nils-herrmann/clean_scirepeval huggingface dataset:
Detection and identification of heart sounds using homomorphic envelogram and self-organizing probabilistic model

This work presents a novel method for automatic detection and identification of heart sounds. Homomorphic filtering is used to obtain a smooth envelogram of the phono cardiogram, which enables a robust detection of events of interest in heart sound signal. Sequences of features extracted from the detected events are used as observations of a hidden Markov model. It is demonstrated that the task of detection and identification of the major heart sounds can be learned from unlabelled phono cardiograms by an unsupervised training process and without the assistance of any additional synchronizing channels
Neuronal Fiber Delineation in Area of Edema from Diffusion Weighted MRI

Diffusion Tensor Magnetic Resonance Imaging (DT-MRI) is a non invasive method for brain neuronal fibers delineation. Here we show a modification for DT-MRI that allows delineation of neuronal fibers which are infiltrated by edema. We use the Muliple Tensor Variational (MTV) framework which replaces the diffusion model of DT-MRI with a multiple component model and fits it to the signal attenuation with a variational regularization mechanism. In order to reduce free water contamination we estimate the free water compartment volume fraction in each voxel, remove it, and then calculate the anisotropy of the remaining compartment. The variational framework was applied on data collected with conventional clinical parameters, containing only six diffusion directions. By using the variational framework we were able to overcome the highly ill posed fitting. The results show that we were able to find fibers that were not found by DT-MRI.
Mosaicing of acoustic camera images

An algorithm for image registration and mosaicing on underwater sonar image sequences characterised by a high noise level, inhomogeneous illumination and low frame rate is presented. Imaging geometry of acoustic cameras is significantly different from that of pinhole cameras. For a planar surface viewed through a pinhole camera undergoing translational and rotational motion, registration can be obtained via a projective transformation. For an acoustic camera, it is shown that, under the same conditions, an affine transformation is a good approximation. A novel image fusion method, which maximises the signal-to-noise ratio of the mosaic image is proposed. The full procedure includes illumination correction, feature based transformation estimation, and image fusion for mosaicing.
Non-iterative construction of super-resolution image from an acoustic camera video sequence

The low visibility condition of underwater environments is often a limiting factor in the exploration of rivers or coastal areas. In such situations, relatively high resolution images produced by an acoustic camera can be an informative measure for the detection of terrain and objects. In this paper, we present a non-iterative super-resolution algorithm for the construction of a high resolution image from multiple frames of acoustic camera images. We discuss the geometry of insonification and image acquisition of an acoustic camera, and describe the detailed procedures for the registration, insonification correction, and fusion
Sonar object discrimination via spectral density analysis

Current underwater sonar exploration often neglects the information that exists in the spectral density of object returns. The attempt to perform spectral density exploration of objects is complicated by the difficulty to present informative spectral content of each location pixel on a 2D image. Spectral content is best represented by the spectral density; however, it does not make sense to explore the detailed spectral density for each pixel in the image. In this work, we outline a general approach for spectral density analysis which is intended for enhancing object discrimination and delivers an easy to interpret, image enhancement of sonar returns. This method actually enhances the sonar image with acoustic color which emphasizes an optimal combination of frequency bands of the returned spectrum for the purpose of object discrimination. This is achieved by analyzing the discriminating power of different frequency bands and creating an optimal association with different bands to a corresponding color map.
Sonar image enhancement via acoustic color

A novel method to enhance synthetic aperture sonar (SAS) images using the acoustic color of the returns from different targets is presented. The method enhances the SAS image using the internal absorption properties of targets in a way that enhances the differences between different types of targets (target signature). First, the algorithm detects the highlights in the sonar image as is found from the beam‐formed processed image. These highlights represent edges of targets. Then the analysis is performed only at the echo returns which correspond to edges of targets. For each edge the raw data corresponding to the return from that edge is found. This data which has not yet been pulse‐compressed using the pinging signal is analyzed using a detailed frequency representation. Then, the frequency representation of the pinging signal is subtracted from it so that a resulting frequency representation of the difference between the pinging signal and the returning signal is obtained. A normalization of the returne...
On Different Model Selection Criteria In A Forward And Backward Regression Hybrid Network

An assessment of the performance of a hybrid network with different model selection criteria is considered. These criteria are used in an automatic model selection algorithm for constructing a hybrid network of radial and perceptron hidden units for regression. A forward step builds the full hybrid network; a model selection criterion is used for controlling the network-size and another criterion is used for choosing the appropriate hidden unit for different regions of input space. This is followed by a conservative pruning step using Likelihood Ratio Test, Bayesian or Minimum Description Length, which leads to robust estimators with low variance. The result is a small architecture that performs well on difficult, realistic, benchmark data-sets of high dimensionality and small training size. Best results are obtained by using the Bayesian approach for the model selection.
Unsupervised statistical learning in vision : computational principles , biological evidence ∗

Unsupervised statistical learning is the standard setting for the development of the only advanced visual system that is both highly sophisticated and versatile, and extensively studied: that of monkeys and humans. In this extended abstract, we invoke philosophical observations, computational arguments, behavioral data and neurobiological findings to explain why computer vision researchers should care about (1) unsupervised learning, (2) statistical inference, and (3) the visual brain. We then outline a neuromorphic approach to structural primitive learning motivated by these considerations, survey a range of neurobiological findings and behavioral data consistent with it, and conclude by mentioning some of the more challenging directions for future research. 1 Why computer vision should care about unsupervised learning As the goals of computer vision grow more ambitious, the importance of learning becomes more difficult to deny: nobody wants to have to enter object and scene representations into his or her system by hand. But why should we insist that such learning be, in the first instance, unsupervised? Because we should not trust our analytical intuitions about the ontology of visual objects.Although the increasing availability of annotated image databases encourages the development of highly sophisticated supervised learning methods that combine linguistic and visual information (Duygulu et al., 2002), the success of such methods is limited by the poverty of the annotations, usually lexical labels. Indeed, a label (such as{cat, forest, grass, tiger }, shown in the work just cited) attached to a picture is both ontologically deficient in that it leaves out a host of possible complementary or alternative labels (Akins, 1996; Smith, 2001), and descriptively deficient in that it falls far short of providing a listener with a clear notion of the scene depicted (Kitcher and Varzi, 2000; Edelman, 2002). Thus, language conceived as a source of supervision for visual learning introduces biases that at best may be irrelevant, and at worst pernicious for any attempt to understand visual perception of objects and scenes. It would be prudent therefore to distrust ∗This is an extended abstract of a manuscript in preparation. Address correspondence to the first author at se37@cornell.edu . †On leave from the School of Computer Science, Tel-Aviv University, Tel Aviv 69678,
IMAGE REGISTRATION AND MOSAICING OF ACOUSTIC CAMERA IMAGES

This paper presents an algorithm for image registration and mosaicing on underwater sonar image sequences characterized by a high noise level, inhomogeneous illumination and low frame rate. Imaging geometry of acoustic cameras is significantly different from that of pinhole cameras. For a planar surface viewed through a pinhole camera undergoing translational and rotational motion, registration can be obtained via a projective transformation. We show that, under the same condition, an affine transformation is a good approximation for an acoustic camera. We further propose a novel image fusion method, which maximizes the signalto-noise ratio of the mosaic image. The full procedure includes illumination correction, feature based transformation estimation, and image fusion for mosaicing.
Image registration and mosaicing of noisy acoustic camera images

We introduce an algorithm for image registration and mosaicing on underwater sonar image sequences characterized by a high noise level, inhomogeneous illumination and low frame rate. For a planar surface viewed through a pinhole camera undergoing translational and rotational motion, registration can be obtained via a projective transformation. For an acoustic camera, we show that, under the same conditions, an affine transformation is a good approximation. We propose a novel image fusion, which maximizes the signal-to-noise ratio of the mosaic image. The full procedure includes illumination correction, feature based transformation estimation, and image fusion for mosaicing.
Acoustic camera image mosaicing and super-resolution

This paper presents an algorithm for image registration and mosaicing on video sequences acquired by an underwater acoustic camera. The sonar images of our interest can be characterized by a high noise level, inhomogeneous illumination and low frame rate. Imaging geometry of acoustic cameras is significantly different from that of pinhole cameras. For a planar surface viewed through a pinhole camera undergoing translational and rotational motion, registration can be obtained via a projective transformation. We show that, under the same condition, an affine transformation is a good approximation for an acoustic camera. We introduce image fusion algorithms which reduce the geometrical distortions which are caused by sharp camera movement. This reduces image blur and increases image resolution.
Lecture 12 : June 7 , 2004 Bayesian Networks

Bayesian networks are a useful tool. First, they are particularly useful for describing processes composed of locally interacting components; that is, the value of each component directly depends on the values of a relatively small number of components. Second, statistical foundations for learning Bayesian networks from observations, and computational algorithms to do so are well understood and have been used successfully in many applications. Finally, Bayesian networks provide models of causal influence: although Bayesian networks are mathematically defined strictly in terms of probabilities and conditional independence statements, a connection can be made between this characterization and the notion of direct causal influence.
The effect of noisy bootstrapping on the robustness of supervised classification of gene expression data

This paper discusses the role of noisy bootstrapping in the analysis of microarray data. We apply linear discriminant analysis, according to Fisher's method, to perform feature selection and classification, creating a linear model which enables clinicians easier interpretation of the results. We present the effects of bootstrapping in the improvement of the results, and specifically robustifying classification with an increased number of genes. The performance of our method is demonstrated on the publicly available datasets, and a comparison with state of the art published results is included. In particular, we show the effect of the number of features (genes) on the result, as well as the effect of bootstrapping. The results show that our classifier is accurate and quite competitive to other classifiers, although it is simpler, and enables considering a larger set of genes in the classification
Pulse-train based time-delay estimation improves resiliency to noise

Time-delay estimation accuracy in echolocating systems decays for increasing levels of noise until a breakpoint is reached, after which accuracy deteriorates by several orders of magnitude. In this paper we present a robust fusion of time-delay estimates from multiple pings that significantly reduces the signal-to-noise ratio corresponding to the accuracy breakpoint. We further show that a simple average of the time-delay estimates does not shift the breakpoint to a lower signal-to-noise ratio. The proposed fusion of multiple pings has the potential of improving the resilience to noise of echolocating systems such as sonar, medical ultrasound and geo-seismic surveys, hence increasing their potential operating range and reducing health/environmental hazards
Delay accuracy in bat sonar is related to the reciprocal of normalized echo bandwidth, or Q.

Big brown bats (Eptesicus fuscus) emit wideband, frequency-modulated biosonar sounds and perceive the distance to objects from the delay of echoes. Bats remember delays and patterns of delay from one broadcast to the next, and they may rely on delays to perceive target scenes. While emitting a series of broadcasts, they can detect very small changes in delay based on their estimates of delay for successive echoes, which are derived from an auditory time/frequency representation of frequency-modulated sounds. To understand how bats perceive objects, we need to know how information distributed across the time/frequency surface is brought together to estimate delay. To assess this transformation, we measured how alteration of the frequency content of echoes affects the sharpness of the bat's delay estimates from the distribution of errors in a psychophysical task for detecting changes in delay. For unrestricted echo frequency content and high echo signal-to-noise ratio, bats can detect extremely small changes in delay of about 10 ns. When echo bandwidth is restricted by filtering out low or high frequencies, the bat's delay acuity declines in relation to the reciprocal of relative echo bandwidth, expressed as Q, which also is the relative width of the target impulse response in cycles rather than time. This normalized-time dimension may be efficient for target classification if it leads to target shape being displayed independent of size. This relation may originate from cochlear transduction by parallel frequency channels with active amplification, which creates the auditory time/frequency representation itself.
Adaptive pulse optimization for improved sonar range accuracy

Using the theory of optimal receivers, the range accuracy of echolocating systems can be expressed as a function of pulse bandwidth and SNR through the well-known Woodward equation. That equation, however, was developed in the limit of very high SNRs and assumes that the correct peak of the cross-correlation function is known a priori. We show that for increasing levels of noise, the accuracy of the cross-correlation receiver undergoes a sharp transition from the Woodward equation for a coherent receiver to a modified Woodward equation for a semicoherent receiver. Since this transition appears at different SNRs for pulses with different center frequencies and bandwidths, it is possible to choose the optimal pulse for any given SNR. We show that the adaptive method we propose outperforms the classical cross-correlation receiver for low SNRs. The same ideas can be applied to the case of a fixed broadband signal, by performing the cross correlation at the receiver end separately in a set of frequency bands with the appropriate center frequencies and bandwidths.
Image Mosaicing of Noisy Acoustic Camera Images

This paper presents an algorithm for image registration and mosaicing on underwater sonar image sequences characterized by a high noise level, inhomogeneous illumination and low frame rate. Imaging geometry of acoustic cameras is significantly different from that of pinhole cameras. For a planar surface viewed through a pinhole camera undergoing translational and rotational motion, registration can be obtained via a projective transformation. For an acoustic camera, we show that, under the same conditions, an affine transformation is a good approximation. We further propose a novel image fusion method, which maximizes the signal-to-noise ratio of the mosaic image. The full procedure includes illumination correction, feature based transformation estimation, and image fusion for mosaicing.
A Study of Ensemble of Hybrid Networks with Strong Regularization

We study various ensemble methods for hybrid neural networks. The hybrid networks are composed of radial and projection units and are trained using a deterministic algorithm that completely defines the parameters of the network for a given data set. Thus, there is no random selection of the initial (and final) parameters as in other training algorithms. Network independent is achieved by using bootstrap and boosting methods as well as random input sub-space sampling. The fusion methods are evaluated on several classification benchmark data-sets. A novel MDL based fusion method appears to reduce the variance of the classification scheme and sometimes be superior in its overall performance.
Delay axis of impulse-response images in biosonar

Theoretical and experimental work on echo-delay acuity in bat sonar reveals a unique, previously unknown feature of the bat's internal representation of sonar targets. Big brown bats (Eptesicus fuscus) broadcast wideband, frequency-modulated (FM) biosonar sounds containing frequencies of 20-100 kHz and perceive objects from echoes of these sounds that return to their ears. Echolocating bats determine target distance, or range, from the delay of echoes. These biosonar sounds have high center frequencies (fc) and wide bandwidths (?f), which in principle could support very accurate determination of echo delay. Using the auditory system as a sonar receiver, big brown bats are able to achieve this potential. For echoes with relatively unrestricted bandwidths, they can detect changes in echo delay as small as 10 to 40 ns at high echo signal-to-noise ratios. To understand how the wide frequency span of echoes contributes to the bat's delay images, we restricted the frequency content of echoes by removing progressively greater portions of the low-frequency or the high-frequency end of the broadcast spectrum. We have found that the bat's delay accuracy deteriorates systematically in a manner that depends on the ratio of echo center frequency to bandwidth, which equals the value for Q, the width of the target impulse response in number of cycles. The decline is not consistently proportional to either the reciprocal of the bandwidth or the reciprocal of the center frequency alone, as would be expected from sonar theory. The bat's internal representation of targets appears to consist of the target impulse response scaled by the number of cycles rather than by units of time. Use of a normalized time axis for impulse responses may facilitate comparison of images for the same target at different aspects and could contribute to the bat's ability to rapidly identify targets from their shape using a small number of pings.
A filterbank model for echo delay estimation in biosonar

In a jittering-echo task, big brown bats achieve an echo-delay acuity of 10-20 ns (40 ns at echo signal-to-noise ratio of 36 dB). To understand the origin of this unusually fine acuity, a filterbank model of transduction in the bat's peripheral auditory system was developed and tested in Monte Carlo simulations to determine its delay accuracy and test its acuity in a jittering-echo paradigm at different signal-to-noise levels. Several versions of the filterbank model were tested to learn how the model's smoothing filter affected performance. When tested at an echo signal-to-noise ratio of 36 dB, the best filterbank delay-estimation method had a jitter threshold of 75 nanoseconds. To achieve 15 nanoseconds, this method required an echo signal-to-noise ratio of 50 dB and the use of a 2nd order lowpass smoothing filter with a cutoff frequency of 8 kHz. Jitter thresholds for filterbank models with either a lower cutoff frequency or a higher order were more than an order of magnitude worse than the behavioral threshold. These results predict that the smoothing filter for echolocating bats may have a higher cutoff frequency and shallower slope than typical mammalian values.
Time-frequency model for echo-delay resolution in wideband biosonar.

A time/frequency model of the bat's auditory system was developed to examine the basis for the fine (approximately 2 micros) echo-delay resolution of big brown bats (Eptesicus fuscus), and its performance at resolving closely spaced FM sonar echoes in the bat's 20-100-kHz band at different signal-to-noise ratios was computed. The model uses parallel bandpass filters spaced over this band to generate envelopes that individually can have much lower bandwidth than the bat's ultrasonic sonar sounds and still achieve fine delay resolution. Because fine delay separations are inside the integration time of the model's filters (approximately 250-300 micros), resolving them means using interference patterns along the frequency dimension (spectral peaks and notches). The low bandwidth content of the filter outputs is suitable for relay of information to higher auditory areas that have intrinsically poor temporal response properties. If implemented in fully parallel analog-digital hardware, the model is computationally extremely efficient and would improve resolution in military and industrial sonar receivers.
The Effect of Noise on a Class of Energy-Based Learning Rules

We study the selectivity properties of neurons based on BCM and kurtosis energy functions in a general case of noisy high-dimensional input space. The proposed approach, which is used for characterization of the stable states, can be generalized to a whole class of energy functions. We characterize the critical noise levels beyond which the selectivity is destroyed. We also perform a quantitative analysis of such transitions, which shows interesting dependency on data set size. We observe that the robustness to noise of the BCM neuron (Bienenstock, Cooper, & Munro, 1982; Intrator & Cooper, 1992) increases as a function of dimensionality. We explicitly compute the separability limit of BCM and kurtosis learning rules in the case of a bimodal input distribution. Numerical simulations show a stronger robustness of the BCM rule for practical data set size when compared with kurtosis.