Miracle-dz/UniFAR-FTUs huggingface dataset:
Descemetorhexis Without Endothelial Keratoplasty Versus DMEK for Treatment of Fuchs Endothelial Corneal Dystrophy

Purpose: To compare the visual outcomes and associated morbidity of patients with Fuchs endothelial corneal dystrophy who were treated with either Descemet membrane endothelial keratoplasty (DMEK) or descemetorhexis without endothelial keratoplasty (DWEK). Methods: This is a retrospective comparative cohort study of 27 eyes with mild to moderate Fuchs dystrophy (with corneal guttae/edema limited to the central cornea with relatively clear periphery) that were treated at the University of Pittsburgh Medical Center from 2015 to 2017 with either DMEK (n = 15) or DWEK (n = 12). Descemetorhexis was performed by removing the central 4 mm of diseased Descemet membrane at the end of phacoemulsification for cataract surgery. Visual acuity was measured using the Snellen chart and then converted to logMAR for analysis. Results: Average postoperative pinhole visual acuity was 20/25 − 1 (logMAR 0.16 ± 0.09) for DMEK eyes and 20/30 + 1 (logMAR 0.13 ± 0.10) for DWEK eyes (P = 0.44). The average time to 20/40 vision for DMEK was 2.2 ± 2.8 weeks compared with 7.1 ± 2.7 weeks for DWEK (P < 0.01). In our DMEK group, 8 (53%) patients had adverse events, including increased intraocular pressure (n = 7), anterior chamber inflammation (n = 1), and graft nonadherence (n = 1), with 1 patient requiring anterior chamber paracentesis (6.7%) and 1 patient (6.7%) requiring a rebubbling procedure. Our DWEK group had no adverse events (P < 0.01). Conclusions: DWEK effectively treats select patients with mild to moderate Fuchs dystrophy with equivalent visual outcomes compared with the current standard of care, DMEK. Although the recovery time may be longer, DWEK patients had reduced adverse events and need for additional procedures and did not require long-term immunosuppression or donor corneal tissue.
Safe and Efficient Human-Robot Collaboration Part I: Estimation of Human Arm Motions

A significant barrier regarding a successful implementation of fenceless robot cells into manufacturing areas with humans is given by the inefficiency due to safety requirements. Robot motions have to be slowed down so that an unexpected collision with a human does not result in human injuries. This velocity reduction leads to longer cycle times and, hence, fenceless robot cells turn out as uneconomic. In this paper, a new approach for human-robot collaboration in assembly tasks is presented. For a better performance of the robot, methods are investigated on how the robot can exploit a maximum performance while maintaining the safety of collaborating humans. For this purpose, the kinematics and dynamics of a human arm are described by a control-oriented dynamic model to determine its capability and reachability. Successful experiments validate the dynamic model as well as a corresponding projection approach for calculating possible movements of the human arm that may lead to a collision with the robot. Finally, this information is used to calculate an admissible path velocity that minimizes the danger of human injuries.
A Geometric and Unified Approach for Modeling Soft-Rigid Multi-Body Systems with Lumped and Distributed Degrees of Freedom

In this paper, a geometric and unified model of soft-rigid multi-body systems is presented, based on a discrete Cosserat approach of the soft-body dynamics. The model is in fact a generalization to soft and hybrid systems of the geometric theory of rigid robotics characterized by the exponential map. A generalization of the recursive Newton-Euler algorithm is also presented, able to solve inverse and forward dynamic problems with linear $O(N)$ complexity. The proposed model provides several improvements with respect to the existing flexible multi-body models, which make it particularly suitable to study the dynamics of modern soft robots as shown for a multi-body system inspired by motile bacteria.
Enhancing Long Tail Recommendation Based on User's Experience Evolution

At present, recommender system has become a hot research area. It has been widely used, especially in e-commerce, news promotion, online education and other fields. Collaborative filtering algorithm based on user behavior analysis is a popular algorithm in recommender system research. In the research of user taste evolution, most of the work considered from the perspective of the group, thus ignoring the difference of individual learning ability of users. On the other hand, how to effectively mine long tail items has become one of the problems that need to be solved in recommender systems. In this paper, we model the evolution of user tastes from the individual level by user rating sequence. Then we build the recommendation scoring model according to the user experience level and the corresponding popularity of the items, and make reasonable topN recommendation. Experiments show that our model can achieve good results. In addition, the recommendation based on user experience level can effectively solve the long tail mining problem.
Identification of Compounds Targeting Hepatitis B Virus Core Protein Dimerization through a Split Luciferase Complementation Assay

The capsid of the hepatitis B virus is an attractive antiviral target for developing therapies against chronic hepatitis B infection. Currently available core protein allosteric modulators (CpAMs) mainly affect one of the two major types of protein-protein interactions involved in the process of capsid assembly, namely, the interaction between the core dimers. ABSTRACT The capsid of the hepatitis B virus is an attractive antiviral target for developing therapies against chronic hepatitis B infection. Currently available core protein allosteric modulators (CpAMs) mainly affect one of the two major types of protein-protein interactions involved in the process of capsid assembly, namely, the interaction between the core dimers. Compounds targeting the interaction between two core monomers have not been rigorously screened due to the lack of screening models. We report here a cell-based assay in which the formation of core dimers is indicated by split luciferase complementation (SLC). Making use of this model, 2 compounds, Arbidol (umifenovir) and 20-deoxyingenol, were identified from a library containing 672 compounds as core dimerization regulators. Arbidol and 20-deoxyingenol inhibit the hepatitis B virus (HBV) DNA replication in vitro by decreasing and increasing the formation of core dimer and capsid, respectively. Our results provided a proof of concept for the cell model to be used to screen new agents targeting the step of core dimer and capsid formation.
Spectral Smoothing by Variationalmode Decomposition and its Effect on Noise and Pitch Robustness of ASR System

A novel front-end speech parameterization technique that is robust towards ambient noise and pitch variations is proposed in this paper. In the proposed technique, the short-time magnitude spectrum obtained by discrete Fourier transform is first decomposed in several components using variational mode decomposition (VMD). For sufficiently smoothing the spectrum, the higher-order components are discarded. The smoothed spectrum is then obtained by reconstructing the spectrum using the first-two modes only. The Mel-frequency cepstral coefficients computed using the VMD-based smoothed spectra are observed to be affected less by ambient noise and pitch variations. To validate the same, an automatic speech recognition system is developed on clean speech from adult speakers and evaluated under noisy test conditions. Furthermore, experimental evaluations are also performed on another test set which consists of speech data from children to simulate large pitch differences. The experimental evaluations as well as signal domain analyses presented in this paper support these claims.
Limited-Memory BFGS Optimization of Recurrent Neural Network Language Models for Speech Recognition

Recurrent neural network language models (RNNLM) have become an increasingly popular choice for state-of-the-art speech recognition systems. RNNLMs are normally trained by minimizing the cross entropy (CE) using the stochastic gradient descent (SGD) algorithm. The SGD method only uses first-order derivatives and no higher order gradient information is used to consider the correlation between model parameters. It is unable to fully capture the curvature of the error cost function. This can lead to slow convergence in model training. In this paper, a limited-memory Broyden Fletcher Goldfarb Shannon (L-BFGS) based second order optimization technique is proposed for RNNLMs. This method efficiently approximates the matrix-vector product between the inverse Hessian and gradient vector via a recursion over past gradients with a compact memory requirement. Consistent perplexity and error rate reductions are obtained over the SGD method on two speech recognition tasks: Switchboard English and Babel Cantonese. A faster convergence and speed up in RNNLM training time was also obtained. Index Terms: recurrent neural network, language model, second order optimization, limited-memory BFGS, speech recognition
Configuration of Perception Systems via Planning Over Factor Graphs

Sensor guided, automated systems require the composition of various sensors and data processing algorithms to obtain relevant information for performing their task. Many applications have additional requirements such as a certain accuracy, which has to be achieved despite sensor noise and calibration errors. In this paper we model the configuration of perception systems as a planning problem over probabilistic graphical models. We work on a subset of the full configuration space of perceptions systems, specifically the used sensors, data processing algorithms and view poses. Based on a semantic description of the goal, available sensors and data processing algorithms, our system plans perception steps and sensor data fusion autonomously. The planner operates by constructing a factor graph until the accuracy requirements of tasks are fulfilled or unobtainable with the available action set. We validate our approach in an industrial assembly scenario.
Incorporating ASR Errors with Attention-Based, Jointly Trained RNN for Intent Detection and Slot Filling

The real-world performance of slot filling and intent detection task generally degrades due to transcription errors generated by speech recognition engine. The insertion, deletion, and mis-recognition errors from speech recognizer's front-end cause the mis-interpretation and mis-alignment of the language understanding models. In this work, we propose a new jointly trained model of intent detection and slot filling with consideration of speech recognition errors. The attention-based encoder-decoder recurrent neural network first decodes the intent information from an utterance, and then corrects errors in the word sequence, if any, before extracting the slot information. The triple joint training framework maximizes the probability of a correct understanding given an input utterance. Our experimental results showed that the proposed model obtained 2.87% absolute gain over the joint model without ASR error correction for slot filling and 0.73% absolute error rate reduction for intent detection.
Online Multi-Object Tracking with Structural Invariance Constraint

Under the framework of tracking-by-detection, data association is one of the most important issues in multi-object tracking (MOT). Given a video sequence labeled with bounding boxes, data association aims at adopting a graph matching or network ﬂow to maximize (minimize) the sum of the association probabilities (costs), which are generally elaborated by objects’ appearance features and motion cues. However, both analogous appearances and moving cameras inevitably increase matching ambiguity and thus make data association intricate and challenging. In this paper, we propose a new data association method to address the online MOT problem by exploiting structural invariance constraint, which is insensitive to both akin appearances and dynamic camera situation. Furthermore, we develop a total probability frame that is able to jointly reason on both appearance and structure cues without adjusting parameters manually. We evaluate our online multi-object tracking algorithm on public MOT Challenge datasets and achieve comparable performance with other state-of-the-art approaches.
Clothoid-Based Global Path Planning for Autonomous Vehicles in Urban Scenarios

Intelligent vehicles require an efficient way to compute a feasible path that connects its current localization to a destination point. To achieve that, the knowledge about the road network, including its geometry, is important since connections between roads can be used in the path planning. This work consists on computing a feasible global path for autonomous vehicles with kinematic constraints. Piecewise linear continuous-curvature paths composed of clothoids, circular arcs, and straight lines are used for this purpose. Low curvature derivatives provide comfort to passengers. This approach provides a compact road network representation as only the parameters of the curves are stored. A real urban scenario with straight and curved roads, multiple lanes, intersections, and roundabouts is modeled and the proposed approach is validated. As a result of the proposed approaches, door-to-door global continuous-curvature paths are generated considering the shortest traveled distance.
Semi-Supervised Multi-Task Word Embeddings

Word embeddings have been shown to benefit from ensembling several word embedding sources, often carried out using straightforward mathematical operations over the set of vectors to produce a meta-embedding representation. More recently, unsupervised learning has been used to find a lower-dimensional representation, similar in size to that of the word embeddings within the ensemble. However, these methods do not use the available manual labeled datasets that are often used solely for the purpose of evaluation. We propose to improve word embeddings by simultaneously learning to reconstruct an ensemble of pretrained word embeddings with supervision from various labeled word similarity datasets. This involves reconstructing word meta-embeddings while simultaneously using a Siamese Network to also learn word similarity where both processes share a hidden layer. Experiments are carried out on 6 word similarity datasets and 3 analogy datasets. We find that performance is improved for all word similarity datasets when compared to unsupervised learning methods with a mean increase of 11.33 in the Spearman Correlation coefficient. Moreover, 4 of 6 of word similarity datasets from our approach show best performance when using of a cosine loss for reconstruction and Brier's loss for word similarity.
Geometry-Consistent Adversarial Networks for One-Sided Unsupervised Domain Mapping

Unsupervised domain mapping aims at learning a function to translate domain X to Y (GXY : X to Y) in the absence of paired (X,Y) samples. Finding the optimal GXY without paired data is an ill-posed problem and hence appropriate constraints are required to obtain reasonable solutions. One of the most prominent constraint is cycle-consistency, which enforces the translated image by GXY to be translated back to the input image by an inverse mapping GYX. While cycle-consistency requires simultaneous training of GXY and GYX, recent methods have demonstrated one-sided domain mapping (only learn GXY) can be achieved by preserving pairwise distance between images before and after translation. Although cycle-consistency and distance preserving successfully constrain the solution space, they overlook the special properties of images that simple geometric transformations do not change the semantics of an image. Based on this special property, we develop a geometry-consistent adversarial network (GcGAN) which enables one-sided unsupervised domain mapping. Our GcGAN takes the original image and its counterpart image transformed by a predefined geometric transformation as inputs and generates two images in the new domain with the corresponding geometry-consistency constraint. The geometry-consistency constraint eliminates unreasonable solutions and produce more reliable solutions. Quantitative comparisons against baseline (GAN alone) and the state-of-the-art methods, including DistanceGAN and CycleGAN, demonstrate the superiority of our method in generating realistic images.
Assigning Rated Items to Locations in Non-List Display Layouts

One of the most common ways in which results are displayed by an information retrieval system is in the form of a list, in which the most relevant results appear in the first positions. Today's large screens, however, allow one to create more complex displays of results, especially in cases such as image retrieval, in which each unit returned is fairly compact. For these layouts the simple list model is no longer valid, since the relations between the slots in which the results are placed do not form a sequence, that is, the relation among them is no longer that of a total order. In this paper we model these layouts as partial orders and show that a “stalwart display” property (a layout in which items’ relevance is unambiguously conveyed by their display position) can be obtained only in the case of lists. For the other layouts, we define two classes of representation functions: “safe” functions (which display results without adding spurious structure) and “rich” functions (which do not drop any structure from the result set), as well as an algorithm to optimally display fully ordered result sets in arbitrary display layouts.
Vehicle Routing Problem with Time Windows in Aged Service: A Delivery Service Model for the Elderly

In recent years, the number of elderly population is increasing in China with the decline of population mortality and the prolongation of life expectancy. The elderly population is also gradually increasing, making the aging phenomenon more and more serious worldwide. Accompanied by the high demands in material life and spiritual life, people pay more and more attention to pension services. Therefore, the service problem for the elderly arises many researchers' interests. As a typical distribution problem for the elderly, in order to reduce the cost of logistics activities and create more economic benefits for the enterprise, the distribution vehicle routing problem certainly becomes the research hotspot. We firstly introduce the background of the vehicle routing problem with time windows and the delivery service for the elderly. Then we analyze the present situation in both domestic and external research. We also summarize the elements of the elderly distribution problem and classifies the characteristics of this problem through an investigation in a typical Beijing community. Finally, we refine the vehicle routing problem with time windows in the delivery service for the elderly and the corresponding mathematical model is established.
Personalized Recommendation Considering Secondary Implicit Feedback

In e-commerce, recommendation is an essential feature to provide users with potentially interesting items to purchase. However, people are often faced with an unpleasant situation, where the recommended items are simply the ones similar to what they have purchased previously. One of the main reasons is that existing recommender systems in e-commerce mainly utilize primary implicit feedback (i.e., purchase history) for recommendation. Little attention has been paid to secondary implicit feedback (e.g., viewing items, adding items to shopping cart, adding items to favorite list, etc), which captures users' potential interests that may not be reflected in their purchase history. We therefore propose a personalized recommendation approach to combine the primary and secondary implicit feedback to generate the recommendation list, which is optimized towards a Bayesian objective criterion for personalized ranking. Experiments with a large-scale real-world e-commerce dataset show that the proposed approach presents a superior performance in comparison with the state-of-the-art baselines.
Risk and Resilience Factors for Psychobehavioral Symptom Trajectories Among Child Welfare–Involved Youth

The primary aim of the study was to investigate the development of psychobehavioral symptoms (internalizing, externalizing, and posttraumatic stress [PTS]) of child welfare–involved adolescents over a 3-year period and to identify predictors of the trajectories. Using three waves of data from the National Survey of Child and Adolescent Well-Being (NSCAW-II), latent growth curve modeling was conducted on a sample of 350 adolescents who were between 11 and 13 years of age at baseline. Findings indicated that adolescents’ internalizing symptoms decreased over time, whereas externalizing symptoms remained stable over time. PTS symptoms decreased over time in girls, but not boys. Physical abuse predicted higher levels of externalizing behaviors and sexual abuse predicted a slower decrease in PTS symptoms. Greater child prosocial skills and higher quality of caregiver–child relationship were associated with lower initial levels of externalizing symptoms. In addition, a higher quality caregiver–child relationship and greater satisfaction with peer relationships were associated with lower initial levels of internalizing and PTS symptoms. Findings suggest that intervention strategies should consider gender, maltreatment type, and interpersonal and social skills to effectively prevent the development of psychobehavioral symptoms among child welfare–involved youth.
Ecg Delineation for Qt Interval Analysis Using an Unsupervised Learning Method

This paper presents a novel approach for automatic ECG delineation with focus on QT interval estimation, using an unsupervised learning algorithm. A three-dimensional feature space is created which uses the characteristics of ECG waveform at its inflection points. To this end, three features are introduced, including the Truncated Energy, which makes our method robust to baseline wandering and noise. Using the fact that the logarithm of features exhibits a mixture of four Gaussian distributions, each for one of the P wave, QRS complex, T wave and baseline, an unsupervised clustering algorithm based on Expectation Maximization is applied. The experimental results reveal that the proposed algorithm extracts the ECG waves accurately, even if they have a very low energy and amplitude. No pre-processing and windowing approach is required in the proposed method resulting in a significantly higher resolution and lower computational complexity in estimating the onset and offset of ECG waves. Furthermore, the proposed algorithm is robust to noise and baseline wandering, thanks to the Laplacian of Gaussian filter employed for inflection point detection.