nils-herrmann/clean_scirepeval huggingface dataset:
Comparison of two multisheet transmission windows for millimeter-wave radiometers

The design and performance of a triple-sheet Mylar window and a Teflon-and-foam window are studied theoretically and experimentally to determine which window produces the smaller brightness temperature variations and, therefore, leads to better accuracy of radiometers operating at frequencies of 20-90 GHz. Theoretical brightness temperatures due to emission and reflection by the windows are calculated and compared; experimental measurements obtained by using the clear atmosphere as a radiation source are also compared. Both theory and experiment indicate that the Teflon-and-foam window produces smaller brightness temperature variations at these operating frequencies. Effects of the windows on integrated water vapor and liquid measurements are also examined; errors are significant for the triple-sheet Mylar window but are negligible for the Teflon-and-foam window. It is observed that use of the Teflon-and-foam design results in acceptably small errors in radiometric measurements of water vapor and cloud liquid.
The STRANDS Project: Long-Term Autonomy in Everyday Environments

Thanks to the efforts of the robotics and autonomous systems community, the myriad applications and capacities of robots are ever increasing. There is increasing demand from end users for autonomous service robots that can operate in real environments for extended periods. In the Spatiotemporal Representations and Activities for Cognitive Control in Long-Term Scenarios (STRANDS) project (http://strandsproject.eu), we are tackling this demand head-on by integrating state-of-the-art artificial intelligence and robotics research into mobile service robots and deploying these systems for long-term installations in security and care environments. Our robots have been operational for a combined duration of 104 days over four deployments, autonomously performing end-user-defined tasks and traversing 116 km in the process. In this article, we describe the approach we used to enable long-term autonomous operation in everyday environments and how our robots are able to use their long run times to improve their own performance.
An efficient method for contour tracking using active shape models

There has been considerable research interest recently, in the areas of real time contour tracking and active shape models. This paper demonstrates how dynamic filtering can be used in combination with a modal-based flexible shape model to track an articulated non-rigid body in motion. The results show the method being used to track the silhouette of a walking pedestrian in real time. The active shape model used was generated automatically from real image data and incorporates variability in shape due to orientation as well as object flexibility. A Kalman filter is used to control spatial scale for feature search over successive frames. Iterative refinement allows accurate contour localisation where feasible. The shape model incorporates knowledge of the likely shape of the contour and speeds up tracking by reducing the number of system parameters. A further increase in speed is obtained by filtering the shape parameters independently.<<ETX>>
Atmospheric limitations on satellite communications at frequencies exceeding 15 GHz

Interest in use of frequencies exceeding 15 GHz for satellite communications is steadily increasing, and indeed it is planned to share frequency bands between satellite and terrestrial systems. It is advantageous to consider use of two polarizations, thereby increasing the information capacity by perhaps a factor of two. However, fundamental limitations are imposed on such systems by effects of rain: attenuation, scattering, and depolarization. The present status of measurement and theory of these effects in the 15 to 100 GHz range is discussed. From terrestrial measurements, attenuation is known to depend on polarization; both this dependence and depolarization of the waves are caused by the oblateness of the raindrops. The role that path diversity can play in improving the reliability of satellite systems is evaluated from measured data. The techniques involved in measuring at the various frequencies are discussed as time permits. The use of radiometer measurements of noise from the rain for determining the attenuation is discussed in some detail. Since the measuring equipments are required to operate continuously in order to produce reliable statistics, advantage of solid-state technology is taken where possible.
Towards 3D hand tracking using a deformable model

In this paper we first describe how we have constructed a 3D deformable Point Distribution Model of the human hand, capturing training data semi-automatically from volume images via a physically-based model. We then show how we have attempted to use this model in tracking an unmarked hand moving with 6 degrees of freedom (plus deformation) in real time using a single video camera. In the course of this we show how to improve on a weighted least-squares pose parameter approximation at little computational cost. We note the successes and shortcomings of our system and discuss how it might be improved.
Wormholes in shape space: tracking through discontinuous changes in shape

Existing object tracking algorithms generally use some form of local optimisation, assuming that an object's position and shape change smoothly over time. In some situations this assumption is not valid: the track able shape of an object may change discontinuously, for example if it is the 2D silhouette of a 3D object. In this paper we propose a novel method for modelling temporal shape discontinuities explicitly. Allowable shapes are represented as a union of (learned) bounded regions within a shape space. Discontinuous shape changes are described in terms of transitions between these regions. Transition probabilities are learned from training sequences and stored in a Markov model. In this way we can create 'wormholes' in shape space. Tracking with such models is via an adaptation, of the CONDENSATION algorithm.
Who knows who - Inverting the Social Force Model for finding groups

Social groups based on friendship or family relations are very common phenomena in human crowds and a valuable cue for a crowd activity recognition system. In this paper we present an algorithm for automatic on-line inference of social groups from observed trajectories of individual people. The method is based on the Social Force Model (SFM) - widely used in crowd simulation applications - which specifies several attractive and repulsive forces influencing each individual relative to the other pedestrians and their environment. The main contribution of the paper is an algorithm for inference of the social groups (parameters of the SFM) based on analysis of the observed trajectories through attractive or repulsive forces which could lead to such behaviour. The proposed SFM-based method shows its clear advantage especially in more crowded scenarios where other state-of-the-art methods fail. The applicability of the algorithm is illustrated on an abandoned bag scenario.
Rain, radiometry, and radar

A remote sensing system is proposed for quantitative operational measurement of the amount of liquid per unit volume in heavy rain. In particular, a 6-GHz radiometer in conjunction with a 10-cm wavelength radar is discussed. An experimental example involving a discrete heavy rain cell with a derived rainrate of 150 mm/h measured by a 6-GHz radiometer/3-GHz radar combination is given. The effect of clear air brightness temperatures, from oxygen and water vapor, on the 6-GHz radiometric rain measurements is analyzed, and an equation relating the 6-GHz brightness temperature from the rain to the radar backscattered power is derived. Design factors important in an operational observing system are also discussed.
Weakly supervised pedestrian detector training by unsupervised prior learning and cue fusion in videos

The growth in the amount of collected video data in the past decade necessitates automated video analysis for which pedestrian detection plays a key role. Training a pedestrian detector using supervised machine learning requires tedious manual annotation of pedestrians in the form of precise bounding boxes. In this paper, we propose a novel weakly supervised algorithm to train a pedestrian detector that only requires annotations of estimated centers of pedestrians instead of bounding boxes. Our algorithm makes use of a pedestrian prior learnt in an unsupervised way from the video and this prior is fused with the given weak supervision information in a principled manner. We show on publicly available datasets that our weakly supervised algorithm reduces the cost of manual annotation by over 4 times while achieving similar performance to a pedestrian detector trained with bounding box annotations.
Process mining in oncology: A literature review

Process mining, an emerging data analytics method, has been used effectively in various healthcare contexts including oncology, the study of cancer. Cancer is a complex disease with many complicated care requirements and there is an urgent need to improve the cost and clinical effectiveness of cancer care pathways. Process mining of the e-health records of cancer patients may play an important future role and this paper presents a literature review of process mining in oncology as a contribution to this research. The search produced 758 articles which were manually reviewed by title, abstract, and full paper text review to develop the original pool of papers. An in-depth ancestor search was used to gather additional articles from the references of the original pool. These steps resulted in 37 papers. Through a thematic review process, the papers were analysed and five themes emerged. These were: 1) process and data types; 2) research questions; 3) techniques, perspectives and tools; 4) methodologies; 5) limitations and future work.
Radio-wave profiling of the earth's atmosphere from the surface

Nine AP-S members have been e l ec t ed to the grade of  Fellow i n the IEEE, e f e c t i v e 1  January  1982. The grade of Fellow i s one of unusual professional dis t inct ion conferred only by inv i t a t ion o f the Board of Directors upon a person of outstanding and ex t raord inary qua l i f ica t ions and experience i n IEEE designated f ie lds , who has made important contributions to one o r more of t h e s e f i e l d s . The nine AP-S members are:
Radiometric correction of atmospheric path length fluctuations in interferometric experiments. [in radio astronomy]

To support very long baseline interferometric experiments, the Jet Propulsion Laboratory has developed a system for estimating atmospheric water vapor path delay. The system consists of dual microwave radiometers, one operating at 20.7 GHz and the other at 31.4 GHz. The measured atmospheric brightness temperatures at these two frequencies yield the estimate of the precipitable water present in both vapor and droplets. To determine the accuracy of the system, a series of observations were undertaken comparing the outputs of two water vapor radiometers with the phase variation observed with two connected elements of the very large array (VLA). The results show that (1) water vapor fluctuations dominate the residual VLA phase and (2) the microwave radiometers can measure and correct these effects. The rms phase error after correction is typically 15° at a wavelength of 6 cm, corresponding to an uncertainty in the path delay of 0.25 cm. The residual uncertainty is consistent with the stability of the microwave radiometer but is still considerably larger than the stability of the VLA. The technique is less successful under conditions of heavy cloud.
Detecting Carried Objects from Sequences of Walking Pedestrians

This paper proposes a method for detecting objects carried by pedestrians, such as backpacks and suitcases, from video sequences. In common with earlier work ,  on the same problem, the method produces a representation of motion and shape (known as a temporal template) that has some immunity to noise in foreground segmentations and phase of the walking cycle. Our key novelty is for carried objects to be revealed by comparing the temporal templates against view-specific exemplars generated offline for unencumbered pedestrians. A likelihood map of protrusions, obtained from this match, is combined in a Markov random field for spatial continuity, from which we obtain a segmentation of carried objects using the MAP solution. We also compare the previously used method of periodicity analysis to distinguish carried objects from other protrusions with using prior probabilities for carried-object locations relative to the silhouette. We have reimplemented the earlier state-of-the-art method  and demonstrate a substantial improvement in performance for the new method on the PETS2006 data set. The carried-object detector is also tested on another outdoor data set. Although developed for a specific problem, the method could be applied to the detection of irregularities in appearance for other categories of object that move in a periodic fashion.
Using neural network to learn spatial-temporal models for moving deformable objects tracking

We investigate the effectiveness of using neural networks for multivariate time series prediction on data from a practical machine vision domain. The data, taking the form of eight short sequences of compact state vectors, are descriptions of typical walking behaviors of a pedestrian in an outdoor scene. The prediction of the changes in the state vector based on a bank of neural network models opens a new way to track the motion of the pedestrian within the scene, showing some unique features. The particulars of this data set are analyzed, the emphasis has been laid on the design of network architecture and the formulation of training patterns to accommodate the spatial-temporal variations of the problem. The prediction results of the models trained using the noise regularization method have been obtained with great success.
Motion segmentation by consensus

We present a method for merging multiple partitions into a single partition, by minimising the ratio of pairwise agreements and contradictions between the equivalence relations corresponding to the partitions. The number of equivalence classes is determined automatically. This method is advantageous when merging segmentations obtained independently. We propose using this consensus approach to merge segmentations of features tracked on video. Each segmentation is obtained by clustering on the basis of mean velocity during a particular time interval.
Feature Space Analysis for Human Activity Recognition in Smart Environments

Activity classification from smart environment data is typically done employing ad hoc solutions customised to the particular dataset at hand. In this work we introduce a general purpose collection of features for recognising human activities across datasets of different type, size and nature. The first experimental test of our feature collection achieves state of the art results on well known datasets, and we provide a feature importance analysis in order to compare the potential relevance of features for activity classification in different datasets.
The role of rain in satellite communications

The most fundamental obstacle encountered in design of satellite communication systems at frequencies above 10 GHz is attenuation by rain. The microwave power radiated toward an earth station, being limited by factors such as available primary power and size of antenna on the satellite, is insufficient, with present technology, to overcome the large attenuation produced by intense rain cells on the earth-space path. The resultant loss of signal makes for unreliable transmission. In what follows, methods of measurement of this attenuation at various frequencies and a technique called path diversity that substantially improves the reliability are presented. Other degradations produced by rain, such as depolarization, interference, increase in earth-station noise, and deterioration of earth-station antenna performance, are also discussed.
Context Aware Detection and Tracking

This paper presents a novel approach to incorporate multiple contextual factors into a tracking process, for the purpose of reducing false positive detections. While much previous work has focused on improving object detection on static images using context, these have not been integrated into the tracking process. Our hypothesis is that a significant improvement can result from the use of context in dynamically influencing the linking of object detections, during the tracking process. To verify this hypothesis, we augment a state of the art dynamic programming based tracker with contextual information by reformulating the maximum a posteriori (MAP) estimation formulation. This formulation introduces contextual factors that first of all augment detection strengths and secondly provides temporal context. We allow both these types of factors to contribute organically to the linking process by learning the relative contribution of each of these factors jointly during a gradient decent based optimisation process. Our experiments demonstrate that the proposed approach contributes to a significantly superior performance on a recent challenging video dataset, which captures complex scenes with a wide range of object types and diverse backgrounds.
The experiment research on diamond string bead wire for cutting the seabed oil pipelines

Seabed oil pipelines are kinds of complex material. During the maintenance and repair of Seabed oil pipelines which need cut off when they are breakage, and it is very difficult to complete the task with common underwater cutting ways. For cutting complex material and materials which have irregular form with diamond wire saw is a perfect technique. The paper gives an introduction of constitution and principle of operation about diamond bead wire saw. The experiments of the diamond wire saw for cutting the seabed oil pipelines have finished. We have got all of the information and relation about the technical parameters which contain feed rate override and cutting force, cutting force and cutting speed, the wear rate of diamond bead wire and the feed rate override, the wear rate of diamond bead wire and the cutting speed through analysis and treat experiment records, and create a mathematic mode of cutting force with regression orthogonal experiment. The conclusion that application the diamond wire saw for cutting the seabed oil pipelines is practical and beneficial ways especially in underwater circumstance and special construct condition of seabed oil pipelines.
Query Adaptive Similarity Measure for RGB-D Object Recognition

This paper studies the problem of improving the top-1 accuracy of RGB-D object recognition. Despite of the impressive top-5 accuracies achieved by existing methods, their top-1 accuracies are not very satisfactory. The reasons are in two-fold: (1) existing similarity measures are sensitive to object pose and scale changes, as well as intra-class variations, and (2) effectively fusing RGB and depth cues is still an open problem. To address these problems, this paper first proposes a new similarity measure based on dense matching, through which objects in comparison are warped and aligned, to better tolerate variations. Towards RGB and depth fusion, we argue that a constant and golden weight doesn't exist. The two modalities have varying contributions when comparing objects from different categories. To capture such a dynamic characteristic, a group of matchers equipped with various fusion weights is constructed, to explore the responses of dense matching under different fusion configurations. All the response scores are finally merged following a learning-to-combination way, which provides quite good generalization ability in practice. The proposed approach win the best results on several public benchmarks, e.g., achieves 92.7% top-1 test accuracy on the Washington RGB-D object dataset, with a 5.1% improvement over the state-of-the-art.
Better proposal distributions: object tracking using unscented particle filter

Tracking objects involves the modeling of non-linear non-Gaussian systems. On one hand, variants of Kalman filters are limited by their Gaussian assumptions. On the other hand, conventional particle filter, e.g., CONDENSATION, uses transition prior as the proposal distribution. The transition prior does not take into account current observation data, and many particles can therefore be wasted in low likelihood area. To overcome these difficulties, unscented particle filter (UPF) has recently been proposed in the field of filtering theory. In this paper, we introduce the UPF framework into audio and visual tracking. The UPF uses the unscented Kalman filter to generate sophisticated proposal distributions that seamlessly integrate the current observation, thus greatly improving the tracking performance. To evaluate the efficacy of the UPF framework, we apply it in two real-world tracking applications. One is the audio-based speaker localization, and the other is the vision-based human tracking. The experimental results are compared against those of the widely used CONDENSATION approach and have demonstrated superior tracking performance.
Efficient 2D-to-3D Correspondence Filtering for Scalable 3D Object Recognition

3D model-based object recognition has been a noticeable research trend in recent years. Common methods find 2D-to-3D correspondences and make recognition decisions by pose estimation, whose efficiency usually suffers from noisy correspondences caused by the increasing number of target objects. To overcome this scalability bottleneck, we propose an efficient 2D-to-3D correspondence filtering approach, which combines a light-weight neighborhood-based step with a finer-grained pairwise step to remove spurious correspondences based on 2D/3D geometric cues. On a dataset of 300 3D objects, our solution achieves ~10 times speed improvement over the baseline, with a comparable recognition accuracy. A parallel implementation on a quad-core CPU can run at ~3fps for 1280×720 images.
A Distributed Approach Toward Discriminative Distance Metric Learning

Distance metric learning (DML) is successful in discovering intrinsic relations in data. However, most algorithms are computationally demanding when the problem size becomes large. In this paper, we propose a discriminative metric learning algorithm, develop a distributed scheme learning metrics on moderate-sized subsets of data, and aggregate the results into a global solution. The technique leverages the power of parallel computation. The algorithm of the aggregated DML (ADML) scales well with the data size and can be controlled by the partition. We theoretically analyze and provide bounds for the error induced by the distributed treatment. We have conducted experimental evaluation of the ADML, both on specially designed tests and on practical image annotation tasks. Those tests have shown that the ADML achieves the state-of-the-art performance at only a fraction of the cost incurred by most existing methods.