Miracle-dz/UniFAR-FTUs huggingface dataset:
An algorithm for music recommendation based on the user's musical preferences and desired emotions

Studies show that music is an effective form of emotional induction and can change the emotional behavior of the user. In view of this, this article seeks to create a "selection" algorithm that includes a subset of songs chosen by the user, while taking account of their current emotional state and allowing them to achieve the emotional state that is desired. In carrying out the experiment, we established the current emotional state, the desired emotional state and 20 suggestions of musical items from a sample of 20 users. With the aim of achieving the desired emotional state, the proposed algorithm returned a new list of musical items for each participant. The participants underwent an evaluation during and after playing the music to assess the effectiveness of the returned list. The results suggest that when listening to the songs selected by the algorithm, the user can approach the desired emotional state.
Preparing Drivers for Planned Control Transitions in Automated Cars

In the near future we expect automated driving to be available for specific segments of a journey, e.g., when driving on the highway. At the end of such a route segment, a (planned) control transition from system to driver occurs. While immediate (unpredictable) take-over situations are heavily investigated, there is still a gap in understanding how to present planned take-over requests, especially while drivers might be involved in non-driving-related activities (NDRAs). We investigated the effect of three different visual representations to indicate planned take-over requests (TOR) on usability, comfort, and driving quality. Additionally, we explored the influences of different NDRAs and the device used for this activity. The results of our simulator study (N=24) indicate that (1) upcoming take-over requests should be displayed dynamically, (2) preferred devices depend on the performed task and (3) take-over requests should be presented with auditory, visual, and tactile cues. Based on our findings, we contribute design recommendations to support the development of safe and comfortable planned control transitions.
Intrinsic properties of human germinal center B cells set antigen affinity thresholds

Human germinal center B cells are intrinsically designed for optimal affinity-based selection. Optimal affinity Germinal center (GC) B cells are essential to generating protective antibody responses and are selected through a process of affinity maturation. Kwak et al. now define intrinsic properties of human GC B cells that are critical to antigen affinity discrimination. They identified BCR-containing actin-rich pod-like structures that facilitated formation of highly stable synapses and antigen internalization but only when GC B cells engaged high-affinity antigens. These findings reveal the importance of these structures in setting thresholds for affinity selection and driving GC B cell responses. Protective antibody responses to vaccination or infection depend on affinity maturation, a process by which high-affinity germinal center (GC) B cells are selected on the basis of their ability to bind, gather, and present antigen to T follicular helper (Tfh) cells. Here, we show that human GC B cells have intrinsically higher-affinity thresholds for both B cell antigen receptor (BCR) signaling and antigen gathering as compared with naïve B cells and that these functions are mediated by distinct cellular structures and pathways that ultimately lead to antigen affinity– and Tfh cell–dependent differentiation to plasma cells. GC B cells bound antigen through highly dynamic, actin- and ezrin-rich pod-like structures that concentrated BCRs. The behavior of these structures was dictated by the intrinsic antigen affinity thresholds of GC B cells. Low-affinity antigens triggered continuous engagement and disengagement of membrane-associated antigens, whereas high-affinity antigens induced stable synapse formation. The pod-like structures also mediated affinity-dependent antigen internalization by unconventional pathways distinct from those of naïve B cells. Thus, intrinsic properties of human GC B cells set thresholds for affinity selection.
Normalization in Training U-Net for 2-D Biomedical Semantic Segmentation

Two-dimensional (2-D) biomedical semantic segmentation is important for robotic vision in surgery. Segmentation methods based on deep convolutional neural network (DCNN) can out-perform conventional methods in terms of both accuracy and levels of automation. One common issue in training a DCNN for biomedical semantic segmentation is the internal covariate shift where the training of convolutional kernels is encumbered by the distribution change of input features, hence both the training speed and performance are decreased. Batch normalization (BN) is the first proposed method for addressing internal covariate shift and is widely used. Instance normalization (IN) and layer normalization (LN) have also been proposed. Group normalization (GN) is proposed more recently and has not yet been applied to 2-D biomedical semantic segmentation (GN was used in 3-D biomedical semantic segmentation in [P.-Y. Kao, T. Ngo, A. Zhang, J. Chen, and B. Manjunath, Brain tumor segmentation and tractographic feature extraction from structural MR images for overall survival prediction 2018, arXiv:1807.07716], however, no specific validations on GN were given). Most DCNNs for biomedical semantic segmentation adopt BN as the normalization method by default, without reviewing its performance. In this letter, four normalization methods—BN, IN, LN, and GN are compared in details, specifically for 2-D biomedical semantic segmentation. U-Net is adopted as the basic DCNN structure. Three datasets regarding the right ventricle, aorta, and left ventricle are used for the validation. The results show that detailed subdivision of the feature map, i.e., GN with a large group number or IN, achieves higher accuracy. This accuracy improvement mainly comes from better model generalization. Codes are uploaded and maintained at Xiao-Yun Zhou's Github.
Effects of Ropivacaine in Patient-Controlled Epidural Analgesia on Uterine Electromyographic Activities during Labor

Epidural analgesia is effective in relieving pain during labor. However, concerns as to compromised labor progress and outcomes arise. This study aimed to assess the effect of patient-controlled epidural analgesia (PCEA) with ropivacaine on uterine electromyography (EMG) activities and outcomes in labor. A total of 213 pregnant women were divided into three groups: the PCEA with ropivacaine group (n = 78), the PCEA with levobupivacaine group (n = 66), and a control group that did not receive PCEA (n = 69). Uterine EMG activities were recorded during the first stage of labor. Maternal and fetal outcomes also were assessed. The primary outcomes of this study were EMG activities. No significant differences were observed in patient demographics or neonatal weight among the three groups. Compared to the PCEA with levobupivacaine group, the control and PCEA with ropivacaine groups had lower rates of oxytocin administration (P < 0.05) and shorter durations of the first stage of labor (P < 0.05). For the EMG activities, the PCEA with ropivacaine group showed a higher power (P < 0.01) and higher peak frequency (P < 0.05) than the PCEA with levobupivacaine group. With ropivacaine, the EMG activities remained stable 30–120 min. Compared with levobupivacaine, the use of ropivacaine in PCEA has no suppressive effect on uterine EMG activities during the first stage of labor. In addition, ropivacaine leads to labor progress and delivery outcomes similar to those in the control group, as well as similar and favorable analgesic satisfaction with the use of levobupivacaine.
APPLIED FEDERATED LEARNING: IMPROVING GOOGLE KEYBOARD QUERY SUGGESTIONS

Federated learning is a distributed form of machine learning where both the training data and model training are decentralized. In this paper, we use federated learning in a commercial, global-scale setting to train, evaluate and deploy a model to improve virtual keyboard search suggestion quality without direct access to the underlying user data. We describe our observations in federated training, compare metrics to live deployments, and present resulting quality increases. In whole, we demonstrate how federated learning can be applied endto-end to both improve user experiences and enhance user privacy.
Cognitive Deficit-Related Interhemispheric Asynchrony within the Medial Hub of the Default Mode Network Aids in Classifying the Hyperthyroid Patients

Background and Purpose Recent studies suggest that abnormal structure and function in the brain network were related to cognitive and emotional impairment in hyperthyroid patients (HPs). The association between altered voxel-mirrored homotopic connectivity (VMHC) and neuropsychological impairment in HPs remains unclear. This study is aimed at investigating the association between the disrupted functional coordination and psychological dysfunction in hyperthyroidism. Method Thirty-three hyperthyroid patients and thirty-three matched healthy controls (HCs) were recruited, and they received resting-state functional magnetic resonance imaging (fMRI) scans and neuropsychological evaluation. The VMHC value was computed to reveal the functional coordination between homotopic regions in both groups. The neurobehavioral relevancy method was employed to explore the relationship between the altered VMHC and emotional, cognition measures. Further receiver operating characteristic (ROC) curve analysis was adopted to examine the power of changed regional VMHC in classifying the patients with hyperthyroidism. Results Compared with the HCs, the HPs exhibited significantly declined VMHC values in the bilateral medial frontal gyrus (MeFG). The interhemispheric asynchrony in the MeFG was positively correlated with Z scores of episodic memory. The ROC analysis further determined that abnormal VMHC in the MeFG could efficiently distinguish the HPs from the HCs (area under the curve (AUC) = 0.808, P < 0.001). Conclusion The altered interhemispheric coordination in the hub of the default mode network may implicated in the modulation of episodic memory in HPs patients and the distinct feature of the interhemispheric asynchrony may be treated as a potential target for the early recognition and intervention for the HPs with cognitive impairments. Clinical Trial Registration This is a study of the neurological basis for dysfunction of mood and cognition in hyperthyroid patients: a resting-state fMRI study (registration number: ChiCTR-OOC-16008607).
Verification of deep probabilistic models

Probabilistic models are a critical part of the modern deep learning toolbox - ranging from generative models (VAEs, GANs), sequence to sequence models used in machine translation and speech processing to models over functional spaces (conditional neural processes, neural processes). Given the size and complexity of these models, safely deploying them in applications requires the development of tools to analyze their behavior rigorously and provide some guarantees that these models are consistent with a list of desirable properties or specifications. For example, a machine translation model should produce semantically equivalent outputs for innocuous changes in the input to the model. A functional regression model that is learning a distribution over monotonic functions should predict a larger value at a larger input. Verification of these properties requires a new framework that goes beyond notions of verification studied in deterministic feedforward networks, since requiring worst-case guarantees in probabilistic models is likely to produce conservative or vacuous results. We propose a novel formulation of verification for deep probabilistic models that take in conditioning inputs and sample latent variables in the course of producing an output: We require that the output of the model satisfies a linear constraint with high probability over the sampling of latent variables and for every choice of conditioning input to the model. We show that rigorous lower bounds on the probability that the constraint is satisfied can be obtained efficiently. Experiments with neural processes show that several properties of interest while modeling functional spaces can be modeled within this framework (monotonicity, convexity) and verified efficiently using our algorithms
Somatosensory Training Improves Proprioception and Untrained Motor Function in Parkinson's Disease

Background: Proprioceptive impairment is a common feature of Parkinson's disease (PD). Proprioceptive function is only partially restored with anti-parkinsonian medication or deep brain stimulation. Behavioral exercises focusing on somatosensation have been promoted to overcome this therapeutic gap. However, conclusive evidence on the effectiveness of such somatosensory-focused behavioral training for improving somatosensory function is lacking. Moreover, it is unclear, if such training has any effect on motor performance in PD. Objective: To investigate, whether proprioception improves with a somatosensory focused, robot-aided training in people with PD (PWPs), and whether enhanced proprioception translates to improved motor performance. Method: Thirteen PWPs of mild-moderate clinical severity were assessed and trained ON medication using a robotic wrist exoskeleton. Thirteen healthy elderly participants served as controls. Training involved making increasingly accurate, continuous, precise small amplitude wrist flexion/extension movements. Wrist position sense acuity, as a marker of proprioception function, and spatial error during wrist pointing, as a marker of untrained motor performance, were recorded twice before and once after training. Functional hand writing kinematics exhibited during training were evaluated in the PD group for determining training-induced changes. Results: Training improved position sense acuity in all PWPs (mean change: 28%; p < 0.001) and healthy controls (mean change: 23%; p < 0.01). Second, 10/13 PD participants and 10/13 healthy control participants had reduced spatial movement error in the untrained wrist pointing task after training. Third, spatial error for the functional handwriting tasks (line tracing and tracking) did not improve with training in the PD group. Conclusion: Proprioceptive function in mild to moderate PD is trainable and improves with a somatosensory-focused motor training. Learning showed a local transfer within the trained joint degree-of-freedom as improved spatial accuracy in an unpracticed motor task. No learning gains were observed for the untrained functional handwriting task, indicating that training may be specific to the trained joint degree-of-freedom.
Scale-aware multi-level guidance for interactive instance segmentation

In interactive instance segmentation, users give feedback to iteratively refine segmentation masks. The user-provided clicks are transformed into guidance maps which provide the network with necessary cues on the whereabouts of the object of interest. Guidance maps used in current systems are purely distance-based and are either too localized or non-informative. We propose a novel transformation of user clicks to generate scale-aware guidance maps that leverage the hierarchical structural information present in an image. Using our guidance maps, even the most basic FCNs are able to outperform existing approaches that require state-of-the-art segmentation networks pre-trained on large scale segmentation datasets. We demonstrate the effectiveness of our proposed transformation strategy through comprehensive experimentation in which we significantly raise state-of-the-art on four standard interactive segmentation benchmarks.
Don’t Turn Blind! The Relationship Between Exploration Before Ball Possession and On-Ball Performance in Association Football

Visual exploratory action – scanning movements expressed through left and right rotation of the head – allows perception of a surrounding environment and supports prospective actions. In the dynamically changing football environment, the extent to which exploratory action benefits a player’s subsequent performance with the ball is likely influenced by how and when the exploratory action occurs. Although few studies have examined the relationship between visual exploration and on-pitch football performance, it has been reported that a higher frequency of exploratory head movement up to 10-s before receiving the ball increases the likelihood of successful performance with the ball. This study investigated the relationship between head turn frequency and head turn excursion, and how and when exploratory head movement – within 10-s before ball possession – is related to performance with the ball in 11v11 match-play. Thirty-two semi-elite football players competed in 11v11 match-play. Head turn frequency and head turn excursion before ball possession were quantified with wearable inertial measurement units, and actions with the ball were coded via notational analysis. Odds ratio calculations were conducted to determine the associations between exploration variables and on-ball performance outcomes. A total of 783 actions with the ball were analyzed. Results revealed a strong relationship between head turn frequency and head turn excursion. Further, a higher than average head turn frequency and head turn excursion before receiving the ball resulted in a higher likelihood of turning with the ball, playing a pass in the attacking direction, and playing a pass to an area that is opposite to which it was received from. The strength of these outcomes varied for different time periods before receiving the ball. When players explored their environment with higher than average head turn frequency and excursion, they used more complex action opportunities afforded by the surrounding environment. Considerations for future research and practical implications are discussed.
Prevalence and Antimicrobial Susceptibility Profile of Salmonella Serovars Isolated from Slaughtered Cattle in Addis Ababa, Ethiopia

Salmonella is one of the top causes of foodborne bacterial illnesses in humans. The primary sources of human Salmonella infection are food producing animals such as cattle, poultry, and swine. A cross-sectional study was undertaken to estimate the prevalence and to determine the serovar distribution and antimicrobial susceptibility profiles of Salmonella spp. isolated from fecal (n=567) and carcass swab (n=159) samples of slaughtered cattle at Addis Ababa Abattoir Enterprise and Kara'alo PLC, Abattoirs, in Addis Ababa, Ethiopia between January 2014 and April 2015. Salmonella isolation was conducted according to Global Foodborne Infections Network Laboratory Protocol and isolates were confirmed by genus specific PCR and serotyped by slide agglutination test. Susceptibility of the isolates to 17 antimicrobials was testedusing the Kirby-Bauer disk diffusion method according to the guidelines of the Clinical and Laboratory Standards Institute. Out of the total 726 samples examined, 27 (3.7%) were positive for Salmonella. Salmonella was detected in 4.1% (23/567) fecal and 2.5% (4/159) carcass swab samples. Twelve different serovars were identified and the most predominant serovars were S. Dublin (n=10, 35.7%) and S. Virchow (n=5, 17.9%), followed by S. Braendrerup, S. Haifa, and S. Saintpaul which were isolated from 2 samples each (7.1%). All of the Salmonella isolates investigated were resistant or intermediately resistant to four or more of the 17 drugs tested. High resistance rate was recorded to streptomycin 25 (89.3%), cephalothin 20 (71.4%), ampicillin 19 (67.9%), and amoxicillin+clavulanic acid 19 (67.9%). Resistance to five or more antimicrobials was detected in 20 (71.5%) of the isolates. Multidrug resistance to more than 7 antimicrobials was detected in 5 (17.9%) of the isolates. Isolation of such multidrug resistant strains of Salmonella from slaughtered cattle poses a major public health concern. These findings imply the need for a strict biosecurity and regulation of antimicrobial use across the country.
Nonlinear Conjugate Gradients For Scaling Synchronous Distributed DNN Training

Nonlinear conjugate gradient (NLCG) based optimizers have shown superior loss convergence properties compared to gradient descent based optimizers for traditional optimization problems. However, in Deep Neural Network (DNN) training, the dominant optimization algorithm of choice is still Stochastic Gradient Descent (SGD) and its variants. In this work, we propose and evaluate the stochastic preconditioned nonlinear conjugate gradient algorithm for large scale DNN training tasks. We show that a nonlinear conjugate gradient algorithm improves the convergence speed of DNN training, especially in the large mini-batch scenario, which is essential for scaling synchronous distributed DNN training to large number of workers. We show how to efficiently use second order information in the NLCG pre-conditioner for improving DNN training convergence. For the ImageNet classification task, at extremely large mini-batch sizes of greater than 65k, NLCG optimizer is able to improve top-1 accuracy by more than 10 percentage points for standard training of the Resnet-50 model for 90 epochs. For the CIFAR-100 classification task, at extremely large mini-batch sizes of greater than 16k, NLCG optimizer is able to improve top-1 accuracy by more than 15 percentage points for standard training of the Resnet-32 model for 200 epochs.
“First in Man”: Case Report of Selective C-Reactive Protein Apheresis in a Patient with Acute ST Segment Elevation Myocardial Infarction

C-reactive protein (CRP) may be causative in cardiovascular disease. As yet, no specific CRP inhibitor for human application has been described. A 69-year-old male was referred with ST segment elevation myocardial infarction (STEMI). Typical symptoms of chest pain started at 10.00 p.m. The patient was admitted to the hospital at 1.30 a.m. the next day. As ECG showed anterior wall myocardial infarction, the patient was immediately transferred to successful emergency angioplasty/drug-eluting- (DE-) stenting of the subtotally occluded left anterior descending artery. Consecutively, the hemodynamically stable patient was monitored at the chest pain unit. C-reactive protein (CRP) apheresis using the CRP adsorber (PentraSorb® CRP) within CAMI-1 trial was performed 34 h and 58 h after the onset of symptoms. In each apheresis session, 6000 ml plasma was treated via peripheral venous access. Plasma CRP levels decreased from 28.77 mg/l to 12.58 mg/l during the first apheresis session and from 24.17 mg/l to 11.55 mg/l during the second session, respectively. No side effects were observed. This is the first report of selective CRP apheresis in a man. The technology offers multiple opportunities to clarify the immunological/pathogenic role of CRP in health and disease.