Miracle-dz/UniFAR-FTUs huggingface dataset:
Ellipse Detection on Images Using Conic Power of Two Points

Elliptical features are evident in abundance, in a wide variety of digital images. Much of these features carry useful statistical and geometrical information that can be explored for a broad range of real-world applications. In this paper, we propose and analyze a novel method which is independent of the popular Hough transform or random sample consensus approaches, to detect elliptical features in a digital image. Our method is based upon the circle power theorem and its extension to conics. The key idea is to transform an image into several power ratio histograms that record the distribution of power ratios computed from some reference point pairs. We show that the peaks in a power ratio histograms provide strong evidence for the existence of ellipses. Peak veriﬁcation based on the parallel chord property together with multi histogram cross validation increase the conﬁdence of an identiﬁed peak. Experiments over several synthetic as well as natural images validate the efﬁcacy of the proposed method in terms of its accuracy and robustness.
Anatomy-Guided Inverse-Gradient Susceptibility Artifact Correction Method for High-Resolution FMRI

Functional Magnetic Resonance Imaging (fMRI) is a widely used and non-invasive technique for recording changes in brain activity. However, susceptibility artifacts are ubiquitous distortions in fMRI, especially strong in high-resolution images, causing the misrepresentation of brain function and structure in the affected regions. Here, we present a novel method for correcting these distortions in high-resolution fMRI images based on the hyper-elastic susceptibility artifact correction (HySCO) method. The novelty of the proposed method is the utilization of the easily-acquired T1-weighted ($\mathrm{T}_{1w}$) anatomy image as a ground-truth measurement to regularize deformations, thereby obtaining meaningful corrections. The performance of the new method is compared to that of HySCO. Results from high-resolution (1mm) EPI data are presented demonstrating the robustness of the new method for image correction and its suitability for subsequent fMRI analysis.
A New Scheme for Robust Control of Uncertain Series Elastic Actuator System

Series elastic actuator (SEA) is widely used in new generation of robotic systems, particularly rehabilitation robots. The existence of noise and disturbance in the model of most industrial systems is inevitable, where SEA model is also not an exception. Presence of disturbance and uncertainty leads to deviation of the response of system from desired inputs. Kalman filter is a practical method to identify the model and also filtration of noisy data. The approach of this paper is to improve the efficiency of uncertain SEAs in control engineering aspects. Hence, a robust control design including combination of unscented Kalman filter (UKF) and sliding mode control (SMC) is developed for linear force-controlled SEA system. Simulation results show improved performance of the proposed controller to track desired force.
Grasp a Moving Target from the Air: System & Control of an Aerial Manipulator

Grasping a moving target has been investigated extensively for fixed-base manipulator. However, such a task becomes much more challenging when the manipulator is free flying in the air with an UAV. Towards moving target grasping, this paper presents an aerial manipulator system composed of a hex-rotor and a 7-DoF (Degree of Freedom) manipulator. An independent control structure is used in the aerial manipulator control system, i.e., the hex-rotor and the manipulator are controlled separately. In the hex-rotor's controller, the system CoM (Center of Mass) offset motion is used to compensate disturbance of the robotic arm. In the manipulator's controller, the relative kinematics between the target and the aerial vehicle is taken into consideration to grasp the target. At last aerial grasping experiments are conducted to validate the feasibility of the proposed control scheme and the reliability of our aerial manipulator system.
Foggy windows: Pupillary responses during task preparation

We investigated pupil dilation in 96 subjects during task preparation and during a post-trial interval in a visual search task and an auditory working memory task. Completely informative difficulty cues (easy, medium, or hard) were presented right before task preparation to examine whether pupil dilation indicated advance mobilisation of attentional resources; functional magnetic resonance imaging (fMRI) studies have argued for the existence of such task preparation, and the literature shows that pupil dilation tracks attentional effort during task performance. We found, however, little evidence for such task preparation. In the working memory task, pupil size was identical across cues, and although pupil dilation in the visual search task tracked the cue, pupil dilation predicted subsequent performance in neither task. Pupil dilation patterns in the post-trial interval were more consistent with an effect of emotional reactivity. Our findings suggest that the mobilisation of attentional resources in the service of the task does not occur during the preparatory interval, but is delayed until the task itself is initiated.
SUMO-Targeted Ubiquitin Ligases (STUbLs) Reduce the Toxicity and Abnormal Transcriptional Activity Associated With a Mutant, Aggregation-Prone Fragment of Huntingtin

Cell viability and gene expression profiles are altered in cellular models of neurodegenerative disorders such as Huntington’s Disease (HD). Using the yeast model system, we show that the SUMO-targeted ubiquitin ligase (STUbL) Slx5 reduces the toxicity and abnormal transcriptional activity associated with a mutant, aggregation-prone fragment of huntingtin (Htt), the causative agent of HD. We demonstrate that expression of an aggregation-prone Htt construct with 103 glutamine residues (103Q), but not the non-expanded form (25Q), results in severe growth defects in slx5Δ and slx8Δ cells. Since Slx5 is a nuclear protein and because Htt expression affects gene transcription, we assessed the effect of STUbLs on the transcriptional properties of aggregation-prone Htt. Expression of Htt 25Q and 55Q fused to the Gal4 activation domain (AD) resulted in reporter gene auto-activation. Remarkably, the auto-activation of Htt constructs was abolished by expression of Slx5 fused to the Gal4 DNA-binding domain (BD-Slx5). In support of these observations, RNF4, the human ortholog of Slx5, curbs the aberrant transcriptional activity of aggregation-prone Htt in yeast and a variety of cultured human cell lines. Functionally, we find that an extra copy of SLX5 specifically reduces Htt aggregates in the cytosol as well as chromatin-associated Htt aggregates in the nucleus. Finally, using RNA sequencing, we identified and confirmed specific targets of Htt’s transcriptional activity that are modulated by Slx5. In summary, this study of STUbLs uncovers a conserved pathway that counteracts the accumulation of aggregating, transcriptionally active Htt (and possibly other poly-glutamine expanded proteins) on chromatin in both yeast and in mammalian cells.
Online Safe Trajectory Generation for Quadrotors Using Fast Marching Method and Bernstein Basis Polynomial

In this paper, we propose a framework for online quadrotor motion planning for autonomous navigation in unknown environments. Based on the onboard state estimation and environment perception, we adopt a fast marching-based path searching method to find a path on a velocity field induced by the Euclidean signed distance field (ESDF) of the map, to achieve better time allocation. We generate a flight corridor for the quadrotor to travel through by inflating the path against the environment. We represent the trajectory as piecewise Bézier curves by using Bernstein polynomial basis and formulate the trajectory generation problem as typical convex programs. By using Bézier curves, we are able to bound positions and higher order dynamics of the trajectory entirely within safe regions. The proposed motion planning method is integrated into a customized light-weight quadrotor platform and is validated by presenting fully autonomous navigation in unknown cluttered indoor and outdoor environments. We also release our code for trajectory generation as an open-source package.
Effective Method for Sentiment Lexical Dictionary Enrichment Based on Word2Vec for Sentiment Analysis

Recently, many researchers have shown interest in using lexical dictionary for sentiment analysis. The SentiWordNet is the most used sentiment lexical to determine the polarity of texts. However, there are huge number of terms in the corpus vocabulary that are not in the SentiWordNet due to the curse of dimensionality, which will limit the performance of the sentiment analysis. This paper proposed a method to enlarge the size of opinion words by learning the polarity of those non-opinion words in the vocabulary based on the SentiWordNet. The effectiveness of the method is evaluated by using the Internet Movie Review Dataset. The result is promising, showing that the proposed Senti2Vec method can be more effective than the SentiWordNet as the sentiment lexical resource.
Block Bayesian Sparse Topical Coding

Learning low dimensional representations from a large number of short corpora has a profound practical significance but with vital challenge in content analysis and data mining applications. In this paper, we propose a novel topic model called Block Bayesian Sparse Topic Coding (Block-BSTC), which is capable of discovering the latent semantic representation of short texts. The Block-BSTC relaxes the normalization constraint of the inferred representations with word embeddings and block sparse Bayesian learning, which is convenient to directly control the sparsity of word codes with exploiting the intra-block correlations. Furthermore, the experimental results show that Block-BSTC achieves great performance on the sparsity ratio of word codes. Meanwhile, it can improve the accuracy of document classification.
Cyclic Autocorrelation based Spectrum Sensing: Theoretical Derivation Framework

In this article, we propose a theoretical framework to derive the stochastic behavior of the cyclic autocorrelation power (CAP). This function is especially used in cyclostationarity-based spectrum sensing for its robustness to noise uncertainty and its low computational cost. We first express the theoretical probability density function (PDF) of the cyclic autocorrelation power - which proves to follow a central scaled (respectively noncentral) chi-square distribution if the received samples consist of additive Gaussian noise (respectively noise plus a cyclostationary signal). In order to verify the accuracy of the proposed theoretical derivation, simulation results are then provided in terms of detection and false alarm probabilities.
Matrix Completion as Graph Bandlimited Reconstruction

This paper develops new designs for recommender systems inspired by recent advances in graph signal processing. Recommender systems aim to predict unknown ratings by exploiting the information revealed in a subset of user-item observed ratings. Leveraging the notions of graph frequency and graph filters, we demonstrate that linear latent factor models, such as low-rank matrix completion, can be viewed as bandlimited interpolation algorithms that operate in a frequency domain given by the spectrum of a joint user and item network. This new interpretation paves the way to new methods for enhanced rating prediction. We propose a low complexity method by exploiting the eigenvector of correlation matrices constructed from known ratings. In the MovieLens 100k dataset, our designs reduce the root mean squared error compared to the ones in benchmark matrix completion by 0.6% and benchmark nearest neighbor methods by 4.2%.
Strong Duality of Sparse Functional Optimization

Signal processing is rich in inherently continuous applications, such as radar, MRI, and source localization, in which sparsity priors play a key role in obtaining state-of-the-art results. To cope with the infinite dimensionality and non-convexity of these estimation problems, they are typically discretized and solved by means of convex relaxations, e.g., using atomic norms. Although successful, this approach is not without issues. Discretization often leads to high dimensional, potentially ill-conditioned optimization problems. Moreover, due to grid mismatch and other coherence issues, a sparse signal in the continuous domain may no longer be sparse when discretized. Finally, performance guarantees for atomic norm relaxations hold under assumptions that may be hard to meet in practice. We address these issues by directly tackling the continuous problem cast as a sparse functional optimization program. We prove that these problems have no duality gap and show that they can be solved efficiently using duality and a stochastic gradient ascent-type algorithm. We illustrate the performance of this new approach on a line spectral estimation problem.
Sparse Representation Using SVR

This paper represents super resolution technique for higher level of scale. It describes categorization like if lower the resolution, the lesser details are captured and for higher resolution the image details are higher and with greater image quality. Image sensors (e.g. CCD) and optics, camera, lens are the parameter which limits the imagine system. In real applications to capture high resolution images using constructing imaging tools like optical chips, lens, and optical component is not practical due to its high cost. Where, it is all time acceptable to improve quality of images or video captured already as the legacy from the development of digital imaging. Key way to overcome the resolution problem is to use machine learning techniques to post-process the captured images i.e. Super-resolution (SR).
Proof-of-Concept of the Incorporation of Ultrasound-Derived Structural Information Into Microwave Radar Imaging

In this work, we develop a method of extracting structural information of the breast from ultrasound signals and integrating this information into microwave radar based image reconstructions. The synergistic combination of these modalities exploits the dielectric contrast between breast tissues at microwave frequencies and short wavelengths of ultrasound signals to enhance the imaging resolution and target localization accuracy. A key feature of this approach is its ability to detect fine structural details of both the breast exterior and interior, which may provide insight into the distribution of breast tissues and can be used as a context for results interpretation. The methods are tested through numerical simulations using anatomically realistic numerical breast models derived from magnetic resonance imaging scans and the improvements in performance are quantified using metrics.
Learning and Thinking Strategy for Training Sequence Generation Models

In order to alleviate the exposure bias problem caused by the discrepancy between training and testing strategies, we propose an Inverse Reinforcement Learning (IRL) based learning and thinking strategy for sequence generation. First, a task-agnostic reward is learned to evaluate the appropriateness of the generated tokens at each time step with the knowledge of ground truth token and current RNN models. With this reward, a deep SARSA network is then designed to meditate among the whole space. Therefore, it can ﬁll in the space that has not been exposed during training with a better policy than the original RNN model. Sequence generation experiments on various text corpus show signiﬁcant improvements over strong baseline and demonstrate the effectiveness of our method.
Music Structure Boundary Detection and Labelling by a Deconvolution of Path-Enhanced Self-Similarity Matrix

We propose a music structure analysis method that converts a path-enhanced self-similarity matrix (SSM) into a block-enhanced SSM using non-negative matrix factor 2-D deconvolution (NMF2D). With a non-negative constraint, the deconvolution intuitively corresponds to the repeated stripes in the path-enhanced SSM. Then the block-enhanced SSM is constructed without any clustering technique. We fuse block-enhanced SSMs obtained using different parameters, resulting in better and more robust results. Discussion shows that the proposed method can be a potential tool for analysing music structure at different scales.
Hidden Biases in Automated Image-Based Plant Identification

Plant identification is critical to support important biodiversity conservation actions such as biodiversity inventories, monitoring of populations of endangered organisms, and assessing climate change impact, among many others. Because deep learning has demonstrated impressive results in the field of computer vision in general, research on automatic plant identification has been shifting its attention towards deep learning approaches. However, some authors have noticed that an important methodological issue may have been overlooked in the design of many experiments, which may explain why, on one hand, some studies based on hand-crafted feature extraction approaches report very high accuracy levels, but, on the other hand, newer deep learning approaches used in events such as the PlantCLEF challenge report relatively lower accuracy levels. Because PlantCLEF uses same specimen photos exclusively in either the training dataset or the testing dataset, we postulate that this may explain the lower accuracies achieved. Specifically, we explore the following two questions: does using different images of the same specimen for training and testing introduce a significant bias in deep learning experiments as well as in those that use handcrafted features in classical computer vision techniques? Does it affect the accuracy of species identifications even in the more restricted domain of leaf-based automated species identifications? We also address the issue of scalability of accuracy results for both, a particular feature extraction approach and a deep learning approach. All experiments are conducted on a dataset of 7,262 photos of leaves of 255 species of plants from Costa Rica.
The PIN-FORMED Auxin Efflux Carriers in Plants

Auxin plays crucial roles in multiple developmental processes, such as embryogenesis, organogenesis, cell determination and division, as well as tropic responses. These processes are finely coordinated by the auxin, which requires the polar distribution of auxin within tissues and cells. The intercellular directionality of auxin flow is closely related to the asymmetric subcellular location of PIN-FORMED (PIN) auxin efflux transporters. All PIN proteins have a conserved structure with a central hydrophilic loop domain, which harbors several phosphosites targeted by a set of protein kinases. The activities of PIN proteins are finely regulated by diverse endogenous and exogenous stimuli at multiple layers—including transcriptional and epigenetic levels, post-transcriptional modifications, subcellular trafficking, as well as PINs’ recycling and turnover—to facilitate the developmental processes in an auxin gradient-dependent manner. Here, the recent advances in the structure, evolution, regulation and functions of PIN proteins in plants will be discussed. The information provided by this review will shed new light on the asymmetric auxin-distribution-dependent development processes mediated by PIN transporters in plants.
Flexible Instruments for Endovascular Interventions: Improved Magnetic Steering, Actuation, and Image-Guided Surgical Instruments

Endovascular surgery has gained broad acceptance in the last few years. The current practice of endovascular procedures is limited by factors including patient-specific operation requirements, high-risk surgery procedures, and time-consuming operations. To address this, magnetically actuated surgical catheters have been introduced to the field of surgical robotics. Recently, advances in steerable catheters and developments in magnetic steering have been studied. However, limited research has been conducted to quantify the effectiveness of magnetic actuation for catheterization procedures. Endovascular interventions employing magnetically actuated catheters deliver the promise of higher accuracy and shorter duration when compared to current, manual techniques. Moreover, they allow surgeons access to areas of cardiovascular systems that cannot be reached with standard, minimally invasive techniques.
End-to-End Continuous Emotion Recognition from Video Using 3D Convlstm Networks

Conventional continuous emotion recognition consists of feature extraction step followed by regression step. However, the objective of the two steps is not consistent as they are parted. Besides, there is still no consensus about appropriate emotional features. In this study, we propose an end-to-end continuous emotion recognition framework which merges feature extraction and regressor into a unified system. We employ 3D convolutional networks with Long Short-Term Memory Neutral Network (ConvLSTM) to handle spatiotemporal information for continuous emotion recognition. This model is applied on AVEC 2017 database. The experiment results reveal that ConvLSTM model makes a positive effect on the performance improvement, which outperforms the baseline results for arousal of 0.583 vs 0.525 (baseline) and for valence of 0.h54 vs 0.507.
Feature Based Adaptation for Speaking Style Synthesis

Speaking style plays an important role in the expressivity of speech for communication. Hence speaking style is very important for synthetic speech as well. Speaking style adaptation faces the difficulty that the data of specific styles may be limited and difficult to obtain in large amounts. A possible solution is to leverage data from speaking styles that are more available, to train the speech synthesizer and then adapt it to the target style for which the data is scarce. Conventional DNN adaptation approaches directly update the top layers of a well-trained, style-dependent model towards the target style. The detailed local context-level mismatch between the original and the target styles is not considered. In order to address this issue, two frame-level input feature-based style adaptation techniques are investigated in this paper. We will use style features extracted from (1) a target-style data trained bottleneck DNN, and (2) a novel cross-style residual feature regression DNN. These features are used for top-layer adaptation of a well-trained style-dependent synthesis network. Experimental results on adapting the declarative sty le to the interrogative sty le demonstrate the effectiveness of our proposed style features in improving the expressiveness of synthesizing speech for the interrogative style, while maintaining speech quality.