nils-herrmann/clean_scirepeval huggingface dataset:
Unsupervised Cross-Modal Deep-Model Adaptation for Audio-Visual Re-identification with Wearable Cameras

Model adaptation is important for the analysis of audiovisual data from body worn cameras in order to cope with rapidly changing scene conditions, varying object appearance and limited training data. In this paper, we propose a new approach for the on-line and unsupervised adaptation of deep-learning models for audio-visual target reidentification. Specifically, we adapt each mono-modal model using the unsupervised labelling provided by the other modality. To limit the detrimental effects of erroneous labels, we use a regularisation term based on the Kullback-Leibler divergence between the initial model and the one being adapted. The proposed adaptation strategy complements common audio-visual late fusion approaches and is beneficial also when one modality is no longer reliable. We show the contribution of the proposed strategy in improving the overall re-identification performance on a challenging public dataset captured with body worn cameras.
Prioritized target tracking with active collaborative cameras

Mobile cameras on robotic platforms can support fixed multi-camera installations to improve coverage and target localization accuracy. We propose a novel collaborative framework for prioritized target tracking that complement static cameras with mobile cameras, which track targets on demand. Upon receiving a request from static cameras, a mobile camera selects (or switches to) a target to track using a local selection criterion that accounts for target priority, view quality and energy consumption. Mobile cameras use a receding horizon scheme to minimize tracking uncertainty as well as energy consumption when planning their path. We validate the proposed framework in simulated realistic scenarios and show that it improves tracking accuracy and target observation time with reduced energy consumption compared to a framework with only static cameras and compared to a state-of-the-art motion strategy.
Business Model Change and Refinement along Business Model Lifecycle: Evidences from a Multiple Case Study on Mobile Telecommunications New Ventures

Few studies describe in depth the dynamic aspect of the design process of the business model (BM) construct. This dynamic feature is interrelated with the changes in the environment, and this is specifically true for new ventures in rapidly changing context, who need to adjust their BMs several times to succeed. This research attempts to clarify the mechanisms of BM change during a firm's startup phase. The study aims at: (i) understanding the main phases constituting the business model lifecycle (BML), and (ii) analyzing in which BM dimensions entrepreneurs focus their design efforts during the refining cycle of the BML. The analysis was conducted through the case study methodology, by interacting with 12 mobile new ventures. Our findings offer an original representation of the BM development in Mobile New Ventures through the BML, and suggest a correlation between founders' background and the refinement activity on some BM dimensions.
Microstructural Description of Cerebral Tissues from Diffusion Spectrum Imaging Data

Micro-structural indexes based on a novel reconstruction method for diffusion MRI data have recently been proposed. Nevertheless, the validation of such descriptors is still on the way and no data are currently available in the literature on human subjects. In this work we derive such descriptors on a a group of human Diffusion Spectrum Imaging (DSI) data. First results show that such measures hold higher sensitivity and specificity as numerical biomarkers with respect to classical measures.
A parametric hybrid model used for multidimensional object representation

In this paper, we present a parametric hybrid model used in the framework of multidimensional object representation, for applications to both object visualization and object-based data compression. Our model is defined as a set of hybrid ellipsoids suitable for both globally and locally deforming the reconstructed shape. Its new parameterization, as compared to classical techniques, allows us to preserve its analytical representation during the fitting process. It is fitted to the object contours by means of a genetic algorithm minimizing a mean-square error criterion. Several criteria are proposed and discussed according to the stability of the optimization process, as well as the ability to efficiently initialize the model parameters. Finally, fitting results are presented for 2D and 3D data and different applications are proposed.
Validation through accuracy prediction in neuroimage registration

Validation and accuracy assessment are the main bottlenecks preventing the adoption of many medical image processing algorithms in the clinical practice. In the classical approach, a-posteriori analysis is performed based on some predefined objective metrics. The main limitation of this methodology is in the fact that it does not provide a mean to estimate what the performance would be a-priori, and thus to shape the processing workflow in the most suitable way. In this paper, we propose a different approach based on Petri Nets. The basic idea consists in predicting the accuracy that will result from a given processing on a given type of data based on the identification and characterization of the sources of inaccuracy intervening along the whole chain. Here we propose a proof of concept in the specific case of image registration. A Petri Net is constructed after the detection of the possible sources of inaccuracy and the evaluation of their respective impact on the estimation of the deformation field. A training set of five different synthetic volumes is used. Afterward, validation is performed on a different set of five synthetic volumes by comparing the estimated inaccuracy with the posterior measurements according to a set of predefined metrics. Two real cases are also considered. Results show that the proposed model provides a good prediction performance. An extended set of clinical data will allow the complete characterization of the system for the considered task.
3D/2D object-based coding of head MRI data

We propose a coding system featuring 3D encoding/2D decoding object-based functionalities. Any object of any 2D image of the dataset can be recovered at a finely graded up to lossless quality. Compression is improved by exploiting the full correlation among data samples by means of 3D DWT. A swift access to 2D images is obtained by enabling 2D decoding. Given the index of the image of interest along the z axis, only the concerned portion of the bitstream is decoded, at the desired quality. The selective access to data can be improved by splitting the image in regions corresponding to the different objects. Then, a suitable ordering of the encoded information within the bitstream enables random access to any object at the desired rate. This enables a pseudo-lossless regime, where the diagnostically relevant parts of the image are represented without loss, while a lower quality is assumed to be acceptable for the others. Results show that the proposed system is a good compromise between the gain in compression efficiency provided by 3D systems and the fast access to the data of 2D ones.
A multi-view approach to consensus clustering in multi-modal MRI

It has been shown that the combination of multi-modal MRI images can improve the discrimination of diseased tissue. The fusion of dissimilar imaging data for classification and segmentation purposes however, is not a trivial task, as there is an inherent difference in information domains, dimensionality and scales. This work proposes a multi-view consensus clustering methodology for the integration of multi-modal MR images into a unified segmentation of tumoral lesions for heterogeneity assessment. Using a variety of metrics and distance functions this multi-view imaging approach calculates multiple vectorial dissimilarity-spaces for each MRI modality and makes use of cluster ensembles to combine a set of un-supervised base segmentations into an unified partition of the voxel-based data. The methodology is demonstrated in application to DCE-MRI and DTI-MR, for which a manifold learning step is implemented in order to account for the geometric constrains of the high dimensional diffusion information.
Shore-based microstructural indices: do they tell us more?

Recent methods for diffusion weighted magnetic resonance convey information about tissue microstructure. In the last years, many models have been proposed for recovering the diffusion signal and extracting information to constitute new families of microstructural indices. Here we focus on three leading diffusion MRI models: NODDI (Neurite Orientation Dispersion and Density Imaging), 3D-SHORE (3D Simple Harmonic Oscillator-based Reconstruction and Estimation) and its formulation in the Cartesian space, the MAPMRI (Mean Apparent Propagator MRI) and analyze the information conveyed by the respective set of indices based on information-theoretic measures. This will allow to objectively assess the ability of each index of capturing microstructural features and thus to shed light on their exploitability in discriminative tasks. To this end, the microstructural descriptors are treated as machine learning features and analyzed via information-theoretic methods. First results on in-vivo data suggest that 3D-SHORE and MAPMARI could be more eloquent in describing microstructure and that a combination of descriptors obtained from all models may provide the best subset of features for a classification task.
Integer wavelet transform for embedded lossy to lossless image compression

The use of the discrete wavelet transform (DWT) for embedded lossy image compression is now well established. One of the possible implementations of the DWT is the lifting scheme (LS). Because perfect reconstruction is granted by the structure of the LS, nonlinear transforms can be used, allowing efficient lossless compression as well. The integer wavelet transform (IWT) is one of them. This is an interesting alternative to the DWT because its rate-distortion performance is similar and the differences can be predicted. This topic is investigated in a theoretical framework. A model of the degradations caused by the use of the IWT instead of the DWT for lossy compression is presented. The rounding operations are modeled as additive noise. The noise are then propagated through the LS structure to measure their impact on the reconstructed pixels. This methodology is verified using simulations with random noise as input. It predicts accurately the results obtained using images compressed by the well-known EZW algorithm. Experiment are also performed to measure the difference in terms of bit rate and visual quality. This allows to a better understanding of the impact of the IWT when applied to lossy image compression.
Towards Computational Proxemics: Inferring Social Relations from Interpersonal Distances

This paper proposes a study corroborated by preliminary experiments on the inference of social relations based on the analysis of interpersonal distances, measured with on obtrusive computer vision techniques. The experiments have been performed over 13 individuals involved in casual standing conversations and the results show that people tend to get closer when their relation is more intimate. In other words, social and physical distances tend to match one another. In this respect, the results match the findings of proxemics, the discipline studying the social and affective meaning of space use and organization in social gatherings. The match between results and expectations of proxemics is observed also when changing one of the most important contextual factors in this type of scenarios, namely the amount of space available to the interactants.
Multirate coding of 3D medical data

The last generation medical imaging equipment produce multidimensional (3D or 3D+time) data distributions. On a coding perspective, it is reasonable to expect that the exploitation of the full dimensional correlation among data samples would lead to a sensible improvement in compression performances, especially for isotropic datasets. We propose a fully three-dimensional wavelet-based coding system providing a finely-graded up to lossless data representation in a single bistream. The data are first decorrelated by a 3D discrete wavelet transform, performed by the non-linear lifting scheme mapping integers to integers. This enables the lossless mode and permits the in-place implementation of the transform at a reduced computational complexity. The coding scheme is inspired to the layered-zero coding proposed by Taubman and Zakhor (1994), extended to handle fully 3D subband structures. Performances are characterized with respect to both the 2D version of the same algorithm and the JPEG standard. The rate-saving is strongly influenced by the amount of the data correlation in the z dimension, ranging between 16.5% and 5.5% for the considered datasets.
Cortical network modulation during paced arm movements

In this paper we investigate task-related changes in brain functional connectivity (FC) by applying different methods namely event-related desynchronization (ERD), coherence and graph-theoretical analysis to electroencephalographic (EEG) recordings. While ERD provides an estimate of the differences in power spectral densities between task and rest conditions, coherence allows assessing the level of synchronization between the recorded signals and graph analysis enables the estimation of the functional network topology. EEGs were recorded on 10 subjects during left/right arm movements. Conventional analysis showed a significant ERD in both alpha and beta bands over the sensorimotor cortex. Connectivity assessment highlighted that stronger connections are those involving the motor regions for which graph analysis revealed reduced accessibility and an increased cen-trality during the movement. This highlights that network analysis brings complementary knowledge with respect to established approaches for modeling motor-induced FC.
Semantics Driven Resampling of the OSA-UCS

In this paper, we propose a resampling of the OSA-UCS. Following the same sampling criterion that used to define the 424 specimens of the OSA-UCS set, we enlarged such an ensemble by adding 590 samples located in the outer region of the original volume. This allows to overcome the bottleneck in the use of the original color basis for computer vision applications due to lack of saturated colors. The outcomes of a color categorization experiment performed on the extended basis were used to train a discrete color naming model that we have recently proposed. The model was validated through the analysis of its performance for segmenting natural images. Results show that the extended basis removes the inability of the model to deal with saturated colors which significantly improves segmentation results and makes the extended bases exploitable for computer vision applications.
Diffusion MRI Compartmental Model Analysis of DSI Data

Recently proposed compartmental models can provide new micro-structural indices for characterizing tissue micro structural properties from diffusion MRI data. The state-of the-art of compartmental model for human brain is Neurite Orientation Dispersion and Density Imaging (NODDI), which is able to describe the diffusion process in both intra-cellular and extra-cellular spaces in the human brain. In our work we applied this model to a group of human Diffusion Spectrum Imaging (DSI) data showing that the computation of its indices can provide new insights about the structural differences among gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF).
Three-dimensional encoding/two-dimensional decoding of medical data

We propose a fully three-dimensional (3-D) wavelet-based coding system featuring 3-D encoding/two-dimensional (2-D) decoding functionalities. A fully 3-D transform is combined with context adaptive arithmetic coding; 2-D decoding is enabled by encoding every 2-D subband image independently. The system allows a finely graded up to lossless quality scalability on any 2-D image of the dataset. Fast access to 2-D images is obtained by decoding only the corresponding information thus avoiding the reconstruction of the entire volume. The performance has been evaluated on a set of volumetric data and compared to that provided by other 3-D as well as 2-D coding systems. Results show a substantial improvement in coding efficiency (up to 33%) on volumes featuring good correlation properties along the z axis. Even though we did not address the complexity issue, we expect a decoding time of the order of one second/image after optimization. In summary, the proposed 3-D/2-D multidimensional layered zero coding system provides the improvement in compression efficiency attainable with 3-D systems without sacrificing the effectiveness in accessing the single images characteristic of 2-D ones.
The confinement tensor model improves characterization of diffusion-weighted magnetic resonance data with varied timing parameters

Diffusion imaging with confinement tensor (DICT) is a new model that employs a tensorial representation of the geometry confining the movements of water molecules. The model differs substantially from the commonly employed diffusion tensor imaging (DTI) technique even at small diffusion weightings when the dependence of the signal on the timing parameters of the pulse sequence is concerned. In this work, we assess the accuracy of the two models on a data set acquired from an excised monkey brain. The publicly available data set features differing values for diffusion pulse duration and separation. Our results indicate that the normalized mean squared error is reduced in an overwhelming portion of the voxels when the DICT model is employed, suggesting the superiority of DICT in characterizing the temporal dependence of the diffusion process in nervous tissue.
A measure for spatial dependence in natural stochastic textures

Random textures differ from natural textures because they lack structure. Structure is a concept that is difficult to formalize, but we generally observe that it is associated with spatial dependence between adjacent pixels. Completely random textures, in fact, are characterized by independent pixels, while natural textures show some local dependence. In this paper, we propose a measure for such local dependence. The texture is scanned on a random walk path and the pixels values encountered are collected in form of a time-series. The statistical properties of the time-series are used to characterize the spatial dependence of the texture. We assume that the proposed measure can be used to help the model textures, to set the size of operating windows in texture synthesis algorithms, and to compute a simple indicator of texture scale. Moreover, we show how the measure can be linked to texture perception.
Exploring the Epileptic Brain Network Using Time-Variant Effective Connectivity and Graph Theory

The application of time-varying measures of causality between source time series can be very informative to elucidate the direction of communication among the regions of an epileptic brain. The aim of the study was to identify the dynamic patterns of epileptic networks in focal epilepsy by applying multivariate adaptive directed transfer function (ADTF) analysis and graph theory to high-density electroencephalographic recordings. The cortical network was modeled after source reconstruction and topology modulations were detected during interictal spikes. First a distributed linear inverse solution, constrained to the individual grey matter, was applied to the averaged spikes and the mean source activity over 112 regions, as identified by the Harvard–Oxford Atlas, was calculated. Then, the ADTF, a dynamic measure of causality, was used to quantify the connectivity strength between pairs of regions acting as nodes in the graph, and the measure of node centrality was derived. The proposed analysis was effective in detecting the focal regions as well as in characterizing the dynamics of the spike propagation, providing evidence of the fact that the node centrality is a reliable feature for the identification of the epileptogenic zones. Validation was performed by multimodal analysis as well as from surgical outcomes. In conclusion, the time-variant connectivity analysis applied to the epileptic patients can distinguish the generator of the abnormal activity from the propagation spread and identify the connectivity pattern over time.
Lossy to lossless object-based coding of 3-D MRI data

We propose a fully three-dimensional (3-D) object-based coding system exploiting the diagnostic relevance of the different regions of the volumetric data for rate allocation. The data are first decorrelated via a 3-D discrete wavelet transform. The implementation via the lifting steps scheme allows to map integer-to-integer values, enabling lossless coding, and facilitates the definition of the object-based inverse transform. The coding process assigns disjoint segments of the bitstream to the different objects, which can be independently accessed and reconstructed at any up-to-lossless quality. Two fully 3-D coding strategies are considered: embedded zerotree coding (EZW-3D) and multidimensional layered zero coding (MLZC), both generalized for region of interest (ROI)-based processing. In order to avoid artifacts along region boundaries, some extra coefficients must be encoded for each object. This gives rise to an overheading of the bitstream with respect to the case where the volume is encoded as a whole. The amount of such extra information depends on both the filter length and the decomposition depth. The system is characterized on a set of head magnetic resonance images. Results show that MLZC and EZW-3D have competitive performances. In particular, the best MLZC mode outperforms the others state-of-the-art techniques on one of the datasets for which results are available in the literature.
A sensitivity analysis of q-space indices with respect to changes in axonal diameter, dispersion and tissue composition

In Diffusion MRI, q-space indices are scalar quantities that describe properties of the ensemble average propagator (EAP). Their values are often linked to the axonal diameter - assuming that the diffusion signal originates from inside an ensemble of parallel cylinders. However, histological studies show that these assumptions are incorrect, and axonal tissue is often dispersed with various tissue compositions. Direct interpretation of these q-space indices in terms of tissue change is therefore impossible, and we must treat them as scalars that only give non-specific contrast - just as DTI indices. In this work, we analyze the sensitivity of q-space indices to tissue structure changes by simulating axonal tissue with changing axonal diameter, dispersion and tissue compositions. Using human connectome project data, we then predict which indices are most sensitive to tissue changes in the brain. We show that, in both multi-shell and single-shell (DTI) data, q-space indices have higher sensitivity to tissue changes than DTI indices in large parts of the brain. Based on these results, it may be interesting to revisit older DTI studies using q-space indices as markers for pathology.