Miracle-dz/UniFAR-FTUs huggingface dataset:
3D segmentation of plant root systems using spatial pyramid pooling and locally adaptive field-of-view inference

Background The non-invasive 3D-imaging and successive 3D-segmentation of plant root systems has gained interest within fundamental plant research and selectively breeding resilient crops. Currently the state of the art consists of computed tomography (CT) scans and reconstruction followed by an adequate 3D-segmentation process. Challenge Generating an exact 3D-segmentation of the roots becomes challenging due to inhomogeneous soil composition, as well as high scale variance in the root structures themselves. Approach (1) We address the challenge by combining deep convolutional neural networks (DCNNs) with a weakly supervised learning paradigm. Furthermore, (2) we apply a spatial pyramid pooling (SPP) layer to cope with the scale variance of roots. (3) We generate a fine-tuned training data set with a specialized sub-labeling technique. (4) Finally, to yield fast and high-quality segmentations, we propose a specialized iterative inference algorithm, which locally adapts the field of view (FoV) for the network. Experiments We compare our segmentation results against an analytical reference algorithm for root segmentation (RootForce) on a set of roots from Cassava plants and show qualitatively that an increased amount of root voxels and root branches can be segmented. Results Our findings show that with the proposed DCNN approach combined with the dynamic inference, much more, and especially fine, root structures can be detected than with a classical analytical reference method. Conclusion We show that the application of the proposed DCNN approach leads to better and more robust root segmentation, especially for very small and thin roots.
Visual feedback-dependent modulation of arousal, postural control, and muscle stretch reflexes assessed in real and virtual environments

Introduction The mechanisms regulating neuromuscular control of standing balance can be influenced by visual sensory feedback and arousal. Virtual reality (VR) is a cutting-edge tool for probing the neural control of balance and its dependence on visual feedback, but whether VR induces neuromodulation akin to that seen in real environments (eyes open vs. closed or ground level vs. height platform) remains unclear. Methods Here we monitored 20 healthy young adults (mean age 23.3 ± 3.2 years; 10 females) during four conditions of quiet standing. Two real world conditions (eyes open and eyes closed; REO and REC) preceded two eyes-open virtual ‘low’ (ground level; VRL) and ‘high’ (14 m height platform; VRH) conditions. We measured arousal via electrodermal activity and psychosocial questionnaires rating perceived fear and anxiety. We recorded surface electromyography over the right soleus, medial gastrocnemius, and tibialis anterior, and performed force plate posturography. As a proxy for modulations in neural control, we assessed lower limb reflexive muscle responses evoked by tendon vibration and electrical stimulation. Results Physiological and perceptual indicators of fear and anxiety increased in the VRH condition. Background soleus muscle activation was not different across conditions; however, significant increases in muscle activity were observed for medial gastrocnemius and tibialis anterior in VRH relative to REO. The mean power frequency of postural sway also increased in the VRH condition relative to REO. Finally, with a fixed stimulus level across conditions, mechanically evoked reflexes remained constant, while H-reflex amplitudes decreased in strength within virtual reality. Discussion Notably, H-reflexes were lower in the VRL condition than REO, suggesting that these ostensibly similar visual environments produce different states of reflexive balance control. In summary, we provide novel evidence that VR can be used to modulate upright postural control, but caution that standing balance in analogous real and virtual environments may involve different neural control states.
Optical repulsive force control of lower-index microparticles in a liquid

A micro object with lower refractive index than surrounding liquid is subjected its motion to an optical repulsive force. The optical repulsive force is generated with a focused beam array, which is dynamically formed by a computer-generated hologram displayed on a liquid crystal spatial light modulator. We demonstrate manipulations of hollow glass spheres in water and water droplets in organic solvent using the optical repulsive forces.
Method for optimal determination of parameters in permutation entropy algorithm

Permutation entropy(PE) algorithm can better magnify tiny change of a time series of data.It is simple in computation and shows good quality in real-time application,so,it gives us a good application prospect in detection of the sudden change of a signal.However,the parameters in the algorithm,namely the embedding dimension and delay time are usually still determined by experience or trial.This forms a bottle-neck of PE algorithm for engineering application.According to the theory of PE algorithm,a method based on reconstructing optimal phase space of time series was put forward to determine these model parameters.Considering two points of view about phase space reconstruction,basic theories of independent and joint determination methods were introduced to determine the delay time and embedding dimension.The two determination methods were validated and compared by using simulated signals and whole life data of rolling bearings.It is concluded that the independent determination of model parameters was better than joint determination for abnormality detection.
On a nonlinear observer with pseudo-linearized error dynamics

The problem of selecting state and output coordinate changes that transform a given nonlinear system into a pseudoobserver form is considered. A system in this form has the property that its family of linearizations about constant operating points is in observer form in the sense of linear systems. For a system in pseudoobserver form, it is straightforward to construct a state observer with the property that the family of linearized error systems is independent of the operating point. Therefore, the error dynamics are said to be pseudolinearized. Such an observer can then be used as part of a stabilizing dynamic output feedback control law. <<ETX>>
Temperature dependence of current-voltage characteristics in highly doped Ag/p-GaN/In Schottky diodes

To obtain detailed information about the conduction process of the Ag/p-GaN Schottky diodes (SDs) fabricated by us, we measured the I-V characteristics over the temperature range of 80–360 K by the steps of 20 K. The slope of the linear portion of the forward bias I-V plot and nkT=E0 of the device remained almost unchanged as independent of temperature with an average of 25.71±0.90 V−1 and 41.44±1.38 meV, respectively. Therefore, it can be said that the experimental I-V data quite well obey the field emission model rather than the thermionic emission or thermionic field emission model. The study is a very good experimental example for the FE model. Furthermore, the reverse bias saturation current ranges from 8.34×10−8 A at 80 K to 2.10×10−7 A at 360 K, indicating that the charge transport mechanism in the Ag/p-GaN SD is tunneling due to the weak temperature dependence of the saturation current. The possible origin of high experimental characteristic tunneling energy of E00=39 meV, which is ten times large.
XStore: Xcelerated performance in time-series key/value STORagE systems

In recent years, we have seen an unprecedented growth of data in our daily lives ranging from health data from an Apple Watch, financial stock price data, volatile crypto-currency data, to diagnostic data of nuclear/rocket simulations. The increase in high-precision, high-sample-rate time-series data is a challenge to existing database technologies. We have developed a novel technique that utilizes sparse-file support to achieve O(1) time complexity in create, read, update, and delete (CRUD) operations while supporting arbitrarily small time granularity. We designed and implemented XStore 1 to be lightweight and offer high performance without the need to maintain an index of the time-series data. We conducted a detailed evaluation between XStore and existing best-of-breed systems such as MongoDB and InfluxDB using synthetic data spanning 20-years of 1-second granularity data, totaling 662 million rows of data and 170GB of data.
Trend in BMI z-score among Private Schools’ Students in Delhi using Multiple Imputation for Growth Curve Model

Objective: The aim of the study is to assess the trend in mean BMI z-score among private schools’ students from their anthropometric records when there were missing values in the outcome. Methodology: The anthropometric measurements of student from class 1 to 12 were taken from the records of two private schools in Delhi, India from 2005 to 2010. These records comprise of an unbalanced longitudinal data that is not all the students had measurements recorded at each year. The trend in mean BMI z-score was estimated through growth curve model. Prior to that, missing values of BMI z-score were imputed through multiple imputation using the same model. A complete case analysis was also performed after excluding missing values to compare the results with those obtained from analysis of multiply imputed data. Results: The mean BMI z-score among school student significantly decreased over time in imputed data (β= -0.2030, se=0.0889, p=0.0232) after adjusting age, gender, class and school. Complete case analysis also shows a decrease in mean BMI z-score though it was not statistically significant (β= -0.2861, se=0.0987, p=0.065). Conclusions: The estimates obtained from multiple imputation analysis were better than those of complete data after excluding missing values in terms of lower standard errors. We showed that anthropometric measurements from schools records can be used to monitor the weight status of children and adolescents and multiple imputation using growth curve model can be useful while analyzing such data
S CRIBBLE -S UPERVISED S EMANTIC S EGMENTATION FOR H AUSTRAL F OLD D ETECTION

Colonoscopy is considered the "gold standard" screening procedure for colorectal cancers. But its effectiveness is limited by the fact that endoscopists sometimes fail to view all parts of the colon surface. We seek to build a system for detecting these missed areas during the procedure and alerting the endoscopist.
Threshold unit root models

One of the main criticisms of unit root models is based on the theoretical fact that economic variables measured in rates cannot have unit roots. Nevertheless, standard unit root tests do not reject the existence of unit roots in many of those variables. In this paper we present a class of threshold models capable of replicating the behavior of economic variables such as unemployment, inflation and interest rates. Depending on the values of a threshold variable these models can have either a unit root or a stable root. However, despite the presence of the unit root, we prove they are stationary and geometrically ergodic. Least squares estimates of the parameters of these models are shown to be consistent and asymptotically normal. We propose the supremum of a e test in order to test the null of no threshold against the alternative of threshold when the threshold value is unknown. The limiting distribution is derived under the null of I (0) as well as under the null of 1(1). Critical values for both asymptotic distributions are computed and a finite sample study of the performance (size and power) of the tests developed in this paper is made. The paper concludes with an application to interest rates.
Introducing Feedback Connections for Vision Transformers

The introduction of Transformer networks in computer vision has resulted in rapid progress of deep models in a variety of vision tasks. These performance gains are strongly tied to the core component of Transformers, namely self-attention that enables such models to capture important long-range spatial interactions. While several methods have been developed to improve the efficiency or modeling capacity of Vision Transformers, most of them rely on a feedforward architecture where lower-level features are sequentially processed to form higher-level features that contain semantically-rich information. Such a design is pro-hibiting and inefficient as the network is unable to use the already learned high-level features to better extract relevant information from the lower-level features. The human visual system (HVS) in contrast relies on a series of feedforward and feedback connections to efficiently process visual stimuli. In this work, we propose a Adaptive Gated Attention Block which adds lightweight top-down and bottom-up connections to enhance information flow be-tween features at different levels. In addition, our module is flexible and can be integrated with both local and global-attention based methods. Experiments on benchmark datasets demonstrate that our Adaptive Gated Attention Block consistently outperforms existing state-of-the-art with a negligible increase in parameters and FLOPs.
The mean drift force and yaw moment on marine structures in waves and current

The effect of the steady second-order velocities on the drift forces and moments acting on marine structures in waves and a (small) current is considered. The second-order velocities are found to arise due to first-order evanescent modes and linear body responses. Their contributions to the horizontal drift forces and yaw moment, obtained by pressure integration at the body, and to the yaw drift moment, obtained by integrating the angular momentum flux in the far field, are expressed entirely in terms of the linear first-order solution. The second-order velocities may considerably increase the forward speed part of the mean yaw moment on realistic marine structures, with the most important contribution occurring where the wave spectrum often has its maximal value. The contribution to the horizontal forces obtained by pressure integration is, however, always found to be small. The horizontal drift forces obtained by the linear momentum flux in the far field are independent of the second-order velocities, provided that there is no velocity circulation in the fluid.
Geometric median and its application in the identification of multiple outliers

Geometric mean (GM) is having growing and wider applications in statistical data analysis as a measure of central tendency. It is generally believed that GM is less sensitive to outliers than the arithmetic mean (AM) but we suspect likewise the AM the GM may also suffer a huge set back in the presence of outliers, especially when multiple outliers occur in a data. So far as we know, not much work has been done on the robustness issue of GM. In quest of a simple robust measure of central tendency, we propose the geometric median (GMed) in this paper. We show that the classical GM has only 0% breakdown point while it is 50% for the proposed GMed. Numerical examples also support our claim that the proposed GMed is unaffected in the presence of multiple outliers and can maintain the highest possible 50% breakdown. Later we develop a new method for the identification of multiple outliers based on this proposed GMed. A variety of numerical examples show that the proposed method can successfully identify all potential outliers while the traditional GM fails to do so.
Comparison of Ocular Biomery in Primary Open Angle Glaucoma and Non-glaucoma in South West Nigeria

Background: Glaucoma is a public health problem in Nigeria. The number of individuals affected by glaucoma in Nigeria is much higher than the individuals known to have the disease. Ocular parameters such as intraocular pressure, central cornea thickness, axial length and refractive error have all been documented as risk factors of glaucoma especially among Caucasians and African Americans, with little documentation in Africa where there’s an alarming rate of blindness. Aim and Objectives: To compare central cornea thickness (CCT), intraocular pressure (IOP), axial length (AL) and refractive state in participants with primary open angle glaucoma (POAG) and non-glaucoma in South-West Nigeria. Materials and Methods: This hospital-based case-control study was carried out among 184 newly diagnosed POAG and non-glaucoma adult participants attending the outpatient clinic of Eleta eye institute. The CCT, IOP, AL and refractive state were measured in each participant. Test of significance between proportions in categorical variables were assessed using chi square test (χ2) in both groups. The means were compared using independent t-test while correlation between parameters were analyzed using Pearson correlation coefficient. Results: The mean age of the POAG participants was 57.16 + 13.3 years and the mean age of the non-glaucoma participants was 54.15 + 13.4 years. The mean IOP in the POAG group was 30.2 + 8.9mmHg while non- glaucoma group was 14.2 + 2.6mmHg (P < 0.001), other ocular parameters were not significantly different in both groups. In the POAG group, decreased spherical equivalent refractive error (i.e increasing myopia) was significantly associated with increased axial length (r= -0.252, P = 0.01), but not significant in the non- glaucoma group. However, in the non-glaucoma group, central cornea thickness increased with increasing intraocular pressure (r= 0.305, P = 0.003), which was not significant in the glaucoma group. Conclusion: Patients with POAG had much higher IOP and thus, IOP remains a significant risk factor in its development. There was a significant relationship between refractive state and axial length in the POAG group while a significant relationship was identified between central cornea thickness and intraocular pressure in the non- glaucoma group.
Variable Selection in PCA Using Fuzzy Clustering and Its Application to Data Mining

Knowledge discovery in databases (KDD) or data mining involves fitting models to or determining patterns from high dimensional data sets, and extraction of correlation rules plays an important role. This paper proposes a new approach to knowledge discovery with linear model estimation, in which principal component analysis (PCA) is performed by selecting variables. The proposed algorithm is a hybrid of fuzzy clustering and PCA based on lower rank approximation of data matrix, in which the relative responsibilities of the variables are estimated by using possibilistic constraint for memberships. The proposed algorithm is also enhanced to a local PCA model that can be used for data mining by performing both of linear model estimation and stratified sampling.
Immunohistochemical Profiling of HER-2/neu, Steroid Hormone Receptors and KI-67 Biomarkers in A Cohort of Nigerian Women with Breast Cancer

Background: Progressive improvement in the accuracy of profiling of hormone receptors in breast cancer provides the basis for targeted endocrine therapy, a major pillar of multimodal breast cancer treatment. However, the disparity in findings from comparatively smaller sample-sized studies in West Africa has led to somewhat conflicting conclusions and recommendations. Objectives: This study investigates the immunohistochemical (IHC) profile of breast cancer specimens for estrogen receptor (ER), progesterone receptor (PR), human epidermal receptor-2 (HER2)/neu, and Ki-67 in a tertiary hospital in Ibadan, Nigeria over 12 years. Materials and Methods: We reviewed 998 IHC reports, documented clinicopathologic parameters, computed patterns of biomarkers, and stratified them based on the American Society of Clinical Oncology/College of American Pathologists recommendations. Descriptive analysis including frequency, mean, and median were generated from the data extracted. Results: Out of the 998 cases, 975 (97.7%) were females and 23 (2.3%) were males. The mean age was 48.84 ± 11.99 years. Open biopsies were the most common types of specimens (320, 41.6%): lumpectomy and incisional biopsy of ulcerated, fungating or unresectable tumours. In those cases, 246 (32.0%) were samples of breast-conserving or ablative surgical extirpation (mastectomy/wide local excision/quadrantectomy), and 203 (26.4%) were obtained by core needle biopsies. Invasive ductal carcinoma was the most common histopathological type (673, 94.5%). The majority of graded tumours were intermediate grade (444, 53.5%). Four hundred and sixty-nine (48.4%) were ER positive, 414 (42.8%) were PR positive, and 180 (19.4%) were HER2/neu positive. Three hundred and thirty-four (34.0%) were triple-negative. Eighty-nine cases had Ki-67 staining done, and of these 61 (68.5%) had positive nuclear staining. Conclusion: Steroid hormone receptors and HER-2/neu proportions in our cohort are likely to be more representative than the widely varied figures hitherto reported in the sub-region. We advocate routine IHC analysis of breast cancer samples as a guide to personalized endocrine therapy.
Eight-space quaternion approach for robotic hand-eye calibration

In this paper, normalized quaternions (Euler parameters) are used to transform the kinematic equation into two simple and well-structured linear systems; where the kinematic equation, H/sub l/H/sub x/=H/sub x/H/sub c/, is formulated for the problem of finding the relative position and orientation between the reference frames of a link-mounted sensor and the link. Two distinct robot movements are required to obtain a unique solution. This leaps to an overdetermined system of equations, and the least-squares solution are obtained. Further, least-squares closed-form solutions to these systems are derived using the Gaussian elimination and Schur decomposition analysis. The selection criterion and the solution formulae can be easily incorporated in application programs which require the calculation of the relative position and orientation of the sensor.
A geometrical theory for eddy-current non-destructive evaluation

In eddy-current non-destructive evaluation, an electromagnetic field is induced in a metal by means of an eddy–current probe and the presence of a flaw is indicated by a change in probe impedance. In order to predict the impedance change, it is necessary to calculate the electromagnetic field in the region of the flaw. We present a new method, based on the geometrical theory of diffraction, by which approximate analytical solutions for the electromagnetic field near to a crack can be calculated for intermediate and high frequencies. Two-dimensional field solutions are found for (i) a buried crack and (ii) a surface-breaking crack in a conducting half-space. The fields are constructed using solutions which satisfy the field equations exactly but approximate the boundary conditions. The degree to which the predicted fields match the required boundary values on the plane of the crack and the air––conductor interface plane is assessed and very good agreement is obtained for cracks whose edges lie half a skin–depth or more below the conductor surface. The impedance change due to the crack is calculated in closed form.