nils-herrmann/clean_scirepeval huggingface dataset:
Probabilistic Deformation Models for Challenging Periocular Image Verification

The periocular region as a biometric trait has recently gained considerable traction, especially under challenging scenarios where reliable iris information is not available for human authentication. In this paper, we consider the problem of one-to-one (1 : 1) matching of highly nonideal periocular images captured in-the-wild under unconstrained imaging conditions. Such images exhibit considerable appearance variations, including nonuniform illumination variations, motion and defocus blur, off-axis gaze, and nonstationary pattern deformations. To address these challenges, we propose periocular probabilistic deformation models (PPDMs) that: 1) reduce the image matching problem to matching local image regions and 2) approximate the periocular distortions by local patch level spatial translations whose relationships are modeled by a Gaussian Markov random field. Given a periocular image pair, we determine the distortion-tolerant similarity metric by regularizing local match scores by the maximum aposteriori probability estimate of the relative local deformations between them. Unlike the existing global periocular image matching techniques, by accounting for local image deformations in the periocular matching process, PPDM exhibits greater tolerance to pattern variations. We demonstrate the effectiveness of our model via extensive evaluation on a large number of in-the-wild periocular images. We find that PPDMs outperform many benchmark 1 : 1 image matching techniques (improving verification rates at 0.1% false accept rate by ~30% over previous work and ~40% when compared with the best baseline) in challenging scenarios leading to state-of-the-art verification performance on multiple real-world periocular data sets.
Structured Hough Voting for Vision-Based Highway Border Detection

We propose a vision-based highway border detection algorithm using structured Hough voting. Our approach takes advantage of the geometric relationship between highway road borders and highway lane markings. It uses a strategy where a number of trained road border and lane marking detectors are triggered, followed by Hough voting to generate corresponding detection of the border and lane marking. Since the initially triggered detectors usually result in large number of positives, conventional frame-wise Hough voting is not able to always generate robust border and lane marking results. Therefore, we formulate this problem as a joint detection-and-tracking problem under the structured Hough voting model, where tracking refers to exploiting inter-frame structural information to stabilize the detection results. Both qualitative and quantitative evaluations show the superiority of the proposed structured Hough voting model over a number of baseline methods.
Two-Dimensional Pulse Response and Media Noise Modeling for Bit-Patterned Media

In high density bit-patterned media (BPM), bits or magnetic islands are very close leading to significant inter-track interference (ITI) in addition to inter-symbol interference (ISI). Also, in BPM, major sources of media noise are the fluctuations in the location of the magnetic islands and fluctuations in the sizes of the magnetic islands. The location fluctuations can happen in both along-the-track and in cross-track directions. The size fluctuations affect both ISI and ITI. In order to obtain a more realistic channel model that includes the 2-D interference and the 2-D impact of media noise, we have developed a 2-D pulse response simulator for BPM. Utilizing the 2-D pulse response, we model the ITI and the media noise. Simulation results show that to achieve an acceptable bit error rate (BER), the location and size fluctuations should be smaller than 8% of the bit period at a density of 1.5 Tb/in2 .
Locally rewritable codes for resistive memories

We propose locally rewritable codes (LWC) for resistive memories inspired by locally repairable codes (LRC) for distributed storage systems. Small values of repair locality of LRC enable fast repair of a single failed node since the lost data in the failed node can be recovered by accessing only a small fraction of other nodes. By using rewriting locality, LWC can improve endurance and power consumption which are major challenges for resistive memories. We point out the duality between LRC and LWC, which indicates that existing construction methods of LRC can be applied to construct LWC.
Modeling of 2-D Magnetic Recording and a Comparison of Data Detection Schemes

Two-dimensional magnetic recording (TDMR) together with shingled magnetic recording (SMR) are technologies proposed to extend the life of conventional granular magnetic recording. The grain flipping probability (GFP) model has been proposed to mimic the performance of micromagnetic ( μ-mag) simulations for the purpose of signal reproduction. Other work in TDMR includes the proposal of a Gaussian mixture model (GMM) that produces improved likelihood information at the output of the detector, combined with low density parity check (LDPC) codes. The contribution of this paper is threefold. First, we aim to simulate a TDMR/SMR recording system with the GFP model, both with and without the GMM detector, and with various random and structured LDPC codes, of both 4 k and 16 k block lengths, to determine areal densities that might be achieved. Second, we perform a comparison of the various model order reduced (MOR) GFP implementations to compare the effect of writing with various factors taken out of the picture. Third, we perform a validation of the GFP model and the setup as a whole, by running the system with a parameter set close to that of conventional recording. The results of these experiments give an assurance of the validity of our model, give an indication of the expected density that might be achieved in a TDMR/SMR system, and give a direction for which parameter(s) in magnetic recording systems might be optimized to yield the most gain.
Redundancy allocation of partitioned linear block codes

Most memories suffer from both permanent defects and intermittent random errors. The partitioned linear block codes (PLBC) were proposed by Heegard to efficiently mask stuck-at defects and correct random errors. The PLBC have two separate redundancy parts for defects and random errors. In this paper, we investigate the allocation of redundancy between these two parts. The optimal redundancy allocation will be investigated using simulations and the simulation results show that the PLBC can significantly reduce the probability of decoding failure in memory with defects. In addition, we will derive the upper bound on the probability of decoding failure of PLBC and estimate the optimal redundancy allocation using this upper bound. The estimated redundancy allocation matches the optimal redundancy allocation well.
LDPC Codes for Memory Systems with Scrubbing

In space, radiation particles can introduce temporary or permanent errors in memory systems. To protect against potential memory faults, either thick shielding or error correcting codes (ECC) are used. Thick shielding translates into increased mass and conventional ECCs designed for memories are typically capable of only correcting a single error and detecting a double error. Decoding is usually performed through hard-decisions where bits are treated as either correct or flipped in polarity. In this work, we demonstrate that low-density parity-check (LDPC) codes that are already prevalent in many communication applications can also be used to protect memories in space. We develop a channel that models memory error events in a space radiation environment. We describe how to compute soft symbol reliabilities on our channel and compare the performance of softdecision decoding LDPC codes against conventional hard-decision decoding of Reed-Solomon (RS) codes and Bose-Chaudhuri- Hoquenghem (BCH) codes for a specific memory structure.
Random Features for Sparse Signal Classification

Random features is an approach for kernel-based inference on large datasets. In this paper, we derive performance guarantees for random features on signals, like images, that enjoy sparse representations and show that the number of random features required to achieve a desired approximation of the kernel similarity matrix can be significantly smaller for sparse signals. Based on this, we propose a scheme termed compressive random features that first obtains low-dimensional projections of a dataset and, subsequently, derives random features on the low-dimensional projections. This scheme provides significant improvements in signal dimensionality, computational time, and storage costs over traditional random features while enjoying similar theoretical guarantees for achieving inference performance. We support our claims by providing empirical results across many datasets.
Evaluation of the Concatenation of LDPC and RS Codes in Magnetic Recording Channel Using Field Programmable Gate Arrays

Concatenations of inner low-density parity-check (LDPC) codes and outer Reed-Solomon (RS) codes were evaluated in magnetic recording channel models by simulations using field programmable gate arrays (FPGAs). Fixed inner LDPC codes were used and the optimal code rates for the outer RS code were obtained. It is observed that the outer RS codes do not always improve sector error rates for a fixed user bit density. The effect of various parameters on the sector error rates was evaluated and discussed.
Robust rear-view ground surface detection with hidden state conditional random field and confidence propagation

We address the problem of detecting rear-view (obstacle free) ground surface using a vehicle production camera. This task is considerably more challenging than general front-view road detection, as the associated challenges widely range from low picture quality, fisheye distortion and large objects, to the absence of useful priors such as vanishing points and road structure. Regarding the challenges, we propose a feature that can simultaneously capture local appearance and context information. In addition, the task suffers from strong appearance variations such as shadows and ground markers. Therefore, we propose a novel conditional random field (CRF) model which includes hidden states indicating confident nodes and propagate their confidence to neighboring nodes. We show that our proposed feature and model can jointly achieve robustness against large objects and shadows/markers, showing excellent detection performance under low quality inputs.
Selecting discriminative regions for periocular verification

A fundamental step in biometric recognition is to identify discriminative features in order to maximize user separation. Matching systems will often require manually choosing these discriminative regions of interest for feature extraction and/or score fusion. Specifically within periocular recognition scenarios, previous works segment the eyebrow and/or eye. While such efforts demonstrate the discriminative power of these regions, in this paper we show that there are various scenarios where blindly employing this type of segmentation is not consistently effective. Thus, we introduce a novel unsupervised approach to automatically select regions in the periocular image for improved match performance. A periocular image is segmented into rectangular regions (this process is referred to as patch segmentation) which improve the overall discrimination ability of the bio-metric samples being matched. We demonstrate the efficacy of this approach via extensive numerical results on multiple periocular biometric databases exhibiting challenges commonly found in uncontrolled acquisition environments. As the proposed approach is shown to be equivalent to or better than state-of-the-art on each dataset, our results indicate that our patch segmentation is an important step which can greatly influence system performance.
Write Modeling and Read Signal Processing for Heat-Assisted Bit-Patterned Media Recording

Heat-assisted bit-patterned media recording (HABPMR) can significantly improve channel areal density due to the increased effective write field gradient provided by heat assistance and decreased media jitter offered by bit-patterned media. In this paper, an analytical write channel model, which can accurately capture the effects of Curie temperature, magnetic island position and size, anisotropy field, and write clock frequency fluctuations, is introduced and investigated for HABPMR. For read signal processing study, a read channel characterizing the media noise and reader geometry is combined with a data-dependent write channel to obtain the readback signal. In addition, the allowable write window offsets, write error rate during the write process and the bit error rate after readback as function of various noise sources are studied. Furthermore, 2-D equalization followed by the Bahl–Cocke–Jelenik–Raviv detector combined with picket-shift code is used to mitigate the 2-D interference and correct the burst errors caused by the write clock mis-synchronization. It is shown, via numerical experiments, that the proposed approach can provide significant symbol error rate performance improvement compared to a channel with conventional Reed Solomon code.
Performance of timing recovery methods in turbo coded magnetic recording channels

Turbo codes, with potential for 5 dB coding gain over the conventional Viterbi detection schemes, are good candidates for the next generation detection/decoding schemes for magnetic recording systems. In this paper, timing recovery is included in the evaluation of turbo codes. Three timing recovery methods, namely voltage control oscillator (VCO) method, interpolated timing recovery (ITR) method and adaptive filter timing recovery (AFTR) method are examined for two turbo decoder architectures, full turbo and serial turbo. Bit error rate (BER) results from simulations suggest that full turbo decoder is more robust than the serial turbo decoder. For serial turbo decoder, error floor is seen earlier in VCO and ITR methods than AFTR methods. Using s-random interleaver seems to be able to remove such an error floor.
A multi-sensor fusion system for moving object detection and tracking in urban driving environments

A self-driving car, to be deployed in real-world driving environments, must be capable of reliably detecting and effectively tracking of nearby moving objects. This paper presents our new, moving object detection and tracking system that extends and improves our earlier system used for the 2007 DARPA Urban Challenge. We revised our earlier motion and observation models for active sensors (i.e., radars and LIDARs) and introduced a vision sensor. In the new system, the vision module detects pedestrians, bicyclists, and vehicles to generate corresponding vision targets. Our system utilizes this visual recognition information to improve a tracking model selection, data association, and movement classification of our earlier system. Through the test using the data log of actual driving, we demonstrate the improvement and performance gain of our new tracking system.
An improved bit-flipping scheme to achieve run length control in coded systems

We propose an improved bit-flipping scheme that combines error-correcting codes such as low-density parity check codes with run length limit (RLL) constraints. In addition to its compatibility with soft decision decoding, the proposed scheme has the advantages of no error propagation and fixed block length. Testing with simulated data and real data show good performance. The loss in performance due to the introduction of RLL constraint is less than 0.1 dB at bit-error rate 10/sup -6/ and block-error rate 10/sup -4/.
Error Floor Estimation of Long LDPC Codes on Magnetic Recording 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 code to maintain the near capacity error correcting performance down to frame error rates of 10-12. In order to investigate the error floor of LDPC codes under magnetic recording channels used in data storage systems, we extended the trapping set based estimation method to predict the error floor under magnetic recording channels. The goal is to accurately estimate the error rates in the error floor region for certain types of LDPC codes under the partial response channel. First, we 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 that are key to the decoding failure caused by this trapping set. Then we use a much simpler in situ simulation with these parameters to obtain the conditional decoding failure rate. By considering all the trapping sets, we obtain the overall frame error rate in the error floor region. The estimation results under the magnetic recording channel are within 0.3 dB of the direct simulation results.
Efficient modeling and iterative magnitude-squared decision feedback equalization (DFE) for volume holographic storage channels

Data pages retrieved from a volume holographic data storage channel (VHSC) suffer mainly from intersymbol interference (ISI) and noise. ISI can be combatted using equalizers that are designed based on careful modeling of the VHSC. In this paper, we present a simple and accurate ISI model for the VHSC and utilize this efficient model to suggest a new iterative magnitude-squared decision feedback equalization (IMSDFE) method. Numerical simulations show that the outputs from the proposed model agree well with expected results and that the IMSDFE is a promising equalization/detection scheme to reduce the bit error rate (BER) of VHSC.
Multi-source variable-rate sampled signal reconstructions in vehicular CPS

Crowdsourcing data from multiple sensing agents has become a fundamental mechanism for extracting information in large-scale cyber-physical systems (CPS). However, there has been little attention paid to the difficulties crowdsourcing presents when the desired information is represented by continuous signals. Crowdsourcing can result in unique types of noise in the aggregated data sample set. Errors from clock synchronization, GPS location determination, or sensor heterogeneity result in biased and correlated errors in the sampled data from each individual sensor. Furthermore, in CPS sensors are often mobile and operate asynchronously. This affects the spatial sampling rates among sensors and results in nonuniform sample spacing. We refer to these sampling issues as the noisy multi-source, variable-rate (MSVR) sampling problem. This work introduces and investigates signal reconstruction algorithms given MSVR sampling conditions. These signal reconstructions are investigated for vehicular applications, using a joint road inclination and bank angle signal estimation algorithm that has access to MSVR sampled vehicle GPS and accelerometer data. The signal reconstruction and angle determination algorithms are validated on simulated and real-world vehicle data, where we reconstruct a road elevation signal with 0.89 m root-mean-square error.
Bi-Directional Pattern-Dependent Noise Prediction for Heat-Assisted Magnetic Recording With High Jitter Noise

In this paper, we first report the results of an investigation of perpendicular heat-assisted magnetic recording (HAMR) channels containing high jitter noise that is likely to be the case at very high areal densities (e.g., 4 Tb/in2). To model the HAMR channel, we use the thermal Williams-Comstock model and the microtrack model to derive the transition response, without large thermal spot approximation. Further, we propose a novel bi-directional pattern-dependent noise prediction (biPDNP) detector to improve the performance of the HAMR channel under high jitter noise conditions. The biPDNP detector utilizes backward linear prediction in the noise prediction process, as well as the conventional forward linear prediction. At bit error rate of 10-3, biPDNP detector offers 0.6-1.4 dB performance gain over the conventional PDNP detector.
Robust low-resolution face identification and verification using high-resolution features

In this work, we elaborate on a rather intuitive hypothesis: face recognition of low-resolution faces can be improved if the processes of reconstruction and recognition are considered simultaneously, instead of sequentially, without feedback or any interaction. Given a high-resolution training set, matching low-resolution probe images with good accuracy is an open problem. We have recently introduced [Hennings-Yeomans, Baker, and Kumar, CVPR, June 2008] a new framework for low-resolution face recognition that uses models from an image formation process, super-resolution priors and face feature extraction methods. By measuring how well an intermediate super-resolution reconstruction of the probe image fits into the models used in the process, the proposed matching algorithm extracts new features for recognition. In this paper, we present results for an improved design of these new features. We show that the proposed algorithm improves performance in both, identification and verification tasks on a large database of 337 subjects that also captures illumination variations.
An Automatic Subject-Adaptable Heartbeat Classifier Based on Multiview Learning

In this paper, a novel subject-adaptable heartbeat classification model is presented, in order to address the significant interperson variations in ECG signals. A multiview learning approach is proposed to automate subject adaptation using a small amount of unlabeled personal data, without requiring manual labeling. The designed subject-customized models consist of two models, namely, general classification model and specific classification model. The general model is trained using similar subjects out of a population dataset, where a pattern matching based algorithm is developed to select the subjects that are “similar” to the particular test subject for model training. In contrast, the specific model is trained mainly on a small amount of high-confidence personal dataset, resulting from multiview-based learning. The learned general model represents the population knowledge, providing an interperson perspective for classification, while the specific model corresponds to the specific knowledge of the subject, offering an intraperson perspective for classification. The two models supplement each other and are combined to achieve improved personalized ECG analysis. The proposed methods have been validated on the MIT-BIH Arrhythmia Database, yielding an average classification accuracy of 99.4% for ventricular ectopic beat class and 98.3% for supraventricular ectopic beat class, which corresponds to a significant improvement over other published results.
Learning in competitively inhibited neural nets

The competitively inhibited neural network (CINN) is a competitive learning paradigm which is modeled by a collection of ordinary differential equations. A sliding threshold condition has been derived for determining the activity of a CINN neuron. This condition allows the development of a mathematical model for CINN learning. The model is a nonlinear diffusion equation whose solution quantitatively characterizes the learning process. Simulation experiments have validated this model