Miracle-dz/UniFAR-FTUs huggingface dataset:
A Comparison between Background Modelling Methods for Vehicle Segmentation in Highway Traffic Videos

The objective of this paper is to compare the performance of three background-modeling algorithms in segmenting and detecting vehicles in highway traffic videos. All algorithms are available in OpenCV and were all coded in Python. We analyzed seven videos, totaling 2 hours of recording. To compare the algorithms, we created 35 ground-truth images, five from each video, and we used three different metrics: accuracy rate, precision rate, and processing time. By using accuracy and precision, we aim to identify how well the algorithms perform in detection and segmentation, while using the processing time to evaluate the impact on the computational system. Results indicate that all three algorithms had more than 90% of precision rate, while obtaining an average of 80% on accuracy. The algorithm with the lowest impact on processing time allowed the computation of 60 frames per second.
Crossplatform References on Social Media: Methodological Challenges and Research Avenues

This paper studies the uses of social media from a crossplatform perspective. It firstly defines the concept of crossplatform references and the different forms they can take. A set of methodological challenges that may hinder the analysis of such references is then described, alongside with some possible solutions and tools. Promising research avenues are finally presented as a potential research agenda.
Comparison of clinical and echocardiographic outcomes and quality of life in patients with severe mitral regurgitation treated by MitraClip implantation or treated conservatively

Introduction The most common alternative method of treatment for patients with severe mitral regurgitation (MR) is the implantation of a MitraClip device. Aim To evaluate clinical and echocardiographic outcomes and quality of life (QoL) in patients with severe secondary MR, disqualified from surgical intervention, treated by implantation of a MitraClip in comparison to conservative therapy. Material and methods A total of 33 patients were included. Patients were stratified by treatment method: group A, MitraClip implantation (n = 10); group B, conservative treatment (n = 23). Clinical, echocardiographic, and QoL (EQ-5D-3L, SF-12v2 Health Survey) characteristics were compared at baseline and at follow-up of 8.0 ±2.3 months. Results In group A, 2 deaths were observed: one patient died 7 days after MitraClip implantation, and the second patient died 4 months after the procedure. No cases of rehospitalization were reported. In group B, 4 (17.4%) deaths and 6 (26.1%) hospitalizations were reported. After MitraClip implantation a significant reduction of the NYHA class (p = 0.02), decrease in grade of MR (p = 0.01), vena contracta width (p = 0.006), effective regurgitant orifice area (EROA) (p = 0.003), regurgitant volume (p = 0.03) and end-diastolic left ventricle diameter (p = 0.02) as well as an improvement in QoL were reported. There were no significant changes in the NYHA class and QoL in the group treated conservatively. In those patients, we observed increased intercommissural mitral annulus diameter (p = 0.03), left atrium diameter (p = 0.002), and right ventricle dimension (p = 0.008), more severe tricuspid regurgitation (p = 0.02) and lower mitral annular plane systolic excursion (p = 0.01). Conclusions Patients with severe secondary MR treated with the MitraClip achieved a significant reduction in symptoms and MR grade, as well as an improvement in QoL, as compared to patients treated conservatively.
Eiffel: Efficient and Flexible Software Packet Scheduling

Packet scheduling determines the ordering of packets in a queuing data structure with respect to some ranking function that is mandated by a scheduling policy. It is the core component in many recent innovations to optimize network performance and utilization. Our focus in this paper is on the design and deployment of packet scheduling in software. Software schedulers have several advantages over hardware including shorter development cycle and flexibility in functionality and deployment location. We substantially improve current software packet scheduling performance, while maintaining flexibility, by exploiting underlying features of packet ranking; namely, packet ranks are integers and, at any point in time, fall within a limited range of values. We introduce Eiffel, a novel programmable packet scheduling system. At the core of Eiffel is an integer priority queue based on the Find First Set (FFS) instruction and designed to support a wide range of policies and ranking functions efficiently. As an even more efficient alternative, we also propose a new approximate priority queue that can outperform FFS-based queues for some scenarios. To support flexibility, Eiffel introduces novel programming abstractions to express scheduling policies that cannot be captured by current, state-of-the-art scheduler programming models. We evaluate Eiffel in a variety of settings and in both kernel and userspace deployments. We show that it outperforms state of the art systems by 3-40x in terms of either number of cores utilized for network processing or number of flows given fixed processing capacity.
Atherosclerosis Might Be Responsible for Branch Artery Disease: Evidence From White Matter Hyperintensity Burden in Acute Isolated Pontine Infarction

Objective: To investigate an MRI-based etiological classification for acute isolated pontine infarcts and to assess differences in vascular risk factors, clinical characteristics and WMH burden among the etiological subtypes. Methods: All participants from SMART cohort with DWI-proven acute isolated pontine infarcts (AIPI) were included and categorized into 3 groups: large-artery-occlusive disease (LAOD), basilar artery branch disease (BAD), and small vessel disease (SVD), according to basilar artery atherosclerosis severity and lesion extent of the transverse axial plane. The vascular risk factors and 6-month functional outcome was analyzed among 3 groups. Results: Of the 1129 patients enrolled, 175 had AIPI. BAD was the most frequent subtype of AIPI (46.3%), followed by SVD (36.0%) and LAOD (17.7%). Neurological impairment on admission was more severe in the LAOD group, followed by BAD. The BAD group had greater frequencies of female sex, hypertension, diabetes mellitus compared to the SVD group (P < 0.05). NIHSS on admission were significantly higher in the BAD group as compared with the SVD group (P < 0.001), but no difference was found between BAD and LAOD group. Poor outcome (mRS≥3) was found in only 13.7% of patients at 6-month post-stroke and there was no difference among 3 groups. WMH severity was significant higher in the SVD group compared to the BAD group for the deep subcortical region; however, there was no difference for the periventricualr region. There was no significant difference in either DWMH or PVWMH severity between the BAD and LAOD groups. Conclusion: BAD is the most frequent etiology of AIPI followed by SVD and LAOD. WMH burden, vascular risk factors and clinical characteristics in BAD group were more similar to the LAOD group, rather than to the SVD group, suggesting the atherothrombotic nature of BAD.
Spatially-Aware Tangibles Using Mouse Sensors

We demonstrate a simple technique that allows tangible objects to track their own position on a surface using an off-the-shelf optical mouse sensor. In addition to measuring the (relative) movement of the device, the sensor also allows capturing a low-resolution raw image of the surface. This makes it possible to detect the absolute position of the device via marker patterns at known positions. Knowing the absolute position may either be used to trigger actions or as a known reference point for tracking the device. This demo allows users to explore and evaluate affordances and applications of such tangibles.
Accelerating Imitation Learning with Predictive Models

Sample efficiency is critical in solving real-world reinforcement learning problems, where agent-environment interactions can be costly. Imitation learning from expert advice has proved to be an effective strategy for reducing the number of interactions required to train a policy. Online imitation learning, which interleaves policy evaluation and policy optimization, is a particularly effective technique with provable performance guarantees. In this work, we seek to further accelerate the convergence rate of online imitation learning, thereby making it more sample efficient. We propose two model-based algorithms inspired by Follow-the-Leader (FTL) with prediction: MoBIL-VI based on solving variational inequalities and MoBIL-Prox based on stochastic first-order updates. These two methods leverage a model to predict future gradients to speed up policy learning. When the model oracle is learned online, these algorithms can provably accelerate the best known convergence rate up to an order. Our algorithms can be viewed as a generalization of stochastic Mirror-Prox (Juditsky et al., 2011), and admit a simple constructive FTL-style analysis of performance.
Adolescent binge ethanol-induced loss of basal forebrain cholinergic neurons and neuroimmune activation are prevented by exercise and indomethacin

Basal forebrain cholinergic neurons mature in adolescence coinciding with development of adult cognitive function. Preclinical studies using the rodent model of adolescent intermittent ethanol (AIE; 5.0 g/kg, i.g., 2-days on/2-days off from postnatal day [P]25 to P55) reveal persistent increases of brain neuroimmune genes that are associated with cognitive dysfunction. Adolescent intermittent ethanol exposure also reduces basal forebrain expression of choline acetyltransferase (ChAT), an enzyme critical for acetylcholine synthesis in cholinergic neurons similar to findings in the post-mortem human alcoholic basal forebrain. We report here that AIE decreases basal forebrain ChAT+IR neurons in both adult female and male Wistar rats following early or late adolescent ethanol exposure. In addition, we find reductions in ChAT+IR somal size as well as the expression of the high-affinity nerve growth factor (NGF) receptor tropomyosin receptor kinase A (TrkA) and the low-affinity NGF receptor p75NTR, both of which are expressed on cholinergic neurons. The decrease in cholinergic neuron marker expression was accompanied by increased phosphorylation of NF-κB p65 (pNF-κB p65) consistent with increased neuroimmune signaling. Voluntary wheel running from P24 to P80 prevented AIE-induced cholinergic neuron shrinkage and loss of cholinergic neuron markers (i.e., ChAT, TrkA, and p75NTR) as well as the increase of pNF-κB p65 in the adult basal forebrain. Administration of the anti-inflammatory drug indomethacin (4.0 mg/kg, i.p prior to each ethanol exposure) during AIE also prevented the loss of basal forebrain cholinergic markers and the concomitant increase of pNF-κB p65. In contrast, treatment with the proinflammatory immune activator lipopolysaccharide (1.0 mg/kg, i.p. on P70) caused a loss of cholinergic neuron markers that was paralleled by increased pNF-κB p65 in the basal forebrain. These novel findings are consistent with AIE causing lasting activation of the neuroimmune system that contributes to the persistent loss of basal forebrain cholinergic neurons in adulthood.
Abnormal Global Functional Connectivity Patterns in Medication-Free Major Depressive Disorder

Mounting studies have applied resting-state functional magnetic resonance imaging (rs-fMRI) to study major depressive disorder (MDD) and have identified abnormal functional activities. However, how the global functional connectivity patterns change in MDD is still unknown. Using rs-fMRI, we investigated the alterations of global resting-state functional connectivity (RSFC) patterns in MDD using weighted global brain connectivity (wGBC) method. First, a whole brain voxel-wise wGBC map was calculated for 23 MDD patients and 34 healthy controls. Two-sample t-tests were applied to compare the wGBC and RSFC maps and the significant level was set at p < 0.05, cluster-level correction with voxel-level p < 0.001. MDD patients showed significantly decreased wGBC in left temporal pole (TP) and increased wGBC in right parahippocampus (PHC). Subsequent RSFC analyses showed decreased functional interaction between TP and right posterior superior temporal cortex and increased functional interaction between PHC and right inferior frontal gyrus in MDD patients. These results revealed the abnormal global FC patterns and its corresponding disrupted functional connectivity in MDD. Our findings present new evidence for the functional interruption in MDD.
An AMR Aligner Tuned by Transition-based Parser

In this paper, we propose a new rich resource enhanced AMR aligner which produces multiple alignments and a new transition system for AMR parsing along with its oracle parser. Our aligner is further tuned by our oracle parser via picking the alignment that leads to the highest-scored achievable AMR graph. Experimental results show that our aligner outperforms the rule-based aligner in previous work by achieving higher alignment F1 score and consistently improving two open-sourced AMR parsers. Based on our aligner and transition system, we develop a transition-based AMR parser that parses a sentence into its AMR graph directly. An ensemble of our parsers with only words and POS tags as input leads to 68.4 Smatch F1 score, which outperforms the current state-of-the-art parser.
Development and Validation of an HPLC-MS/MS Method for Rapid Simultaneous Determination of Cefprozil Diastereomers in Human Plasma

Background Both cis- and trans-cefprozil have antimicrobial activity, but their potencies are quite different. It is therefore necessary to develop a sensitive method to simultaneously determine both isomers for pharmacokinetic and bioequivalence studies. Methods An LC-MS/MS method, using stable isotope-labeled cefprozil as the internal standard, was developed and validated. The analytes were extracted from plasma by protein precipitation and separated on a reverse-phase C18 column using a gradient program consisting of 0.5% formic acid and acetonitrile within 4 min. The mass spectrometry acquisition was performed with multiple reaction monitoring in positive ion mode using the respective [M+H]+ ions, m/z 391.2→114.0 for cefprozil and 395.0→114.5 for cefprozil-D4. Results The calibration curves were linear over the ranges of 0.025–15 μg/mL for cis-cefprozil and 0.014–1.67 μg/mL for trans-cefprozil. The accuracies for the cis and trans isomers of cefprozil were 93.1% and 103.0%, respectively. The intra- and interassay precisions for the QC samples of the isomers were < 14.3%. The intra- and interassay precisions at the LLOQ were < 16.5%. Conclusions The method was sensitive and reproducible and was applied in a pilot pharmacokinetic study of healthy volunteers.
The equivalence between two classic algorithms for the assignment problem

We give a detailed review of two algorithms that solve the minimization case of the assignment problem. The Bertsekas' auction algorithm and the Goldberg & Kennedy algorithm. We will show that these algorithms are equivalent in the sense that both perform equivalent steps in the same order. We also present experimental results comparing the performance of three algorithms for the assignment problem. They show the auction algorithm performs and scales better in practice than algorithms that are harder to implement but have better theoretical time complexity.
Power and sample size requirements for GEE analyses of cluster randomized crossover trials

The cluster randomized crossover design has been proposed to improve efficiency over the traditional parallel cluster randomized design, which often involves a limited number of clusters. In recent years, the cluster randomized crossover design has been increasingly used to evaluate the effectiveness of health care policy or programs, and the interest often lies in quantifying the population‐averaged intervention effect. In this paper, we consider the two‐treatment two‐period crossover design, and develop sample size procedures for continuous and binary outcomes corresponding to a population‐averaged model estimated by generalized estimating equations, accounting for both within‐period and interperiod correlations. In particular, we show that the required sample size depends on the correlation parameters through an eigenvalue of the within‐cluster correlation matrix for continuous outcomes and through two distinct eigenvalues of the correlation matrix for binary outcomes. We demonstrate that the empirical power corresponds well with the predicted power by the proposed formulae for as few as eight clusters, when outcomes are analyzed using the matrix‐adjusted estimating equations for the correlation parameters concurrently with a suitable bias‐corrected sandwich variance estimator.
Safe- to-Explore State Spaces: Ensuring Safe Exploration in Policy Search with Hierarchical Task Optimization

Policy search reinforcement learning allows robots to acquire skills by themselves. However, the learning procedure is inherently unsafe as the robot has no a-priori way to predict the consequences of the exploratory actions it takes. Therefore, exploration can lead to collisions with the potential to harm the robot and/or the environment. In this work we address the safety aspect by constraining the exploration to happen in safe-to-explore state spaces. These are formed by decomposing target skills (e.g., grasping) into higher ranked sub-tasks (e.g., collision avoidance, joint limit avoidance) and lower ranked movement tasks (e.g., reaching). Sub-tasks are defined as concurrent controllers (policies) in different operational spaces together with associated Jacobians representing their joint-space mapping. Safety is ensured by only learning policies corresponding to lower ranked sub-tasks in the redundant null space of higher ranked ones. As a side benefit, learning in sub-manifolds of the state-space also facilitates sample efficiency. Reaching skills performed in simulation and grasping skills performed on a real robot validate the usefulness of the proposed approach.
Working life trajectories with hearing impairment

Abstract Purpose: The aim was to identify and explore factors, which facilitate or hinder work participation for people with hearing impairment. Materials and methods: In-depth interviews with 21 hearing impaired individuals of 32–67 years of age with a present or recent vocational affiliation were conducted. The analysis was conducted using a grounded theory approach. Results: The analysis resulted in a conceptual framework of working life trajectories evolving through three phases of acknowledgement of hearing loss impact: the pre-acknowledgement, acknowledgement, and post-acknowledgement phase. The phases were influenced by the qualities of three contexts: the personal, the workplace, and the service provider. The qualities of the contexts, together with the amount of time spent in a pre-acknowledgement phase, formed the trajectories towards continuation of work participation or towards a disconnection. Accumulated risk factors constituted increased likelihood of disconnecting trajectories, while accumulated facilitating factors supported sustainable trajectories. Conclusions: The results revealed a need for extended support at the workplaces, which includes the manager, colleagues, and professionals in the aim of preventing exhaustion and facilitate work participation among employees with hearing impairments. Joint action in facilitating communicative participation would share the responsibility for accommodation measures and broaden the room for manoeuver at the workplace. Implications for rehabilitation Fatigue prevention in employees with hearing loss needs to be addressed in occupational rehabilitation. Knowledge transfer on hearing loss implications needs to be included in aural rehabilitation. Occupational rehabilitation professionals and professionals targeting hearing impairments should enter into systematic, multidisciplinary follow-up at the worksite.
A Demo of Talkit++: Interacting with 3D Printed Models Using an iOS Device

Tactile models are important learning materials for visually impaired students. With the adoption of 3D printing technologies, visually impaired students and teachers will have more access to 3D printed tactile models. We designed Talkit++, an iOS application that plays audio and visual content as a user touches parts of a 3D print. With Talkit++, a visually impaired student can explore a printed model tactilely, and use finger gestures and speech commands to get more information about certain elements in the model. Talkit++ detects the model and finger gestures using computer vision algorithms, simple accessories like paper stickers and printable trackers, and the built-in RGB camera on an iOS device. Based on the model's position and the user's input, Talkit++ speaks textual information, plays audio recordings, and displays visual animations.
Graphlet Count Estimation via Convolutional Neural Networks

Graphlets are defined as k-node connected induced subgraph patterns. For instance, for an undirected graph, 3-node graphlets include close triangle and open triangle. The number of each graphlet, called graphlet count, is a signature which characterizes the local network structure of a given graph. Graphlet count plays a prominent role in network analysis of many fields, most notably bioinformatics and social science. However, computing exact graphlet count is inherently difficult and computational expensive because the number of graphlets grows exponentially large as the graph size and/or graphlet size grow. To deal with this difficulty, many sampling methods were proposed to estimate graphlet count with bounded error. Nevertheless, these methods require large number of samples to be statistically reliable, which is still computationally demanding. Intuitively, learning from historic graphs can make estimation more accurate and avoid many repetitive counting to reduce computational cost. Based on this idea, we propose a convolutional neural network (CNN) framework and two preprocessing techniques to estimate graphlet count. Extensive experiments on two types of random graphs and real world biochemistry graphs show that our framework can offer substantial speedup on estimating graphlet count of new graphs with high accuracy.
Deep LDA Hashing

The conventional supervised hashing methods based on classification do not entirely meet the requirements of hashing technique, but Linear Discriminant Analysis (LDA) does. In this paper, we propose to perform a revised LDA objective over deep networks to learn efficient hashing codes in a truly end-to-end fashion. However, the complicated eigenvalue decomposition within each mini-batch in every epoch has to be faced with when simply optimizing the deep network w.r. t. the LDA objective. In this work, the revised LDA objective is transformed into a simple least square problem, which naturally overcomes the intractable problems and can be easily solved by the off-the-shelf optimizer. Such deep extension can also overcome the weakness of LDA Hashing in the limited linear projection and feature learning. Amounts of experiments are conducted on three benchmark datasets. The proposed Deep LDA Hashing shows nearly 70 points improvement over the conventional one on the CIFAR-10 dataset. It also beats several state-of-the-art methods on various metrics.