nils-herrmann/clean_scirepeval huggingface dataset:
Long-term movement tracking from Local Field Potentials with an adaptive open-loop decoder

One of the challenges in using intra-cortical recordings like Local Field Potentials for Brain Computer Interface (BCI) is their inherent day-to-day variability and non-stationarity caused by subject motivation and learning. Practical Brain Computer Interfaces need to overcome these variations, as models trained on characteristic features from one day fail to represent new characteristics of another. This paper proposes a novel adaptive model that adjusts to signal variation by appending new features to the existing model and without knowledge of actual hand kinetics in an unsupervised way. With this adapting model we investigated the effects of learning and model adaptation on BCI performance. Using this new model we dramatically improve on all previously published long term decoding and show that target direction is accurately decoded in 95% of the trials over two weeks and in 85% of the trials in varying environments. Since the model needs no separate re-calibration, it can reduce user frustration and improve BCI experience.
An efficient scheme for image transmission over error-prone channels: sigma filtering and image interpolation

Wireless networks and packet networks such as the Internet are prone to long burst errors and transient failures. In contrast to the traditional schemes that first introduce some amount of redundancy among descriptions before transmitting multimedia data to combat channel impairments, we investigate a new scheme to tackle this problem. The proposed scheme is to first capture the most important visual features of a given image extracted from Sigma filtering preprocessing, into independent equal length packets. At the receiver side, we adopt a smart interpolation-based technique. The reconstruction quality at the receiver depends only on the number of packets received, but is independent of the place from where they were cropped. The preliminary results on standard test images show that our reconstructed image is very pleasant to human eyes, and achieves reasonable PSNR values, while at the same time using a lower bit rate than other traditional reported schemes based on multiple description coding.
Parallel smoothing for time-invariant two-point boundary value systems

A parallel and stable algorithm is presented for solving the smoothing problem for two-point boundary value systems. The algorithm uses the generalized Schur decomposition of matrix pencils to decouple the Hamiltonian system that governs the solution to the two-point boundary value smoothing problem into forward-stable and backward-stable recursions. Associative fan-in algorithms are then used to compute in a parallel stable fashion the various quantities that are needed to evaluate the solution of the forward and backward recursions. Those solutions are finally combined to obtain the desired smoothed estimates. The total running time of this procedure is O(log K) time units for 1-D smoothing problems defined on the interval (0, K). The algorithm is characterized by a high efficiency and low interprocessor communications requirements.<<ETX>>
Sensing of transmission opportunities at the Medium Access Control layer for cognitive radio networks

Detection of transmission opportunities is the critical part of establishing a cognitive radio network. Traditionally, it is achieved via spectrum sensing carried out at the physical layer to detect unused frequency bands. In this paper, we propose a novel approach to detect transmission opportunities at the Medium Access Control (MAC) layer based on the concept of interference temperature. In this approach, secondary transmissions are overlayed on top of primary transmissions as long as they do not affect the primary transmission. A Transmit Margin is defined and used to quantify the spare capacity in the primary network. We setup a system to estimate and track the Packet Error Rates (PER) and use it as a metric for transmit opportunity detection. A distributed estimation procedure to increase the robustness of the estimate is also proposed for cognitive radio networks. The scope of this hitherto unexplored area of cognitive radios is demonstrated and applied to IEEE 802.11 wireless networks through simulation studies.
Long-term decoding of arm movement using Spatial Distribution of Neural Patterns

Day to day variability and non-stationarity caused by changes in subject motivation, learning and behavior pose a challenge in using local field potentials (LFP) for practical Brain Computer Interfaces. Pattern recognition algorithms require that the features possess little to no variation from the training to test data. As such models developed on one day fail to represent the characteristics on the other day. This paper provides a solution in the form of adaptive spatial features. We propose an algorithm to capture the local spatial variability of LFP patterns and provide accurate long-term decoding. This algorithm achieved more than 95% decoding of eight movement directions two weeks after its initial training.
Collision resolution and interference elimination in multiaccess communication networks

We define a multiaccess communication scheme that effectively eliminates interference and resolves collisions in many-to-one and many-to-many communication scenarios. Each transmitter is uniquely identified by a coding vector. Using these vectors, all signals issued from a specific transmitter will be aligned along a unique dimension at all receivers hearing this transmission. This dimension is characteristic of the transmitter. It also lies within a signal-and-noise subspace that is orthogonal to the noise-only subspace at the receiver. Signals along each dimension of the signal-and-noise subspace can be extracted separately using the properties of the Vandermonde matrix. The decoding algorithm is thus able to asymptotically achieve full network capacity at high signal-to-noise ratio (SNR) compared to 50% and 36.79% asymptotic throughputs for interference alignment and Ethernet respectively. Synchronization is assumed between the transmitters and the receiver(s). The number of transmitters is not necessarily known to each receiver.
Under-sampled functional MRI using low-rank plus sparse matrix decomposition

High spatial resolution in functional magnetic resonance imaging improves its sensitivity to brain activation signals by reducing partial volume effects. However, the long acquisition times required for high spatial resolution limit the temporal resolution in fMRI studies. Consequently, the low temporal sampling bandwidth leads to increase in physiological noise and poor modeling of the functional activation dynamics. Thus, fast techniques capable of recovering fMRI time-series from under-sampled data are desirable to improve the sensitivity and specificity of fMRI for functional brain mapping. This paper presents an under-sampled fMRI recovery using low-rank plus sparse matrix decomposition signal model. This model is suited for blocked or slow event-related fMRI studies, where the low-rank matrix captures the temporally static T*2-weighted image patterns and, the sparse matrix captures the pseudo-periodic brain activation signal. The preliminary results of under-sampled recovery on in-vivo fMRI data show recovery of BOLD activation in human superior colliculus with contrast-to-noise ratio ≥ 4.4 (85% of reference) up to acceleration factors of 3.
Uncovering potential biomarkers in ovarian carcinoma via biclustering of DNA microarray data

The NIH/NCI estimates that one out of 57 women will develop ovarian cancer during their lifetime. Ovarian cancer is 90 percent curable when detected early. Unfortunately, many cases of ovarian cancer are not diagnosed until advanced stages because most women do not develop noticeable symptoms. This paper presents an exhaustive identification of all potential biomarkers for the diagnosis of early-stage and/or recurrent ovarian cancer using a unique and comprehensive set of gene expression data. The data set was generated by Gene Logic Inc. from ovarian normal and cancerous tissues as well as non-ovarian tissues collected at the University of Minnesota by Skubitz et al. In particular, the paper shows the ability of a modified biclustering technique combined with sensitivity analysis of gene expression levels to identify all potential biomarkers found by prior studies as well as several more promising candidates that had been missed in the literature. Furthermore, unlike most prior studies, this work screens all candidate biomarkers using two additional techniques: immunohistochemical analysis and reverse transcriptase polymerase chain reaction.
In vivo tracking of 3D organs using spherical harmonics and subspace clustering

Deformable organ tracking has been a challenge in various medical applications. This paper proposes an algorithm for 3D organ tracking based on spherical harmonics (SH) and subspace clustering. The potential deformation subspaces are identified from training data, based on which an extremely low density sampling strategy and a low cost deformation construction method are designed. Both theoretical analysis and simulations verified that the presented tracking algorithm minimizes the number of sampling locations, storage and computation complexity, while maintaining high accuracy. The designed approach can be applied to in vivo 3D organ tracking and visualization during surgical intervention.
Low bit rate transparent audio compression using a dynamic dictionary and optimized wavelets

The authors propose an audio synthesis/coding method which employs an optimized wavelet-transform (WT)-based adaptive transform coding to exploit perceptual masking. In addition, a dynamic dictionary is used to extract source redundancies. Experiments indicated that transparent coding of monophonic CD quality signals (sampled at 44.1 KHz) is possible at bit rates approaching 64 kbit/s when the WT-based method alone is used, and in the range of 48-64 kbit/s when the dynamic dictionary is also employed.<<ETX>>
Empirical likelihood ratio test with density function constraints

In this work, we study non-parametric hypothesis testing problem with density function constraints. The empirical likelihood ratio test has been widely used in testing problems with moment (in)equality constraints. However, some detection problems cannot be described using moment (in)equalities. We propose a density function constraint along with an empirical likelihood ratio test. This detector is applicable to a wide variety of robust parametric/non-parametric detection problems. Since the density function constraints provide a more exact description of the null hypothesis, the test outperforms many other alternatives such as the empirical likelihood ratio test with moment constraints and robust Kolmogorov-Smirnov test, especially when the alternative hypothesis has a special structure.
Outage capacity of pulsed-OFDM ultra wideband communications

Pulsed-OFDM is a novel modulation scheme proposed to enhance the baseline multi-band OFDM proposal for the IEEE 802.15.3a wireless personal area networks. Pulsating the OFDM symbols spreads the spectrum of the modulated signals in the frequency domain, leading to enhanced performance in multipath fading environments and reduced complexity and power consumption for transceivers. We study the information theoretic aspects of the pulsed-OFDM system. We provide formulas for average capacity and capacity versus outage probability characteristics in Rayleigh fading channels and use these formulas to determine the optimum spreading factor for the pulsed-OFDM system in a given channel condition. We also show that the optimum spreading factor is a function of the signal-to-noise ratio and capacity versus outage probability characteristics of the channel.
Handling Updates of a Pairwise Sequence Alignment

Sequence alignment or decoding in molecular biology is mostly done via computationally expensive dynamic programming (DP) based approaches. Unfortunately, as sequencing errors are discovered frequently, researchers must repeat all previous similarity analysis for the erroneous sequence. This can take hours or days. In this work, we derive relative tolerance bounds on node distances from a root node that guarantee that partial shortest path distances remain optimal. We then propose an algorithm that uses these bounds to skip all unperturbed parts of a sequence when recomputing an alignment. We also discuss techniques to reduce the memory requirements of the algorithm by focusing on the highly conserved segments of the sequence. Experimental results establish that our proposed alignment procedure can update alignment decisions of modified sequence with 4.6% to 18% of the number of computations required by the normal Needleman-Wunsch algorithm, depending on sequence length. Higher computational savings are achieved with longer sequences
Classification of movement EEG with local discriminant bases

We use local discriminant bases and linear discriminant analysis to classify EEG of left and right hand movement execution and imagination. The local discriminant bases adaptively segment and extract features from real and imagined movement EEG (2003 BCI competition) using cosine packets and Kullback-Leibler, Euclidean and Hellinger class separability (CS) criteria. We also tried principal component analysis (PCA) as another feature reduction method. In our case, CS ordered coefficients resulted in lower classification error than PCA using a smaller number of coefficients. We observed that the most discriminative components were located on the post movement beta and alpha synchronization. Pre-movement features were also selected by the algorithm. We believe that these segments correspond to the mental state and strategy of the subject during the movement execution/imagination. The main advantage of the algorithm is that it adaptively finds these physiological states in an ongoing EEG. This may eliminate the inter- and intra-subject variability. The average error rate of the classification was 12.7% for movement execution and 14.2% for movement imagination. Accordingly, the algorithm would be the 3rd best in the 2003 BCI (brain-computer interface) competition.
Dissolve transition detection using B-splines interpolation

We present a novel identification technique for dissolve transitions in video sequences. Our scheme is to estimate the actual transition curve by polynomial data interpolation. We make use of "goodness" of fit to determine the presence of dissolve transitions. A B-spline polynomial curve fitting technique is used. Our approach is able to recover the original transition behavior of a dissolve edit effect even if it was distorted by various post-processing stages.
On the choice of a wavelet for signal coding and processing

It is argued that a good wavelet for representing a given signal is one that leads to a compact representation of the signal up to a given scale. Unfortunately, minimizing the distance between a given signal and its approximation up to a given scale using any wavelet is a difficult optimization problem. Instead, a tight upper bound on that distance is developed. The bound is an explicit function of the discrete set of coefficients defining the wavelet. By minimizing this upper bound one then obtains a good wavelet for representing the given signal.<<ETX>>
Completeness and stability of partial dyadic wavelet domain signal representations

The authors present a necessary and sufficient condition for the completeness of any partial dyadic wavelet transform domain representation (PDWTDR) of discrete finite data length signals (including dyadic wavelet transform extrema and zero-crossing representations). It is shown that completeness depends only on the locations of the retained samples of the dyadic wavelet transform. The present completeness test is more convenient and easier to verify than previously derived tests. It is also shown how to ensure the completeness of the representation by adding additional information in those cases where the PDWTDR is incomplete. The numerical stability of such a representation is also discussed. A fast-Fourier-transform (FFT)-based reconstruction algorithm from such a signal representation is also described.<<ETX>>
Blind adaptive modulation systems for wireless channels with binary feedback

In this paper, we investigate the performance of a blind adaptive modulation scheme that does not require any channel knowledge and just uses binary feedback, thereby decreasing feedback load. Retransmission of erroneous packet is not considered. In particular, we present an analytical framework for the performance evaluation of a simple wireless system in terms of receiver and transmitter structure. The system requires no knowledge of the channel and relies on a binary feedback. Slow and fast Rayleigh fading channel conditions are considered. The paper includes the derivation of closed-form expressions of the packet error rate (PER) and the spectral efficiency in the case of slow Rayleigh fading. In some cases, also closed-form expressions for the spectral efficiency are derived. Our results show relatively high PER but some applications such as voice communication can still operate in a satisfactory fashion in these conditions. Using coded modulation with high coding gain and increasing the number of blocks per time slot decrease the PER even more.
Multiscale difference equation signal modeling and analysis techniques

A novel signal modeling technique in which an arbitrary sequence of data points is represented as samples of the solution to a multiscale difference equation is proposed. Such a model completely characterizes a number of higher derivatives of the signal as well as the signal itself. It provides a recursive signal interpolation mechanism as a function of scale. It also leads to multigrid type signal filtering, detection and estimation algorithms. The existence and uniqueness of L/sub 1/ and L/sub 2/ solutions to the multiscale difference equation are first investigated. The paper generalizes the results obtained for the two scale difference equation. Next, we provide conditions for the existence of unique solution to such an equation. Finally, techniques for modeling an arbitrary set of data samples as samples of the solution to a multiscale difference equation are presented. Specifically, we describe the encoding and decoding steps in these techniques. We also prevent audio signal modeling examples to illustrate multiscale difference equation models.<<ETX>>
Transferring multimedia files using adaptive transmission in infostation technology

Infostations is a new system concept that has been proposed to provide convenient and cost effective access to high date rate mobile data services. An efficient IP packet transmission protocol is required to compensate for the high error rate inherent to fading radio channels. In this paper we propose to investigate the performance of a selective re- peat automatic-repeat-request (SR-ARQ) scheme combined with adaptive modulation and Maximum Ratio Combining (MRC) to transfer fragmented large multimedia files over Infostations networks. Based on the channel state information (CSI), the modulation level can be switched dynamically. We will consider two access options: (i) sit-through access, and (ii) drive-through access. Closed-form expressions and computer simulations will be provided to evaluate the performance of the system.
Sparse magnetic resonance imaging using tagging RF pulses

Few fast magnetic resonance (MR) imaging techniques have proposed modifications to the MR signal encoding formulation in order to improve the performance guarantees of image recovery using compressed sensing. A limitation of the previously proposed encoding formulations is their difficult realization on the physical hardware. The deviation of realizable formulation from the theoretical model leads to operating characteristics which are clinically infeasible. In this paper, a novel MR signal encoding formulation using tagging radio-frequency pulses is proposed. The proposed formulation uses tagging pulses to uniquely modulate the longitudinal magnetization in the field-of-view for each MR excitation. The modulation of magnetization leads to mixing of information in the spatial Fourier space which improves the incoherence between the sensing and the sparsifying basis. The physical realization of the proposed formulation is promising due to the use of clinically active RF pulses. The preliminary results for image recovery experiments using the proposed formulation on an in-vivo dataset are comparably close and at times better than the results of the difficult-to-realize state-of-the-art formulation.
Wavelet domain bearing estimation in unknown correlated noise

It is shown that the M-band wavelet domain correlation matrix of a noise process consists of linear structures as long as the spatial correlation function of the noise process decays asymptotically to zero as the distance between samples of the noise tends to infinity and the analyzing M-band wavelet has a large (/spl ges/5) number of vanishing moments. On the other hand, the M-band wavelet domain correlation matrix corresponding to plane waves consists of "plateau" like structures. Next, a procedure that discriminates between the signal information carried by plane waves and the noise information based on the structure of the M-band wavelet domain correlation matrix of the array outputs is proposed.<<ETX>>
Segmented rapid magnetic resonance imaging using structured sparse representations

This paper presents a novel approach for segmented rapid magnetic resonance (MRI) imaging using structured sparse representations. In recent years, MR imaging speed has been improved through techniques relying on reduced data acquisition either to allow less-perceivable artifacts or to exploit the data-redundancy. But, these techniques require design of randomized sampling patterns which prove to be sub-optimal from the point of attaining high acceleration gains. The proposed approach treats the imaging problem as a segmented imaging problem constrained to structured dictionaries learned from a small training dataset. The solution to which, is further relaxed to allow recovery of segmented component images from the under-sampled Fourier space. The under-sampling corresponds to traversing few commonly used sampling trajectories only (such as: spirals and radials), i.e., no special sampling pattern design is needed. This highly reduces the number of trajectory acquisitions needed per image formation and, reconstruction in learned dictionaries leads to recovery of automatic segmented images. Experimental results on a real-patient cardiac cine-MR data-set recover high quality segmented images at an acceleration factor of R=13.5 using only 2-spiral sampling trajectories.
Liver segmentation using structured sparse representations

Segmentation of liver from volumetric images forms the basis for surgical planning required for living donor transplantations and tumor resections surgeries. This paper introduces a novel idea of using sparse representations of liver shapes in a learned structured dictionary to produce an accurate preliminary segmentation, which is further evolved using a joint image and shape based level-set framework to obtain the final segmented volume. Structured dictionary for liver shapes can be learned from an available training dataset. The proposed approach requires only 3 orthogonal segmented masks as user-input, which is less than half the number required by current state-of-the-art interaction-based methods. The increased accuracy of the preliminary segmentation translates into faster convergence of the evolution step and highly accurate final segmentations with mean average symmetric surface distances (ASSD)  of (1.03±0.3)mm when tested on a challenging dataset containing 62 volumes. Our approach segments a volume on an average of 5 mins and, is 25% (approx.) faster than comparably performing techniques.