Miracle-dz/UniFAR-FTUs huggingface dataset:
Embedded Real-Time Fall Detection with Deep Learning on Wearable Devices

Unintentional falls are the leading cause of fatal injuries and nonfatal trauma among older adults. An automated monitoring system that detects occurring falls and issues remote notifications will prove very valuable for improving the level of care that could be provided to people at higher risk. The work presented focuses on the design of embedded software for wearable devices that are connected in wireless mode to a remote monitoring system. The work focuses on the implementation of recurrent neural networks (RNNs) architectures of micro controller units (MCU) for fall detection with tri-axial accelerometers. A few general formulas for determining memory, computing power and power consumption for such architectures are presented. These formulas have been validated with an actual implementation for the SensorTile device by STMicroelectronics.
Multi-label Feature Selection Techniques for Hierarchical Multi-label Protein Function Prediction

Protein Function Prediction is a complex Hierarchical Multi-label Classification task where the functional classes involved are organized in a hierarchy. While many Machine Learning methods have been proposed for this task, very few studies were performed for feature selection in such hierarchical scenarios. In this paper, we investigate feature selection techniques for hierarchical multi-label classification of protein functions. As decision trees are natural feature selectors, we rely on a hierarchical multi-label decision tree induction algorithm to extract features represented by the internal nodes of the tree. We also investigated the performance of a ReliefF-based non-hierarchical multi-label feature selection technique on the hierarchical scenario. We tested the different techniques on two classifiers, based on neural networks and genetic algorithms. The experimental results show that, in very few cases, the existing feature selection techniques were able to improve the classifiers performances, showing the need for developing feature selectors specifically to consider hierarchical class relationships.
The Advantages and Application Prospects of CAN FD Bus Technology for Vehicle Network Communication

The fact that the degree of electronic control of automobiles increases rapidly has promoted the research of new vehicle-mounted buses. CAN FD bus, released by Bosch based on CAN bus, is the most promising one. Its biggest feature is that the data field length is increased to 64 bytes and it has a flexible and variable baud rate. It inherits many advantages of traditional CAN bus and it has made some improvements. Due to these changes, some advantages of CAN FD bus, compared with CAN bus, can be verified. Firstly, the data transmission efficiency and the synchronization of the signal transmission are greatly improved. Secondly, encryption design can be done to improve the security of communication. The CRC-16 check code is stored in the 63rd and 64th bytes. In addition, a CAN node can be compatible with a CAN FD node in the same network by letting the traditional CAN node enter two different working states, sleeping and waking up. Through experiments, it is found that compared with traditional CAN bus, CAN FD bus greatly reduces the load rate of the bus. Transmitting the same amount of information, as for physical nodes, CAN FD bus can reduce the bus load from 45.64% to 8.19%. Also it can significantly reduce the flash writing time based on the bus when the data field is extended to 64 bytes.
Think Outside the Band: Design and Miniaturization of Absorptive Filters

A teaching assistant once told me that, as microwave engineers, impedance matching is "what we do. " We do much more than that, of course. But I think it is true that this is what most of our more complex tasks ultimately boil down to. To design an amplifier, you have to match the impedance of the transistor. To design an antenna, you have to match its radiation impedance. We do it by rote, without thinking, like a writer punctuating sentences. It is no wonder that the Smith chart-the classic graphical tool for analyzing and displaying an impedance match-has become our field's own personal totem.
Continuous Control With a Combination of Supervised and Reinforcement Learning

Reinforcement learning methods have recently achieved impressive results on a wide range of control problems. However, especially with complex inputs, they still require an extensive amount of training data in order to converge to a meaningful solution. This limits their applicability to complex input spaces such as video signals, and makes them impractical for use in complex real world problems, including many of those for video based control. Supervised learning, on the contrary, is capable of learning on a relatively limited number of samples, but relies on arbitrary hand-labelling of data rather than task-derived reward functions, and hence do not yield independent control policies. In this article we propose a novel, model-free approach, which uses a combination of reinforcement and supervised learning for autonomous control and paves the way towards policy based control in real world environments. We use SpeedDreams/TORCS video game to demonstrate that our approach requires much less samples (hundreds of thousands against millions or tens of millions) comparing to the state-of-the-art reinforcement learning techniques on similar data, and at the same time overcomes both supervised and reinforcement learning approaches in terms of quality. Additionally, we demonstrate applicability of the method to MuJoCo control problems.
An Ensemble of Convolutional Neural Networks for Unbalanced Datasets: A case Study with Wagon Component Inspection

Railway component inspection is a technique widely used for maintenance because defective components pose safety issues. Nevertheless, finding defective components is a hard task because they are normally hidden by dusty, which poses hard problems for the image segmentation algorithms. To approach this problem, manual inspection by humans is normally used, but it is time consuming, expensive and sometimes dangerous. Meanwhile, automatic approaches that uses machine learning algorithms are also difficult because the datasets are strongly unbalanced. Such datasets usually induce biased classification models that identify new instances as members of the class with the greatest abundance of examples in the training data. In this paper, we propose a new method that combines the use of Convolutional Neural Networks (CNN) with imbalanced learning to address the challenge of using machine learning to identify defective components. Our method was tested with realworld data from images used for wagon component inspection. Moreover, we compared our method with an ensemble of MLP networks using features extraction, such as the LeNet, and a CNN network without ensemble learning. Results indicate that our proposed method produced the higher overall accuracy compared to the other methods.
Opinion Dynamics via Search Engines (and Other Algorithmic Gatekeepers)

Ranking algorithms are the information gatekeepers of the Internet era. We develop a stylized framework to study the effects of ranking algorithms on opinion dynamics. We consider rankings that depend on popularity and on personalization. We find that popularity driven rankings can enhance asymptotic learning while personalized ones can both inhibit or enhance it, depending on whether individuals have common or private value preferences. We also find that ranking algorithms can contribute towards the diffusion of misinformation (e.g., “fake news”), since lower ex-ante accuracy of content of minority websites can actually increase their overall traffic share.
Classification of malware based on file content and characteristics

In general, the industry of malware has come to be a market which brings on loads of money by investing and implementing high end technology to escape traditional detection while vendors of anti-malware spend thousands if not millions of dollars to stop the malware breach since it not only causes financial losses but also emotional ones. This paper study the classification of malware based on file content and characteristics, this was done through use of Clamp Integrated dataset that includes 5210 instances. There are different algorithms were applied using Weka software, which are; ZeroR, bayesNet, SMO, KNN, J48, as well as Random Forest. The obtained results showed that Random Forest that achieved the highest overall accuracy of (99.0979%). This means that Random Forest algorithm is efficient to be used in malware classification based on file content and characteristics.
A Supervised Multi-Spike Learning Algorithm for Spiking Neural Networks

The formulation of efficient supervised learning algorithms for Spiking Neural Network (SNN) is difficult and remains challenging. This paper presents a supervised multispike learning algorithm, which is used to train neurons to output spike train with a target firing rate. The proposed algorithm simplifies the expression of the membrane potential by assuming a special condition of the threshold, thus allows the application of a gradient descent to optimize the synaptic weights. Additionally, in the presented experimental results, the proposed algorithm is evaluated regarding its initial setups, its classification performance for rate-based and timing-based patterns and its capability to sound recognition. The results also demonstrate that the proposed algorithm can achieve a competitive accuracy in temporal pattern classification and sound recognition.
Emergent Turing Machine as a General Purpose Approximator

An autonomous navigation agent needs to learn the navigation rules and the transition among different navigation states, which can be summarized in Turing Machine (TM) controlled by a navigation Finite Automata. However, real-world inputs to this TM is always noisy with numerous appearance variances and distractors, which made it impossible to handcraft symbolic representations or features to detect. Developmental Network-l (DN-1) incrementally learns a Finite Automata error-free using emergent representation but lacks hierarchy in its internal representation. In this paper, we extend DN-1 by adding lateral connection and multiple types of neurons to form hierarchical representation both internally and in the motor area. The formed hierarchical representation is more robust against distractors compared to the global feature patches in DN-1. Compared to other FA based methods, DN-2 directly uses natural input without handcrafting. The navigation states are emergent from the firing patterns in network areas instead of the hand-crafted symbolic states in the neural network output layer. Real-world and simulated navigation experiments showed that DN-2 successfully approximated rules of navigation using natural inputs. The agent learns different levels of concepts from simple to complex with the help of internal hierarchical representation.
Remote Sensing Image Classification Using the Spectral-Spatial Distance Based on Information Content

Among many types of efforts to improve the accuracy of remote sensing image classification, using spatial information is an effective strategy. The classification method integrates spatial information into spectral information, which is called the spectral-spatial classification approach, has better performance than traditional classification methods. Construct spectral-spatial distance used for classification is a common method to combine the spatial and spectral information. In order to improve the performance of spectral-spatial classification based on spectral-spatial distance, we introduce the information content (IC) in which two pixels are shared to measure spatial relation between them and propose a novel spectral-spatial distance measure method. The IC of two pixels shared was computed from the hierarchical tree constructed by the statistical region merging (SRM) segmentation. The distance we proposed was applied in two distance-based contextual classifiers, the k-nearest neighbors-statistical region merging (k-NN-SRM) and optimum-path forest-statistical region merging (OPF-SRM), to obtain two new contextual classifiers, the k-NN-SRM-IC and OPF-SRM-IC. The classifiers with the novel distance were implemented in four land cover images. The classification results of the classifier based on our spectral-spatial distance outperformed all the other competitive contextual classifiers, which demonstrated the validity of the proposed distance measure method.
Eggs: A high potential food for improving maternal and child nutrition.

Recent research has demonstrated that egg consumption can have a dramatic impact on preventing stunting and possibly on the cognitive development of young children (Iannotti, Lutter, Stewart, et al., 2017; Iannotti, Lutter, Waters, et al., 2017). This leaves us with the question: How do we best utilize this new evidence to help improve the lives of children around the world? This supplement tackles this question by providing a set of nine papers that progress from the role of eggs in improving maternal and child nutrition to a scenario ‐ based analysis of possible pathways to reaching one billion low ‐ income households with access to eggs. The first paper provides an overview of the role of eggs in the diet of maternal and child nutrition and updated data on egg consumption (Lutter, Iannotti, & Stewart, 2018), while the second summa-rizes how social marketing was used in a randomized controlled trial of eggs early in the complementary feeding period to foster brand loyalty andhighcompliance,aswellasempowermentofparticipantsandpolicy change in Ecuador (Gallegos et al., 2018). The third and fourth papers examine how a controlled intervention to foster poultry production affected child dietary diversity and nutritional status in Ghana (Marquis et al., 2018) and Zambia (Dumas, Lewis, & Travis, 2018), while the fifth paper reviews successes and lessons learned from a project on small ‐ scale poultry production to increase egg production and household egg intake in four diverse African contexts (Nordhagen & Klemn,
Closed loop image aided optimization for cold spray process based on molecular dynamics

This study proposed a closed loop image aided optimization (CLIAO) method to improve the quality of deposition during the cold spray process. Some recent research shows that the quality of deposition measured by flattening ratio of the bonded particle is associated with impact velocity, angle and particle size. Therefore, the original idea of CLIAO is to improve the quality of deposition by obtaining the maximum flattening ratio which is extracted from the molecular dynamics (MD) simulation snapshots directly. To complete this strategy, a Python script is suggested to generate the required snapshots from result files automatically and the image processing technique is used to evaluate the flattening ratio from the snapshots. Moreover, three optimization methods including surrogate optimization (Efficient Global Optimization) and heuristic algorithms (Particle Swarm Optimization, Different Evolution algorithm) are engaged. Then a back propagation neural network (BPNN) is used to accelerate the process of optimization, where the BPNN is used to build the meta-model instead of the forward calculation. The optimization result demonstrates that all the above methods can obtain the acceptable solution. The comparison between those methods is also given and the selection of them should be determined by the trade-off between efficiency and accuracy.
A Versatile PCM-Based Circuits Emulator and Its Use on Implementing Linear Algebra Functions

Phase-change memory (PCM) is a technology that can be exploited not only on next-generation storage systems but also on new computing paradigms, like the in-memory computing approach. For experimenting with different characteristics of PCM technology in large-scale PCM-based circuits and devices, we developed an accurate and precise hardware PCM emulator, which can model PCM circuits at the cell, array and matrix level. In this work, we present details of the PCM emulator, its architecture and experimental results on its use for implementing basic linear algebra functions.
Assessing the Value of Routine Pathologic Examination of Resected Femoral Head Specimens After Femoral Neck Fracture.

INTRODUCTION The purpose of this study was to evaluate the efficacy of routine pathologic examination (PE) of femoral head (FH) specimens after arthroplasty for acute femoral neck fractures and to determine the cost. METHODS This was a retrospective chart review of 850 acute femoral neck fractures treated with hemiarthroplasty or total hip arthroplasty These were evaluated to determine whether the FH was sent for PE, the resultant findings, alterations in medical treatment, and cost. RESULTS A total of 466 FH specimens (54.8%) were sent to pathology. Four (0.9%) were positive for a neoplastic process. All four had a known history of cancer, antecedent hip pain, or an inappropriate injury mechanism. None of the findings resulted in an alteration in medical treatment. The average cost of PE was $195 USD. DISCUSSION The routine PE of FH specimens after arthroplasty for femoral neck fractures is not warranted and uneconomic. Sending the FH for PE, only when clinically indicated, rather than routine, will result in notable savings for the healthcare system. LEVEL OF EVIDENCE Level IV.
Multi-modal Remote Sensing Image Classification for Low Sample Size Data

Recently, multiple and heterogeneous remote sensing images have provided a new development opportunity for Earth observation research. utilizing deep learning to gain the shared representative information between different modalities is important to resolve the problem of geographical region classification. In this paper, a CNN-based multi-modal framework for low-sample-size data classification of remote sensing images is introduced. This method has three main stages. Firstly, features are extracted from high- and low-resolution remote sensing images separately using multiple convolution layers. Then, the two types of features are fused at the fusion algorithm layer. Finally, the fused features are used to train a classifier. The novelty of this method is that not only it considers the complementary relationship between the two modalities, but enhances the value of a small number of samples. Based on our experiments, the proposed model can obtain a state-of-the-art performance, being more accurate than the comparable architectures, such as single-modal LeNet, NanoNets and multi-modal H&L-LeNet that are trained with a double size of samples.
Analysis of Human Brain Structure Reveals that the Brain “Types” Typical of Males Are Also Typical of Females, and Vice Versa

Findings of average differences between females and males in the structure of specific brain regions are often interpreted as indicating that the typical male brain is different from the typical female brain. An alternative interpretation is that the brain types typical of females are also typical of males, and sex differences exist only in the frequency of rare brain types. Here we contrasted the two hypotheses by analyzing the structure of 2176 human brains using three analytical approaches. An anomaly detection analysis showed that brains from females are almost as likely to be classified as “normal male brains,” as brains from males are, and vice versa. Unsupervised clustering algorithms revealed that common brain “types” are similarly common in females and in males and that a male and a female are almost as likely to have the same brain “type” as two females or two males are. Large sex differences were found only in the frequency of some rare brain “types. ” Last, supervised clustering algorithms revealed that the brain “type(s)” typical of one sex category in one sample could be typical of the other sex category in another sample. The present findings demonstrate that even when similarity and difference are defined mathematically, ignoring biological or functional relevance, sex category (i.e., whether one is female or male), is not a major predictor of the variability of human brain structure. Rather, the brain types typical of females are also typical of males, and vice versa, and large sex differences are found only in the prevalence of some rare brain types. We discuss the implications of these findings to studies of the structure and function of the human brain.
Transforming Growth Factor β1 (TGF-β1)-Stimulated Integrin-Linked Kinase (ILK) Regulates Migration and Epithelial-Mesenchymal Transition (EMT) of Human Lens Epithelial Cells via Nuclear Factor κB (NF-κB)

Background In view of the high incidence of posterior capsule opacification (PCO) and the effects of TGF-β signaling on the epithelial-mesenchymal transition (EMT) of human lens epithelial cells (LECs), our study aimed to explore the mechanism of the function of TGF-β signaling in LECs EMT. Material/Methods Human lens epithelial cells (HLEC-h3) were treated with TGF-β, ILK siRNA, ILK inhibitor, and NF-κB inhibitor to study the effects of TGF-β, ILK, and NF-κB on cell migration and EMT. Cell migration assay was used to measure cell migration ability. Western blot was performed to detect the expression of ILK, E-cadherin, and α-SMA at the protein level. QRT-PCR was used to detect the expression of ILK at the mRNA level. Results Compared with control cells, TGF-β treatment increased the expression level of ILK HLEC-h3, promoted migration of HLEC-h3 cells, increased the expression level of E-cadherin protein, and decreased the expression level of α-SMA protein. However, treatment with ILK siRNA, ILK inhibitor, and NF-κB inhibitor reversed the effects of TGF-β on HLEC-h3 cells. Conclusions TGF-β-stimulated ILK regulates the migration and EMT of human LECs via NF-κB.
RND1 regulates migration of human glioblastoma stem-like cells according to their anatomical localization and defines a prognostic signature in glioblastoma

Despite post-operative radio-chemotherapy, glioblastoma systematically locally recurs. Tumors contacting the periventricular zone (PVZ) show earlier and more distant relapses than tumors not contacting the PVZ. Since glioblastoma stem-like cells (GSCs) have been proposed to play a major role in glioblastoma recurrence, we decided to test whether GSC migration properties could be different according to their anatomical location (PVZ+/PVZ–). For that purpose, we established paired cultures of GSCs from the cortical area (CT) and the PVZ of glioblastoma patient tumors. We demonstrated that PVZ GSCs possess higher migration and invasion capacities than CT GSCs. We highlighted specific transcriptomic profiles in PVZ versus CT populations and identified a down-regulation of the RhoGTPase, RND1 in PVZ GSCs compared to CT GSCs. Overexpression of RND1, dramatically inhibited PVZ GSC migration and conversely, downregulation of RND1 increased CT GSC migration. Additionally, transcriptomic analyses also revealed a down-regulation of RND1 in glioblastoma compared to normal brain. Using the glioblastoma TCGA database, low levels of RND1 were also shown to correlate with a decreased overall survival of patients. Finally, based on signaling pathways activated in patients with low levels of RND1, we identified an RND1low signature of six genes (MET, LAMC1, ITGA5, COL5A1, COL3A1, COL1A2) that is an independent prognostic factor in glioblastoma. These findings contribute to explain the shorter time to progression of patients with PVZ involvement and, point out genes that establish the RND1low signature as key targets genes to impede tumor relapse after treatment.