nils-herrmann/clean_scirepeval huggingface dataset:
An Ensemble Adjustment Kalman Filter for Data Assimilation

A theory for estimating the probability distribution of the state of a model given a set of observations exists. This nonlinear filtering theory unifies the data assimilation and ensemble generation problem that have been key foci of prediction and predictability research for numerical weather and ocean prediction applications. A new algorithm, referred to as an ensemble adjustment Kalman filter, and the more traditional implementation of the ensemble Kalman filter in which ‘‘perturbed observations’’ are used, are derived as Monte Carlo approximations to the nonlinear filter. Both ensemble Kalman filter methods produce assimilations with small ensemble mean errors while providing reasonable measures of uncertainty in the assimilated variables. The ensemble methods can assimilate observations with a nonlinear relation to model state variables and can also use observations to estimate the value of imprecisely known model parameters. These ensemble filter methods are shown to have significant advantages over four-dimensional variational assimilation in low-order models and scale easily to much larger applications. Heuristic modifications to the filtering algorithms allow them to be applied efficiently to very large models by sequentially processing observations and computing the impact of each observation on each state variable in an independent calculation. The ensemble adjustment Kalman filter is applied to a nondivergent barotropic model on the sphere to demonstrate the capabilities of the filters in models with state spaces that are much larger than the ensemble size. When observations are assimilated in the traditional ensemble Kalman filter, the resulting updated ensemble has a mean that is consistent with the value given by filtering theory, but only the expected value of the covariance of the updated ensemble is consistent with the theory. The ensemble adjustment Kalman filter computes a linear operator that is applied to the prior ensemble estimate of the state, resulting in an updated ensemble whose mean and also covariance are consistent with the theory. In the cases compared here, the ensemble adjustment Kalman filter performs significantly better than the traditional ensemble Kalman filter, apparently because noise introduced into the assimilated ensemble through perturbed observations in the traditional filter limits its relative performance. This superior performance may not occur for all problems and is expected to be most notable for small ensembles. Still, the results suggest that careful study of the capabilities of different varieties of ensemble Kalman filters is appropriate when exploring new applications.
ENCOPLOT: Pairwise Sequence Matching in Linear Time Applied to Plagiarism Detection ∗

In this paper we describe a new general plagiarism detection method, that we used in our winning entry to the 1st International Competition on Plagiarism Detection, the external plagiarism detection task, which assumes the source documents are available. In the first phase of our method, a matrix of kernel values is computed, which gives a similarity value based on n-grams between each source and each suspicious document. In the second phase, each promising pair is further investigated, in order to extract the precise positions and lengths of the subtexts that have been copied and maybe obfuscated – using encoplot, a novel linear time pairwise sequence matching technique. We solved the significant computational challenges arising from having to compare millions of document pairs by using a library developed by our group mainly for use in network security tools. The performance achieved is comparing more than 49 million pairs of documents in 12 hours on a single computer. The results in the challenge were very good, we outperformed all other methods.
Object Detection with Discriminatively Trained Part Based Models

We describe an object detection system based on mixtures of multiscale deformable part models. Our system is able to represent highly variable object classes and achieves state-of-the-art results in the PASCAL object detection challenges. While deformable part models have become quite popular, their value had not been demonstrated on difficult benchmarks such as the PASCAL data sets. Our system relies on new methods for discriminative training with partially labeled data. We combine a margin-sensitive approach for data-mining hard negative examples with a formalism we call latent SVM. A latent SVM is a reformulation of MI--SVM in terms of latent variables. A latent SVM is semiconvex, and the training problem becomes convex once latent information is specified for the positive examples. This leads to an iterative training algorithm that alternates between fixing latent values for positive examples and optimizing the latent SVM objective function.
Fortuna: Model Checking Priced Probabilistic Timed Automata

Fortuna is the first tool for model checking priced probabilistic timed automata (PPTAs). PPTAs are an important model that can handle the combination of real-time, probabilistic and cost features. Only model checkers that incorporate all these features can address the key design trade-offs that arise in many practical applications such as: the Zeroconf, Bluetooth, IEEE802.11 and Firewire protocols, protocols for sensor networks, and scheduling problems with failures. PPTAs are an extension of probabilistic timed automata (PTAs), by having cost-rates and discrete cost increments on states. Fortuna is able to compute the maximal probability by which a class of states can be reached under a certain cost-bound (and time bound.) Although the problem is undecidable in general, there exists a semi-algorithm that produces a non-decreasing sequence of maximal probabilities. This paper enhances that algorithm. We compared the performance of Fortuna with existing approaches for PTAs. Surprisingly, although PPTAs are more general, our techniques exhibit superior performance.
New Routes from Minimal Approximation Error to Principal Components

We introduce two new methods of deriving the classical PCA in the framework of minimizing the mean square error upon performing a lower-dimensional approximation of the data. These methods are based on two forms of the mean square error function. One of the novelties of the presented methods is that the commonly employed process of subtraction of the mean of the data becomes part of the solution of the optimization problem and not a pre-analysis heuristic. We also derive the optimal basis and the minimum error of approximation in this framework and demonstrate the elegance of our solution in comparison with a recent solution in the framework.
The 3D Model Acquisition Pipeline

Three-dimensional (3D) image acquisition systems are rapidly becoming more affordable, especially systems based on commodity electronic cameras. At the same time, personal computers with graphics hardware capable of displaying complex 3D models are also becoming inexpensive enough to be available to a large population. As a result, there is potentially an opportunity to consider new virtual reality applications as diverse as cultural heritage and retail sales that will allow people to view realistic 3D objects on home computers. Although there are many physical techniques for acquiring 3D data—including laser scanners, structured light and time-of-flight—there is a basic pipeline of operations for taking the acquired data and producing a usable numerical model. We look at the fundamental problems of range image registration, line-of-sight errors, mesh integration, surface detail and color, and texture mapping. In the area of registration we consider both the problems of finding an initial global alignment using manual and automatic means, and refining this alignment with variations of the Iterative Closest Point methods. To account for scanner line-of-sight errors we compare several averaging approaches. In the area of mesh integration, that is finding a single mesh joining the data from all scans, we compare various methods for computing interpolating and approximating surfaces. We then look at various ways in which surface properties such as color (more properly, spectral reflectance) can be extracted from acquired imagery. Finally, we examine techniques for producing a final model representation that can be efficiently rendered using graphics hardware.
Bidirectional Recurrent Neural Networks as Generative Models

Bidirectional recurrent neural networks (RNN) are trained to predict both in the positive and negative time directions simultaneously. They have not been used commonly in unsupervised tasks, because a probabilistic interpretation of the model has been difficult. Recently, two different frameworks, GSN and NADE, provide a connection between reconstruction and probabilistic modeling, which makes the interpretation possible. As far as we know, neither GSN or NADE have been studied in the context of time series before. As an example of an unsupervised task, we study the problem of filling in gaps in high-dimensional time series with complex dynamics. Although unidirectional RNNs have recently been trained successfully to model such time series, inference in the negative time direction is non-trivial. We propose two probabilistic interpretations of bidirectional RNNs that can be used to reconstruct missing gaps efficiently. Our experiments on text data show that both proposed methods are much more accurate than unidirectional reconstructions, although a bit less accurate than a computationally complex bidirectional Bayesian inference on the unidirectional RNN. We also provide results on music data for which the Bayesian inference is computationally infeasible, demonstrating the scalability of the proposed methods.
Predicting Trust and Distrust in Social Networks

As user-generated content and interactions have overtaken the web as the default mode of use, questions of whom and what to trust have become increasingly important. Fortunately, online social networks and social media have made it easy for users to indicate whom they trust and whom they do not. However, this does not solve the problem since each user is only likely to know a tiny fraction of other users, we must have methods for inferring trust - and distrust - between users who do not know one another. In this paper, we present a new method for computing both trust and distrust (i.e., positive and negative trust). We do this by combining an inference algorithm that relies on a probabilistic interpretation of trust based on random graphs with a modified spring-embedding algorithm. Our algorithm correctly classifies hidden trust edges as positive or negative with high accuracy. These results are useful in a wide range of social web applications where trust is important to user behavior and satisfaction.
EYE CONTROLLED WHEELCHAIR BASED ON ARDUINO CIRCUIT

Statistics suggests that there are around 40 cases per million of quadriplegia every year. Great people like Stephen Hawking have been suffering from this phenomenon. Our project attempts to make lives of the people suffering from this phenomenon simple by helping them move around on their own and not being a burden on others. The idea is to create an Eye Controlled System which enables the movement of the patient’s wheelchair depending on the movements of eyeball. A person suffering from quadriplegia can move his eyes and partially tilt his head, thus giving is an opportunity for detecting these movements. There are various kinds of interfaces developed for powered wheelchair and also there are various new techniques invented but these are costly and not affordable to the poor and needy people. In this paper, we have proposed the simpler and cost effective method of developing wheelchair. We have created a system wherein a person sitting on this automated Wheel Chair with a camera mounted on it, is able to move in a direction just by looking in that direction by making eye movements. The captured camera signals are then send to PC and controlled MATLAB, which will then be send to the Arduino circuit over the Serial Interface which in turn will control motors and allow the wheelchair to move in a particular direction. The system is affordable and hence can be used by patients spread over a large economy range. KeywordsAutomatic wheelchair, Iris Movement Detection, Servo Motor, Daugman’s algorithm, Arduino.
Dense Variational Reconstruction of Non-rigid Surfaces from Monocular Video

This paper offers the first variational approach to the problem of dense 3D reconstruction of non-rigid surfaces from a monocular video sequence. We formulate non-rigid structure from motion (nrsfm) as a global variational energy minimization problem to estimate dense low-rank smooth 3D shapes for every frame along with the camera motion matrices, given dense 2D correspondences. Unlike traditional factorization based approaches to nrsfm, which model the low-rank non-rigid shape using a fixed number of basis shapes and corresponding coefficients, we minimize the rank of the matrix of time-varying shapes directly via trace norm minimization. In conjunction with this low-rank constraint, we use an edge preserving total-variation regularization term to obtain spatially smooth shapes for every frame. Thanks to proximal splitting techniques the optimization problem can be decomposed into many point-wise sub-problems and simple linear systems which can be easily solved on GPU hardware. We show results on real sequences of different objects (face, torso, beating heart) where, despite challenges in tracking, illumination changes and occlusions, our method reconstructs highly deforming smooth surfaces densely and accurately directly from video, without the need for any prior models or shape templates.
Driver drowsiness monitoring based on yawning detection

Fatigue and drowsiness of drivers are amongst the significant causes of road accidents. In this paper, we discuss a method for detecting drivers' drowsiness and subsequently alerting them. The aim is to reduce the number of accidents due to drivers fatigue and hence increase the transportation safety. Many special body and face gestures are used as sign of driver fatigue, including yawning, eye tiredness and eye movement, which indicate that the driver is no longer in a proper driving condition. Here, we propose a method of yawning detection based on the changes in the mouth geometric features.
Community Detection in Networks with Node Attributes

Community detection algorithms are fundamental tools that allow us to uncover organizational principles in networks. When detecting communities, there are two possible sources of information one can use: the network structure, and the features and attributes of nodes. Even though communities form around nodes that have common edges and common attributes, typically, algorithms have only focused on one of these two data modalities: community detection algorithms traditionally focus only on the network structure, while clustering algorithms mostly consider only node attributes. In this paper, we develop Communities from Edge Structure and Node Attributes (CESNA), an accurate and scalable algorithm for detecting overlapping communities in networks with node attributes. CESNA statistically models the interaction between the network structure and the node attributes, which leads to more accurate community detection as well as improved robustness in the presence of noise in the network structure. CESNA has a linear runtime in the network size and is able to process networks an order of magnitude larger than comparable approaches. Last, CESNA also helps with the interpretation of detected communities by finding relevant node attributes for each community.
Approximate Policy Iteration with a Policy Language Bias: Solving Relational Markov Decision Processes

We explore approximate policy iteration (API), replacing t he usual costfunction learning step with a learning step in policy space. We give policy-language biases that enable solution of very large relational Markov decision processes (MDPs) that no previous techniqu e can solve. In particular, we induce high-quality domain-specific plan ners for classical planning domains (both deterministic and stochastic variants) by solving such domains as extremely large MDPs.
Ontology-Driven Geographic Information Systems

Theintegration of information of different kinds, such asspatial and alphanumeric, at different levels of detail is a challenge. While a solution is not reached, it is widely recognized that the need to integrate information is so pressing that it does not matter if detail is lost, as long as integration is achieved. This paper shows the potential for extraction of different levels of information, within the framework of ontology-driven geographic information systems.
Learning monocular reactive UAV control in cluttered natural environments

Autonomous navigation for large Unmanned Aerial Vehicles (UAVs) is fairly straight-forward, as expensive sensors and monitoring devices can be employed. In contrast, obstacle avoidance remains a challenging task for Micro Aerial Vehicles (MAVs) which operate at low altitude in cluttered environments. Unlike large vehicles, MAVs can only carry very light sensors, such as cameras, making autonomous navigation through obstacles much more challenging. In this paper, we describe a system that navigates a small quadrotor helicopter autonomously at low altitude through natural forest environments. Using only a single cheap camera to perceive the environment, we are able to maintain a constant velocity of up to 1.5m/s. Given a small set of human pilot demonstrations, we use recent state-of-the-art imitation learning techniques to train a controller that can avoid trees by adapting the MAVs heading. We demonstrate the performance of our system in a more controlled environment indoors, and in real natural forest environments outdoors.
Information, Community, and Action: How Nonprofit Organizations Use Social Media

The rapid diffusion of ‘‘microblogging’’ services such as Twitter is ushering in a new era of possibilities for organizations to communicate with and engage their core stakeholders and the general public. To enhance understanding of the communicative functions microblogging serves for organizations, this study examines the Twitter utilization practices of the 100 largest nonprofit organizations in the United States. The analysis reveals there are three key functions of microblogging updates—‘‘information,’’ ‘‘community,’’ and ‘‘action.’’ Though the informational use of microblogging is extensive, nonprofit organizations are better at using Twitter to strategically engage their stakeholders via dialogic and community-building practices than they have been with traditional websites. The adoption of social media appears to have engendered new paradigms of public engagement.
System-level integrated server architectures for scale-out datacenters

A System-on-Chip (SoC) integrates multiple discrete components into a single chip, for example by placing CPU cores, network interfaces and I/O controllers on the same die. While SoCs have dominated high-end embedded products for over a decade, system-level integration is a relatively new trend in servers, and is driven by the opportunity to lower cost (by reducing the number of discrete parts) and power (by reducing the pin crossings from the cores to the I/O). Today, the mounting cost pressures in scale-out dat-acenters demand technologies that can decrease the Total Cost of Ownership (TCO). At the same time, the diminshing return of dedicating the increasing number of available transistors to more cores and caches is creating a stronger case for SoC-based servers.
 This paper examines system-level integration design options for the scale-out server market, specifically targeting datacenter-scale throughput computing workloads. We develop tools to model the area and power of a variety of discrete and integrated server configurations. We evaluate the benefits, trade-offs, and trends of system-level integration for warehouse-scale datacenter servers, and identify the key "uncore" components that reduce cost and power. We perform a comprehensive design space exploration at both SoC and datacenter level, identify the sweet spots, and highlight important scaling trends of performance, power, area, and cost from 45nm to 16nm. Our results show that system integration yields substantial benefits, enables novel aggregated configurations with a much higher compute density, and significantly reduces total chip area and dynamic power versus a discrete-component server.
 Finally, we use utilization traces and architectural profiles of real machines to evaluate the dynamic power consumption of typical scale-out cloud applications, and combine them in an overall TCO model. Our results show that, for example at 16nm, SoC-based servers can achieve more than a 26% TCO reduction at datacenter scale.
Full Flow: Optical Flow Estimation By Global Optimization over Regular Grids

We present a global optimization approach to optical flow estimation. The approach optimizes a classical optical flow objective over the full space of mappings between discrete grids. No descriptor matching is used. The highly regular structure of the space of mappings enables optimizations that reduce the computational complexity of the algorithm's inner loop from quadratic to linear and support efficient matching of tens of thousands of nodes to tens of thousands of displacements. We show that one-shot global optimization of a classical Horn-Schunck-type objective over regular grids at a single resolution is sufficient to initialize continuous interpolation and achieve state-of-the-art performance on challenging modern benchmarks.
LSH Ensemble: Internet-Scale Domain Search

We study the problem of domain search where a domain is a set of distinct values from an unspecified universe. We use Jaccard set containment score, defined as |Q ∩X|/|Q|, as the measure of relevance of a domain X to a query domain Q. Our choice of Jaccard set containment over Jaccard similarity as a measure of relevance makes our work particularly suitable for searching Open Data and data on the web, as Jaccard similarity is known to have poor performance over sets with large differences in their domain sizes. We demonstrate that the domains found in several real-life Open Data and web data repositories show a power-law distribution over their domain sizes. We present a new index structure, Locality Sensitive Hashing (LSH) Ensemble, that solves the domain search problem using set containment at Internet scale. Our index structure and search algorithm cope with the data volume and skew by means of data sketches using Minwise Hashing and domain partitioning. Our index structure does not assume a prescribed set of data values. We construct a cost model that describes the accuracy of LSH Ensemble with any given partitioning. This allows us to formulate the data partitioning for LSH Ensemble as an optimization problem. We prove that there exists an optimal partitioning for any data distribution. Furthermore, for datasets following a powerlaw distribution, as observed in Open Data and Web data corpora, we show that the optimal partitioning can be approximated using equi-depth, making it particularly efficient to use in practice. We evaluate our algorithm using real data (Canadian Open Data and WDC Web Tables) containing up to three million domains. The experiments demonstrate that our index consistently outperforms other leading alternatives in accuracy and performance. The improvements are most dramatic for data with large skew in the domain sizes. Even at three million domains, our index sustains query performance with under 50 milliseconds response time.