Notes

Evaluation along with Discriminability involving Doppler Ultrasound exam Baby Heart Rate Variation Measures.
Channel selection or electrode placement for neural decoding is a commonly encountered problem in electroencephalography (EEG). Since evaluating all possible channel combinations is usually infeasible, one usually has to settle for heuristic methods or convex approximations without optimality guarantees. To date, it remains unclear how large the gap is between the selection made by these approximate methods and the truly optimal selection. The goal of this paper is to quantify this optimality gap for several state-of-the-art channel selection methods in the context of least-squares based neural decoding. To this end, we reformulate the channel selection problem as a mixed-integer quadratic program (MIQP), which allows the use of efficient MIQP solvers to find the optimal channel combination in a feasible computation time for up to 100 candidate channels. As this reveals the exact solution to the combinatorial problem, it allows to quantify the performance losses when using state-of-the-art sub-optimal (yet faster) channel selection methods. In a context of auditory attention decoding, we find that a greedy channel selection based on the utility metric does not show a significant optimality gap compared to optimal channel selection, whereas other state-of-the-art greedy or l1 -norm penalized methods do show a significant loss in performance. Furthermore, we demonstrate that the MIQP formulation also provides a natural way to incorporate topology constraints in the selection, e.g., for electrode placement in neuro-sensor networks with galvanic separation constraints. Furthermore, a combination of this utility-based greedy selection with an MIQP solver allows to perform a topology constrained electrode placement, even in large scale problems with more than 100 candidate positions.Speech disorders linked to neurological problems affect person's ability to communicate through speech. Dysarthria is one of the speech disorders caused due to muscle weakness producing slow, slurred and less intelligible speech. Automatic intelligibility assessment of dysarthria from speech can be used as a promising clinical tool in treatment. This paper explores the use of perceptually enhanced Fourier transform spectrograms and Constant-Q transform spectrograms with CNN to assess word level and sentence level intelligibility of dysarthric speech from UA and TORGO databases. Constant-Q transform and perceptually enhanced mel warped STFT spectrograms performed better in the classification task.Evaluating the transmittance between two points along a ray is a key component in solving the light transport through heterogeneous participating media and entails computing an intractable exponential of the integrated medium's extinction coefficient. While algorithms for estimating this transmittance exist, there is a lack of theoretical knowledge about their behaviour, which also prevent new theoretically sound algorithms from being developed. For this purpose, we introduce a new class of unbiased transmittance estimators based on random sampling or truncation of a Taylor expansion of the exponential function. In contrast to classical tracking algorithms, these estimators are non-analogous to the physical light transport process and directly sample the underlying extinction function without performing incremental advancement. We present several versions of the new class of estimators, based on either importance sampling or Russian roulette to provide finite unbiased estimators of the infinite Taylor series expansion. We also show that the well known ratio tracking algorithm can be seen as a special case of the new class of estimators. Lastly, we conduct performance evaluations on both the central processing unit (CPU) and the graphics processing unit (GPU), and the results demonstrate that the new algorithms outperform traditional algorithms for heterogeneous mediums.In machine learning, the idea of maximizing the margin between two classes is widely used in classifier design. Selleckchem Molidustat Enlighted by the idea, this paper proposes a novel supervised dimensionality reduction method for tensor data based on local decision margin maximization. The proposed method seeks to preserve and protect the local discriminant information of the original data in the low-dimensional data space. Firstly, we depart the original tensor dataset into overlapped localities with discriminant information. Then, we extract the similarity and anti-similarity coefficients of each high-dimensional locality and preserve these coefficients in the embedding data space via the multilinear projection scheme. Under the combined effect of these coefficients, each dimension-reduced locality tends to be a convex set where strongly correlated intraclass points gather. Simultaneously, the local decision margin, which is defined as the shortest distance from the boundary of each locality to the nearest point of each side, will be maximized. Therefore, the local discriminant structure of the original data could be well maintained in the low-dimensional data space. Moreover, a simple iterative scheme is proposed to solve the final optimization problem. Finally, the experiment results on 6 real-world datasets demonstrate the effectiveness of the proposed method.Different from Visual Question Answering task that requires to answer only one question about an image, Visual Dialogue task involves multiple rounds of dialogues which cover a broad range of visual content that could be related to any objects, relationships or high-level semantics. Thus one of the key challenges in Visual Dialogue task is to learn a more comprehensive and semantic-rich image representation that can adaptively attend to the visual content referred by variant questions. In this paper, we first propose a novel scheme to depict an image from both visual and semantic views. Specifically, the visual view aims to capture the appearance-level information in an image, including objects and their visual relationships, while the semantic view enables the agent to understand high-level visual semantics from the whole image to the local regions. Furthermore, on top of such dual-view image representations, we propose a Dual Encoding Visual Dialogue (DualVD) module, which is able to adaptively select question-relevant information from the visual and semantic views in a hierarchical mode.
My Website: https://www.selleckchem.com/products/molidustat-(bay85-3934).html