nils-herrmann/clean_scirepeval huggingface dataset:
Esthetic outcomes following immediate and early implant placement in the anterior maxilla--a systematic review.

The objectives of this systematic review are (1) to quantitatively estimate the esthetic outcomes of implants placed in postextraction sites, and (2) to evaluate the influence of simultaneous bone augmentation procedures on these outcomes.



Electronic and manual searches of the dental literature were performed to collect information on esthetic outcomes based on objective criteria with implants placed after extraction of maxillary anterior and premolar teeth. All levels of evidence were accepted (case series studies required a minimum of 5 cases).



From 1,686 titles, 114 full-text articles were evaluated and 50 records included for data extraction. The included studies reported on single-tooth implants adjacent to natural teeth, with no studies on multiple missing teeth identified (6 randomized controlled trials, 6 cohort studies, 5 cross-sectional studies, and 33 case series studies). Considerable heterogeneity in study design was found. A meta-analysis of controlled studies was not possible. The available evidence suggests that esthetic outcomes, determined by esthetic indices (predominantly the pink esthetic score) and positional changes of the peri-implant mucosa, may be achieved for single-tooth implants placed after tooth extraction. Immediate (type 1) implant placement, however, is associated with a greater variability in outcomes and a higher frequency of recession of > 1 mm of the midfacial mucosa (eight studies; range 9% to 41% and median 26% of sites, 1 to 3 years after placement) compared to early (type 2 and type 3) implant placement (2 studies; no sites with recession > 1 mm). In two retrospective studies of immediate (type 1) implant placement with bone graft, the facial bone wall was not detectable on cone beam CT in 36% and 57% of sites. These sites had more recession of the midfacial mucosa compared to sites with detectable facial bone. Two studies of early implant placement (types 2 and 3) combined with simultaneous bone augmentation with GBR (contour augmentation) demonstrated a high frequency (above 90%) of facial bone wall visible on CBCT. Recent studies of immediate (type 1) placement imposed specific selection criteria, including thick tissue biotype and an intact facial socket wall, to reduce esthetic risk. There were no specific selection criteria for early (type 2 and type 3) implant placement.



Acceptable esthetic outcomes may be achieved with implants placed after extraction of teeth in the maxillary anterior and premolar areas of the dentition. Recession of the midfacial mucosa is a risk with immediate (type 1) placement. Further research is needed to investigate the most suitable biomaterials to reconstruct the facial bone and the relationship between long-term mucosal stability and presence/absence of the facial bone, the thickness of the facial bone, and the position of the facial bone crest.
Supervised Learning for Insider Threat Detection Using Stream Mining

Insider threat detection requires the identification of rare anomalies in contexts where evolving behaviors tend to mask such anomalies. This paper proposes and tests an ensemble-based stream mining algorithm based on supervised learning that addresses this challenge by maintaining an evolving collection of multiple models to classify dynamic data streams of unbounded length. The result is a classifier that exhibits substantially increased classification accuracy for real insider threat streams relative to traditional supervised learning (traditional SVM and one-class SVM) and other single-model approaches.
An efficient new algorithm for 2-D line clipping: Its development and analysis

This paper describes a new alorithm for clipping a line in two dimensions against a rectangular window. This algorithm avoids computation of intersection points which are not endpoints of the output line segment. The performance of this algorithm is shown to be consistently better than existing algorithms, including the Cohen-Sutherland and Liang-Barsky algorithms. This performance comparison is machine-independent, based on an analysis of the number of arithmetic operations and comparisons required by the different algorithms. We first present the algorithm using procedures which perform geometric transformations to exploit symmetry properties and then show how program transformation techniques may be used to eliminate the extra statements involved in performing geometric transformations.
Modality-Dependent Cross-Media Retrieval

In this article, we investigate the cross-media retrieval between images and text, that is, using image to search text (I2T) and using text to search images (T2I). Existing cross-media retrieval methods usually learn one couple of projections, by which the original features of images and text can be projected into a common latent space to measure the content similarity. However, using the same projections for the two different retrieval tasks (I2T and T2I) may lead to a tradeoff between their respective performances, rather than their best performances. Different from previous works, we propose a modality-dependent cross-media retrieval (MDCR) model, where two couples of projections are learned for different cross-media retrieval tasks instead of one couple of projections. Specifically, by jointly optimizing the correlation between images and text and the linear regression from one modal space (image or text) to the semantic space, two couples of mappings are learned to project images and text from their original feature spaces into two common latent subspaces (one for I2T and the other for T2I). Extensive experiments show the superiority of the proposed MDCR compared with other methods. In particular, based on the 4,096-dimensional convolutional neural network (CNN) visual feature and 100-dimensional Latent Dirichlet Allocation (LDA) textual feature, the mAP of the proposed method achieves the mAP score of 41.5%, which is a new state-of-the-art performance on the Wikipedia dataset.
gvnn: Neural Network Library for Geometric Computer Vision

We introduce gvnn, a neural network library in Torch aimed towards bridging the gap between classic geometric computer vision and deep learning. Inspired by the recent success of Spatial Transformer Networks, we propose several new layers which are often used as parametric transformations on the data in geometric computer vision. These layers can be inserted within a neural network much in the spirit of the original spatial transformers and allow backpropagation to enable end-toend learning of a network involving any domain knowledge in geometric computer vision. This opens up applications in learning invariance to 3D geometric transformation for place recognition, end-to-end visual odometry, depth estimation and unsupervised learning through warping with a parametric transformation for image reconstruction error.
Probabilistic Data Analysis with Probabilistic Programming

Probabilistic techniques are central to data analysis, but different approaches can be difficult to apply, combine, and compare. This paper introduces composable generative population models (CGPMs), a computational abstraction that extends directed graphical models and can be used to describe and compose a broad class of probabilistic data analysis techniques. Examples include hierarchical Bayesian models, multivariate kernel methods, discriminative machine learning, clustering algorithms, dimensionality reduction, and arbitrary probabilistic programs. We also demonstrate the integration of CGPMs into BayesDB, a probabilistic programming platform that can express data analysis tasks using a modeling language and a structured query language. The practical value is illustrated in two ways. First, CGPMs are used in an analysis that identifies satellite data records which probably violate Kepler’s Third Law, by composing causal probabilistic programs with non-parametric Bayes in under 50 lines of probabilistic code. Second, for several representative data analysis tasks, we report on lines of code and accuracy measurements of various CGPMs, plus comparisons with standard baseline solutions from Python and MATLAB libraries.
Going the Distance for TLB Prefetching: An Application-Driven Study

The importance of the Translation Lookaside Buffer (TLB) on system performance is well known. There have been numerous prior efforts addressing TLB design issues for cutting down access times and lowering miss rates. However, it was only recently that the first exploration [26] on prefetching TLB entries ahead of their need was undertaken and a mechanism called Recency Prefetching was proposed. There is a large body of literature on prefetching for caches, and it is not clear how they can be adapted (or if the issues are different) for TLBs, how well suited they are for TLB prefetching, and how they compare with the recency prefetching mechanism.This paper presents the first detailed comparison of different prefetching mechanisms (previously proposed for caches) - arbitrary stride prefetching, and markov prefetching - for TLB entries, and evaluates their pros and cons. In addition, this paper proposes a novel prefetching mechanism, called Distance Prefetching, that attempts to capture patterns in the reference behavior in a smaller space than earlier proposals. Using detailed simulations of a wide variety of applications (56 in all) from different benchmark suites and all the SPEC CPU2000 applications, this paper demonstrates the benefits of distance prefetching.
A Study on Dual-Scale Data Charts

We present the results of a user study that compares different ways of representing Dual-Scale data charts. Dual-Scale charts incorporate two different data resolutions into one chart in order to emphasize data in regions of interest or to enable the comparison of data from distant regions. While some design guidelines exist for these types of charts, there is currently little empirical evidence on which to base their design. We fill this gap by discussing the design space of Dual-Scale cartesian-coordinate charts and by experimentally comparing the performance of different chart types with respect to elementary graphical perception tasks such as comparing lengths and distances. Our study suggests that cut-out charts which include collocated full context and focus are the best alternative, and that superimposed charts in which focus and context overlap on top of each other should be avoided.
Complex Event Detection using Semantic Saliency and Nearly-Isotonic SVM

We aim to detect complex events in long Internet videos that may last for hours. A major challenge in this setting is that only a few shots in a long video are relevant to the event of interest while others are irrelevant or even misleading. Instead of indifferently pooling the shots, we first define a novel notion of semantic saliency that assesses the relevance of each shot with the event of interest. We then prioritize the shots according to their saliency scores since shots that are semantically more salient are expected to contribute more to the final event detector. Next, we propose a new isotonic regularizer that is able to exploit the semantic ordering information. The resulting nearly-isotonic SVM classifier exhibits higher discriminative power. Computationally, we develop an efficient implementation using the proximal gradient algorithm, and we prove new, closed-form proximal steps. We conduct extensive experiments on three real-world video datasets and confirm the effectiveness of the proposed approach.
Baseline CNN structure analysis for facial expression recognition

We present a baseline convolutional neural network (CNN) structure and image preprocessing methodology to improve facial expression recognition algorithm using CNN. To analyze the most efficient network structure, we investigated four network structures that are known to show good performance in facial expression recognition. Moreover, we also investigated the effect of input image preprocessing methods. Five types of data input (raw, histogram equalization, isotropic smoothing, diffusion-based normalization, difference of Gaussian) were tested, and the accuracy was compared. We trained 20 different CNN models (4 networks × 5 data input types) and verified the performance of each network with test images from five different databases. The experiment result showed that a three-layer structure consisting of a simple convolutional and a max pooling layer with histogram equalization image input was the most efficient. We describe the detailed training procedure and analyze the result of the test accuracy based on considerable observation.
Exponentiated Gradient Algorithms for Conditional Random Fields and Max-Margin Markov Networks

Log-linear and maximum-margin models are two commonly-used methods in supervised machine learning, and are frequently used in structured prediction problems. Efficient learning of parameters in these models is therefore an important problem, and becomes a key factor when learning from very large data sets. This paper describes exponentiated gradient (EG) algorithms for training such models, where EG updates are applied to the convex dual of either the log-linear or maxmargin objective function; the dual in both the log-linear and max-margin cases corresponds to minimizing a convex function with simplex constraints. We study both batch and online variants of the algorithm, and provide rates of convergence for both cases. In the max-margin case, O( ε ) EG updates are required to reach a given accuracy ε in the dual; in contrast, for log-linear models only O(log( ε )) updates are required. For both the max-margin and log-linear cases, our bounds suggest that the online EG algorithm requires a factor of n less computation to reach a desired accuracy than the batch EG algorithm, where n is the number of training examples. Our experiments confirm that the online algorithms are much faster than the batch algorithms in practice. We describe how the EG updates factor in a convenient way for structured prediction problems, allowing the algorithms to be efficiently applied to problems such as sequence learning or natural language parsing. We perform extensive evaluation of the algorithms, comparing them to L-BFGS and stochastic gradient descent for log-linear models, and to SVM-Struct for max-margin models. The algorithms are applied to a multi-class problem as well as to a more complex large-scale parsing task. In all these settings, the EG algorithms presented here outperform the other methods.
Structured Determinantal Point Processes

We present a novel probabilistic model for distributions over sets of structures— for example, sets of sequences, trees, or graphs. The critical characteristic of our model is a preference for diversity: sets containing dissimilar structures are more likely. Our model is a marriage of structured probabilistic models, like Markov random fields and context free grammars, with determinantal point processes, which arise in quantum physics as models of particles with repulsive interactions. We extend the determinantal point process model to handle an exponentially-sized set of particles (structures) via a natural factorization of the model into parts. We show how this factorization leads to tractable algorithms for exact inference, including computing marginals, computing conditional probabilities, and sampling. Our algorithms exploit a novel polynomially-sized dual representation of determinantal point processes, and use message passing over a special semiring to compute relevant quantities. We illustrate the advantages of the model on tracking and articulated pose estimation problems.
Recommendation Systems for Software Engineering

Software development can be challenging because of the large information spaces that developers must navigate. Without assistance, developers can become bogged down and spend a disproportionate amount of their time seeking information at the expense of other value-producing tasks. Recommendation systems for software engineering (RSSEs) are software tools that can assist developers with a wide range of activities, from reusing code to writing effective bug reports. The authors provide an overview of recommendation systems for software engineering: what they are, what they can do for developers, and what they might do in the future.
ATLAS: A Small but Complete SQL Extension for Data Mining and Data Streams

DBMSs have long suffered from SQL’s lack of power and extensibility. We have implemented ATLaS , a powerful database language and system that enables users to develop complete data-intensive applications in SQL—by writing new aggregates and table functions in SQL, rather than in procedural languages as in current Object-Relational systems. As a result, ATLaS’ SQL is Turing-complete [7], and is very suitable for advanced data-intensive applications, such as data mining and stream queries. The ATLaS system is now available for download along with a suite of applications [1] including various data mining functions, that have been coded in ATLaS’ SQL, and execute with a modest (20–40%) performance overhead with respect to the same applications written in C/C++. Our proposed demo will illustrate the key features and applications of ATLaS. In particular, we will demonstrate:
High-Speed FMCW Radar Frequency Synthesizer With DDS Based Linearization

Recently , we introduced a software linearization technique for frequency-modulated continuous-wave (FMCW) radar applications using a nonlinear direct digital synthesizer based frequency source. In this letter, we present a method that uses this unconventional, cost efficient, basically nonlinear synthesizer concept, but is capable of linearizing the frequency chirp directly in hardware by means of defined sweep predistortion. Additionally, the concept is extended for the generation of defined nonlinear frequency courses and verified on measurements with a 2.45-GHz FMCW radar prototype
Machine learning approach for forecasting crop yield based on climatic parameters

With the impact of climate change in India, majority of the agricultural crops are being badly affected interms of their performance over a period of last two decades. Predicting the crop yield well ahead of its harvest would help the policy makers and farmers for taking appropriate measures for marketing and storage. Such predictions will also help the associated industries for planning the logistics of their business. Several methods of predicting and modeling crop yields have been developed in the past with varying rateof success, as these don't take into account characteristicsoftheweather, a n d aremostly empirical. In the present study a software tool named `Crop Advisor' has been developed as an user friendly web page for predicting the influence of climatic parameters on the crop yields.C4.5 algorithm is used to find out the most influencing climatic parameter on the crop yields of selected crops in selected districts of Madhya Pradesh. This software provides an indication of relative influence of different climatic parameters on the crop yield, other agro-input parameters responsible for crop yield are not considered in this tool, since, application of these input parameters varies with individual fields in space and time.
Gender Asymmetries in Reality and Fiction: The Bechdel Test of Social Media

The subjective nature of gender inequality motivates the analysis and comparison of data from real and fictional human interaction. We present a computational extension of the Bechdel test: A popular tool to assess if a movie contains a male gender bias, by looking for two female characters who discuss about something besides a man. We provide the tools to quantify Bechdel scores for both genders, and we measure them in movie scripts and large datasets of dialogues between users of MySpace and Twitter. Comparing movies and users of social media, we find that movies and Twitter conversations have a consistent male bias, which does not appear when analyzing MySpace. Furthermore, the narrative of Twitter is closer to the movies that do not pass the Bechdel test than to
An improved algorithm to accelerate regular expression evaluation

Modern network intrusion detection systems need to perform regular expression matching at line rate in order to detect the occurrence of critical patterns in packet payloads. While deterministic finite automata (DFAs) allow this operation to be performed in linear time, they may exhibit prohibitive memory requirements. In , Kumar et al. propose Delayed Input DFAs (D2FAs), which provide a trade-off between the memory requirements of the compressed DFA and the number of states visited for each character processed, which corresponds directly to the memory bandwidth required to evaluate regular expressions.
 In this paper we introduce a general compression technique that results in at most 2N state traversals when processing a string of length N. In comparison to the D2FA approach, our technique achieves comparable levels of compression, with lower provable bounds on memory bandwidth (or greater compression for a given bandwidth bound). Moreover, our proposed algorithm has lower complexity, is suitable for scenarios where a compressed DFA needs to be dynamically built or updated, and fosters locality in the traversal process. Finally, we also describe a novel alphabet reduction scheme for DFA-based structures that can yield further dramatic reductions in data structure size.
WebNav: A New Large-Scale Task for Natural Language based Sequential Decision Making

We propose a goal-driven web navigation as a benchmark task for evaluating an agent with abilities to understand natural language and plan on partially observed environments. In this challenging task, an agent navigates through a web site, which is represented as a graph consisting of web pages as nodes and hyperlinks as directed edges, to find a web page in which a query appears. The agent is required to have sophisticated high-level reasoning based on natural languages and efficient sequential decision making capability to succeed. We release a software tool, called WebNav, that automatically transforms a website into this goal-driven web navigation task, and as an example, we make WikiNav, a dataset constructed from the English Wikipedia containing approximately 5 million articles and more than 12 million queries for training. We evaluate two different agents based on neural networks on the WikiNav and provide the human performance. Our results show the difficulty of the task for both humans and machines. With this benchmark, we expect faster progress in developing artificial agents with natural language understanding and planning skills.
Type-based race detection for Java

This paper presents a static race detection analysis for multithreaded Java programs. Our analysis is based on a formal type system that is capable of capturing many common synchronization patterns. These patterns include classes with internal synchronization, classes thatrequire client-side synchronization, and thread-local classes. Experience checking over 40,000 lines of Java code with the type system demonstrates that it is an effective approach for eliminating races conditions. On large examples, fewer than 20 additional type annotations per 1000 lines of code were required by the type checker, and we found a number of races in the standard Java libraries and other test programs.