nils-herrmann/clean_scirepeval huggingface dataset:
Dropout-tolerant read channels

Dropouts are intermittent losses of signal commonly seen in magnetic tape recording readout. The main reason for such losses is the increased spacing between the head and the medium due to media defects or debris particles. The resulting signal is not only degraded by the apparent amplitude loss, but the characteristics of the pulses due to transitions are also changed. Moreover, in many cases, the locations of the pulses are altered, causing excessive amounts of peakshift. In this paper, a model, linking the liftoff to these effects, is presented. Experimental verification of the model using actual signals from a test tape drive is also given. Artificial dropout waveforms generated using this model are used to test two different read channel strategies. The first approach is an equalization-based correction scheme that attempts to undo the dropout. Results indicate that the dropout effects can be almost completely eliminated if an appropriate equalization procedure is applied. As an alternative approach, it is shown that the use of turbo coding in the presence of dropouts appears to be promising.
Iterative decoding for partial response (PR), equalized, magneto-optical (MO) data storage channels

Turbo codes and low-density parity check (LDPC) codes with iterative decoding have received significant research attention because of their remarkable near-capacity performance for additive white Gaussian noise (AWGN) channels. Previously, turbo code and LDPC code variants are being investigated as potential candidates for high-density magnetic recording channels suffering from low signal-to-noise ratios (SNR). We address the application of turbo codes and LDPC codes to magneto-optical (MO) recording channels. Our results focus on a variety of practical MO storage channel aspects, including storage density, partial response targets, the type of precoder used, and mark edge jitter. Instead of focusing just on bit error rates (BER), we also study the block error statistics. Our results for MO storage channels indicate that turbo codes of rate 16/17 can achieve coding gains of 3-5 dB over partial response maximum likelihood (PRML) methods for a 10/sup -4/ target BER. Simulations also show that the performance of LDPC codes for MO channels is comparable to that of turbo codes, while requiring less computational complexity. Both LDPC codes and turbo codes with iterative decoding are seen to be robust to mark edge jitter.
Extended Kalman Filter Based Acquisition Timing Recovery for Magnetic Recording Read Channels

In synchronous timing recovery approaches often employed in magnetic recording readback channels, the timing error detector (TED) output is assumed to be linearly related to the underlying phase offset. However, the noisy readback signal samples in high-density magnetic recording systems are nonlinearly related to the underlying timing disturbances. Thus, a nonlinear state space models may describe such systems more accurately and an improved timing recovery solution may be obtained via an extended Kalman filter (EKF) approach. In this paper, EKF based timing recovery is investigated for the acquisition stage. Numerical simulations indicate that the EKF based timing recovery scheme yields smaller root mean square (RMS) error in timing estimates, which enables it to handle larger timing errors more reliably. In addition, the proposed method converges faster than the conventional timing recovery schemes, which may allow us to reduce the length of the preamble pattern used for the acquisition.
Masked correlation filters for partially occluded face recognition

Face recognition is widely used for a variety of applications, such as identifying people for security purposes, as well as photo album organization. A challenge is to perform accurate face recognition when there exist partial occlusions of the face such as scarves or sunglasses. Correlation Filters (CFs) are an occlusion-tolerant object recognition method, potentially suited to deal with partial occlusions. In this paper, we introduce a new class of correlation filters called Masked Correlation Filters (MCFs), that are designed specifically to handle partial occlusions in face images. The benefits of using MCFs are illustrated using well-known face image data sets.
One Network to Solve Them All — Solving Linear Inverse Problems Using Deep Projection Models

While deep learning methods have achieved state-of-theart performance in many challenging inverse problems like image inpainting and super-resolution, they invariably involve problem-specific training of the networks. Under this approach, each inverse problem requires its own dedicated network. In scenarios where we need to solve a wide variety of problems, e.g., on a mobile camera, it is inefficient and expensive to use these problem-specific networks. On the other hand, traditional methods using analytic signal priors can be used to solve any linear inverse problem; this often comes with a performance that is worse than learning-based methods. In this work, we provide a middle ground between the two kinds of methods — we propose a general framework to train a single deep neural network that solves arbitrary linear inverse problems. We achieve this by training a network that acts as a quasi-projection operator for the set of natural images and show that any linear inverse problem involving natural images can be solved using iterative methods. We empirically show that the proposed framework demonstrates superior performance over traditional methods using wavelet sparsity prior while achieving performance comparable to specially-trained networks on tasks including compressive sensing and pixel-wise inpainting.
Use of adaptive filter for timing recovery for data storage channels

Adaptive filters have been traditionally used in data storage channels mainly for equalization purposes. In this paper, an adaptive filters is investigated for timing recovery so that equalization and timing recovery can be carried out simultaneously. Simulation results show that the bit error rate (BER) performance of the adaptive filter timing recovery (AFTR) method is comparable to other existing schemes. The main advantages of AFTR are that it is potentially simpler and can lead to a fully digital implementation.
GEN04-4: Improved Runlength Control When Using Low-Density Parity-Check Codes

In high-density magnetic recording systems, a runlength constraint (typically (0, k)) is imposed to aid then timing recovery system. However, the traditional encoding structure where the runlength limited (RLL) encoder follows the ECC encoder is not a good fit when using LDPC codes. The main difficulty is that the LDPC decoder requires soft information, which is incompatible the typical RLL decoders which output hard decoding decisions. In this paper, we propose a structure so that soft information can be delivered directly to the LDPC decoder while the runlength constraint is satisfied. By integrating the bit-flipping scheme proposed in [Vasic and Pedagani,  May 2004] and an interleaver into the system, we are able to take advantage of the near-capacity performance of the LDPC code given the runlength limit. The simulations show that the BER performance loss of the LDPC code (rate 8/9, length 4932) due to the RLL constraint is less than 0.1 dB. The error floor introduced by bit-flipping scheme is limited to bit error rate (BER) levels lower than 10-7.
An Analytical Approach for Performance Evaluation of Bit-Patterned Media Channels

In this work, a new analytical approach is used to evaluate the error performance of bit-patterned media (BPM) magnetic recording channels that employ one-dimensional (1D) and two-dimensional (2D) generalized partial response (GPR) equalizers to combat the significant inter-track interference (ITI) expected in BPM magnetic recording systems. The probability density function of ITI is obtained analytically and is used to estimate the bit error rate (BER) from the Viterbi detector. The proposed method takes into account most of the important factors affecting the BER such as ITI, un-equalized intersymbol interference (ISI), colored noise and the distance and the multiplicity of error events. In this work, it is shown that for 1D channels, modeling ITI and un-equalized ISI by Gaussian PDFs leads to inaccurate BERs and that the non-Gaussian distribution of the ITI and un-equalized ISI must be taken into account for more accurate BER estimates. This method provides fast and accurate estimates of BERs for moderate to high signal-to-noise ratios (SNRs). By using this analytical method, time-consuming numerical simulations for error performance evaluation can be avoided.
Correlation of Binarized Images

Correlator technique has been used in pattern recognition applications. We introduce a correlation performance measure, peak to background ratio (PBR), defined as the ratio of the peak intensity to the average background variation in a single correlation output. Using this performance measure, we analyze the binary correlator and show that the binarization of the images can result in better performance. We consider a real-time implementation of the binary correlator, using VLSI technology. The analytical results are supported by computer simulations.
Shallow arc detection in disk surface images for disk forensics

In disk forensics application, the goal is to determine whether a particular floppy disk was recorded in a particular drive. This paper discusses how image processing is used to determine whether a disk was recorded in drive A or drive B. Microscope images of floppy disk surfaces are used to extract features. Each image exhibits tracks and gaps as parallel shallow arcs, and the track and gap widths are extracted as features. We apply an image processing method to the shallow arcs to extract their widths. The Hough transform (PVC Hough, 1962) and the concept of entropy are used to segment each arc, and a simple and unbiased parabola fitting is used to fit a circle to the arc segment. Results show this method extracts track and gap widths of real images successfully resulting in the two floppy drives being distinguishable.
Representational oriented component analysis (ROCA) for face recognition with one sample image per training class

Subspace methods such as PCA, LDA, ICA have become a standard tool to perform visual learning and recognition. In this paper we propose representational oriented component analysis (ROCA), an extension of OCA, to perform face recognition when just one sample per training class is available. Several novelties are introduced in order to improve generalization and efficiency: (1) combining several OCA classifiers based on different image representations of the unique training sample is shown to greatly improve the recognition performance. (2) To improve generalization and to account for small misregistration effect, a learned subspace is added to constrain the OCA solution, (3) a stable/efficient generalized eigenvector algorithm that solves the small size sample problem and avoids overfitting. Preliminary experiments in the FRGC Ver 1.0 dataset show that ROCA outperforms existing linear techniques (PCA, OCA) and some commercial systems.
Magneto-optic read channel modeling in the presence of bloom

Bloom, the thermal expansion or contraction of the written domains, is present in magneto-optic (MO) recording. With high density MO recording, bloom and intersymbol interference (ISI) become problems. To study the effect of bloom on the soft bit error rate (BER), we modeled the MO channel in the presence of bloom. Our study indicates that while the signal-to-noise ratios (SNRs) of both PWM and PPM degrade about the same amount In the presence of bloom, overall performance of PWM is better than PPM.
On the average codeword length of optimal binary codes for extended sources

Although optimal binary source coding using symbol blocks of increasing length must eventually yield a code having an average codeword length arbitrarily close to the source entropy, it is known that the sequence of average codeword lengths need not be nonincreasing. The sequence is, however, bounded above by the average codeword length of the source, and certain subsequences must be nondecreasing. Sufficient conditions are obtained describing sources for which a decrease in average codeword length is achieved when coding pairs of symbols. A sufficient condition specifying sources for which no such decrease is possible is also obtained.
Iterative soft decoded partial response channels for hybrid magneto-optical recording

In this paper, a physical model of readback signals from a hybrid magneto-optical (MO) recording using magnetic flux detection is presented. The minimum mean-square error (MMSE) technique has been used to design generalized partial response (GPR) targets in order to facilitate the application of the trellis based channel detector. A PR target with integer coefficients is proposed as a good approximation to the GPR target. We also address the application of turbo codes and LDPC codes with iterative soft decoding structure to the hybrid magneto-optical (MO) recording channel. The effects of bloom, transition noise and giant magneto-resistive (GMR) sensor nonlinearity on the bit error rate (BER) performance are investigated both for uncoded partial response maximum likelihood (PRML) and iterative soft decoded recording channels. Our results for the hybrid recording channels indicate that turbo/LDPC codes of rate 16/17 can achieve coding gains of about 5 dB over PRML methods for a 10/sup -5/ target BER at the normalized density S=1.5. Turbo codes with iterative soft decoding are seen to be robust to transition noise.
Competitive learning's global search property

The competitively inhibited neural net (CINN) is a special class of competitive learning paradigm which is amenable to formal analysis. The authors present evidence that the CINN is capable of globally optimizing certain problems. They suggest that the CINN is capable of locating the primary mode of a source density function. In the event that this density represents a performance functional (such as in maximum-likelihood estimation), the CINN can be used to locate the global optimum of the performance functional. The mechanisms behind this global search property have been explained using a nonlinear diffusion model of CINN learning, and simulation experiments have corroborated this capability of the CINN for a minimally deceptive problem
Bidirectional Pattern-Dependent Noise Prediction With LDPC Codes for HAMR

As areal densities increase, substantial jitter noise is expected in heat-assisted magnetic recording (HAMR). To mitigate the effects of jitter noise, in an earlier work we proposed the bidirectional pattern-dependent noise prediction (BiPDNP) detector, which employs backward linear prediction as well as the conventional forward linear prediction. However, no error correction codes were used in this earlier work. In this paper, we implement BiPDNP in the Bahl-Cocke-Jelinek-Raviv (BCJR) detector, and investigate its performance with low-density parity check (LDPC) codes. For the LDPC coded channel, by combining the BCJR detector with BiPDNP, we observe that a SNR gain of 1 dB (at bit error rate 10$^{-4}$  with 30% microtrack jitter) is achieved over the conventional BCJR detector. Further, in HAMR channel modeling we employ the thermal Williams-Comstock (TWC) model. Conventionally, a linear relationship for coercivity with temperature is used. In this paper, we update the TWC model by using a nonlinear relationship for coercivity with temperature.
Error Floor Estimation of Long LDPC Codes on Partial Response Channels

The presence of error floor in low density parity check (LDPC) codes is of great concern for potential applications of LDPC codes to data storage channels, which require the error correcting code (ECC) to maintain the near-capacity error correcting performance at frame error rate as low as 10-12. In order to investigate the error floor of LDPC codes under partial response channels used in data storage systems, we propose a new estimation method combining analytical tools and simulation, based on the concept of trapping sets. The definition of trapping sets is based on the dominant error patterns observed in the decoding process. The goal is to accurately estimate the error rate in the error floor region for certain types of LDPC codes under the partial response channel and further extend the frame error rate down to 10-14 or lower. Towards this goal, we first use field programmable gate array (FPGA) hardware simulation to find the trapping sets that cause the decoding failure in the error floor region. For each trapping set, we extract the parameters which are key to the decoding failure rate caused by this trapping set. Then we use a much simpler in situ hardware simulation with these parameters to obtain the conditional decoding failure rate. By considering all the trapping sets we find, we obtain the overall frame error rate in the error floor region. The estimation results for a length -4623 QC-LDPC code under the EPR4 channel are within 0.3 dB of the direct simulation results. In addition, this method allows us to estimate the frame error rate of a LDPC code down to 10-14 or lower.
Gaussian Conditional Random Fields for Face Recognition

We propose a Gaussian Conditional Random Field (GCRF) approach to modeling the non-stationary distortions that are introduced from changing facial expressions during acquisition. While previous work employed a Gaussian Markov Random Field (GMRF) to perform deformation tolerant matching of periocular images, we show that the approach is not well-suited for facial images, which can contain significantly larger and more complex deformations across the image. Like the GMRF, the GCRF tries to find the maximum scoring assignment between a match pair in the presence of non-stationary deformations. However, unlike the GMRF, the GCRF directly computes the posterior probability that the observed deformation is consistent with the distortions exhibited in other authentic match pairs. The difference is the inclusion of a derived mapping between an input comparison and output deformation score. We evaluate performance on the CMU Multi-PIE facial dataset across all sessions and expressions, finding that the GCRF is significantly more effective at capturing naturally occurring large deformations than the previous GMRF approach.
Iterative Cross-Entropy Encoding for Memory Systems with Stuck-At Errors

In this paper, a novel iterative encoding scheme is proposed for memory systems suffering from stuck-at errors. The stuck-at errors can be efficiently managed by using side information about stuck-at memory cells during encoding, while encoding for unconstrained number of stuck-at errors is intractable due to its exponential complexity. The proposed coding scheme employs an iterative encoding algorithm using cross-entropy method, which has a polynomial time complexity. In addition, any linear block code (LBC) can be concatenated with the proposed code, to correct for both residual stuck-at errors and random (soft) errors. The proposed coding schemes are evaluated by numerical simulations using a memory channel undergoing both stuck-at and random errors. Simulation results show that the cross-entropy based coding scheme provides an improved block error rate (BLER) performance, or alternatively, a higher overall storage capacity.
Partial-Aliasing Correlation Filters

Correlation filters (CFs) are useful tools for detecting and locating signals or objects within a larger signal or scene of interest. Typically, these filters are designed during the training stage without worrying about how the cross-correlation between a test signal and the designed CF template will be carried out during the testing or use stage. Because of its computational benefits, the Fast Fourier Transform (FFT) algorithm is usually used for performing cross-correlations, leading to circular correlations and aliasing in the resulting correlation outputs. The aliasing effects can be suppressed by zero-padding, but at the expense of using longer FFTs and thus incurring more computational complexity. In this paper, we present a new approach where CFs are designed to explicitly allow partial aliasing at test time (thus allowing the use of shorter FFTs). This approach of allowing aliasing in the cross-correlation output and explicitly taking such partial aliasing into account when designing the CF is diametrically opposite to the conventional CF approaches which try to avoid aliasing effects. We demonstrate through numerical results that these new partial-aliasing correlation filters (PACFs) achieve better recognition performance than conventional CFs when used in block filtering architectures that allow aliasing.
Large girth cycle codes for partial response channels

In this paper, we present a general approach for constructing high-rate cycle codes having girth 12 which achieve the lower bound on the block length. An efficient encoding algorithm is also proposed for this class of codes. We estimate the word error rate of the cycle codes for additive white Gaussian noise (AWGN) channels under maximum likelihood decoding, using the minimum distance and its multiplicity. These estimates are compared to simulation results with iterative soft decoding. The performance of these codes for partial response channels is studied under iterative soft decoding using computer simulations.
Normalized face image generation with perceptron generative adversarial networks

This paper presents a deep neural architecture for synthesizing the frontal and neutral facial expression image of a subject given a query face image with arbitrary expression. This is achieved by introducing a combination of feature space perceptual loss, pixel-level loss, adversarial loss, symmetry loss, and identity-preserving loss. We leverage both the frontal and neutral face distributions and pre-trained discriminative deep perceptron models to guide the identity-preserving inference of the normalized views from expressive profiles. Unlike previous generative methods that utilize their intermediate features for the recognition tasks, the resulting expression- and pose-disentangledface image has potential for several downstream applications, such as facial expression or face recognition, and attribute estimation. We show that our approach produces photorealistic and coherent results, which assist the deep metric learning-based facial expression recognition (FER) to achieve promising results on two well-known FER datasets.