nils-herrmann/clean_scirepeval huggingface dataset:
Toothpic: Who took this picture?

Managing photos over the Internet is an increasingly bigger problem with sizeable social and financial implications. More than 1 billion Facebook users upload in excess of 350 millions of new pictures per day and 250 billions overall, not to mention other social media sites such as Flickr, Instagram, Google+, Tumblr and Pinterest, and these figures are growing steadily. It is hence not surprising that a huge problem has arisen for the users and social media sites alike: it is very difficult to track down a wide range of improper uses of the photos, such as exploiting them for commercial purposes, re-posting others' photos without consent or infringing copyright, posting photos containing unethical or illegal contents, and so forth. To quantify the magnitude of these issues, consider that social media company The Content Factory reportedly got sued $8,000 for using an image on a blog post that got less than 100 visitors. Pinterest's users are held responsible for the pictures they post or re-post, plus they automatically grant licence for reuse on Pinterest. Since many of Pinterest's 70+ million users arguably do not own these rights, they may greatly suffer from legal actions, and this may in turn disrupt the service's popularity.
Distributed scheduling for scalable P2P video streaming with Network Coding

Previous research has shown the benefits of random-push Network Coding (NC) for P2P video streaming. On the other hand, scalable video coding provides graceful quality adaptation to heterogeneous network conditions. Nevertheless, packet scheduling for scalable media streaming with P2P NC is still a largely unexplored problem. Our ongoing research aims at designing a packet scheduling scheme that maximizes the quality of the video with minimal coordination among peers. In this work, we provide a preliminary description of our scheduling scheme and preliminary performance measurements.
Distributed Iterative Thresholding for  $\ell _{0}/\ell _{1}$ -Regularized Linear Inverse Problems

The ℓ0/ℓ1-regularized least-squares approach is used to deal with linear inverse problems under sparsity constraints, which arise in mathematical and engineering fields. In particular, multiagent models have recently emerged in this context to describe diverse kinds of networked systems, ranging from medical databases to wireless sensor networks. In this paper, we study methods for solving ℓ0/ℓ1-regularized leastsquares problems in such multiagent systems. We propose a novel class of distributed protocols based on iterative thresholding and input driven consensus techniques, which are well-suited to work in-network when the communication to a central processing unit is not allowed. Estimation is performed by the agents themselves, which typically consist of devices with limited computational capabilities. This motivates us to develop low-complexity and low-memory algorithms that are feasible in real applications. Our main result is a rigorous proof of the convergence of these methods in regular networks. We introduce a suitable distributed, regularized, least-squares functional, and we prove that our algorithms reach their minima using results from dynamical systems theory. Furthermore, we propose numerical comparisons with the alternating direction method of multipliers and the distributed subgradient methods, in terms of performance, complexity, and memory usage. We conclude that our techniques are preferable for their good memory-accuracy tradeoff.
Concealment of whole-frame losses for wireless low bit-rate video based on multiframe optical flow estimation

In low bit-rate packet-based video communications, video frames may have very small size, so that each frame fills the payload of a single network packet; thus, packet losses correspond to whole-frame losses, to which the existing error concealment algorithms are badly suited and generally not applicable. In this paper, we deal with the problem of concealment of whole frame-losses, and propose a novel technique which is capable of handling this very critical case. The proposed technique presents other two major innovations with respect to the state-of-the-art: i) it is based on optical flow estimation applied to error concealment and ii) it performs multiframe estimation, thus optimally exploiting the multiple reference frame buffer featured by the most modern video coders such as H.263+ and H.264. If data partitioning is employed, by e.g., sending headers, motion vectors, and coding modes in prioritized packets as can be done in the DiffServ network model, the algorithm is capable of exploiting the motion vectors to improve the error concealment results. The algorithm has been embedded in the H.264 test model software, and tested under both independent and correlated packet loss models with parameters typical of the wireless environment. Results show that the proposed algorithm significantly outperforms other techniques by several dBs in peak signal-to-noise ratio (PSNR), provides good visual quality, and has a rather low complexity, which makes it possible to perform real-time operation with reasonable computational resources.
Pattern recognition using higher-order local autocorrelation coefficients

The autocorrelations have been previously used as features for 1D or 2D signal classification in a wide range of applications, like texture classification, face detection and recognition, EEG signal classification, and so on. However, in almost all the cases, the high computational costs have hampered the extension to higher orders (more than the second order). We present a method which avoids the computation of the autocorrelation coefficients and which can be applied to a large set of tools commonly used in statistical pattern recognition. We discuss different scenarios of using the autocorrelations and we show that the order of autocorrelations is no longer an obstacle.
Relevant Feature Selection for Audio-Visual Speech Recognition

We present a feature selection method based on information theoretic measures, targeted at multimodal signal processing, showing how we can quantitatively assess the relevance of features from different modalities. We are able to find the features with the highest amount of information relevant for the recognition task, and at the same having minimal redundancy. Our application is audio-visual speech recognition, and in particular selecting relevant visual features. Experimental results show that our method outperforms other feature selection algorithms from the literature by improving recognition accuracy even with a significantly reduced number of features.
Cardiac output measured by electrical impedance tomography: Applications and limitations

Cardiac output is one of the most determining parameters to assess cardiac function and manage hemodynamically unstable patients. Its continuous and non-invasive estimation, however, remains a technical challenge. In that context, electrical impedance tomography (EIT), an emerging modality for a new class of safe and portable hemodynamic monitors, appears as an appealing candidate for the continuous monitoring of cardiac output. However, the interpretation of EIT signals requires a feasibility study to be carried out. In the context of the Nano-Tera ObeSense project, we thus proposed a model and numerical simulations to evaluate the impact of myocardial motion in the estimation of cardiac output by EIT. It was found that 64% of the cardiac EIT signal originates from the mechanical interaction of the heart muscle with its surrounding tissues. However, because these movements are correlated with ventricular blood volume, the EIT-based estimation of cardiac output is not compromised. In parallel, regarding the technical aspect of EIT, we proposed innovative solutions for its measurement setup where the wiring complexity is drastically reduced, thus opening the way towards portable EIT systems.
Sample and Pixel Weighting Strategies for Robust Incremental Visual Tracking

In this paper, we introduce the incremental temporally weighted principal component analysis (ITWPCA) algorithm, based on singular value decomposition update, and the incremental temporally weighted visual tracking with spatial penalty (ITWVTSP) algorithm for robust visual tracking. ITWVTSP uses ITWPCA for computing incrementally a robust low dimensional subspace representation (model) of the tracked object. The robustness is based on the capacity of weighting the contribution of each single sample to the subspace generation to reduce the impact of bad quality samples, reducing the risk of model drift. Furthermore, ITWVTSP can exploit the a priori knowledge about important regions of a tracked object. This is done by penalizing the tracking error on some predefined regions of the tracked object, which increases the accuracy of tracking. Several tests are performed on several challenging video sequences, showing the robustness and accuracy of the proposed algorithm, as well as its superiority with respect to state-of-the-art techniques.
Live demonstration: Inexpensive 1024-channel 3D telesonography system on FPGA

Volumetrie ultrasound (US) is a very promising development of medical US imaging. An under-exploited advantage of volumetric US is the mitigation of the strict probe positioning constrains necessary to acquire 2D scans, potentially allowing the decoupling of US image acquisition and diagnosis. However, today's 3D US systems are large and beset by high power and cost requirements, making them only available in well-equipped hospitals. In this work , , we propose the first telesonography-capable medical imaging system that supports up to 1024 channels, on par with the state of the art. As a first embodiment, we have implemented our design in a single development FPGA board of 26.7cm × 14cm × 0.16cm, with an estimated power consumption of 6.1 W. The imager exploits a highly scalable architecture which can be either downscaled for 2D imaging, or further upscaled on a larger FPGA. Moreover, our design supports two types of data inputs: real-time via an optical connection and offline over Ethernet. The reconstructed images can be visualized on an HDMI screen. The estimated cost of the proposed prototype materials is less than 4000€. TABLE I shows the resources utilization of the current design and the extrapolated utilization in case of further upscaling on a larger FPGA.
Estimation of soil moisture from airborne hyperspectral imagery with support vector regression

In this paper, we propose to estimate soil moisture in bare soils directly from hyperspectral imagery using support vector regression (υ-SVR). υ-SVR is a supervised non-parametric learning technique, e.g. making no assumption on the underlying data distribution, which shows good generalization properties even when only a limited number of training samples is available (which is often the case in soil moisture estimation). Estimation in six tilled bare soil fields shows the potential of using non-linear υ-SVR for the prediction of gravimetric soil moisture. Dependence to the origin of training samples, as well as their number, is thoroughly considered.
Text identification in complex background using SVM

The paper presents a fast and robust algorithm to identify text in image or video frames with complex backgrounds and compression effects. The algorithm first extracts the candidate text line on the basis of edge analysis, baseline location and heuristic constraints. Support Vector Machine (SVM) is then used to identify text line from the candidates in edge-based distance map feature space. Experiments based on a large amount of images and video frames from different sources showed the advantages of this algorithm compared to conventional methods in both identification quality and computation time.
Towards Ultrasound Everywhere: A Portable 3D Digital Back-End Capable of Zone and Compound Imaging

Ultrasound imaging is a ubiquitous diagnostic technique, but does not fit the requirements of the telemedicine approach, because it relies on the real-time manipulation and image recognition skills of a trained expert, called sonographer. Sonographers are only available in hospitals and clinics, negating or at least delaying access to ultrasound scans in many locales—rural areas, developing countries—as well as in medical rescue operations. Telesonography would require an advanced imager that supports three-dimensional (3-D) acquisition; this would allow untrained operators to acquire broad scans and upload them remotely for diagnosis. Such advanced imagers do exist, but do not meet several other requirements for telesonography, such as being portable, inexpensive, and sufficiently low power to enable battery operation. In this work, we present our prototype of the first portable 3-D digital ultrasound back-end system. The prototype is implemented in a single midrange Xilinx field programmable gate array (FPGA), for an estimated power consumption of 5 W. The device supports up to 1024 input channels, which is state of the art and could be scaled further, and supports multiple image reconstruction modes. We evaluate the resource utilization of the FPGA and provide various quality metrics to ascertain the output image quality.
Atlas-based segmentation of pathological MR brain images using a model of lesion growth

We propose a method for brain atlas deformation in the presence of large space-occupying tumors, based on an a priori model of lesion growth that assumes radial expansion of the lesion from its starting point. Our approach involves three steps. First, an affine registration brings the atlas and the patient into global correspondence. Then, the seeding of a synthetic tumor into the brain atlas provides a template for the lesion. The last step is the deformation of the seeded atlas, combining a method derived from optical flow principles and a model of lesion growth. Results show that a good registration is performed and that the method can be applied to automatic segmentation of structures and substructures in brains with gross deformation, with important medical applications in neurosurgery, radiosurgery, and radiotherapy.
Lower and upper bounds for approximation of the Kullback-Leibler divergence between Gaussian Mixture Models

Many speech technology systems rely on Gaussian Mixture Models (GMMs). The need for a comparison between two GMMs arises in applications such as speaker verification, model selection or parameter estimation. For this purpose, the Kullback-Leibler (KL) divergence is often used. However, since there is no closed form expression to compute it, it can only be approximated. We propose lower and upper bounds for the KL divergence, which lead to a new approximation and interesting insights into previously proposed approximations. An application to the comparison of speaker models also shows how such approximations can be used to validate assumptions on the models.
Action Units and Their Cross-Correlations for Prediction of Cognitive Load during Driving

Driving requires the constant coordination of many body systems and full attention of the person. Cognitive distraction (subsidiary mental load) of the driver is an important factor that decreases attention and responsiveness, which may result in human error and accidents. In this paper, we present a study of facial expressions of such mental diversion of attention. First, we introduce a multi-camera database of 46 people recorded while driving a simulator in two conditions, baseline and induced cognitive load using a secondary task. Then, we present an automatic system to differentiate between the two conditions, where we use features extracted from Facial Action Unit (AU) values and their cross-correlations in order to exploit recurring synchronization and causality patterns. Both the recording and detection system are suitable for integration in a vehicle and a real-world application, e.g., an early warning system. We show that when the system is trained individually on each subject we achieve a mean accuracy and F-score of <inline-formula><tex-math notation="LaTeX">$\sim 95$</tex-math><alternatives> <inline-graphic xlink:href="yuce-ieq1-2584042.gif"/></alternatives></inline-formula> percent, and for the subject independent tests <inline-formula><tex-math notation="LaTeX">$\sim 68$</tex-math><alternatives> <inline-graphic xlink:href="yuce-ieq2-2584042.gif"/></alternatives></inline-formula> percent accuracy and <inline-formula><tex-math notation="LaTeX">$\sim 66$</tex-math><alternatives> <inline-graphic xlink:href="yuce-ieq3-2584042.gif"/></alternatives></inline-formula> percent F-score, with person-specific normalization to handle subject dependency. Based on the results, we discuss the universality of the facial expressions of such states and possible real-world uses of the system.
USSR: An ultrasound sparse regularization framework

Ultrafast ultrasound (US) imaging uses unfocused waves to insonify the whole medium of interest at once, allowing pulse-echo US imaging to achieve very high frame rates, at the cost of a lower image quality. In this paper, we present USSR, an UltraSound Sparse Regularization framework which permits high-quality imaging at fast rates and with a very low memory footprint. The framework, based on highly parallelizable, parametric, matrix-free formulations of the measurement model and its adjoint as well as on well-chosen sparsity priors, is implemented on multi-threaded architectures and evaluated on the publicly available PICMUS dataset.
Bayesian integration of a discrete choice pedestrian behavioral model and image correlation techniques for automatic multiobject tracking

In this paper we deal with the multiobject tracking problem in the particular case of pedestrians, assuming the detection step already done. We use a Bayesian framework to combine the likelihood term provided by an image correlation algorithm with a prior distribution given by a discrete choice model for pedestrian behavior, calibrated on real data. We aim to show how the combination of the image information with a model of pedestrian behavior can provides appreciable results in real and complex scenarios.
Accurate segmentation of 3-D magnetic resonance images of the head using a directional watershed transform

A new algorithm for automatic and accurate segmentation of 3-D magnetic resonance images (MRI) is presented. It improves the classical watershed transformation (WST) whose results are inaccurate when applied on 3-D noisy data. It uses directional information and is less sensitive to noise since it does not require the gradient image. Initial results are presented.
Morphological registration of 3D medical images

Registration of three-dimensional medical image modalities (PET and MRI) of the human brain is performed by a multi-scale morphological shape description combined with 3D Chamfer matching. Objects of Interest are first segmented. For MR images of the head, a Directional Watershed algorithm is used to obtain an accurate segmentation of the brain. PET images are segmented by thresholding combined with region growing. The shape description is then performed by generalized morphological skeletons, introduced in this article, providing a multi-scale representation of the object shape. Registration is finally operated by Chamfer matching, using a rigid transform for PET-MRI registration. Non-rigid registration is also evoked.
Morphological component analysis for sparse regularization in plane wave imaging

Classical ultrasound image reconstruction mainly relies on the well-known delay-and-sum (DAS) beamforming for its simplicity and real-time capability. Sparse regularization methods propose an alternative to DAS which lead to a better inversion of the ill-posed problem resulting from the acoustic wave propagation. In the following work, a new sparse regularization method is proposed which includes a component-based modelling of the radio-frequency images as well as a point-spread-function-adaptive sparsity prior. The proposed method, evaluated on the PICMUS dataset,outperforms the classical DAS in terms of contrast and resolution.
Selecting relevant visual features for speechreading

A quantitative measure of relevance is proposed for the task of constructing visual feature sets which are at the same time relevant and compact. A feature's relevance is given by the amount of information that it contains about the problem, while compactness is achieved by preventing the replication of information between features. To achieve these goals, we use mutual information both for assessing relevance and measuring the redundancy between features. Our application is speechreading, that is, speech recognition performed on the video of the speaker. This is justified by the fact that the performance of audio speech recognition can be improved by augmenting the audio features with visual ones, especially when there is noise in the audio channel. We report significant improvements compared to the most common method of dimensionality reduction for speechreading, Linear Discriminant Analysis (LDA).
Sparse non-negative decomposition of speech power spectra for formant tracking

Many works on speech processing have dealt with auto-regressive (AR) models for spectral envelope and formant frequency estimation, mostly focusing on the estimation of the AR parameters. However, it is also interesting to be able to directly estimate the formant frequencies, or equivalently the poles of the AR filter. To tackle this issue, we propose in this paper to decompose the signal onto several bases, one for each formant, taking advantage of recent works on nonnegative matrix factorization (NMF) for the estimation stage, further refined by sparsity and smoothness penalties. The results are encouraging, and the proposed system provides formant tracks which seem robust enough to be used in different applications such as phonetic analysis, emotion detection or as visual cue for computer-aided pronunciation training applications. The model can also be extended to deal with multiple-speaker signals.
Geodesic Active Fields on the Sphere

In this paper, we propose a novel method to register images defined on spherical meshes. Instances of such spherical images include inflated cortical feature maps in brain medical imaging or images from omni directional cameras. We apply the Geodesic Active Fields (GAF) framework locally at each vertex of the mesh. Therefore we define a dense deformation field, which is embedded in a higher dimensional manifold, and minimize the weighted Polyakov energy. While the Polyakov energy itself measures the hyper area of the embedded deformation field, its weighting allows to account for the quality of the current image alignment. Iteratively minimizing the energy drives the deformation field towards a smooth solution of the registration problem. Although the proposed approach does not necessarily outperform state-of-the-art methods that are tightly tailored to specific applications, it is of methodological interest due to its high degree of flexibility and versatility.
Structured sparsity through reweighting and application to diffusion MRI

We consider the problem of multiple correlated sparse signals reconstruction and propose a new implementation of structured sparsity through a reweighting scheme. We present a particular application for diffusion Magnetic Resonance Imaging data and show how this procedure can be used for fibre orientation reconstruction in the white matter of the brain. In that framework, our structured sparsity prior can be used to exploit the fundamental coherence between fibre directions in neighbour voxels. Our method approaches the ℓ0 minimisation through a reweighted ℓ1-minimisation scheme. The weights are here defined in such a way to promote correlated sparsity between neighbour signals.