Samsung (India)

Recently, automatic visual data understanding from drone platforms becomes highly demanding. To facilitate the study, the Vision Meets Drone Object Detection in Image Challenge is held the second time in conjunction with the 17-th International Conference on Computer Vision (ICCV 2019), focuses on image object detection on drones. Results of 33 object detection algorithms are presented. For each participating detector, a short description is provided in the appendix. Our goal is to advance the state-of-the-art detection algorithms and provide a comprehensive evaluation platform for them. The evaluation protocol of the VisDrone-DET2019 Challenge and the comparison results of all the submitted detectors on the released dataset are publicly available at the website: http: //www.aiskyeye.com/. The results demonstrate that there still remains a large room for improvement for object detection algorithms on drones.

In this paper, we consider molecular communication, with information conveyed in the time of release of molecules. These molecules propagate to the transmitter through a fluid medium, propelled by a positive drift velocity and Brownian motion. The main contribution of this paper is the development of a theoretical foundation for such a communication system; specifically, the additive inverse Gaussian noise (AIGN) channel model. In such a channel, the information is corrupted by noise that follows an IG distribution. We show that such a channel model is appropriate for molecular communication in fluid media. Taking advantage of the available literature on the IG distribution, upper and lower bounds on channel capacity are developed, and a maximum likelihood receiver is derived. Results are presented which suggest that this channel does not have a single quality measure analogous to signal-to-noise ratio in the additive white Gaussian noise channel. It is also shown that the use of multiple molecules leads to reduced error rate in a manner akin to diversity order in wireless communications. Finally, some open problems are discussed that arise from the IG channel model.

This article considers a practical implementation of massive MIMO systems [1]. Although the best performance can be achieved when a large number of active antennas are placed only in the horizontal domain, BS form factor limitation often makes horizontal array placement infeasible. To cope with this limitation, this article introduces full-dimension MIMO (FD-MIMO) cellular wireless communication system, where active antennas are placed in a 2D grid at BSs. For analysis of the FD-MIMO systems, a 3D spatial channel model is introduced, on which system-level simulations are conducted. The simulation results show that the proposed FD-MIMO system with 32 antenna ports achieves 2-3.6 times cell average throughput gain and 1.5-5 times cell edge throughput gain compared to the 4G LTE system of two antenna ports at the BS.

Developments in mobile robot navigation have enabled robots to operate in warehouses, retail stores, and on sidewalks around pedestrians. Various navigation solutions have been proposed, though few as widely adopted as ROS (Robot Operating System) Navigation. 10 years on, it is still one of the most popular navigation solutions <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> . Yet, ROS Navigation has failed to keep up with modern trends. We propose the new navigation solution, Navigation2, which builds on the successful legacy of ROS Navigation. Navigation2 uses a behavior tree for navigator task orchestration and employs new methods designed for dynamic environments applicable to a wider variety of modern sensors. It is built on top of ROS2, a secure message passing framework suitable for safety critical applications and program lifecycle management. We present experiments in a campus setting utilizing Navigation2 to operate safely alongside students over a marathon as an extension of the experiment proposed in Eppstein et al. [1]. The Navigation2 system is freely available at https://github.com/ros-planning/navigation2 with a rich community and instructions.

Recent advances in smart devices have sustained them as a better alternative for the design of human-machine interaction (HMI), because they are equipped with accelerometer sensor, gyroscope sensor, and an advanced operating system. This paper presents a continuous hand gestures recognition technique that is capable of continuous recognition of hand gestures using three-axis accelerometer and gyroscope sensors in a smart device. To reduce the influence of unstableness of a hand making the gesture and compress the data, a gesture coding algorithm is developed. An automatic gesture spotting algorithm is developed to detect the start and end points of meaningful gesture segments. Finally, a gesture is recognized by comparing the gesture code with gesture database using dynamic time warping algorithm. In addition, a prototype system is developed to recognize the continuous hand gestures-based HMI. With the smartphone, the user is able to perform the predefined gestures and control smart appliances using the Samsung AllShare protocol.

for many decades, there has been a general perception in the literature that Fourier methods are not suitable for the analysis of nonlinear and non-stationary data. In this paper, we propose a novel and adaptive Fourier decomposition method (FDM), based on the Fourier theory, and demonstrate its efficacy for the analysis of nonlinear and non-stationary time series. The proposed FDM decomposes any data into a small number of 'Fourier intrinsic band functions' (FIBFs). The FDM presents a generalized Fourier expansion with variable amplitudes and variable frequencies of a time series by the Fourier method itself. We propose an idea of zero-phase filter bank-based multivariate FDM (MFDM), for the analysis of multivariate nonlinear and non-stationary time series, using the FDM. We also present an algorithm to obtain cut-off frequencies for MFDM. The proposed MFDM generates a finite number of band-limited multivariate FIBFs (MFIBFs). The MFDM preserves some intrinsic physical properties of the multivariate data, such as scale alignment, trend and instantaneous frequency. The proposed methods provide a time-frequency-energy (TFE) distribution that reveals the intrinsic structure of a data. Numerical computations and simulations have been carried out and comparison is made with the empirical mode decomposition algorithms.

Machine Learning has been steadily gaining traction for its use in Anomaly-based Network Intrusion Detection Systems (A-NIDS). Research into this domain is frequently performed using the KDD CUP 99 dataset as a benchmark. Several studies question its usability while constructing a contemporary NIDS, due to the skewed response distribution, non-stationarity, and failure to incorporate modern attacks. In this paper, we compare the performance for KDD-99 alternatives when trained using classification models commonly found in literature: Neural Network, Support Vector Machine, Decision Tree, Random Forest, Naive Bayes and K-Means. Applying the SMOTE oversampling technique and random undersampling, we create a balanced version of NSL-KDD and prove that skewed target classes in KDD-99 and NSL-KDD hamper the efficacy of classifiers on minority classes (U2R and R2L), leading to possible security risks. We explore UNSW-NB15, a modern substitute to KDD-99 with greater uniformity of pattern distribution. We benchmark this dataset before and after SMOTE oversampling to observe the effect on minority performance. Our results indicate that classifiers trained on UNSW-NB15 match or better the Weighted F1-Score of those trained on NSL-KDD and KDD-99 in the binary case, thus advocating UNSW-NB15 as a modern substitute to these datasets.

With the proliferation of Li-ion batteries in smart phones, safety is the main concern and an on-line detection of battery faults is much wanting. Internal short circuit is a very critical issue that is often ascribed to be a cause of many accidents involving Li-ion batteries. A novel method that can detect the Internal short circuit in real time based on an advanced machine leaning approach, is proposed. Based on an equivalent electric circuit model, a set of features encompassing the physics of Li-ion cell with short circuit fault are identified and extracted from each charge-discharge cycle. The training feature set is generated with and without an external short-circuit resistance across the battery terminals. To emulate a real user scenario, internal short is induced by mechanical abuse. The testing feature set is generated from the battery charge-discharge data before and after the abuse. A random forest classifier is trained with the training feature set. The fault detection accuracy for the testing dataset is found to be more than 97%. The proposed algorithm does not interfere with the normal usage of the device, and the trained model can be implemented in any device for online fault detection.

Many challenges emerge as the DRAM enters into a generation of the gigabit density era. Most of the challenges come from the shrink technology which scales down minimum feature size by a factor of 0.84 per year. The need for higher performance to narrow the bandwidth mismatch between fast processors and slower memories and lower power consumption drives the DRAM technology toward smaller cell size, faster memory cell operation, less power consumption, and longer data retention times. In addition, increasingly complicated wafer processing requires simple process. In this paper, the challenges brought from the extremely small minimum feature, high performance, and simple wafer processing will be discussed. The solutions to overcome the challenges will be described focusing on the memory cell scheme, lithography, device, memory cell capacitor, and metallization.

This paper explores the benefits and limitations of in-storage processing on current Solid-State Disk (SSD) architectures. While disk-based in-storage processing has not been widely adopted, due to the characteristics of hard disks, modern SSDs provide high performance on concurrent random writes, and have powerful processors, memory, and multiple I/O channels to flash memory, enabling in-storage processing with almost no hardware changes. In addition, offloading I/O tasks allows a host system to fully utilize devices' internal parallelism without knowing the details of their hardware configurations. To leverage the enhanced data processing capabilities of modern SSDs, we introduce the Smart SSD model, which pairs in-device processing with a powerful host system capable of handling data-oriented tasks without modifying operating system code. By isolating the data traffic within the device, this model promises low energy consumption, high parallelism, low host memory footprint and better performance. To demonstrate these capabilities, we constructed a prototype implementing this model on a real SATA-based SSD. Our system uses an object-based protocol for low-level communication with the host, and extends the Hadoop MapReduce framework to support a Smart SSD. Our experiments show that total energy consumption is reduced by 50% due to the low-power processing inside a Smart SSD. Moreover, a system with a Smart SSD can outperform host-side processing by a factor of two or three by efficiently utilizing internal parallelism when applications have light trafic to the device DRAM under the current architecture.

In one of the earliest events in human cytomegalovirus (HCMV)-infected cells, the major immediate-early (IE) protein IE1 initially targets to and then disrupts the nuclear structures known as PML oncogenic domains (PODs) or nuclear domain 10. Recent studies have suggested that modification of PML by SUMO is essential to form PODs and that IE1 both binds to PML and may disrupt PODs by preventing or removing SUMO adducts on PML. In this study, we showed that in contrast to herpes simplex virus type 1 (HSV-1) IE110 (ICP0), the loss of sumoylated forms of PML by cotransfected IE1 was resistant to the proteasome inhibitor MG132 and that IE1 did not reduce the level of unmodified PML. Reduced sumoylation of PML was also observed in U373 cells after infection with wild-type HCMV and proved to require IE1 protein expression. Mutational analysis revealed that the central hydrophobic domain of IE1, including Leu174, is required for both PML binding and loss of PML sumoylation and confirmed that all IE1 mutants tested that were deficient in these functions also failed both to target to PODs and to disrupt PODs. These same mutants were also inactive in several reporter gene transactivation assays and in inhibition of PML-mediated repression. Importantly, a viral DNA genome containing an IE1 gene with a deletion [IE1(Delta290-320)] that was defective in these activities was not infectious when transfected into permissive fibroblast cells, but the mutant IE1(K450R), which is defective in IE1 sumoylation, remained infectious. Our mutational analysis strengthens the idea that interference by IE1 with both the sumoylation of PML and its repressor activity requires a physical interaction with PML that also leads to disruption of PODs. These activities of IE1 also correlate with several unusual transcriptional transactivation functions of IE1 and may be requirements for efficient initiation of the lytic cycle in vivo.

BACKGROUND AND PURPOSE: Atrial fibrillation (AF) is increasingly prevalent in the elderly, but such patients tend to be under-represented in clinical trials. Increasing age confers a higher risk of stroke and bleeding when antithrombotic therapy is used. We examined risk factors for stroke and bleeding among elderly (age, >75 years) patients within a real world hospitalized cohort from the Loire Valley AF project. METHODS: We identified elderly (age, >75 years) patients with AF, assessed their risk factors, and followed up for stroke, thromboembolism, death, or major bleeding. The effect of vitamin K antagonist (VKA) use on these end points was assessed. RESULTS: We studied 8962 patients with AF, and we identified 4130 elderly (age, ≥75 years) patients. Using Kaplan-Meier analyses, event rates of death, stroke/thromboembolism, the composite of stroke/thromboembolism/death, and major bleeding increased with increasing age. For mortality, VKA-treated patients did better than non-VKA-treated patients. The risk of death and stroke/thromboembolism/death increased with increasing age. The risk of major bleeding did not increase with increasing age strata. VKA treatment was associated with lower mortality in those aged <75 years (adjusted hazard ratio [HR], 0.57; 95% confidence interval [CI], 0.45-0.72), and the effect size was maintained with increasing age strata (Pint=0.67). For stroke/thromboembolism/death, VKA also has a significant benefit in those aged <75 years (adjusted HR, 0.69; [0.57-0.83]), and the effect size was maintained with increasing age strata (Pint=0.58). For major bleeding, there was no statistically significant difference between age strata (Pint=0.67). In elderly patients, age and previous stroke emerged as the main predictors of stroke and thromboembolism. Renal impairment and VKA use were predictors of major bleeding. CONCLUSIONS: Elderly patients with AF have a higher risk of stroke and bleeding, but the benefits of VKA therapy for stroke/thromboembolism or mortality were present regardless of increasing age.

High data rate at high mobile speed will still be an essential requirement for the future 5G mobile cellular system. High frequency bands above 6 GHz are particularly promising for the 5G system because of large signal bandwidths such high frequencies can offer. By using high gain beamforming antennas, the problem of high propagation loss at high frequencies can be overcome. However, the use of beamforming antennas at such high frequencies requires a significant change in the design of a cellular system. In particular, it requires a significant change in key functions such as cell search, random access, measurement of beams for fast beam adaptation, and various physical control and data channels. In this paper, we propose a new radio frame structure for the future mobile cellular communications system at millimeter wave frequency that addresses such challenges. A testbed was built at Samsung Electronics, Korea, based on the proposed frame structure at 28 GHz with bandwidth of 800 MHz. It attained the downlink (DL) data rate of 7.5 Gbps by delivering four streams of 64 QAM data with code rate of 3/4 to two mobile stations (MSs) located in a close distance to the base station antennas at fixed positions. It also achieved the DL data rate of 1.2 Gbps by delivering single stream of 16 QAM data with code rate of 3/4 to an MS moving at 110 km/h in a single cell of up to 800 m in a line-of-sight environment. Finally, it implemented handover and achieved an average handover interruption time of 21 ms in a three-cell environment, and demonstrated feasibility of mobile cellular communications at millimeter wave frequency.

Building on the principles of openness and intelligence, there has been a concerted global effort from the operators towards enhancing the radio access network (RAN) architecture. The objective is to build an operator-defined RAN architecture (and associated interfaces) on open hardware that provides intelligent radio control for beyond fifth generation (5G) as well as future sixth generation (6G) wireless networks. Specifically, the open-radio access network (O-RAN) alliance has been formed by merging xRAN forum and C-RAN alliance to formally define the requirements that would help achieve this objective. Owing to the importance of O-RAN in the current wireless landscape, this article provides an introduction to the concepts, principles, and requirements of the Open RAN as specified by the O-RAN alliance. In order to illustrate the role of intelligence in O-RAN, we propose an intelligent radio resource management scheme to handle traffic congestion and demonstrate its efficacy on a real-world dataset obtained from a large operator. A high-level architecture of this deployment scenario that is compliant with the O-RAN requirements is also discussed. The article concludes with key technical challenges and open problems for future research and development.

PURPOSE The aim of this work is to update key recommendations of the ASCO guideline adaptation of the Cancer Care Ontario guideline on the selection of optimal adjuvant chemotherapy regimens for early breast cancer and adjuvant targeted therapy for breast cancer. METHODS An Expert Panel conducted a targeted systematic literature review guided by a signals approach to identify new, potentially practice-changing data that might translate into revised guideline recommendations. RESULTS The Expert Panel reviewed abstracts from the literature review and identified one article for inclusion that reported results of the phase III, open-label KATHERINE trial. In the KATHERINE trial, patients with stage I to III human epidermal growth factor receptor 2 (HER2)–positive breast cancer with residual invasive disease in the breast or axilla after completing neoadjuvant chemotherapy and HER2-targeted therapy were allocated to adjuvant trastuzumab emtansine (T-DM1; n = 743) or to trastuzumab (n = 743). Invasive disease–free survival was significantly higher in the T-DM1 group than in the trastuzumab arm (hazard ratio, 0.50; 95% CI, 0.39 to 0.64; P &lt; .001), and risk of distant recurrence was lower in patients who received T-DM1 than in patients who received trastuzumab (hazard ratio, 0.60; 95% CI, 0.45 to 0.79). Grade 3 or higher adverse events occurred in 190 patients (25.7%) who received T-DM1 and in 111 patients (15.4%) who received trastuzumab. RECOMMENDATIONS Patients with HER2-positive breast cancer with pathologic invasive residual disease at surgery after standard preoperative chemotherapy and HER2-targeted therapy should be offered 14 cycles of adjuvant T-DM1, unless there is disease recurrence or unmanageable toxicity. Clinicians may offer any of the available and approved formulations of trastuzumab, including trastuzumab, trastuzumab and hyaluronidase-oysk, and available biosimilars. Additional information can be found at www.asco.org/breast-cancer-guidelines

The aim of this study was to investigate the association between adult Internet game addiction (IGA) and mental disorders. A total of 1401 adults aged between 18 and 74 years participated in this study. The IGA group had significantly younger patients, and it showed a higher proportion of unmarried and unemployed adults, and higher rates of suicidal ideation, plan, and attempt than the non-IGA group. Multivariate logistic regression indicated that IGA was significantly associated with major depressive disorder, dysthymia, and depressive disorders adjusting for all variables. The Patient Health Questionnaire-9 score was significantly higher in the IGA group than in the non-IGA group for both young adults and middle groups. "Escape from negative emotions like nervousness, sadness, and anger" was the only significant item associated with depression among symptoms of IGA. This study suggests that adults with IGA and depression may use Internet games to escape from negative emotions.

Abstract Cost, safety, and cycle life have emerged as prime concerns to build robust batteries to cater to the global energy demand. These concerns are impacted by all battery components, but the realizable energy density of lithium‐ion batteries (LIBs) is limited by the performance of cathodes. Thus, cathode materials have a significant role to play in advancing the performance and economics of secondary batteries. To realize next generation Li‐ion and post Li‐ion batteries, a variety of cathode insertion materials have been explored, but finding a cost effective and stable cathode material that can deliver high energy density has been a daunting task. Oxide cathode materials are ubiquitous in commercial applications, as they can deliver high capacity. In comparison, polyanionic insertion materials can offer tuneable (high) redox potential, operational safety, and structural as well as thermal stability. Indeed, a wide range of polyanionic materials like phosphates, borates, sulfates, and their complexes have been reported. In this article, the alkali metal fluorophosphates class of polyanionic cathodes for secondary batteries is discussed. The various reported fluorophosphate insertion materials are discussed in terms of their electrochemical and electrocatalytic properties. The historical overview, recent progress, and remaining challenges for polyanionic fluorophosphates are presented along with suggested future research directions and potential application.

Cuff-less estimation of systolic (SBP) and diastolic (DBP) blood pressure is an efficient approach for non-invasive and continuous monitoring of an individual's vitals. Although pulse transit time (PTT) based approaches have been successful in estimating the systolic and diastolic blood pressures to a reasonable degree of accuracy, there is still scope for improvement in terms of accuracies. Moreover, PTT approach requires data from sensors placed at two different locations along with individual calibration of physiological parameters for deriving correct estimation of systolic and diastolic blood pressure (BP) and hence is not suitable for smartphone deployment. Heart Rate Variability is one of the extensively used non-invasive parameters to assess cardiovascular autonomic nervous system and is known to be associated with SBP and DBP indirectly. In this work, we propose a novel method to extract a comprehensive set of features by combining PPG signal based and Heart Rate Variability (HRV) related features using a single PPG sensor. Further, these features are fed into a DBP feedback based combinatorial neural network model to arrive at a common weighted average output of DBP and subsequently SBP. Our results show that using this current approach, an accuracy of ±6.8 mmHg for SBP and ±4.7 mmHg for DBP is achievable on 1,750,000 pulses extracted from a public database (comprising 3000 people). Since most of the smartphones are now equipped with PPG sensor, a mobile based cuff-less BP estimation will enable the user to monitor their BP as a vital parameter on demand. This will open new avenues towards development of pervasive and continuous BP monitoring systems leading to an early detection and prevention of cardiovascular diseases.

The paper presents a Multi-Head Attention deep learning network for Speech Emotion Recognition (SER) using Log mel-Filter Bank Energies (LFBE) spectral features as the input. The multi-head attention along with the position embedding jointly attends to information from different representations of the same LFBE input sequence. The position embedding helps in attending to the dominant emotion features by identifying positions of the features in the sequence. In addition to Multi-Head Attention and position embedding, we apply multi-task learning with gender recognition as an auxiliary task. The auxiliary task helps in learning the gender specific features that influence the emotion characteristics in speech and results in improved accuracy of Speech Emotion Recognition, the primary task. We conducted all our experiments on IEMOCAP dataset. We are able to achieve an overall accuracy of 76.4% and average class accuracy of 70.1%, which are 5.3% and 6.2% higher respectively than the state-of-the-art models available on SER for four emotion classes.

Symmetry is a pervasive phenomenon presenting itself in all forms and scales in natural and manmade environments. Its detection plays an essential role at all levels of human as well as machine perception. The recent resurging interest in computational symmetry for computer vision and computer graphics applications has motivated us to conduct a US NSF funded symmetry detection algorithm competition as a workshop affiliated with the Computer Vision and Pattern Recognition (CVPR) Conference, 2013. This competition sets a more complete benchmark for computer vision symmetry detection algorithms. In this report we explain the evaluation metric and the automatic execution of the evaluation workflow. We also present and analyze the algorithms submitted, and show their results on three test sets of real world images depicting reflection, rotation and translation symmetries respectively. This competition establishes a performance baseline for future work on symmetry detection.