nils-herrmann/clean_scirepeval huggingface dataset:
Evaluation of an auditory model for echo delay accuracy in wideband biosonar.

In a psychophysical task with echoes that jitter in delay, big brown bats can detect changes as small as 10-20 ns at an echo signal-to-noise ratio of approximately 49 dB and 40 ns at approximately 36 dB. This performance is possible to achieve with ideal coherent processing of the wideband echoes, but it is widely assumed that the bat's peripheral auditory system is incapable of encoding signal waveforms to represent delay with the requisite precision or phase at ultrasonic frequencies. This assumption was examined by modeling inner-ear transduction with a bank of parallel bandpass filters followed by low-pass smoothing. Several versions of the filterbank model were tested to learn how the smoothing filters, which are the most critical parameter for controlling the coherence of the representation, affect replication of the bat's performance. When tested at a signal-to-noise ratio of 36 dB, the model achieved a delay acuity of 83 ns using a second-order smoothing filter with a cutoff frequency of 8 kHz. The same model achieved a delay acuity of 17 ns when tested with a signal-to-noise ratio of 50 dB. Jitter detection thresholds were an order of magnitude worse than the bat for fifth-order smoothing or for lower cutoff frequencies. Most surprising is that effectively coherent reception is possible with filter cutoff frequencies well below any of the ultrasonic frequencies contained in the bat's sonar sounds. The results suggest that only a modest rise in the frequency response of smoothing in the bat's inner ear can confer full phase sensitivity on subsequent processing and account for the bat's fine acuity or delay.
Title: Robust Statistics from Multiple Pings Improves Noise Tolerance in Sonar

N. Neretti is with the Institute for Brain and Neural Systems and Physics Department, Brown University, Providence RI 02912. N. Intrator is with the Institute for Brain and Neural Systems and Physics Department, Brown University, Providence RI 02912, and is on leave from Tel-Aviv University. L. N Cooper is with the Physics Department, Institute for Brain and Neural Systems and Neuroscience Department, Brown University, Providence RI 02912.
Biosonar model for improved range accuracy in a noisy environment

Using the theory of optimal receivers the range accuracy of echolocating systems can be expressed as a function of receiver bandwidth and signal-to-noise ratio through the well-known Woodward equation. That equation however was developed in the limit of very high signal-to-noise ratios, and assumes that the correct peak of the crosscorrelation function is known a-priori. Echolocating animals such as dolphins and bats have developed a highly specialized receiver to optimize echolocation to different environments and conditions. In particular, they use a set of filters with different center frequencies but overlapping bands. We show that this structure can help in improving accuracy in the case of relatively low signal-to-noise ratios when the ambiguity in the choice of the main peak of the crosscorrelation function cannot be avoided.
Target detection in side-scan sonar images : expert fusion reduces false alarms

We integrate several key components of a pattern recognition system for a mine-like targets detection problem. These include several image enhancements, postprocessing and multi-expert fusion. The image enhancement includes wavelet de-noising and classical computer vision methods such as nonlinear and adaptive equalization and other filters. Our approach attempts to make the different experts as independent as possible so as to maximally exploit their fusion. Results are given on a shallow water mine-like targets detection problem using sonar data.
A strong regularization on a hybrid MLP / RBF architecture achieves small bias and small variance error ?

We introduce a Forward Backward and model selection algorithm for constructing a hybrid network of radial and perceptron hidden units for regression. The algorithm determines if a radial or a perceptron unit is required at a given region of input space. Given an error target, the algorithm also determines the number of hidden units. Then the algorithm uses model selection criteria and prunes unnecessary weights. This results in a final architecture which is often much smaller than an RBF network or a MLP.
Unsupervised Learning of Visual Structure

To learn a visual code in an unsupervised manner, one may attempt to capture those features of the stimulus set that would contribute significantly to a statistically efficient representation (as dictated, e.g., by the Minimum Description Length principle). Paradoxically, all the candidate features in this approach need to be known before statistics over them can be computed. This paradox may be circumvented by confining the repertoire of candidate features to actual scene fragments, which resemble the "what+where" receptive fields found in the ventral visual stream in primates. We describe a single-layer network that learns such fragments from unsegmented raw images of structured objects. The learning method combines fast imprinting in the feedforward stream with lateral interactions to achieve single-epoch unsupervised acquisition of spatially localized features that can support systematic treatment of structured objects .
CLASSIFICATION OF MAMMOGRAPHIC IMAGES USING THE JOINT BEST BASIS AND THE APPROXIMATE KLT

Breast cancer is currently one of the major causes of death for women in the U.S. Mammography is currently the most effective method for detection of breast cancer and early detection has proven to be an efficient tool to reduce the number of deaths. Mammography is the most demanding of all clinical imaging applications as it requires high contrast, high signal to noise ratio and resolution with minimal x-radiation. According to studies [16], 10\% to 30\% of women having breast cancer and undergoing mammography have negative mammograms, i.e. are misdiagnosed. Furthermore, only 20\%-40\% of the women who undergo biopsy have cancer. Biopsies are expensive, invasive and traumatic to the patient. The high rate of false positives motivate research aimed to enhance the mammogram images, to provide Computer Aided Diagnostics tools that can alert the radiologist to potentially malignant regions in the mammograms and to develop tools for automated classification of mammograms into benign and malignant classes (see for example [4, 8]). In this paper we present classification results of mammographic images from an early stage of malignancy using feature vectors based on wavelet packets, PCA and the Approximate Karhunen Loeve transform. We employ an innovative method that provides classification results better than the average performance of radiologists. The method was tested using database of mammograms from an early stage of malignancy. Correct detection is harder and more important at an early stage of malignancy.
Gradient-based optimization of mother wavelets

We present a general framework for the design of a mother wavelet best adapted to a specific signal or to a class of signals. The filter’s coefficients are obtained via optimization of a smooth objective function. We develop an unconstrained gradient-based optimization algorithm for a discrete wavelet transform. The algorithm is extended to the joint optimization of the mother wavelet and of the wavelet packets basis.  Wavelets, Wavelet Pakets, Filter Banks, Polyphase Matrix, Lattice Decomposition, Optimization.
Forward and Backward Selection in Regression Hybrid Network

We introduce a Forward Backward and Model Selection algorithm (FBMS) for constructing a hybrid regression network of radial and perceptron hidden units. The algorithm determines whether a radial or a perceptron unit is required at a given region of input space. Given an error target, the algorithm also determines the number of hidden units. Then the algorithm uses model selection criteria and prunes unnecessary weights. This results in a final architecture which is often much smaller than a RBF network or a MLP. Results for various data sizes on the Pumadyn data indicate that the resulting architecture competes and often outperform best known results for this data set.
A Hybrid Projection-based and Radial Basis Function Architecture: Initial Values and Global Optimisation

We introduce a mechanism for constructing and training a hybrid architecture of projection-based units and radial basis functions. In particular, we introduce an optimisation scheme which includes several steps and assures a convergence to a useful solution. During network architecture construction and training, it is determined whether a unit should be removed or replaced. The resulting architecture often has a smaller number of units compared with competing architectures. A specific overfitting resulting from shrinkage of the RBF radii is addressed by introducing a penalty on small radii. Classification and regression results are demonstrated on various benchmark data sets and compared with several variants of RBF networks [1,2]. A striking performance improvement is achieved on the vowel data set .
Hidden loop recovery for handwriting recognition

One significant challenge in the recognition of off-line handwriting is in the interpretation of loop structures. Although this information is readily available in online representation, close proximity of strokes often merges their centers making them difficult to identify. In this paper a novel approach to the recovery of hidden loops in off-line scanned document images is presented. The proposed algorithm seeks blobs that resemble truncated ellipses. We use a sophisticated form analysis method based on mutual distance measurements between the two sides of a symmetric shape. The experimental results are compared with the ground truth of the online representations of each off-line word image. More than 86% percent of the meaningful loops are handled correctly.
Time Frequency Analysis of Dolphin Clicks Reveals Fine Temporal Structure

One of the most effective ways for a dolphin to perceive its environment for the purpose of prey detection is to use its active and passive (listening) sonar capabilities. Acoustic energy propagates in water more efficiently than any other form of energy, so that an active sonar capability is ideal for life in an aquatic environment. Dolphin sound emissions can be classified into two broad categories: narrow-band-frequency-modulated continuous tonal sounds, referred to as whistles, and broadband sonar clicks that have durations between 50 and 200μs (Evans 1973). Passing air back and forth through a set of nasal passages, which creates a narrow beam of sound, first produces the click sound. This sound is then passed through a fat-filled cavity in the head called the melon. In the melon, the sound is culminated so that the dolphin can actively direct the clicks without wasting energy due to scattering the sound. When searching for a target, dolphins usually emit bursts of clicks referred to as a click train. The interval between clicks appears to be a function of the distance from the target. Other factors such as the difficulty of detecting a target, the presence or absence of a target of interest, and the animal's expectation of finding a specific target also affect the click train.
Automatic Model Selection in a Hybrid Perceptron/Radial Network

We introduce an algorithm for incrementaly constructing a hybrid network fo radial and perceptron hidden units. The algorithm determins if a radial or a perceptron unit is required at a given region of input space. Given an error target, the algorithm also determins the number of hidden units. This results in a final architecture which is often much smaller than an RBF network or a MLP. A benchmark on four classification problems and three regression problems is given. The most striking performance improvement is achieved on the vowel data set .
Probabilistic principles in unsupervised learning of visual structure: human data and a model

To find out how the representations of structured visual objects depend on the co-occurrence statistics of their constituents, we exposed subjects to a set of composite images with tight control exerted over (1) the conditional probabilities of the constituent fragments, and (2) the value of Barlow's criterion of "suspicious coincidence" (the ratio of joint probability to the product of marginals). We then compared the part verification response times for various probe/target combinations before and after the exposure. For composite probes, the speedup was much larger for targets that contained pairs of fragments perfectly predictive of each other, compared to those that did not. This effect was modulated by the significance of their co-occurrence as estimated by Barlow's criterion. For lone-fragment probes, the speedup in all conditions was generally lower than for composites. These results shed light on the brain's strategies for unsupervised acquisition of structural information in vision.
Target reconstruction from broadband acoustic backscatter

Broadband acoustic backscatter data are examined for mine reconstruction using LFM and dolphin clicks. Broadband backscatter data from free‐field and bottom mines were collected during tests conducted in ARL/UT’s sand tank and sponsored by ONR. The data set includes multi‐aspect broadband LFM (50–200 kHz) acoustic backscatter returns from a collection of targets. Both free‐field and bottom target data were collected. Bottom target data sets include returns from two different levels of bottom roughness. Each data file is sampled over an interval that is large relative to the echo duration with sufficient sampling both prior to the echo onset and post‐echo return to enable meaningful target characterization. Data collected over 360 deg in 2‐deg increments are processed to generate a descriptive target pattern. The data are pulse compressed and system artifacts are removed using adjacent channels over the entire frequency band and within subbands. The target pattern is generated by applying the new adaptive ...
Panel Summary: Frontiers of Human-Machine Interaction

The hot points presented to the panel were the following: 
 
 
What are the broader definitions of Human/Machine interaction? For example: 
 
 
non direct connection (using currently available computer GUI) 
 
 
first phase direct connection (linking sensors to the sensory system) 
 
 
second phase (direct connections into the Central Nervous System) 
 
 
 
 
 
 
What are the technologies that should be developed in order to enable each of the previous phases? 
 
 
What are the scientific research issues that are related to such phases? 
 
 
What are the possible implications of a direct human-computer link on the society? (compared to the Internet revolution?) 
 
 
There are several phases in Human-Machine interaction. The first, and most trivial one, involves using virtual reality and augmented reality devices, that allow even today manipulation of objects by interactively superpositioning artificial objects and data on real images- The next interesting phases, however, involves direct data transfer from artificial sensors to the nervous system. In that regard, we can show various implementations of artificial auditory and visual sensors. Hearing implants are being coupled into the auditory system, and a direct link to the auditory nerve does exist. Regarding visual data, the task is more complicated, and current technologies are being developed in order to implant a sensor into the retina and connect it to the optic nerve.
Improving Classification via Reconstruction ∗

Learning a many-parameter model is generally an under-constrained problem that requires additional regularization. We propose to use reconstruction as a regularization constraint for image classification. We show that fusing the two models together is an effective regularizer which adds to the improvement achieved by weight decay constraints. This regularization is effective for single networks and network ensembles. Classification results are demonstrated on two facial data-sets which are extended to include various image degradations. We show that combining weight decay and reconstruction constraints improves image classification for a wide range of degradations. In particular, “bagging” ensembles which are composed of regularized networks trained on different cross-folds produce best results.
Global Optimization of RBF Networks

Several modifications to parameter estimation in a Radial Basis Functions network are introduced. These include a better initializing clustering algorithm and a full gradient descent on centers and weights after weights were found via a matrix inversion. Performance comparison with other RBF algorithms is given on several data-sets. It is found that The proposed method was found superior to Bishop’s EM training algorithm, to Orr’s method  for as well as a conventional implementation.
Coarse coding of shape fragments) (Retinotopy) Representation of structure

The ability to deal with object structure--to determine what is where in a given object, rather than merely to categorize or identify it--has been hitherto considered the prerogative of 'structural description' approaches, which represent shapes as categorical compositions of generic parts taken from a small alphabet. In this note, we propose a simple extension to a theoretically motivated and extensively tested appearance-based model of recognition and categorization, which should make it capable of representing object structure. We describe a pilot implementation of the extended model, survey independent evidence supporting its modus operandi, and outline a research program focused on achieving a range of object processing capabilities, including reasoning about structure, within a unified appearance-based framework.
A Productive, Systematic Framework for the Representation of Visual Structure

We describe a unified framework for the understanding of structure representation in primate vision. A model derived from this framework is shown to be effectively systematic in that it has the ability to interpret and associate together objects that are related through a rearrangement of common "middle-scale" parts, represented as image fragments. The model addresses the same concerns as previous work on compositional representation through the use of what+where receptive fields and attentional gain modulation. It does not require prior exposure to the individual parts, and avoids the need for abstract symbolic binding.
Improving Classi cation via Reconstruction

Learning a many-parameter model is generally an under-constrained problem that requires additional regularization. We propose to use reconstruction as a regularization constraint for image classiication. We show that fusing the two models together is an eeective regularizer which adds to the improvement achieved by weight decay constraints. This regularization is eeective for single networks and network ensembles. Classiication results are demonstrated on two facial data-sets which are extended to include various image degradations. We show that combining weight decay and reconstruction constraints improves image classiication for a wide range of degradations. In particular, \bagging" ensembles which are composed of regularized networks trained on diierent cross-folds produce best results.
(Coarse coding of shape fragments) + (retinotopy) approximately = representation of structure.

The ability to deal with object structure--to determine what is where in a given object, rather than merely to categorize or identify it--has been hitherto considered the prerogative of 'structural description' approaches, which represent shapes as categorical compositions of generic parts taken from a small alphabet. In this note, we propose a simple extension to a theoretically motivated and extensively tested appearance-based model of recognition and categorization, which should make it capable of representing object structure. We describe a pilot implementation of the extended model, survey independent evidence supporting its modus operandi, and outline a research program focused on achieving a range of object processing capabilities, including reasoning about structure, within a unified appearance-based framework.
Blurred face recognition via a hybrid network architecture

We introduce a hybrid recognition/reconstruction architecture that is suitable for recognition of images degraded by various forms of blur. This architecture includes an ensemble of feedforward networks each of which is constrained to reconstruct the inputs in addition to performing classification. The strength of the constraints is controlled by a regularization parameter. Networks are trained on original as well as Gaussian-blurred images, so as to achieve higher robustness to different blur operators. Face recognition is used to demonstrate the proposed method and results are compared to those of classical unconstrained feedforward architectures. In addition, the effect of state-of-the-art restoration methods is demonstrated and it is shown that image restoration with the proposed hybrid architecture leads to the best and most robust results under various forms of blur.
Offline cursive script word recognition – a survey

We review the field of offline cursive word recognition. We mainly deal with the various methods that were proposed to realize the core of recognition in a word recognition system. These methods are discussed in view of the two most important properties of such a system: the size and nature of the lexicon involved, and whether or not a segmentation stage is present. We classify the field into three categories: segmentation-free methods, which compare a sequence of observations derived from a word image with similar references of words in the lexicon; segmentation-based methods, that look for the best match between consecutive sequences of primitive segments and letters of a possible word; and the perception-oriented approach, that relates to methods that perform a human-like reading technique, in which anchor features found all over the word are used to boot-strap a few candidates for a final evaluation phase.
Solutions of the BCM learning rule in a network of lateral interacting nonlinear neurons.

We introduce a new method for obtaining the fixed points for neurons that follow the BCM learning rule. The new formalism, which is based on the objective function formulation, permits analysis of a laterally connected network of nonlinear neurons and allows explicit calculation of the fixed points under various network conditions. We show that the stable fixed points, in terms of the postsynaptic activity, are not altered by the lateral connectivity or nonlinearity. We show that the lateral connectivity alters the probability of attaining different states in a network of interacting neurons. We further show the exact alteration in presynaptic weights as a result of the neuronal nonlinearity.
Boosting Regression Estimators

There is interest in extending the boosting algorithm (Schapire, 1990) to fit a wide range of regression problems. The threshold-based boosting algorithm for regression used an analogy between classification errors and big errors in regression. We focus on the practical aspects of this algorithm and compare it to other attempts to extend boosting to regression. The practical capabilities of this model are demonstrated on the laser data from the Santa Fe times-series competition and the Mackey-Glass time series, where the results surpass those of standard ensemble average.