Explanation on Pretraining Bias of Finetuned Vision Transformer

As the number of fine tuning of pretrained models increased, understanding the bias of pretrained model is essential. However, there is little tool to analyse transformer architecture and the interpretation of the attention maps is still challenging. To tackle the interpretability, we propose Input-Attribution and Attention Score Vector (IAV) which measures the similarity between attention map and input-attribution and shows the general trend of interpretable attention patterns. We empirically explain the pretraining bias of supervised and unsupervised pretrained ViT models, and show that each head in ViT has a specific range of agreement on the decision of the classification. We show that generalization, robustness and entropy of attention maps are not property of pretraining types. On the other hand, IAV trend can separate the pretraining types

Localization of cortico-peripheral coherence with electroencephalography.

Background The analysis of coherent networks from continuous recordings of neural activity with functional MRI or magnetoencephalography has provided important new insights into brain physiology and pathology. Here we assess whether valid localizations of coherent cortical networks can also be obtained from high-resolution electroencephalography (EEG) recordings. Methods EEG was recorded from healthy subjects and from patients with ischemic brain lesions during a tonic hand muscle contraction task and during continuous visual stimulation with an alternating checkerboard. These tasks induce oscillations in the primary hand motor area or in the primary visual cortex, respectively, which are coherent with extracerebral signals (hand muscle electromyogram or visual stimulation frequency). Cortical oscillations were reconstructed with different inverse solutions and the coherence between oscillations at each cortical voxel and the extracerebral signals was calculated. Moreover, simulations of coherent point sources were performed. Results Cortico-muscular coherence was correctly localized to the primary hand motor area and the steady-state visual evoked potentials to the primary visual cortex in all subjects and patients. Sophisticated head models tended to yield better localization accuracy than a single sphere model. A Minimum Variance Beamformer (MVBF) provided more accurate and focal localizations of simulated point sources than an L2 Minimum Norm (MN) inverse solution. In the real datasets, the MN maps had less localization error but were less focal than MVBF maps. Conclusions EEG can localize coherent cortical networks with sufficient accuracy

Preceptorship Practice in Healthcare Institutions in Ghana: A Situational Analysis

Preceptors play a vital role in supporting nursing/midwifery students and new employees’ transition and assimilation into their new role. Furthermore, with the increasing focus on educating more qualified nurses and midwives to meet health-related United Nations Sustainable Development Goals, there is a need for a more standardized and coordinated approach to preceptorship training. As former Head of the Nursing/Midwifery Training Institution in Ghana, I observed first-hand that the system of preceptorship needs improvements. Published literature on preceptorship has shown that the practice plays a vital role in healthcare delivery. However, most of the existing literature preceptorship is from developed countries, with little research from developing countries like Ghana. This study explored the practice of preceptorship in selected nursing/midwifery and healthcare institutions in Ghana. Situational analysis was used to examine the complex dynamics of the preceptorship program. It consists of three main procedural tools: situational maps, social worlds/arenas maps, and positional maps. Several important factors were found to impact preceptorship in Ghana. Key ones were motivational (monetary) challenges, lack of training of preceptors, politicking related to the development of preceptorship manuals, supervision, and outdated procedure guidelines for on-the-job teaching students. The study offers a series of recommendations to improve preceptorship practice at micro, meso, and macro levels. Additionally, they may enable regulators and policy makers in Ghana to formulate policies leading to a more robust preceptorship program to strengthen the skills of nursing/midwifery profession. This dissertation is available in open access at AURA (https://aura.antioch.edu) and OhioLINK ETD Center (https://etd.ohiolink.edu)

Real-time motion and dynamic receiver sensitivity correction for CEST-MRI in the human brain at 7T

Chemical Exchange Saturation Transfer (CEST) is a novel Magnetic Resonance Imaging (MRI) technique that utilises exchange reactions between metabolites and tissue water to map metabolite concentration or tissue pH noninvasively. Similarly to Magnetic Resonance Spectroscopy (MRS), CEST is able to detect many endogenous metabolites, but unlike MRS, CEST is based on imaging and thus enjoys the speed of modern MR imaging. On the other hand, CEST also suffers from the same difficulties as MRI and MRS. One of the most common source of image artifacts in MRI is subject motion during imaging. Many different motion correction methods have been devised. Recently, a novel real-time motion correction system was developed for MRS. This method is based on volumetric navigators (vNav) that are performed multiple times interleaved with the parent measurement. Navigator image comparison, affine matrix calculation, and acquisition gradient correction to correct the field of view to match subject head motion are done online and in real-time. The purpose of this thesis is to implement this real-time motion correction method to CEST-MRI and study its efficacy and correction potential in phantoms and in healthy volunteers on 7T MR scanner. Additionally, it is hypothesised that the vNav images may be used to correct for motion related receiver sensitivity (B1-) inhomogeneities. Glutamate was chosen as the metabolite of interest due to it being the most abundant neurotransmitter in the human brain and due to its involvement in both normal cognitive function as well as many brain pathologies. Since glutamate has an amine group, it undergoes chemical exchange with water and is thus a usable metabolite for CEST imaging. A glutamate phantom was constructed to show the glutamate concentration sensitivity of CEST and to test and optimise the CEST sequence. Seven healthy volunteers were imaged over a period of two months. All but one volunteer were imaged more than once (2-4 times). Subjects were measured without voluntary head motion and with controlled left-right and up-down head movements. All measurements were performed with and without motion correction to test the motion and B1- -correction methods. Additionally, three volunteers were measured with a dynamic CEST experiment to assess the reproducibility of CEST. The real-time motion correction method was found to be able to correct for small, involuntary head movements. 18 % of the CEST maps measured without motion correction were found to have motion artifacts whereas the equivalent number for maps with motion correction was 0 % (4/22 maps versus 0/18 maps). Larger (>0.7◦ or >0.7 mm in one coregistration step), voluntary head movements could not be corrected adequately. The vNav images could be used to correct for B1- -inhomogeneities. This was found to improve CEST spectra quality and to remove lateral inhomogeneities from the CEST maps. The reproducibility of the CEST-MRI could not be established, however dynamic CEST measurements were found to be stable with only small contrast fluctuation of 4 % between consecutive maps due to noise

Identifying The Pattern of Material Loss at the Head-Neck Junction Wear Helps Determine the Mechanism of Failure of Metal on Metal Total Hip Replacements

Material loss at the Head-Neck junction accounts for a third of the total volume material loss in contemporary metal-on-metal total hip replacements. It is speculated that the material loss is the result of corrosion and mechanical wear (fretting). High volumes of material loss have been reported, especially from the head taper. There is only one report on characterizing the pattern of material loss and this was in a very small number of cases (n=5). Our aim was to identify the different material loss patterns at the head taper and their corresponding mechanisms We retrospectively analysed a series of retrieved Large Head Metal on Metal Total Hip Replacements (155 cups, 155 femoral heads and 4 stems). We measured material loss on the bearing surfaces and the head-neck junction using well-published metrology methods. Furthermore we collected patient (age, gender and time of primary/revision operations), pre-revision (cobalt and chromium blood metal ion, oxford hip score, cup orientation and implant position) implant (cup and head size, manufacturer and corrosion severity) data. Finally we used surface analysis techniques (microscopy and spectroscopy) to identify fretting, imprinting and the material composition of debris. We devised a novel four-group classification and two blinded engineers classified the material loss patterns using wear maps derived from the metrology analysis We observed four distinct patterns of taper surface material loss at our retrieval centre and we set out to characterize these types and relate them to patient, implant and clinical variables. The four groups of material loss patterns were defined as: (1) Low wear (n= 63), (2) Open-end band (n=32), (3) Stripped material loss (n=54) and (4) Coup-Countercoup (n=6) (Figure). The Interobserver Reliability Kappa score was 0.78 (p<0.001) indicating substantial agreement between the two examiners. Analysis of variables between the groups identified significantly different head sizes (highest: Group 2, p=0.000), corrosion severity (highest: Group 2, p=0.004) and time to revision (highest: Group 3, p=0.040). We identified four different material loss patterns each with its own mechanism. Corrosion was identified as the principal mechanism in Groups 1 and 3. Group 1 head-neck junctions are thought to have a better seal with less fluid ingress in the junction. Group 3 head-neck junctions are attacked by corrosion either circumferentially, or unilaterally, along the whole engagement length. Mechanically assisted corrosion was the principal mechanism in Group 2. The higher friction torque opens up the open-end part of the junction and the ingressing fluid accelerates the corrosion. Extensive fretting was also observed under the scanning electron microscope. Intra-operative surgical damage was identified as the principal mechanism in Group 4, with only 6 components. The patterns and the mechanisms of material loss at the head-neck junction contribute to the understanding of large head metal-on-metal hip replacements. As a result, better implants can be designed in the future. Clinically, these findings suggest that head size and head taper-trunnion fit are the main factors that determine the longevity of the head-neck junction. On the other hand, patients selection does not influence the integrity of the junction

Generating natural word orders in a semi-free word order language: Treebank-based linearization preferences for German

We outline an algorithm capable of generating varied but natural sounding sequences of argument NPs in subordinate clauses of German, a semi-free word order language. In order to attain the right level of output flexibility, the algorithm considers (1) the relevant lexical properties of the head verb (not only transitivity type but also reflexivity, thematic relations expressed by the NPs, etc.), and (2) the animacy and definiteness values of the arguments, and their length. The relevant statistical data were extracted from the NEGRA–II treebank and from hand-coded features for animacy and definiteness. The algorithm maps the relevant properties onto “primary” versus “secondary” placement options in the generator. The algorithm is restricted in that it does not take into account linear order determinants related to the sentence’s information structure and its discourse context (e.g. contrastiveness). These factors may modulate the above preferences or license “tertiary” linear orders beyond the primary and secondary options considered here

Trunk and Hand-Centred Spatial Coordinate Frames

Neurons in early visual cortex represent space within a retinocentric coordinate frame, whereas downstream in motor cortex object location is coded with respect to the effector. The reference frame used by parietal neurons for the spatial analysis required to create movement plans is still contested. One dominant view is that parietal neurons use a retinal-centred reference frame at the single-cell level, but that other coordinate schemes can be ‘read out’ when information is pooled from a population of neurons gain-modulated by the head, hand, or other body part. More recently, there has been a surge of evidence for higher-order reference frames existing at the single-cell level, predominantly within non-human primate research. Furthermore, the range of coding typologies appears to be wide and complex, with the emergence of hybrid and dimension dependent response profiles. The research presented here investigates explicit body-centred and hand-centred coordinate systems in a two-part study using a memory-guided saccade paradigm. In the neuroimaging experiment, time-series analysis was used to test for the reorganisation of topographic maps along the intraparietal sulcus (IPS), following left and right, and near and far changes to hand position. In the behavioural experiment, eye-tracking data from the right eye was used to test for differences in error, following 90 degree torso rotations coupled with left and right hand placements. In both experiments, the retinal coordinates of saccade targets remained constant, and significant differences between conditions would provide evidence for the contribution of hand and body-centred spatial coding. This evidence would support the recently emerging evidence from the non-human primate literature and strengthen the argument for using posterior parietal cortex (PPC) as the source of command signals for the real-time control of neural prosthetics

Head Pose Estimation via Probabilistic High-Dimensional Regression

International audienceThis paper addresses the problem of head pose estimation with three degrees of freedom (pitch, yaw, roll) from a single image. Pose estimation is formulated as a high-dimensional to low-dimensional mixture of linear regression problem. We propose a method that maps HOG-based descriptors, extracted from face bounding boxes, to corresponding head poses. To account for errors in the observed bounding-box position, we learn regression parameters such that a HOG descriptor is mapped onto the union of a head pose and an offset, such that the latter optimally shifts the bounding box towards the actual position of the face in the image. The performance of the proposed method is assessed on publicly available datasets. The experiments that we carried out show that a relatively small number of locally-linear regression functions is sufficient to deal with the non-linear mapping problem at hand. Comparisons with state-of-the-art methods show that our method outperforms several other techniques

Reliability Scores from Saliency Map Clusters for Improved Image-based Harvest-Readiness Prediction in Cauliflower

Cauliflower is a hand-harvested crop that must fulfill high-quality standards in sales making the timing of harvest important. However, accurately determining harvest-readiness can be challenging due to the cauliflower head being covered by its canopy. While deep learning enables automated harvest-readiness estimation, errors can occur due to field-variability and limited training data. In this paper, we analyze the reliability of a harvest-readiness classifier with interpretable machine learning. By identifying clusters of saliency maps, we derive reliability scores for each classification result using knowledge about the domain and the image properties. For unseen data, the reliability can be used to (i) inform farmers to improve their decision-making and (ii) increase the model prediction accuracy. Using RGB images of single cauliflower plants at different developmental stages from the GrowliFlower dataset, we investigate various saliency mapping approaches and find that they result in different quality of reliability scores. With the most suitable interpretation tool, we adjust the classification result and achieve a 15.72% improvement of the overall accuracy to 88.14% and a 15.44% improvement of the average class accuracy to 88.52% for the GrowliFlower dataset.Comment: Preprint, 8 pages, 6 figure