Miracle-dz/UniFAR-FTUs huggingface dataset:
Prospective Analysis of the Effects of Maternal Immune Activation on Rat Cytokines during Pregnancy and Behavior of the Male Offspring Relevant to Schizophrenia

Abstract Influenza during pregnancy is associated with the development of psychopathology in the offspring. We sought to determine whether maternal cytokines produced following administration of viral mimetic polyinosinic-polycytidylic acid (polyI:C) to pregnant rats were predictive of behavioral abnormalities in the adult offspring. Timed-pregnant Sprague Dawley rats received a single intravenous injection of 4-mg/kg polyI:C or saline on gestational day (GD)15. Blood was collected 3 h later for serum analysis of cytokine levels with ELISA. Male offspring were tested in a battery of behavioral tests during adulthood and behavior was correlated with maternal cytokine levels. Maternal serum levels of CXCL1 and interleukin (IL)-6, but not tumor necrosis factor (TNF)-α or CXCL2, were elevated in polyI:C-treated dams. PolyI:C-treated dams experienced post-treatment weight loss and polyI:C pups were smaller than controls at postnatal day (PND)1. Various behavior alterations were seen in the polyI:C-treated offspring. Male polyI:C offspring had enhanced MK-801-induced locomotion, and reduced sociability. PolyI:C offspring failed to display crossmodal and visual memory, and oddity preference was also impaired. Set-shifting, assessed with a lever-based operant conditioning task, was facilitated while touchscreen-based reversal learning was impaired. Correlations were found between maternal serum concentrations of CXCL1, acute maternal temperature and body weight changes, neonatal pup mass, and odd object discrimination and social behavior. Overall, while the offspring of polyI:C-treated rats displayed behavior abnormalities, maternal serum cytokines were not related to the long-term behavior changes in the offspring. Maternal sickness effects and neonatal pup size may be better indicators of later effects of maternal inflammation in the offspring.
Reaching law based DSMC with higher relative degree sliding variables

Discrete-time sliding variables with relative degree higher than one have been shown to reduce oscillations in switching type quasi-sliding mode strategies and to improve system robustness. In this paper we consider the variables with relative degree equal to the actual degree of the plant. For such variables, a general switching type reaching law is presented. It is demonstrated that the control signal obtained from this reaching law always ensures a finite time response of the closed-loop system. Furthermore, it is shown that our strategy reduces state error compared to the classic relative degree one approach.
LOGOS: Local Geometric Support for High-Outlier Spatial Verification

This paper presents LOGOS, a method of spatial verification for visual localization that is robust in the presence of a high proportion of outliers. LOGOS uses scale and orientation information from local neighbourhoods of features to determine which points are likely to be inliers. The inlier points can be used for secondary localization verification and pose estimation. LOGOS is demonstrated on a number of benchmark localization datasets and outperforms RANSAC as a method of outlier removal and localization verification in scenarios that require robustness to many outliers.
Direct Line Guidance Odometry

Modern visual odometry algorithms utilize sparse point-based features for tracking due to their low computational cost. Current state-of-the-art methods are split between indirect methods that process features extracted from the image, and indirect methods that deal directly on pixel intensities. In recent years, line-based features have been used in SLAM and have shown an increase in performance albeit with an increase in computational cost. In this paper, we propose an extension to a point-based direct monocular visual odometry method. Here we that uses lines to guide keypoint selection rather than acting as features. Points on a line are treated as stronger keypoints than those in other parts of the image, steering point-selection away from less distinctive points and thereby increasing efficiency. By combining intensity and geometry information from a set of points on a line, accuracy may also be increased.
Health Service Decision Toolbox (HSDT): Delivering the Right Treatment to the Right Patient with Health Information Technology and Data Analytics

The amount of data being digitally collected and stored by health information technology (health IT) is vast and expanding rapidly. How to convert this huge amount of resources into knowledge that can facilitate delivering the right intervention to the right patient, or how to bridge the gap between prevention research and practice, remain unclear. The objective of this study is to develop a Health Service Decision Toolbox (HSDT) that enables healthcare providers to integrate vast data sources collected by Health IT to improve confidence in decisions of whether or not to adopt an evidence-based intervention. HSDT can guide users through feeding massive health related data into several major input groups, such as healthcare providers, patient population, and intervention procedures. With the HSDT, we can successfully project specific impacts, because of additional tailoring to fit each organization/patient population's unique circumstances. When such tailoring combined with an interactive visualization environment, it would further increase decision makers' confidence. We illustrate the use of HSDT with a case study of delivering Transitional Care Model (TCM) to the most needed hospitals. TCM is a proven care management approach that is designed and tested by University of Pennsylvania via multiple NIH funded clinical trials.
The Promise of Electronic Health Records to Promote Shared Decision Making: A Narrative Review and a Look Ahead

Background. Shared decision making (SDM) involves the sharing of best available evidence between patients and providers in the face of difficult decisions. We examine outcomes that occur when electronic health records (EHRs) are purposefully used with the goal of improving SDM and detail which EHR functions can benefit SDM. Methods. A systematic search of PubMed yielded 1369 articles. Studies were included only if they used EHR interventions to support SDM and included results that showed impact on SDM. Articles were excluded if they did not measure the impact of the intervention on SDM or did not discuss how SDM had been supported by the EHR. Results. Five studies demonstrated improved clinical outcomes, positive lifestyle behavior changes, more deliberation from patients regarding use of imaging, and less decisional conflict about medication use among patients with use of EHRs aiding SDM. Discussion. Few EHRs have integrated SDM, and even fewer evaluations of these exist. EHRs have potential in supporting providers during all steps of SDM. The promise of EHRs to support SDM has yet to be fully exploited.
Refinement of Utterance Database and Concatenation of Utterances for Enhancing System Utterances in Chat-oriented Dialogue System

We have been using an utterance database created from a massive amount of predicate-argument structures extracted from the web for generating utterances of our commercial chat-orientated dialogue system. However, since the creation of this database involves several automated processes, the database often includes non-sentences (ungrammatical or uninterpretable sentences) and utterances with inappropriate topic information (called off-focus utterances). Also, utterances tend to be monotonous and uninformative because they are created from single predicate-argument structures. To tackle these problems, we propose methods for filtering non-sentences by using neuralnetwork-based methods and utterances inappropriate for their associated foci by using co-occurrence statistics. To reduce monotony, we also propose a method for concatenating automatically generated utterances so that the utterances can be longer and richer in content. Experimental results indicate that our non-sentence filter can successfully remove non-sentences with an accuracy of 95% and that we can filter utterances inappropriate for their foci with high recall. We also examined the effectiveness of our filtering and concatenation methods through an experiment involving human participants. The experimental results show that our methods significantly outperformed the baseline in terms of understandability and that the concatenation of two utterances leads to higher familiarity and content richness while retaining understandability.
Tribulus terrestris Protects against Male Reproductive Damage Induced by Cyclophosphamide in Mice

Tribulus terrestris (TT) has been considered as a potential stimulator of testosterone production, which has been related with steroidal saponins prevailing in this plant. Cyclophosphamide (CP) is the most commonly used anticancer and immunosuppressant drug, which causes several toxic effects, especially on the reproductive system. Patients who need to use CP therapy exhibit reduced fertility or infertility, which impacts both physically and emotionally on the decision to use this drug, especially among young men. We hypothesized that the treatment with TT dry extract would protect the male reproductive system against CP toxicity. Mice received dry extract of TT (11 mg/kg) or vehicle by gavage for 14 days. Saline or CP was injected intraperitoneally at a single dose (100 mg/kg) on the 14th day. Animals were euthanized 24 h after CP administration, and testes and epididymis were removed for biochemical and histopathological analysis and sperm evaluation. The dry extract of TT was evaluated by HPLC analysis and demonstrated the presence of protodioscin (1.48%, w/w). CP exposure increased lipid peroxidation, reactive species, and protein carbonylation and altered antioxidant enzymes (SOD, CAT, GPx, GST, and GR). Moreover, acute exposure to CP caused a reduction on 17 β-HSD activity, which may be related to the reduction in serum testosterone levels, histopathological changes observed in the testes, and the quality of the semen. The present study highlighted the role of TT dry extract to ameliorate the alterations induced by CP administration in mice testes, probably due to the presence of protodioscin.
Functional Connectivity States of the Brain Using Restricted Boltzmann Machines

Recent work on resting-state functional magnetic resonance imaging (rs-fMRI) suggests that functional connectivity (FC) is dynamic. A variety of machine learning and signal processing tools have been applied to the study of dynamic functional connectivity networks (dFCNs) of the brain, by identifying a small number of network states that describe the dynamics of connectivity during rest. Recently, deep learning (DL) methods have been applied to neuroimaging data for learning generative models. In this paper, we employ the restricted Boltzmann machine (RBM), to learn FC states from resting-state dFCNs. Unlike previous applications of DL to neuroimaging data that focus on feature extraction based on the voxel level activation data, the current work employs RBM to learn connectivity patterns, where the input to RBMs are a collection of windowed covariances across time and subjects. The extracted FC states are evaluated based on their occurrence rate as well as modularity.
License Plate Recognition and Super-resolution from Low-Resolution Videos by Convolutional Neural Networks

The paper proposes Convolutional Neural Network (CNN) for License Plate Recognition (LPR) from low-resolution videos. The CNN accepts arbitrary long sequence of geometrically registered license plate (LP) images and outputs a distribution over a set of strings with an admissible length. Evaluation on 31k low-resolution videos shows that the proposed CNN signiﬁcantly outperforms both baseline methods and humans by a large margin. Our second contribution is a CNN based super-resolution generator of LP images. The generator converts input low-resolution LP image into its high-resolution counterpart which i) preserves the structure of the input and ii) depicts a string that was previously recognized from video.
Discrete-time stability analysis of grid-connected converters considering the PLL dynamics

The objective of this paper is to develop a discrete-time stability analysis of a three-phase grid-connected converter, considering not only the current and the DC-link voltage/power controllers, but also including the phase-locked loop (PLL) dynamics. This analysis is carried out in the synchronous $dq$ reference frame. Some aspects of the closed-loop stability that involve nonlinear dynamics analysis and equilibrium points are presented in order to get a small-signal linearized model.
A Discriminatively Learned Feature Embedding Based on Multi-Loss Fusion For Person Search

Person search is a challenging task that requires to address pedestrian detection and person re- identification simultaneously. Though significant progress has been made in detection and re-identification respectively, the similar appearances of persons, pedestrian misdetections and false alarms still have adverse effects on person search. To this end, an improved end-to-end person search network with multi -loss is proposed to jointly optimize detection and re-identification. Firstly, a pre-trained network is designed to obtain proper initial state for the whole training network. Then, to enhance the person search model, an improved online instance matching (IOIM) loss is proposed by hardening the distribution of labeled identities and softening the distribution of unlabeled identities. Finally, considering the intra-class compactness of features learned by center loss, the IOIM loss is combined with center loss by the proposed multi-loss fusion strategy, which can learn more discriminative feature embeddings. Experimental results on two challenging datasets CUHK-SYSU and PRW demonstrate our approach significantly outperforms the state-of-the-arts.
Learning In-Place Residual Homogeneity for Image Detail Enhancement

In this paper, we put forward and demonstrate a novel method in image and video detail enhancement–– in-place residual homogeneity (IP). In-place residual homogeneity is a regular law we find in testing different blocks in database, that is, residual blocks with slight different resolutions hold homogenous structures. By learning this homogeneity, we guess that it might be a good description of image's detail layer. Then images are enhanced by designed framework and accelerated by proposed fast in-place search method. Unlike most algorithms that need to adjust parameters by manual to get best performance, our approach is adaptive. Besides, many algorithms will change images' intensity, but our IP can keep natural images from over enhancement. Moreover, IP is also robust to low bit rate H.265 encoder and decoder system and runs faster than most popular methods. The last but not least, it can be easily FPGA implemented as well. Numbers of experiments testify that our algorithm is robust with good performance both subjectively and objectively.
Kinematic Sensitivity Evaluation of Revolute and Prismatic 3-DOF Delta Robots

The Delta parallel robot establishes itself as one of the most successful parallel robot designs thanks to its intrinsic properties. Generally, based on the type of actuator, the 3-DOF Delta robot falls into two groups, namely, revolute-input and prismatic-input. The main aim of this paper consists in evaluating and comparing the kinematic performance of the two foregoing types of Delta robots. So far, different kinematic sensitivity indices have been proposed. In this paper, by employing different kinematic sensitivity indices such as manipulability, dimensionally kinematic sensitivity and weighted kinematic sensitivity indices. the kinetostatic performance of the aforementioned robots are compared over a certain workspace. The obtained results showed that the sensitivity of prismatic Delta robot is high in the center of the workspace and the sensitivity increases when the end-effector moves to edges from the center. However sensitivity of the revolute Delta robot is totally different. It is high on the edges and it decreases by moving to center. weighted kinematic sensitivity results show that geometrical features of the robots are very important and it changes the sensitivity values and trend greatly. Moreover, in order to enhance the kinematic performance of the robots, the configuration of the robots are redesigned using optimization of the kinematic sensitivity indices. The results of this paper revealed that kinematic sensitivity indices can be employed in the design step of the robot to reach an optimal kinematic sensitivity in the workspace.
Edge-Aware Context Encoder for Image Inpainting

We present Edge-aware Context Encoder (E-CE): an image inpainting model which takes scene structure and context into account. Unlike previous CE which predicts the missing regions using context from entire image, E-CE learns to recover the texture according to edge structures, attempting to avoid context blending across boundaries. In our approach, edges are extracted from the masked image, and completed by a full-convolutional network. The completed edge map together with the original masked image are then input into the modified CE network to predict the missing region. The experiments demonstrate that E-CE can generate images with better shapes and structures than CE.
Sliding Mode Control of Fuzzy Singularly Perturbed Systems With Application to Electric Circuit

This paper presents a new sliding mode control (SMC) design methodology for fuzzy singularly perturbed systems (SPSs) subject to matched/unmatched uncertainties. To fully accommodate the model characteristics of the systems, a novel integral-type fuzzy switching surface function is put forward, which contains singular perturbation matrix and state-dependent input matrix simultaneously. Its corresponding sliding mode dynamics is a transformed fuzzy SPSs such that the matched uncertainty/perturbation is completely compensated without amplifying the unmatched one. By adopting a $\boldsymbol \varepsilon $ -dependent Lyapunov function, sufficient conditions are presented to guarantee the asymptotic stability of sliding mode dynamics, and a simple search algorithm is provided to find the stability bound. Then, a fuzzy SMC law is synthesized to ensure the reaching condition despite matched/unmatched uncertainties. A modified adaptive fuzzy SMC law is further constructed for adapting the unknown upper bound of the matched uncertainty. The applicability and superiority of obtained fuzzy SMC methodology are verified by a controller design for an electric circuit system.
Dynamic Super-Rays for Efficient Light Field Video Processing

One challenging problem with light field video editing is the dreadful volume of data to process. Image and video processing frequently rely on over-segmentation methods to reduce the computational burden of subsequent editing tasks. In this paper, we present the first approach for video light field over-segmentation called dynamic super-rays, which can be seen as temporally and angularly consistent superpixels. Our algorithm is memory efficient and fast. The results show timing close to editing time by lever-aging GPU computational power.
Information Fusion Using Particles Intersection

A technique is presented for combining arbitrary empirical probability density estimates whose interdependencies are unspecified. The underlying estimates may be, for example, the particle approximations of a pair of particle filters. In this respect, our approach provides a way to obtain a new particle approximation, which is better in a precise information-theoretic sense than that of any of the particle filters alone. The viability of the proposed approach is demonstrated in a multiple object tracking scenario.
Experimental Uncertainty Analysis of Gear Fatigue Life Theoretical Prediction

Gear design is a good example of a complex engineering activity which involves knowledge of several subjects such as Application Requirements, Sizing Analysis, including Stress Analysis, Material Properties Analysis, Tribology Aspects, Manufacturing Process Capability, Prototypes Requirements, Bench Tests, Failure Analysis and Reliability Studies. All those listed subjects represent the basic elements supporting life estimation activity based on design criteria developed to avoid or define expected occurrence of specific failure mechanisms [2][3].
Learning Task-Based Instructional Policy for Excavator-Like Robots

We explore beyond existing work in learning from demonstration by asking the question: “Can robots learn to guide? ”, that is, can a robot autonomously learn an instructional policy from expert demonstration and use it to instruct humans in executing complex task? As a solution, we propose learning of instructional policy $(\pi^{I})$ that maps the state to an instruction for a human. To learn $\pi^{I}$, we define action primitives that addresses the challenge of mapping continuous state action trajectories to human parse-able instructions. Action primitives are demonstrated to be very effective in automatic segmentation of demonstration trajectories into fewer repetitive and reusable segments, and a highly scalable approach in comparison to the existing state-of-the art. Finally, we construct a non-generic policy model as a generative model for instructional policies to generate instruction for an entire task. With few modifications, the proposed model is demonstrated to perform autonomous execution of complex truck loading task on hydraulic actuated scaled excavator robot. Guidance approach is tested based on a controlled group study involving 75 participants, who learn to perform the same task.
A Joint Perspective of Periodically Excited Efficient NLMS Algorithm and Inverse Cyclic Convolution

Research in static and time-variant system identification has brought up a broad variety of identification algorithms. In acoustics, e.g., static measurements of transfer functions are commonly conducted using Inverse Cyclic Convolution (ICC) with Exponential Sweep excitation. Identification and tracking of time-variant systems, however, often employ adaptive filter algorithms, such as the Normalized Least Mean Square (NLMS) algorithm. An interesting implementation variant is the so-called Efficient NLMS (eNLMS) algorithm for arbitrary periodic excitation. ICC and the eNLMS algorithm originate from different fields and have so far evolved independently. This paper bridges the gap using a theoretical analysis of both algorithms to prove that they can be transferred into each other. This understanding provides a joint perspective, such that know-how from both fields can be combined to further optimize the system identification process.