nils-herrmann/clean_scirepeval huggingface dataset:
Rover traverse science for increased mission science return

Rover traverse distances are increasing at a faster rate than downlink capacity is increasing. As this trend continues, the quantity of data that can be returned to Earth per meter traversed is reduced. The capacity of the rover to collect data, however, remains high. Ths circumstance leads to an opportunity to increase mission science return by carefully selecting the data with the highest science interest for downlink. We have developed an onboard science analysis technology for increasing science return from missions. Our technology evaluates the geologic data gather by the rover. This analysis is used to prioritize the data for transmission, so that the data with the highest science value is transmitted to Earth. In addition, the onboard analysis results are used to identify science opportunities. A planning and scheduling component of the system enables the rover to take advantage of the identified science opportunity. Although our techniques are applicable to a wide range of data modalities, our initial emphasis has focused on image analysis, since images consume a large percentage of downlink bandwidth. We have fkther focused our foundational work on rocks. Rocks are among the primary features populating the Martian landscape. Characterization and understanding of rocks on the surface is a-first step leading towards more complex in situ regional geological assessmeats by the rover.
Sparse Greedy Minimax Probability Machine Classification

The Minimax Probability Machine Classification (MPMC) framework [Lanckriet et al., 2002] builds classifiers by minimizing the maximum probability of misclassification, and gives direct estimates of the probabilistic accuracy bound Ω. The only assumptions that MPMC makes is that good estimates of means and covariance matrices of the classes exist. However, as with Support Vector Machines, MPMC is computationally expensive and requires extensive cross validation experiments to choose kernels and kernel parameters that give good performance. In this paper we address the computational cost of MPMC by proposing an algorithm that constructs nonlinear sparse MPMC (SMPMC) models by incrementally adding basis functions (i.e. kernels) one at a time – greedily selecting the next one that maximizes the accuracy bound Ω. SMPMC automatically chooses both kernel parameters and feature weights without using computationally expensive cross validation. Therefore the SMPMC algorithm simultaneously addresses the problem of kernel selection and feature selection (i.e. feature weighting), based solely on maximizing the accuracy bound Ω. Experimental results indicate that we can obtain reliable bounds Ω, as well as test set accuracies that are comparable to state of the art classification algorithms.
Sparse Greedy Minimax Probability Machine Classification ; CU-CS-956-03

The Minimax Probability Machine Classification (MPMC) framework [Lanckriet et al., 2002] builds classifiers by minimizing the maximum probability of misclassification, and gives direct estimates of the probabilistic accuracy bound Ω. The only assumptions that MPMC makes is that good estimates of means and covariance matrixes of the classes exist. However, as with Support Vector Machines, MPMC is computationally expensive and requires extensive cross validation experiments to choose kernels and kernel parameters that give good performance. In this paper we address the computational cost of MPMC by proposing an algorithm that constructs nonlinear sparse MPMC (SMPMC) models by incrementally adding basis functions (i.e. kernels) one at a time – greedily selecting the next one that maximizes the accuracy bound Ω. SMPMC automatically chooses both kernel parameters and feature weights without using computationally expensive cross validation. Therefore the SMPMC algorithm simultaneously addresses the problem of kernel selection and feature selection (i.e. feature weighting), based solely on maximizing the accuracy bound Ω. Experimental results indicate that we can obtain reliable bounds Ω, as well as test-set accuracies that are comparable to state of the art classification algorithms.
Fast Query-Optimized Kernel Machine Classification Via Incremental Approximate Nearest Support Vectors

Support vector machines (and other kernel machines) offer robust modern machine learning methods for nonlinear classification. However, relative to other alternatives (such as linear methods, decision trees and neural networks), they can be orders of magnitude slower at query-time. Unlike existing methods that attempt to speedup querytime, such as reduced set compression (e.g. (Burges, 1996)) and anytime bounding (e.g. (DeCoste, 2002), we propose a new and efficient approach based on treating the kernel machine classifier as a special form of k nearest-neighbor. Our approach improves upon a traditional k-NN by determining at query-time a good k for each query, based on pre-query analysis guided by the original robust kernel machine. We demonstrate effectiveness on high-dimensional benchmark MNIST data, observing a greater than 100- fold reduction in the number of SVs required per query (amortized over all 45 pairwise MNIST digit classifiers), with no extra test errors (in fact, it happens to make 4 fewer).
Anytime query-tuned kernel machine classifiers via Cholesky factorization

We recently demonstrated 2 to 64-fold query-time speedups of Support Vector Machine and Kernel Fisher classifiers via a new computational geometry method for anytime output bounds (DeCoste,2002). This new paper refines our approach in two key ways. First, we introduce a simple linear algebra formulation based on Cholesky factorization, yielding simpler equations and lower computational overhead. Second, this new formulation suggests new methods for achieving additional speedups, including tuning on query samples. We demonstrate effectiveness on benchmark datasets.
Visualizing data mining models

A stationary cylinder is jacketed to provide a first passage for a first heat exchange medium and forms a chamber for throughflow of a product to be heated or cooled. The cylinder contains a coaxial rotor comprising a shaft having a cylindrical outer surface and forming a second passage for a second heat exchange medium, whereby said stationary cylinder and said shaft surface are heated or cooled by said media. Each of a plurality of tubes has its opposite ends secured to the shaft and has an intermediate portion extending outside the shaft in spaced relation to its cylindrical outer surface. The second heat exchange medium is fed through one end portion of the shaft to adjacent ends of said second passage and said tubes and is discharged from the opposite ends thereof through the other end portion of the shaft. Scraper means protrude from the rotor between adjacent tubes and are operable to sweep the inner surface of the jacketed cylinder.
Learning envelopes for fault detection and state summarization

This paper discusses a data mining approach for overcoming common problems with the traditional red-line limit-checking approach to fault detection and state summarization. It essentially involves learning and adapting parametric functions which provide context-sensitive bounds on historic time-series engineering data. Such bounds are suitable as dynamic plug-in replacements for static red-line values. They enable significantly earlier detection while maintaining low false alarm rates. An example is discussed from onboard tests of this technology during the NASA Deep Space 1 (DS1) mission.
Making an Impact: Artificial Intelligence at the Jet Propulsion Laboratory

The National Aeronautics and Space Administration (NASA) is being challenged to perform more frequent and intensive space-exploration missions at greatly reduced cost. Nowhere is this challenge more acute than among robotic planetary exploration missions that the Jet Propulsion Laboratory (JPL) conducts for NASA. This article describes recent and ongoing work on spacecraft autonomy and ground systems that builds on a legacy of existing success at JPL applying AI techniques to challenging computational problems in planning and scheduling, real-time monitoring and control, scientific data analysis, and design automation.
The Future of Chess-Playing Technologies and the Significance of Kasparov Versus deep Blue

In this paper we argue that the recent Garry Kasparov vs. Deep Blue matches are significant for the field of artificial intelligence in several ways, including providing an example of valuable baseline benchmarks for more complex alternatives to contrast and justify themselves. We will also briefly summarize some of the latest developments on computer chess research and highlight how our own work on a program called Chester tries to build on those developments to provide such justifications.
Localized Content-Based Image Retrieval

We define localized content-based image retrieval as a CBIR task where the user is only interested in a portion of the image, and the rest of the image is irrelevant. In this paper we present a localized CBIR system, ACCIO, that uses labeled images in conjunction with a multiple-instance learning algorithm to first identify the desired object and weight the features accordingly, and then to rank images in the database using a similarity measure that is based upon only the relevant portions of the image. A challenge for localized CBIR is how to represent the image to capture the content. We present and compare two novel image representations, which extend traditional segmentation-based and salient point-based techniques respectively, to capture content in a localized CBIR setting.
Smartacking: Improving TCP Performance from the Receiving End

We present smartacking, a technique that improves performance of Transmission Control Protocol (TCP) via adap- tive generation of acknowledgments (ACKs) at the receiver. When the bottleneck link is underutilized, the receiver transmi ts an ACK for each delivered data segment and thereby allows the connection to acquire the available capacity promptly. When the bottleneck link is at its capacity, the smartacking receiver sends ACKs with a lower frequency reducing the control traffi c overhead and slowing down the congestion window growth to utilize the network capacity more effectively. To promote quick deployment of the technique, our primary implementation of smartacking modifies only the receiver. This implementatio n estimates the sender's congestion window using a novel algo rithm of independent interest. We also consider different implemen- tations of smartacking where the receiver relies on explicit assistance from the sender or network. Experiments in a wide variety of settings show beneficial impacts of smartacking o n TCP performance, especially in environments with low levels of connection multiplexing on bottleneck links.
A fast texture feature extraction method for region-based image segmentation

Region-based image segmentation is one popular approach to image segmentation of generic images. In these methods, an image is partitioned into connected regions by grouping neighboring pixels of similar features, and adjacent regions are then merged with respect to the similarities between the features in these regions. To achieve fine-grain segmentation at the pixel level, we must be able to define features on a per-pixel basis. This is straightforward for color information, but not for texture. Typically texture feature extraction is very computationally intensive for individual pixels. In this paper, we propose a novel fast texture feature extraction method which takes advantage of the similarities between the neighboring pixels. The experiments demonstrate that our method can greatly increase the extraction speed while keeping the distortion within a reasonable range.
Localized Content Based Image Retrieval [ Extended

Classic Content-Based Image Retrieval (CBIR) takes a single non-annotated query image, and retrieves similar images from an image repository. Such a search must rely upon a holistic (or global) view of the image. Yet often the desired content of an image is not holistic, but is localized. Specifically, we define Localized Content-Based Image Retrieval as a CBIR task where the user is only interested in a portion of the image, and the rest of the image is irrelevant. Many classic CBIR systems use relevance feedback to obtain images labeled as desirable or not desirable. Yet, these labeled images are typically used only to re-weight the features used within a global similarity measure. In this paper we present a localized CBIR system, Accio! , that uses labeled images in conjunction with a multiple-instance learning algorithm to first identify the desired object and re-weight the features, and then to rank images in the database using a similarity measure that is based upon individual regions within the image. We evaluate our system using a five-category natural scenes image repository, and benchmark data set, SIVAL, that we have constructed with 25 object categories.
PAC Learning of One-Dimensional Patterns

Developing the ability to recognize a landmark from a visual image of a robot‘s current location is a fundamental problem in robotics. We consider the problem of PAC-learning the concept class of geometric patterns where the target geometric pattern is a configuration of k points on the real line. Each instance is a configuration of n points on the real line, where it is labeled according to whether or not it visually resembles the target pattern. To capture the notion of visual resemblance we use the Hausdorff metric. Informally, two geometric patterns P and Q resemble each other under the Hausdorff metric if every point on one pattern is ’’close‘‘ to some point on the other pattern. We relate the concept class of geometric patterns to the landmark matching problem and then present a polynomial-time algorithm that PAC-learns the class of one-dimensional geometric patterns. We also present some experimental results on how our algorithm performs.
A co-evaluation framework for improving segmentation evaluation

Object segmentation is an important preprocessing step for many target recognition applications. Many segmentation methods have been studied, but there is still no satisfactory effectiveness measure which makes it hard to compare different segmentation methods, or even different parameterizations of a single method. A good segmentation evaluation method not only would enable different approaches to be compared, but could also be integrated within the target recognition system to adaptively select the appropriate granularity of the segmentation which in turn could improve the recognition accuracy. A few stand-alone effectiveness measures have been proposed, but these measures examine different fundamental criteria of the objects, or examine the same criteria in a different fashion, so they usually work well in some cases, but poorly in the others. We propose a em co-evaluation framework, in which different effectiveness measures judge the performance of the segmentation in different ways, and their measures are combined by using a machine learning approach which coalesces the results. Experimental results demonstrate that our method performs better than the existing methods.
Can PAC learning algorithms tolerate random attribute noise?

This paper studies the robustness of PAC learning algorithms when the instance space is {0,1}n, and the examples are corrupted by purely random noise affecting only the attributes (and not the labels). Foruniform attribute noise, in which each attribute is flipped independently at random with the same probability, we present an algorithm that PAC learns monomials for any (unknown) noise rate less than21. Contrasting this positive result, we show thatproduct random attribute noise, where each attributei is flipped randomly and independently with its own probability pi, is nearly as harmful as malicious noise-no algorithm can tolerate more than a very small amount of such noise.
Learning Binary Relations Using Weighted Majority Voting

In this paper we demonstrate how weighted majority voting with multiplicative weight updating can be applied to obtain robust algorithms for learning binary relations. We first present an algorithm that obtains a nearly optimal mistake bound but at the expense of using exponential computation to make each prediction. However, the time complexity of our algorithm is significantly reduced from that of previously known algorithms that have comparable mistake bounds. The second algorithm we present is a polynomial time algorithm with a non-optimal mistake bound. Again the mistake bound of our second algorithm is significantly better than previous bounds proven for polynomial time algorithms.A key contribution of our work is that we define a “non-pure” or noisy binary relation and then by exploiting the robustness of weighted majority voting with respect to noise, we show that both of our algorithms can learn non-pure relations. These provide the first algorithms that can learn non-pure binary relations.
PAC learning of one-dimensional patterns

Developing the ability to recognize a landmark from a visual image of a robot's current location is a fundamental problem in robotics. We consider the problem of PAC-learning the concept class of geometric patterns where the target geometric pattern is a configuration ofk points on the real line. Each instance is a configuration ofn points on the real line, where it is labeled according to whether or not it visually resembles the target pattern. To capture the notion of visual resemblance we use the Hausdorff metric. Informally, two geometric patternsP andQ resemble each other under the Hausdorff metric if every point on one pattern is “close” to some point on the other pattern. We relate the concept class of geometric patterns to the landmark matching problem and then present a polynomial-time algorithm that PAC-learns the class of one-dimensional geometric patterns. We also present some experimental results on how our algorithm performs.
Multiple-Instance Learning of Real-Valued Geometric Patterns

Recently there has been significant research in multiple-instance learning, yet most of this work has only considered this model when there are Boolean labels. However, in many of the application areas for which the multiple-instance model fits, real-valued labels are more appropriate than Boolean labels. We define and study a real-valued multiple-instance model in which each multiple-instance example (bag) is given a real-valued label in [0, 1] that indicates the degree to which the bag satisfies the target concept. To provide additional structure to the learning problem, we associate a real-valued label with each point in the bag. These values are then combined using a real-valued aggregation operator to obtain the label for the bag. We then present on-line agnostic algorithms for learning real-valued multiple-instance geometric concepts defined by axis-aligned boxes in constant-dimensional space and describe several possible applications of these algorithms. We obtain our learning algorithms by reducing the problem to one in which the exponentiated gradient or gradient descent algorithm can be used. We also give a novel application of the virtual weights technique. In typical applications of the virtual weights technique, all of the concepts in a group have the same weight and prediction, allowing a single “representative” concept from each group to be tracked. However, in our application there are an exponential number of different weights and possible predictions. Hence, boxes in each group have different weights and predictions, making the computation of the contribution of a group significantly more involved. However, we are able to both keep the number of groups polynomial in the number of trials and efficiently compute the overall prediction.
A Theoretical and Empirical Study of a Noise-Tolerant Algorithm to Learn Geometric Patterns

Developing the ability to recognize a landmark from a visual image of a robot's current location is a fundamental problem in robotics. We describe a way in which the landmark matching problem can be mapped to that of learning a one-dimensional geometric pattern. The first contribution of our work is an efficient noise-tolerant algorithm (designed using the statistical query model) to PAC learn the class of one-dimensional geometric patterns. The second contribution of our work is an empirical study of our algorithm that provides some evidence that statistical query algorithms may be valuable for use in practice for handling noisy data.
Learning binary relations using weighted majority voting

In this paper we demonstrate how weighted majority voting with multiplicative weight updating can be applied to obtain robust algorithms for learning binary relations. We first present an algorithm that obtains a nearly optimal mistake bound but at the expense of using exponential computation to make each prediction. However, the time complexity of our algorithm is significantly reduced from that of previously known algorithms that have comparable mistake bounds. The second algorithm we present is a polynomial time algorithm with a non-optimal mistake bound. Again the mistake bound of our second algorithm is significantly better than previous bounds proven for polynomial time algorithms.A key contribution of our work is that we define a “non-pure” or noisy binary relation and then by exploiting the robustness of weighted majority voting with respect to noise, we show that both of our algorithms can learn non-pure relations. These provide the first algorithms that can learn non-pure binary relations.
The power of self-directed learning

This article studies self-directed learning, a variant of the on-line (or incremental) learning model in which the learner selects the presentation order for the instances. Alternatively, one can view this model as a variation of learning with membership queries in which the learner is only “charged” for membership queries for which it could not predict the outcome. We give tight bounds on the complexity of self-directed learning for the concept classes of monomials, monotone DNF formulas, and axis-parallel rectangles in {0, 1, ...,n − 1}d. These results demonstrate that the number of mistakes under self-directed learning can be surprisingly small. We then show that learning complexity in the model of self-directed learning is less than that of all other commonly studied on-line and query learning models. Next we explore the relationship between the complexity of self-directed learning and the Vapnik-Chervonenkis (VC-)dimension. We show that, in general, the VC-dimension and the self-directed learning complexity are incomparable. However, for some special cases, we show that the VC-dimension gives a lower bound for the self-directed learning complexity. Finally, we explore a relationship between Mitchell's version space algorithm and the existence of self-directed learning algorithms that make few mistakes.