural computation. Research over the last few years has shown that Granger causality is a key technique to furnish this capability. The main goal of this article is to provide an expository introduction to the concept of Granger causality. Mathematical frameworks for both bivariate Granger causality and conditional Granger causality are developed in detail with particular emphasis on their spectral representations. The technique is demonstrated in numerical examples where the exact answers of causal influences are known. It is then applied to analyze multichannel local field potentials recorded from monkeys performing a visuomotor task. Our results are shown to be physiologically interpretable and yield new insights into the dynamical organization of large-scale oscillatory cortical networks.
Computer-Aided Software Engineering in a Distributed Workstation Environment

Computer-Aided Software Engineering environments are becoming essential for complex software projects, just as CAD systems have become essential for complex hardware projects. DSEE, the DOMAIN Software Engineering Environment, is a distributed, production quality, software development environment that runs on Apollo workstations. DSEE provides source code control, configuration management, release control, advice management, task management, and user-defined dependency tracking with automatic notification.
 DSEE incorporates some of the best ideas from existing systems. This paper describes DSEE, contrasts it other systems, and discusses some of the technical issues involved in the construction of a highly-reliable, safe, efficient, and distributed development environment.
PID, LQR and LQR-PID on a quadcopter platform

This paper aims to present a comparison between different controllers to be used in a dynamic model of a quadcopter platform. The controllers assumed in this work are an ITAE tuned PID, a classic LQR controller and a PID tuned with a LQR loop. The results were obtained through simulations for 10 different attitudes of the quadcopter, however, in this paper simulation results will be presented for the vertical attitude only (the remainder are analogous and were omitted for brevity).
The survival of direct composite restorations in the management of severe tooth wear including attrition and erosion: A prospective 8-year study.

Survival of directly placed composite to restore worn teeth has been reported in studies with small sample sizes, short observation periods and different materials. This study aimed to estimate survival for a hybrid composite placed by one clinician up to 8-years follow-up.



All patients were referred and recruited for a prospective observational cohort study. One composite was used: Spectrum(®) (DentsplyDeTrey). Most restorations were placed on the maxillary anterior teeth using a Dahl approach.



A total of 1010 direct composites were placed in 164 patients. Mean follow-up time was 33.8 months (s.d. 27.7). 71 of 1010 restorations failed during follow-up. The estimated failure rate in the first year was 5.4% (95% CI 3.7-7.0%). Time to failure was significantly greater in older subjects (p=0.005) and when a lack of posterior support was present (p=0.003). Bruxism and an increase in the occlusal vertical dimension were not associated with failure. The proportion of failures was greater in patients with a Class 3 or edge-to-edge incisal relationship than in Class 1 and Class 2 cases but this was not statistically significant. More failures occurred in the lower arch (9.6%) compared to the upper arch (6%) with the largest number of composites having been placed on the maxillary incisors (n=519).



The worn dentition presents a restorative challenge but composite is an appropriate restorative material.



This study shows that posterior occlusal support is necessary to optimise survival.
Dermoscopy patterns of halo nevi.

Halo nevi (HN) are benign melanocytic nevi surrounded by a depigmented area (halo). This study aims to evaluate the dermoscopic features of HN and their changes during digital dermoscopic follow-up and to investigate the frequency of the halo phenomenon in a series of melanomas.



In a retrospective study, digital dermoscopic images of HN from patients who attended the Pigmented Skin Lesions Clinic of the Department of Dermatology, Medical University of Graz, between October 1, 1997, and March 31, 2004, were reviewed and classified by dermoscopic morphologic criteria. For HN that were followed up with digital dermoscopy, the percentages of changes in the size of the nevus and halo components were calculated. In addition, digital dermoscopic images of histopathologically confirmed melanomas obtained from the same database were reviewed for the presence of an encircling halolike depigmentation. We classified 138 HN in 87 patients (mean age, 22.4 years). The most common dermoscopic structures were the globular and/or homogeneous patterns in more than 80% of HN. Follow-up of 33 HN revealed considerable size reduction of the nevus component, but this was not associated with significant structural changes. Of a total of 475 melanomas, only 2 revealed an encircling halo, but both displayed clear-cut melanoma-specific patterns according to dermoscopy.



Halo nevi exhibit the characteristic dermoscopic features of benign melanocytic nevi, represented by globular and/or homogeneous patterns that are typically observed in children and young adults. Halo nevi reveal considerable changes of area over time during digital dermoscopic follow-up, albeit their structural patterns remain unchanged. For this reason and because melanoma with halolike depigmentation, despite being rare, additionally exhibits melanoma-specific dermoscopic criteria, the role of digital dermoscopic follow-up in the diagnosis of HN is insignificant.
Comparison of Different Classification Techniques Using WEKA for Breast Cancer

The development of data-mining applications such as classification and clustering has shown the need for machine learning algorithms to be applied to large scale data. In this paper we present the comparison of different classification techniques using Waikato Environment for Knowledge Analysis or in short, WEKA.  The aim of this paper is to investigate the performance of different classification or clustering methods for a set of large data. The algorithm or methods tested are Bayes Network, Radial Basis Function, Pruned Tree, Single Conjunctive Rule Learner and Nearest Neighbors Algorithm. A fundamental review on the selected technique is presented for introduction purposes. The data breast cancer data with a total data of 6291 and a dimension of 699 rows and 9 columns will be used to test and justify the differences between the classification methods or algorithms. Subsequently, the classification technique that has the potential to significantly improve the common or conventional methods will be suggested for use in large scale data, bioinformatics or other general applications.  Machine Learning, Data Mining, WEKA, Classification, Bioinformatics.
SAP HANA database: data management for modern business applications

The SAP HANA database is positioned as the core of the SAP HANA Appliance to support complex business analytical processes in combination with transactionally consistent operational workloads. Within this paper, we outline the basic characteristics of the SAP HANA database, emphasizing the distinctive features that differentiate the SAP HANA database from other classical relational database management systems. On the technical side, the SAP HANA database consists of multiple data processing engines with a distributed query processing environment to provide the full spectrum of data processing -- from classical relational data supporting both row- and column-oriented physical representations in a hybrid engine, to graph and text processing for semi- and unstructured data management within the same system.
 From a more application-oriented perspective, we outline the specific support provided by the SAP HANA database of multiple domain-specific languages with a built-in set of natively implemented business functions. SQL -- as the lingua franca for relational database systems -- can no longer be considered to meet all requirements of modern applications, which demand the tight interaction with the data management layer. Therefore, the SAP HANA database permits the exchange of application semantics with the underlying data management platform that can be exploited to increase query expressiveness and to reduce the number of individual application-to-database round trips.
Spatiotemporal anomaly detection through visual analysis of geolocated Twitter messages

Analyzing message streams from social blogging services such as Twitter is a challenging task because of the vast number of documents that are produced daily. At the same time, the availability of geolocated, realtime, and manually created status updates are an invaluable data source for situational awareness scenarios. In this work we present an approach that allows for an interactive analysis of location-based microblog messages in realtime by means of scalable aggregation and geolocated text visualization. For this purpose, we use a novel cluster analysis approach and distinguish between local event reports and global media reaction to detect spatiotemporal anomalies automatically. A workbench allows the scalable visual examination and analysis of messages featuring perspective and semantic layers on a world map representation. Our novel techniques can be used by analysts to classify the presented event candidates and examine them on a global scale.
Effective missing data prediction for collaborative filtering

Memory-based collaborative filtering algorithms have been widely adopted in many popular recommender systems, although these approaches all suffer from data sparsity and poor prediction quality problems. Usually, the user-item matrix is quite sparse, which directly leads to inaccurate recommendations. This paper focuses the memory-based collaborative filtering problems on two crucial factors: (1) similarity computation between users or items and (2) missing data prediction algorithms. First, we use the enhanced Pearson Correlation Coefficient (PCC) algorithm by adding one parameter which overcomes the potential decrease of accuracy when computing the similarity of users or items. Second, we propose an effective missing data prediction algorithm, in which information of both users and items is taken into account. In this algorithm, we set the similarity threshold for users and items respectively, and the prediction algorithm will determine whether predicting the missing data or not. We also address how to predict the missing data by employing a combination of user and item information. Finally, empirical studies on dataset MovieLens have shown that our newly proposed method outperforms other state-of-the-art collaborative filtering algorithms and it is more robust against data sparsity.
Design methodology using inversion coefficient for low-voltage low-power CMOS voltage reference

This paper presents an analog design methodology, using the selection of inversion coefficient of MOS devices, to design low voltage and low-power (LVLP) CMOS voltage references. These circuits often work under subthreshold operation. Hence, there is a demand for analog design methods that optimize the sizing process of transistors working in weak and moderate inversion. The advantage of the presented method -- compared with the traditional approach to design circuits -- is the reduction of design cycle time and minimization of trial-and-error simulations, if the proposed equations are used. As a case study, a LVLP voltage reference based on subthreshold MOSFETs with supply voltage of 0.7 V was designed for 0.18-¼m CMOS technology.
Training Region-Based Object Detectors with Online Hard Example Mining

The field of object detection has made significant advances riding on the wave of region-based ConvNets, but their training procedure still includes many heuristics and hyperparameters that are costly to tune. We present a simple yet surprisingly effective online hard example mining (OHEM) algorithm for training region-based ConvNet detectors. Our motivation is the same as it has always been - detection datasets contain an overwhelming number of easy examples and a small number of hard examples. Automatic selection of these hard examples can make training more effective and efficient. OHEM is a simple and intuitive algorithm that eliminates several heuristics and hyperparameters in common use. But more importantly, it yields consistent and significant boosts in detection performance on benchmarks like PASCAL VOC 2007 and 2012. Its effectiveness increases as datasets become larger and more difficult, as demonstrated by the results on the MS COCO dataset. Moreover, combined with complementary advances in the field, OHEM leads to state-of-the-art results of 78.9% and 76.3% mAP on PASCAL VOC 2007 and 2012 respectively.
Decentralized Trust Management

We identify the trust management problem as a distinct and important component of security in network services. Aspects of the trust management problem include formulating security policies and security credentials, determining whether particular sets of credentials satisfy the relevant policies, and deferring trust to third parties. Existing systems that support security in networked applications, including X.509 and PGP, address only narrow subsets of the overall trust management problem and often do so in a manner that is appropriate to only one application. This paper presents a comprehensive approach to trust management, based on a simple language for specifying trusted actions and trust relationships. It also describes a prototype implementation of a new trust management system, called PolicyMaker, that will facilitate the development of security features in a wide range of network services.
Face identification from low resolution near-infrared images

Face identification from low quality and low resolution Near-Infrared (NIR) face images is a challenging problem. Since surveillance cameras typically acquire images at a large standoff distance, the effective resolution of the face is not large enough to identify the individuals. Moreover for a 24-hour surveillance footage, images in low light and at nighttime are acquired in NIR mode which makes the identification problem even more challenging. We propose an effective method using both hand-crafted and learned features for face identification of low resolution NIR images. We show that learned features contribute considerably to the performance of identification algorithm, and that using both feature level and score level fusion in a hierarchal approach gives good performance. The results demonstrate the effectiveness of the proposed approach on images which are of low quality, low resolution and acquired under challenging illumination conditions in near-infrared mode by surveillance cameras.
Experiences with CoralCDN: A Five-Year Operational View

CoralCDN is a self-organizing web content distribution network (CDN). Publishing through CoralCDN is as simple as making a small change to a URL’s hostname; a decentralized DNS layer transparently directs browsers to nearby participating cache nodes, which in turn cooperate to minimize load on the origin webserver. CoralCDN has been publicly available on PlanetLab since March 2004, accounting for the majority of its bandwidth and serving requests for several million users (client IPs) per day. This paper describes CoralCDN’s usage scenarios and a number of experiences drawn from its multi-year deployment. These lessons range from the specific to the general, touching on the Web (APIs, naming, and security), CDNs (robustness and resource management), and virtualized hosting (visibility and control). We identify design aspects and changes that helped CoralCDN succeed, yet also those that proved wrong for its current environment.
Machine Comprehension Based on Learning to Rank

Machine comprehension plays an essential role in NLP and has been widely explored with dataset like MCTest. However, this dataset is too simple and too small for learning true reasoning abilities. (Hermann et al., 2015) therefore release a large scale news article dataset and propose a deep LSTM reader system for machine comprehension. However, the training process is expensive. We therefore try feature-engineered approach with semantics on the new dataset to see how traditional machine learning technique and semantics can help with machine comprehension. Meanwhile, our proposed L2R reader system achieves good performance with efficiency and less training data.
Beyond Doctors: Future Health Prediction from Multimedia and Multimodal Observations

Although chronic diseases cannot be cured, they can be effectively controlled as long as we understand their progressions based on the current observational health records, which is often in the form of multimedia data. A large and growing body of literature has investigated the disease progression problem. However, far too little attention to date has been paid to jointly consider the following three observations of the chronic disease progression: 1) the health statuses at different time points are chronologically similar; 2) the future health statuses of each patient can be comprehensively revealed from the current multimedia and multimodal observations, such as visual scans, digital measurements and textual medical histories; and 3) the discriminative capabilities of different modalities vary significantly in accordance to specific diseases. In the light of these, we propose an adaptive multimodal multi-task learning model to co-regularize the modality agreement, temporal progression and discriminative capabilities of different modalities. We theoretically show that our proposed model is a linear system. Before training our model, we address the data missing problem via the matrix factorization approach. Extensive evaluations on a real-world Alzheimer's disease dataset well verify our proposed model. It should be noted that our model is also applicable to other chronic diseases.
The KeY Platform for Verification and Analysis of Java Programs

The KeY system offers a platform of software analysis tools for sequential Java. Foremost, this includes full functional verification against contracts written in the Java Modeling Language. But the approach is general enough to provide a basis for other methods and purposes: (i) complementary validation techniques to formal verification such as testing and debugging, (ii) methods that reduce the complexity of verification such as modularization and abstract interpretation, (iii) analyses of non-functional properties such as information flow security, and (iv) sound program transformation and code generation. We show that deductive technology that has been developed for full functional verification can be used as a basis and framework for other purposes than pure functional verification. We use the current release of the KeY system as an example to explain and prove this claim.
Competency-based medical education in two Sub-Saharan African medical schools

Relatively little has been written on Medical Education in Sub-Saharan Africa, although there are over 170 medical schools in the region. A number of initiatives have been started to support medical education in the region to improve quality and quantity of medical graduates. These initiatives have led to curricular changes in the region, one of which is the introduction of Competency-Based Medical Education (CBME).



This paper presents two medical schools, Makerere University College of Health Sciences and College of Medicine, University of Ibadan, which successfully implemented CBME. The processes of curriculum revision are described and common themes are highlighted. Both schools used similar processes in developing their CBME curricula, with early and significant stakeholder involvement. Competencies were determined taking into consideration each country's health and education systems. Final competency domains were similar between the two schools. Both schools established medical education departments to support their new curricula. New teaching methodologies and assessment methods were needed to support CBME, requiring investments in faculty training. Both schools received external funding to support CBME development and implementation.



CBME has emerged as an important change in medical education in Sub-Saharan Africa with schools adopting it as an approach to transformative medical education. Makerere University and the University of Ibadan have successfully adopted CBME and show that CBME can be implemented even for the low-resourced countries in Africa, supported by external investments to address the human resources gap.
Gravity compensation in robotics

The actuator power required to resist joint torque caused by the weight of robot links can be a significant problem. Gravity compensation is a well-known technique in robot design to achieve equilibrium throughout the range of motion and as a result to reduce the loads on the actuator. Therefore, it is desirable and commonly implemented in many situations. Various design concepts for gravity compensation are available in the literature. This paper proposes an overview of gravity compensation methods applied in robotics. The examined properties of the gravity compensation are disclosed and illustrated via kinematic schemes. In order to classify the considered balancing schemes three principal groups are distinguished due to the nature of the compensation force: counterweight, spring or active force developed by an auxiliary actuator. Then, each group is reviewed through sub-groups organized via structural features of balancing schemes. The author believes that such an arrangement of gravity compensation methods allows one to carry out a systematized analysis and provides a comprehensive view on the problem.