Miracle-dz/UniFAR-FTUs huggingface dataset:
Design of Novel Deep Learning Models for Real-time Human Activity Recognition with Mobile Phones

In this paper we present deep learning based techniques for human activity classification that are designed to run in real time on mobile devices. Our methods minimize the size of the model and computational overhead in order to run on the embedded processor and preserve battery life. Prior work shows that the inertial measurement unit (IMU) data from waist-mounted mobile phones can be used to develop accurate classification models for various human activities such as walking, running, stair-climbing, etc. However, these models have largely been based on hand crafted features derived from temporal and spectral statistics. More recently, deep learning has been applied to IMU sensor data, but have not been optimized for resourceconstrained devices. We present a detailed study of the traditional hand-crafted features used for shallow/statistical models that consist of a over 561 manually chosen set of dimensions. We show, through principal component analysis (PCA) and application of a published support vector machine (SVM) pipeline, that the number of features can be significantly reduced - less than 100 features that give the same performance. In addition, we show that features derived from frequency-domain transformations do not contribute to the accuracy of these models. Finally, we provide details of our learning technique which creates 2D signal images from windowed samples of IMU data. Our pipeline includes a convolutional neural network (CNN) with several layers (1 convolutional layer and 1 averaging layer and a fully connected layer). We show that by removing the steps in the pipeline and layers in the CNN, we can still achieve 0.98 F1 score but with a much smaller memory footprint and corresponding computational cost. To increase the classification accuracy of our pipeline we added a hybrid bi-class support vector machine (SVM) that was trained using the labeled and flattened convolutional layer after each training image was processed. The learned feature set is almost half the size of the original hand crafted feature set and combining the CNN with the SVM results in 0.99 F1 score. We also investigate a novel application of transfer learning by using the time series 2D signal images to re-train two different publicly available networks, Inception/ImageNet and MobileNet. We find that re-trained ImageNet networks could be created $< 5.5$ MB (suitable for mobile phones) and classification accuracy ranging from 0.83 to 0.93 (F1 score), thus indicating that retraining can be a useful future direction to build new classifiers for continuously evolving activities quickly while also being applicable to mobile device classification. Finally, we show that these deep learning models may be generalizable enough such that classifiers built from a given set of users for a specified set of activities can be used for a new user/subject as well.
An End-to-End Neural Dialog State Tracking for Task-Oriented Dialogs

Dialog state tracking in spoken dialog system is the task that tracks the flow of a dialog and grasps what a user wants from the utterance precisely. Since the dialog success is related to catching the want of the user, dialog state tracking is a necessary component for spoken dialog systems. This paper proposes a neural dialog state tracker with the attention mechanism for focusing on valuable words and the hierarchical softmax for efficient training of the tracker. In addition, the proposed tracker combines a natural language understanding module and a dialog state module in an end-to-end style. As a result, the error propagation within a dialog system is minimized. To prove the effectiveness of the proposed model, we do experiments on dialog state tracking in the human-human task-oriented dialogs. Our experimental results show that the proposed method outperforms both the neural tracker without the attention mechanism and that without the hierarchical softmax.
Efficacy and safety of secukinumab in Japanese patients with active ankylosing spondylitis: 24-week results from an open-label phase 3 study (MEASURE 2-J)

Abstract Objective: Secukinumab, a fully human monoclonal antibody that neutralizes interleukin-17A, improved the signs and symptoms of ankylosing spondylitis (AS) in three Phase 3 global studies (MEASURE 1, 2, and 3). Here, we describe the efficacy and safety results through Week 24 of a study of secukinumab in Japanese patients with active AS. Methods: In this multicenter, open-label, single arm, 52-week study, 30 AS patients self-administered secukinumab 150 mg subcutaneously at baseline, Weeks 1, 2, 3, and 4, and every 4 weeks thereafter. The primary efficacy endpoint was ASAS 20 response at Week 16. Overall safety and tolerability were assessed beyond Week 24 up to the data reporting cut-off date. Results: The ASAS 20 response rate was 70% (21/30) at Week 16, which was sustained to Week 24. Secukinumab was effective in various clinical outcomes including patient’s global assessment of disease activity, spinal pain, nocturnal pain, physical function, spinal mobility, and CRP level. Comparable ASAS 20 and 40 responses were observed regardless of previous anti-TNF therapy. Secukinumab was well-tolerated with a safety profile consistent with previous reports. Conclusion: Secukinumab 150 mg provided sustained improvement in the signs and symptoms of Japanese AS patients through 24 weeks, with no new or unexpected safety signals.
Clustering with Minimum Spanning Tree using TOPSIS with Multi-Criteria Information

Clustering is the process of grouping similar objects into the same partition and keeping dissimilar objects on different partitions. Clustering algorithms based on Minimum Spanning Tree (MST) have been successfully applied in the separation of non-convex clusters, although the use of individual objective functions limits the algorithms to a reduced set of clustering problems, presenting difficulties in cases of unbalanced, noisy, overlapping datasets, etc. In order to make clustering process more robust, this paper proposes an algorithm based on Minimum Spanning Tree that combines different objective functions using TOPSIS. The algorithm performance was evaluated on real and synthetic datasets. Experimental results indicate that the combination of objective functions improves clustering results compared to individual functions.
Fine-Level Semantic Labeling of Large-Scale 3D Model by Active Learning

Semantic labeling of 3D models has been a challenging task in recent years. Due to the various categories and shapes of 3D objects in different scenes, it is hard to develop a versatile method suitable for most scenes. In this paper, we propose an Active Learning based method to tackle the problem. The proposed method takes a 3D mesh model generated from images using SfM and MVS, as well as the calibrated images, as the input, and outputs a semantic mesh model in which each facet takes a fine-level semantic label. Starting with a small annotated image set, we progressively fine-tune a Convolutional Neural Network (CNN) with the ever-enlarging annotated image set for image semantic segmentation. In each iteration, by back-projecting the pixel labels to the 3D model and fusing them in 3D space, a semantic 3D model is generated. The semantic 3D model functions as a supervisor to select a batch of worthy images for annotation to boost the performance of the CNN in next iteration. This process iterates until the label assignment of the 3D model becomes steady. By making full use of the 3D geometric information, the proposed method could significantly reduce the annotation cost without losing the labeling quality of 3D models. Experimental results of fine-level labeling on two large-scale ancient Chinese architectures demonstrate the effectiveness of the proposed method.
A Self-organizing Method for Robot Navigation based on Learned Place and Head-Direction Cells

This paper describes a neural model for a robot learning spatial knowledge and navigating on learned place and head-direction (HD) cell representations. The place and HD cells, which are trained through unsupervised slow feature analysis (SFA) from sequences of visual stimuli, provide positional and directional information for navigation. Based on the ensemble activity of place cells, the robot learns a topological map of the environment through extracting the statistical distribution of the place cell activities covering the traversable areas and realizes self-localization based on the map. The robot’s heading direction, which is encoded by the HD cells, works as a control signal to adjust its behavior. Action representations supporting state transitions are learned through memorizing the same movement from a previous phase where an experimenter drives a robot to explore an environment. Given reward signals spreading from a target location along the topological map, the robot can reach the goal in a reward-ascending way. This work intends to build a practical navigation system by simulating animals’ hippocampal cell firing activities on a robot platform using its self-contained sensor. Experimental results from simulation demonstrate that our system navigates a robot to the desired position smoothly and effectively.
High Il-1β Serum as a Predictor of Decreased Cognitive Function in Mild Traumatic Brain Injury Patients

BACKGROUND: Traumatic brain injury (TBI) exerts a significant impact on society with regards to physical, affective, and cognitive impairment. The consequent cognitive sequelae include a problem in memory, attention, concentration, and processing speed. Following traumatic brain injury, inflammatory response developed, characterised by increased interleukin 1-β (IL-1β) levels in the blood. IL 1-β at pathophysiological concentration has been reported to cause an inhibition of the expression of long-term potentiation (LTP) in the areas CA1, CA3, and dentate gyrus of the hippocampus. AIM: This study aims to determine whether high IL-1β serum is a predictor of decreased cognitive function in mild TBI. METHODS: This is a prospective cohort study conducted at the emergency room, surgical and neurologic ward at Sanglah Hospital from November 2017 until January 2018. As many as thirty-five mild TBI with normal IL-1β serum (< 0.0565 pg/ml) and thirty-five of those with high IL-1β serum (≥ 0.0565 pg/ml) subjects were included within the corresponding period. The decrease of cognition after trauma was measured seven days later. RESULTS: This study demonstrated that group with high IL-1β serum levels were at higher risk of suffering from cognitive impairment after TBI when compared with the group with normal IL-1β serum levels (RR = 2.6; 95% CI 1.49-4.55, p < 0.001). CONCLUSION: Mild TBI with high serum IL-1β levels were more than twice likely to experience decreased cognitive function than those with normal IL-1β levels.
Should Machines Express Sympathy and Empathy? Experiments with a Health Advice Chatbot

When we ask a chatbot for advice about a personal problem, should it simply provide informational support and refrain from offering emotional support? Or, should it show sympathy and empathize with our situation? Although expression of caring and understanding is valued in supportive human communications, do we want the same from a chatbot, or do we simply reject it due to its artificiality and uncanniness? To answer this question, we conducted two experiments with a chatbot providing online medical information advice about a sensitive personal issue. In Study 1, participants (N = 158) simply read a dialogue between a chatbot and a human user. In Study 2, participants (N = 88) interacted with a real chatbot. We tested the effect of three types of empathic expression-sympathy, cognitive empathy, and affective empathy-on individuals' perceptions of the service and the chatbot. Data reveal that expression of sympathy and empathy is favored over unemotional provision of advice, in support of the Computers are Social Actors (CASA) paradigm. This is particularly true for users who are initially skeptical about machines possessing social cognitive capabilities. Theoretical, methodological, and practical implications are discussed.
Clinicopathological features of epiretinal membranes in eyes filled with silicone oil

Purpose The aim of this case series was to clarify the clinicopathological features of epiretinal membranes (ERMs) that developed in eyes after silicone oil (SO) tamponade to treat rhegmatog-enous retinal detachments (RRDs). Patients and methods In the Department of Ophthalmology, Saitama Medical Center, Jichi Medical University, patients with idiopathic ERMs (23 eyes) and ERMs in eyes filled with SO (SO ERMs) after vitreous surgery to treat RRDs (nine eyes) were enrolled from July 2012 to March 2014. ERM tissues obtained intraoperatively were examined histopathologically. Besides the main outcome measure of the pathological findings of the ERM tissues, other outcome measures included the preoperative findings on optical coherence tomography (OCT) images and the surgical findings. Results Eight (89%) of nine eyes with SO ERMs had bilayered membranes composed of a firm layer on the retinal side with glial cells and extracellular matrix and a fragile sponge-like layer on the vitreous side. The sponge-like layer was composed of emulsified SO surrounded by macrophages. Quantitative analysis showed that the areas with cluster of differentiation 68 (CD68)-positive macrophages identified by immunohistochemistry in eyes with SO ERMs were significantly (P<0.001) larger than those in eyes with idiopathic ERMs. The findings on OCT images were consistent with the pathological features of the SO ERMs. Surgical removal of the SO ERMs was difficult because the sponge-like layer was fragile, and the underlying retina was also fragile due to inflammation. Conclusion SO ERMs are bilayered membranes. Long-standing emulsified SO formed a sponge-like layer and SO (foreign body)-induced granulation and caused retinal inflammation in these eyes, making surgical removal difficult. A preoperative OCT examination is necessary to identify SO ERMs.
Bio-Inspired Ganglion Cell Models for Detecting Horizontal and Vertical Movements

The retina performs the earlier stages of image processing in living beings and is composed of six different groups of cells, namely, the rods, cones, horizontal, bipolar, amacrine and ganglion cells. Each of those group of cells can be sub-divided into other types of cells that vary in shape, size, connectivity and functionality. Each cell is responsible for performing specific tasks in these early stages of biological image processing. Some of those cells are sensitive to horizontal and vertical movements. This paper proposes a multi-hierarchical spiking neural network architecture for detecting horizontal and vertical movements using a custom dataset which was generated in laboratory settings. The proposed architecture was designed to reflect the connectivity, behaviour and the number of layers found in the majority of vertebrates retinas, including humans. The architecture was trained using 2303 images and tested using 816 images. Simulation results revealed that each cell model is sensitive to vertical and horizontal movements with a detection error of 6.75 percent.
Conservación de las especies subvaloradas como recursos genéticos agrícolas

In this paper two conservation strategies are presented: In situ conservation through community seed banks where the Neglected and Underutilized Species (NUS) predominate, and ex situ conservation through germplasm banks. In Mexico, there are 26 community banks, where an ample variety of native species cultivated under the milpa system are preserved. In those banks, local farmers actively participate in conservation and improvement tasks. The National Forest, Crops and Livestock Research Institute (INIFAP), has the National Genetic resources Center, with advanced facilities, equipment and technology and current capacity to preserve 800,000 seed accessions, whose main objective is longterm preservation of germplasm, both economically relevant and NUS. There is a need for increasing the number of NUS collections, both in community banks and in the ex situ collections of germplasm banks, as well as for generating information on NUS characterization and use promotion.
Tracking Users across the Web via TLS Session Resumption

User tracking on the Internet can come in various forms, e.g., via cookies or by fingerprinting web browsers. A technique that got less attention so far is user tracking based on TLS and specifically based on the TLS session resumption mechanism. To the best of our knowledge, we are the first that investigate the applicability of TLS session resumption for user tracking. For that, we evaluated the configuration of 48 popular browsers and one million of the most popular websites. Moreover, we present a so-called prolongation attack, which allows extending the tracking period beyond the lifetime of the session resumption mechanism. To show that under the observed browser configurations tracking via TLS session resumptions is feasible, we also looked into DNS data to understand the longest consecutive tracking period for a user by a particular website. Our results indicate that with the standard setting of the session resumption lifetime in many current browsers, the average user can be tracked for up to eight days. With a session resumption lifetime of seven days, as recommended upper limit in the draft for TLS version 1.3, 65% of all users in our dataset can be tracked permanently.
A Framework of Transferring Structures Across Large-scale Information Networks

The existing domain-specific methods for mining information networks in machine learning aims to represent the nodes of an information network into a vector format. However, the real-world large-scale information network cannot make well network representations by one network. When the information of the network structure transferred from one network to another network, the performance of network representation might decrease sharply. To achieve these ends, we propose a novel framework to transfer useful information across relational large-scale information networks (FTLSIN). The framework consists of a 2-layer random walks to measure the relations between two networks and predict links across them. Experiments on real-world datasets demonstrate the effectiveness of the proposed model.
Artificial Neural Networks Applied in the Solution of the Inverse Kinematics Problem of a 3D Manipulator Arm

This paper approaches the classic problem of robotics’ Inverse Kinematics. This problem does not have a trivial solution due its solution complexity, in addition to multiple solutions for the same final position of the robotic actuator. The number of solutions increases with the number of degrees of freedom In this context, the goal of this paper is to present a solution to this problem through the application of an Artificial Neural Network (ANN) in a robotic arm with 4 degrees of freedom in three dimensions. Initial results with a set of seven points using two different ANN configurations were convincing.
Association of Genetically Enhanced Lipoprotein Lipase–Mediated Lipolysis and Low-Density Lipoprotein Cholesterol–Lowering Alleles With Risk of Coronary Disease and Type 2 Diabetes

Importance Pharmacological enhancers of lipoprotein lipase (LPL) are in preclinical or early clinical development for cardiovascular prevention. Studying whether these agents will reduce cardiovascular events or diabetes risk when added to existing lipid-lowering drugs would require large outcome trials. Human genetics studies can help prioritize or deprioritize these resource-demanding endeavors. Objective To investigate the independent and combined associations of genetically determined differences in LPL-mediated lipolysis and low-density lipoprotein cholesterol (LDL-C) metabolism with risk of coronary disease and diabetes. Design, Setting, and Participants In this genetic association study, individual-level genetic data from 392 220 participants from 2 population-based cohort studies and 1 case-cohort study conducted in Europe were included. Data were collected from January 1991 to July 2018, and data were analyzed from July 2014 to July 2018. Exposures Six conditionally independent triglyceride-lowering alleles in LPL, the p.Glu40Lys variant in ANGPTL4, rare loss-of-function variants in ANGPTL3, and LDL-C–lowering polymorphisms at 58 independent genomic regions, including HMGCR, NPC1L1, and PCSK9. Main Outcomes and Measures Odds ratio for coronary artery disease and type 2 diabetes. Results Of the 392 220 participants included, 211 915 (54.0%) were female, and the mean (SD) age was 57 (8) years. Triglyceride-lowering alleles in LPL were associated with protection from coronary disease (approximately 40% lower odds per SD of genetically lower triglycerides) and type 2 diabetes (approximately 30% lower odds) in people above or below the median of the population distribution of LDL-C–lowering alleles at 58 independent genomic regions, HMGCR, NPC1L1, or PCSK9. Associations with lower risk were consistent in quintiles of the distribution of LDL-C–lowering alleles and 2 × 2 factorial genetic analyses. The 40Lys variant in ANGPTL4 was associated with protection from coronary disease and type 2 diabetes in groups with genetically higher or lower LDL-C. For a genetic difference of 0.23 SDs in LDL-C, ANGPTL3 loss-of-function variants, which also have beneficial associations with LPL lipolysis, were associated with greater protection against coronary disease than other LDL-C–lowering genetic mechanisms (ANGPTL3 loss-of-function variants: odds ratio, 0.66; 95% CI, 0.52-0.83; 58 LDL-C–lowering variants: odds ratio, 0.90; 95% CI, 0.89-0.91; P for heterogeneity = .009). Conclusions and Relevance Triglyceride-lowering alleles in the LPL pathway are associated with lower risk of coronary disease and type 2 diabetes independently of LDL-C–lowering genetic mechanisms. These findings provide human genetics evidence to support the development of agents that enhance LPL-mediated lipolysis for further clinical benefit in addition to LDL-C–lowering therapy.
Tetrahedra and Relative Directions in Space, Using 2 and 3-Space Simplexes for 3-Space Localization

This research presents a novel method of determining relative bearing and elevation measurements, to a remote signal, that is suitable for implementation on small embedded systems - potentially in a GPS denied environment. This is an important, currently open, problem in a number of areas, particularly in the field of swarm robotics, where rapid updates of positional information are of great importance. We achieve our solution by means of a tetrahedral phased array of receivers at which we measure the phase difference, or time difference of arrival, of the signal. We then perform an elegant and novel, albeit simple, series of direct calculations, on this information, in order to derive the relative bearing and elevation to the signal. This solution opens up a number of applications where rapidly updated and accurate directional awareness in 3-space is of importance and where the available processing power is limited by energy or CPU constraints.