Showing papers by "Samsung published in 2018"

Skin electronics from scalable fabrication of an intrinsically stretchable transistor array.

Abstract: Skin-like electronics that can adhere seamlessly to human skin or within the body are highly desirable for applications such as health monitoring, medical treatment, medical implants and biological studies, and for technologies that include human-machine interfaces, soft robotics and augmented reality. Rendering such electronics soft and stretchable-like human skin-would make them more comfortable to wear, and, through increased contact area, would greatly enhance the fidelity of signals acquired from the skin. Structural engineering of rigid inorganic and organic devices has enabled circuit-level stretchability, but this requires sophisticated fabrication techniques and usually suffers from reduced densities of devices within an array. We reasoned that the desired parameters, such as higher mechanical deformability and robustness, improved skin compatibility and higher device density, could be provided by using intrinsically stretchable polymer materials instead. However, the production of intrinsically stretchable materials and devices is still largely in its infancy: such materials have been reported, but functional, intrinsically stretchable electronics have yet to be demonstrated owing to the lack of a scalable fabrication technology. Here we describe a fabrication process that enables high yield and uniformity from a variety of intrinsically stretchable electronic polymers. We demonstrate an intrinsically stretchable polymer transistor array with an unprecedented device density of 347 transistors per square centimetre. The transistors have an average charge-carrier mobility comparable to that of amorphous silicon, varying only slightly (within one order of magnitude) when subjected to 100 per cent strain for 1,000 cycles, without current-voltage hysteresis. Our transistor arrays thus constitute intrinsically stretchable skin electronics, and include an active matrix for sensory arrays, as well as analogue and digital circuit elements. Our process offers a general platform for incorporating other intrinsically stretchable polymer materials, enabling the fabrication of next-generation stretchable skin electronic devices.

A Tutorial on UAVs for Wireless Networks: Applications, Challenges, and Open Problems

Abstract: The use of flying platforms such as unmanned aerial vehicles (UAVs), popularly known as drones, is rapidly growing. In particular, with their inherent attributes such as mobility, flexibility, and adaptive altitude, UAVs admit several key potential applications in wireless systems. On the one hand, UAVs can be used as aerial base stations to enhance coverage, capacity, reliability, and energy efficiency of wireless networks. On the other hand, UAVs can operate as flying mobile terminals within a cellular network. Such cellular-connected UAVs can enable several applications ranging from real-time video streaming to item delivery. In this paper, a comprehensive tutorial on the potential benefits and applications of UAVs in wireless communications is presented. Moreover, the important challenges and the fundamental tradeoffs in UAV-enabled wireless networks are thoroughly investigated. In particular, the key UAV challenges such as three-dimensional deployment, performance analysis, channel modeling, and energy efficiency are explored along with representative results. Then, open problems and potential research directions pertaining to UAV communications are introduced. Finally, various analytical frameworks and mathematical tools such as optimization theory, machine learning, stochastic geometry, transport theory, and game theory are described. The use of such tools for addressing unique UAV problems is also presented. In a nutshell, this tutorial provides key guidelines on how to analyze, optimize, and design UAV-based wireless communication systems.

An integrated self-healable electronic skin system fabricated via dynamic reconstruction of a nanostructured conducting network.

Abstract: Electronic skin devices capable of monitoring physiological signals and displaying feedback information through closed-loop communication between the user and electronics are being considered for next-generation wearables and the 'Internet of Things'. Such devices need to be ultrathin to achieve seamless and conformal contact with the human body, to accommodate strains from repeated movement and to be comfortable to wear. Recently, self-healing chemistry has driven important advances in deformable and reconfigurable electronics, particularly with self-healable electrodes as the key enabler. Unlike polymer substrates with self-healable dynamic nature, the disrupted conducting network is unable to recover its stretchability after damage. Here, we report the observation of self-reconstruction of conducting nanostructures when in contact with a dynamically crosslinked polymer network. This, combined with the self-bonding property of self-healing polymer, allowed subsequent heterogeneous multi-component device integration of interconnects, sensors and light-emitting devices into a single multi-functional system. This first autonomous self-healable and stretchable multi-component electronic skin paves the way for future robust electronics.

Self-powered ultra-flexible electronics via nano-grating-patterned organic photovoltaics.

Abstract: Next-generation biomedical devices1-9 will need to be self-powered and conformable to human skin or other tissue. Such devices would enable the accurate and continuous detection of physiological signals without the need for an external power supply or bulky connecting wires. Self-powering functionality could be provided by flexible photovoltaics that can adhere to moveable and complex three-dimensional biological tissues1-4 and skin5-9. Ultra-flexible organic power sources10-13 that can be wrapped around an object have proven mechanical and thermal stability in long-term operation13, making them potentially useful in human-compatible electronics. However, the integration of these power sources with functional electric devices including sensors has not yet been demonstrated because of their unstable output power under mechanical deformation and angular change. Also, it will be necessary to minimize high-temperature and energy-intensive processes10,12 when fabricating an integrated power source and sensor, because such processes can damage the active material of the functional device and deform the few-micrometre-thick polymeric substrates. Here we realize self-powered ultra-flexible electronic devices that can measure biometric signals with very high signal-to-noise ratios when applied to skin or other tissue. We integrated organic electrochemical transistors used as sensors with organic photovoltaic power sources on a one-micrometre-thick ultra-flexible substrate. A high-throughput room-temperature moulding process was used to form nano-grating morphologies (with a periodicity of 760 nanometres) on the charge transporting layers. This substantially increased the efficiency of the organophotovoltaics, giving a high power-conversion efficiency that reached 10.5 per cent and resulted in a high power-per-weight value of 11.46 watts per gram. The organic electrochemical transistors exhibited a transconductance of 0.8 millisiemens and fast responsivity above one kilohertz under physiological conditions, which resulted in a maximum signal-to-noise ratio of 40.02 decibels for cardiac signal detection. Our findings offer a general platform for next-generation self-powered electronics.

Ultra-Reliable and Low-Latency Communications in 5G Downlink: Physical Layer Aspects

Abstract: URLLC is a new service category in 5G to accommodate emerging services and applications having stringent latency and reliability requirements. In order to support URLLC, there should be both evolutionary and revolutionary changes in the air interface named 5G NR. In this article, we provide an up-to-date overview of URLLC with an emphasis on the physical layer challenges and solutions in 5G NR downlink. We highlight key requirements of URLLC and then elaborate the physical layer issues and enabling technologies including packet and frame structure, scheduling schemes, and reliability improvement techniques, which have been discussed in the 3GPP Release 15 standardization.

Averaging Weights Leads to Wider Optima and Better Generalization

Abstract: Deep neural networks are typically trained by optimizing a loss function with an SGD variant, in conjunction with a decaying learning rate, until convergence. We show that simple averaging of multiple points along the trajectory of SGD, with a cyclical or constant learning rate, leads to better generalization than conventional training. We also show that this Stochastic Weight Averaging (SWA) procedure finds much flatter solutions than SGD, and approximates the recent Fast Geometric Ensembling (FGE) approach with a single model. Using SWA we achieve notable improvement in test accuracy over conventional SGD training on a range of state-of-the-art residual networks, PyramidNets, DenseNets, and Shake-Shake networks on CIFAR-10, CIFAR-100, and ImageNet. In short, SWA is extremely easy to implement, improves generalization, and has almost no computational overhead.

Channel Estimation in Broadband Millimeter Wave MIMO Systems With Few-Bit ADCs

Abstract: We develop a broadband channel estimation algorithm for millimeter wave (mmWave) multiple input multiple output (MIMO) systems with few-bit analog-to-digital converters (ADCs). Our methodology exploits the joint sparsity of the mmWave MIMO channel in the angle and delay domains. We formulate the estimation problem as a noisy quantized compressed-sensing problem and solve it using efficient approximate message passing (AMP) algorithms. In particular, we model the angle-delay coefficients using a Bernoulli–Gaussian-mixture distribution with unknown parameters and use the expectation-maximization forms of the generalized AMP and vector AMP algorithms to simultaneously learn the distributional parameters and compute approximately minimum mean-squared error (MSE) estimates of the channel coefficients. We design a training sequence that allows fast, fast Fourier transform based implementation of these algorithms while minimizing peak-to-average power ratio at the transmitter, making our methods scale efficiently to large numbers of antenna elements and delays. We present the results of a detailed simulation study that compares our algorithms to several benchmarks. Our study investigates the effect of SNR, training length, training type, ADC resolution, and runtime on channel estimation MSE, mutual information, and achievable rate. It shows that, in a mmWave MIMO system, the methods we propose to exploit joint angle-delay sparsity allow 1-bit ADCs to perform comparably to infinite-bit ADCs at low SNR, and 4-bit ADCs to perform comparably to infinite-bit ADCs at medium SNR.

Flexible and Stretchable Smart Display: Materials, Fabrication, Device Design, and System Integration

Abstract: Recent technological advances in nanomaterials have driven the development of high‐performance light‐emitting devices with flexible and stretchable form factors. Deformability in such devices is mainly achieved by replacing the rigid materials in the device components with flex...

Random circuit breaker network model for unipolar resistance switching (Advanced Materials (2008) 20 (1154))

Abstract: The existence of reversible resistance switching (RS) behaviors induced by electric stimulus has been known for some time, and these intriguing physical phenomena have been observed in numerous materials, including oxides. As conventional charge-based random access memory is expected to face a size limit in the near future, a surge of renewed interest has been developed in RS phenomena for possible applications in small nonvolatile memory devices called resistance random access memory (RRAM). Of particular interest is unipolar RS, which shows the RS at two values of applied voltage of the same polarity. The unipolar RS exhibits a much larger resistance change than other RS phenomena, and this greatly simplifies the process of reading the memory state. When fabricated with oxide p-n diodes, memory cells using unipolar RS can be stacked vertically, which has the potential for dramatically increasing memory density. Therefore, unipolar RRAM may be a good candidate for multi-stacked, high density, nonvolatile memory. The most important scientific and technical issues concerning unipolar RS are how it works and the identification of its controlling parameters. Some studies have reported that unipolar RS comes from a homogeneous/inhomogeneous transition of current distribution, while others maintain that it comes from the formation and rupture of conducting filaments. Even with recent extensive studies on unipolar RS, its basic origin is still far from being understood. In addition, no model exists that actually explains how the reversible switching can occur at two values of applied voltage. This lack of a quantitative model poses a major barrier for unipolar RRAM applications. In this study, we describe RS behavior in polycrystalline TiO2 film. To explain the basic mechanism of unipolar RS behavior, we propose a new percolation model based on a network of ‘‘circuit breakers’’ with two switchable metastable states. The random circuit breaker (RCB) network model can explain the long-standing material issue of how unipolar RS occurs. This simple percolation model is different from the conventional percolation models, which have dealt only with static or irreversible dynamic processes. In addition, the RCB network model provides an indication of how to overcome the substantial distribution of switching voltages, which is currently considered the most serious obstacle to practical unipolar RRAM applications. The unipolar RS phenomenon can be explained by the current (I)-voltage (V) curves in Figure 1a, which are derived from measurements of our polycrystalline TiO2 thin capacitors. At the pristine state (green dot), they are in an insulating state. As the external voltage Vext increases from zero and reaches a threshold voltage Vforming, a sudden increase occurs in the current. If the current is not limited to a certain value, here called the compliance current Icomp, the TiO2 capacitor would experience a dielectric breakdown and be destroyed. However, [*] Prof. T. W. Noh, S. C. Chae, S. B. Lee, S. H Chang, Dr. C. Liu ReCOE & FPRD, Department of Physics and Astronomy Seoul National University Seoul 151-747 (Korea) E-mail: twnoh@snu.ac.kr

A 28GHz Bulk-CMOS dual-polarization phased-array transceiver with 24 channels for 5G user and basestation equipment

Abstract: Developing next-generation cellular technology (5G) in the mm-wave bands will require low-cost phased-array transceivers [1]. Even with the benefit of beamforming, due to space constraints in the mobile form-factor, increasing TX output power while maintaining acceptable PA PAE, LNA NF, and overall transceiver power consumption is important to maximizing link budget allowable path loss and minimizing handset case temperature. Further, the phased-array transceiver will need to be able to support dual-polarization communication. An IF interface to the analog baseband is desired for low power consumption in the handset or user equipment (UE) active antenna and to enable use of arrays of transceivers for customer premises equipment (CPE) or basestation (BS) antenna arrays with a low-loss IF power-combining/splitting network implemented on an antenna backplane carrying multiple tiled antenna modules.

Selective growth of layered perovskites for stable and efficient photovoltaics

Abstract: Perovskite solar cells (PSCs) are promising alternatives toward clean energy because of their high-power conversion efficiency (PCE) and low materials and processing cost However, their poor stability under operation still limits their practical applications Here we design an innovative approach to control the surface growth of a low dimensional perovskite layer on top of a bulk three-dimensional (3D) perovskite film This results in a structured perovskite interface where a distinct layered low dimensional perovskite is engineered on top of the 3D film Structural and optical properties of the stack are investigated and solar cells are realized When embodying the low dimensional perovskite layer, the photovoltaic cells exhibit an enhanced PCE of 201% on average, when compared to pristine 3D perovskite In addition, superior stability is observed: the devices retain 85% of the initial PCE stressed under one sun illumination for 800 hours at 50 °C in an ambient environment

Google Workloads for Consumer Devices: Mitigating Data Movement Bottlenecks

Abstract: We are experiencing an explosive growth in the number of consumer devices, including smartphones, tablets, web-based computers such as Chromebooks, and wearable devices. For this class of devices, energy efficiency is a first-class concern due to the limited battery capacity and thermal power budget. We find that data movement is a major contributor to the total system energy and execution time in consumer devices. The energy and performance costs of moving data between the memory system and the compute units are significantly higher than the costs of computation. As a result, addressing data movement is crucial for consumer devices. In this work, we comprehensively analyze the energy and performance impact of data movement for several widely-used Google consumer workloads: (1) the Chrome web browser; (2) TensorFlow Mobile, Google's machine learning framework; (3) video playback, and (4) video capture, both of which are used in many video services such as YouTube and Google Hangouts. We find that processing-in-memory (PIM) can significantly reduce data movement for all of these workloads, by performing part of the computation close to memory. Each workload contains simple primitives and functions that contribute to a significant amount of the overall data movement. We investigate whether these primitives and functions are feasible to implement using PIM, given the limited area and power constraints of consumer devices. Our analysis shows that offloading these primitives to PIM logic, consisting of either simple cores or specialized accelerators, eliminates a large amount of data movement, and significantly reduces total system energy (by an average of 55.4% across the workloads) and execution time (by an average of 54.2%).

A General 3-D Non-Stationary 5G Wireless Channel Model

Abstract: A novel unified framework of geometry-based stochastic models for the fifth generation (5G) wireless communication systems is proposed in this paper. The proposed general 5G channel model aims at capturing small-scale fading channel characteristics of key 5G communication scenarios, such as massive multiple-input multiple-output, high-speed train, vehicle-to-vehicle, and millimeter wave communications. It is a 3-D non-stationary channel model based on the WINNER II and Saleh-Valenzuela channel models considering array-time cluster evolution. Moreover, it can easily be reduced to various simplified channel models by properly adjusting model parameters. Statistical properties of the proposed general 5G small-scale fading channel model are investigated to demonstrate its capability of capturing channel characteristics of various scenarios, with excellent fitting to some corresponding channel measurements.

Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs

Abstract: The loss functions of deep neural networks are complex and their geometric properties are not well understood. We show that the optima of these complex loss functions are in fact connected by simple curves, over which training and test accuracy are nearly constant. We introduce a training procedure to discover these high-accuracy pathways between modes. Inspired by this new geometric insight, we also propose a new ensembling method entitled Fast Geometric Ensembling (FGE). Using FGE we can train high-performing ensembles in the time required to train a single model. We achieve improved performance compared to the recent state-of-the-art Snapshot Ensembles, on CIFAR-10, CIFAR-100, and ImageNet.

Factorizable Net: An Efficient Subgraph-based Framework for Scene Graph Generation

Abstract: Generating scene graph to describe the object interactions inside an image gains increasing interests these years. However, most of the previous methods use complicated structures with slow inference speed or rely on the external data, which limits the usage of the model in real-life scenarios. To improve the efficiency of scene graph generation, we propose a subgraph-based connection graph to concisely represent the scene graph during the inference. A bottom-up clustering method is first used to factorize the entire graph into subgraphs, where each subgraph contains several objects and a subset of their relationships. By replacing the numerous relationship representations of the scene graph with fewer subgraph and object features, the computation in the intermediate stage is significantly reduced. In addition, spatial information is maintained by the subgraph features, which is leveraged by our proposed Spatial-weighted Message Passing (SMP) structure and Spatial-sensitive Relation Inference (SRI) module to facilitate the relationship recognition. On the recent Visual Relationship Detection and Visual Genome datasets, our method outperforms the state-of-the-art method in both accuracy and speed. Code has been made publicly available (https://github.com/yikang-li/FactorizableNet).

Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs

Abstract: The loss functions of deep neural networks are complex and their geometric properties are not well understood. We show that the optima of these complex loss functions are in fact connected by simple curves over which training and test accuracy are nearly constant. We introduce a training procedure to discover these high-accuracy pathways between modes. Inspired by this new geometric insight, we also propose a new ensembling method entitled Fast Geometric Ensembling (FGE). Using FGE we can train high-performing ensembles in the time required to train a single model. We achieve improved performance compared to the recent state-of-the-art Snapshot Ensembles, on CIFAR-10, CIFAR-100, and ImageNet.

High-Performance Triboelectric Nanogenerators Based on Electrospun Polyvinylidene Fluoride–Silver Nanowire Composite Nanofibers

Abstract: The preparation of ferroelectric polymer–metallic nanowire composite nanofiber triboelectric layers is described for use in high-performance triboelectric nanogenerators (TENGs). The electrospun polyvinylidene fluoride (PVDF)–silver nanowire (AgNW) composite and nylon nanofibers are utilized in the TENGs as the top and bottom triboelectric layers, respectively. The electrospinning process facilitates uniaxial stretching of the polymer chains, which enhances the formation of the highly oriented crystalline β-phase that forms the most polar crystalline phase of PVDF. The addition of AgNWs further promotes the β-phase crystal formation by introducing electrostatic interactions between the surface charges of the nanowires and the dipoles of the PVDF chains. The extent of β-phase formation and the resulting variations in the surface charge potential upon the addition of nanowires are systematically analyzed using X-ray diffraction (XRD) and Kelvin probe force microscopy techniques. The ability of trapping the induced tribocharges increases upon the addition of nanowires to the PVDF matrix. The enhanced surface charge potential and the charge trapping capabilities of the PVDF–AgNW composite nanofibers significantly enhance the TENG output performances. Finally, the mechanical stability of the electrospun nanofibers is dramatically enhanced while maintaining the TENG performances by applying thermal welding near the melting temperature of PVDF.

Simultaneous Spectral-Spatial Feature Selection and Extraction for Hyperspectral Images.

Abstract: In hyperspectral remote sensing data mining, it is important to take into account of both spectral and spatial information, such as the spectral signature, texture feature, and morphological property, to improve the performances, e.g., the image classification accuracy. In a feature representation point of view, a nature approach to handle this situation is to concatenate the spectral and spatial features into a single but high dimensional vector and then apply a certain dimension reduction technique directly on that concatenated vector before feed it into the subsequent classifier. However, multiple features from various domains definitely have different physical meanings and statistical properties, and thus such concatenation has not efficiently explore the complementary properties among different features, which should benefit for boost the feature discriminability. Furthermore, it is also difficult to interpret the transformed results of the concatenated vector. Consequently, finding a physically meaningful consensus low dimensional feature representation of original multiple features is still a challenging task. In order to address these issues, we propose a novel feature learning framework, i.e., the simultaneous spectral-spatial feature selection and extraction algorithm, for hyperspectral images spectral-spatial feature representation and classification. Specifically, the proposed method learns a latent low dimensional subspace by projecting the spectral-spatial feature into a common feature space, where the complementary information has been effectively exploited, and simultaneously, only the most significant original features have been transformed. Encouraging experimental results on three public available hyperspectral remote sensing datasets confirm that our proposed method is effective and efficient.

Visual Semantic Navigation using Scene Priors

Abstract: How do humans navigate to target objects in novel scenes? Do we use the semantic/functional priors we have built over years to efficiently search and navigate? For example, to search for mugs, we search cabinets near the coffee machine and for fruits we try the fridge. In this work, we focus on incorporating semantic priors in the task of semantic navigation. We propose to use Graph Convolutional Networks for incorporating the prior knowledge into a deep reinforcement learning framework. The agent uses the features from the knowledge graph to predict the actions. For evaluation, we use the AI2-THOR framework. Our experiments show how semantic knowledge improves performance significantly. More importantly, we show improvement in generalization to unseen scenes and/or objects. The supplementary video can be accessed at the following link: this https URL .

Microwave vs. Millimeter-Wave Propagation Channels: Key Differences and Impact on 5G Cellular Systems

Abstract: Fifth generation cellular systems will be deployed in the microwave and millimeterwave (mmWave) frequency bands (i.e., between 0.5100 GHz). Propagation characteristics at these bands have a fundamental impact on each aspect of the cellular architecture, ranging from equipment design to real-time performance in the field. While we have a reasonable understanding of the propagation characteristics at microwave (< 6 GHz) frequencies, the same cannot be said for mmWave. This article explains key differences in the propagation characteristics between the microwave and mmWave bands, and further gives examples of how these differences impact 5G system design.

Triboelectric Series of 2D Layered Materials.

Abstract: Recently, as applications based on triboelectricity have expanded, understanding the triboelectric charging behavior of various materials has become essential. This study investigates the triboelectric charging behaviors of various 2D layered materials, including MoS2 , MoSe2 , WS2 , WSe2 , graphene, and graphene oxide in a triboelectric series using the concept of a triboelectric nanogenerator, and confirms the position of 2D materials in the triboelectric series. It is also demonstrated that the results are obviously related to the effective work functions. The charging polarity indicates the similar behavior regardless of the synthetic method and film thickness ranging from a few hundred nanometers (for chemically exfoliated and restacked films) to a few nanometers (for chemical vapor deposited films). Further, the triboelectric charging characteristics could be successfully modified via chemical doping. This study provides new insights to utilize 2D materials in triboelectric devices, allowing thin and flexible device fabrication.

Spectrum for 5G: Global Status, Challenges, and Enabling Technologies

Abstract: Availability of widely harmonized mobile spectrum is crucial for the success of 5G mobile communications, fulfilling the visions and requirements and delivering the full range of potential capabilities for 5G. This article introduces the global situation of spectrum for 5G, both below and above 6 GHz, in both the regulatory status and technical aspects. In particular, the technical challenges of supporting 5G in millimeter-wave spectrum, such as coverage limitation and implementation aspects, are discussed.

Monocular Relative Depth Perception with Web Stereo Data Supervision

Abstract: In this paper we study the problem of monocular relative depth perception in the wild. We introduce a simple yet effective method to automatically generate dense relative depth annotations from web stereo images, and propose a new dataset that consists of diverse images as well as corresponding dense relative depth maps. Further, an improved ranking loss is introduced to deal with imbalanced ordinal relations, enforcing the network to focus on a set of hard pairs. Experimental results demonstrate that our proposed approach not only achieves state-of-the-art accuracy of relative depth perception in the wild, but also benefits other dense per-pixel prediction tasks, e.g., metric depth estimation and semantic segmentation.

Attention-Based Ensemble for Deep Metric Learning

Abstract: Deep metric learning aims to learn an embedding function, modeled as deep neural network. This embedding function usually puts semantically similar images close while dissimilar images far from each other in the learned embedding space. Recently, ensemble has been applied to deep metric learning to yield state-of-the-art results. As one important aspect of ensemble, the learners should be diverse in their feature embeddings. To this end, we propose an attention-based ensemble, which uses multiple attention masks, so that each learner can attend to different parts of the object. We also propose a divergence loss, which encourages diversity among the learners. The proposed method is applied to the standard benchmarks of deep metric learning and experimental results show that it outperforms the state-of-the-art methods by a significant margin on image retrieval tasks.

Narrow-linewidth lasing and soliton Kerr microcombs with ordinary laser diodes

Abstract: Narrow-linewidth lasers and optical frequency combs generated with mode-locked lasers have revolutionized optical frequency metrology. The advent of soliton Kerr frequency combs in compact crystalline or integrated ring optical microresonators has opened new horizons in academic research and industrial applications. These combs, as was naturally assumed, however, require narrow-linewidth, single-frequency pump lasers. We demonstrate that an ordinary cost-effective broadband Fabry–Perot laser diode at the hundreds of milliwatts level, self-injection-locked to a microresonator, can be efficiently transformed to a powerful single-frequency, ultra-narrow-linewidth light source with further transformation to a coherent soliton comb oscillator. Our findings pave the way to the most compact and inexpensive highly coherent lasers, frequency comb sources, and comb-based devices for mass production. A broadband multi-frequency Fabry–Perot laser diode, when coupled to a high-Q microresonator, can be efficiently transformed to an ~100 mW narrow-linewidth single-frequency light source, and subsequently, to a coherent soliton Kerr comb oscillator.

Toward the Standardization of Non-Orthogonal Multiple Access for Next Generation Wireless Networks

Abstract: Non-orthogonal multiple access (NOMA) as an efficient method of radio resource sharing has its roots in network information theory. For generations of wireless communication systems design, orthogonal multiple access schemes in the time, frequency, or code domain have been the main choices due to the limited processing capability in the transceiver hardware, as well as the modest traffic demands in both latency and connectivity. However, for the next generation radio systems, given its vision to connect everything and the much evolved hardware capability, NOMA has been identified as a promising technology to help achieve all the targets in system capacity, user connectivity, and service latency. This article provides a systematic overview of the state-of-theart design of the NOMA transmission based on a unified transceiver design framework, the related standardization progress, and some promising use cases in future cellular networks, based on which interested researchers can get a quick start in this area.

Privacy Enhanced Matrix Factorization for Recommendation with Local Differential Privacy

Abstract: Recommender systems are collecting and analyzing user data to provide better user experience. However, several privacy concerns have been raised when a recommender knows user's set of items or their ratings. A number of solutions have been suggested to improve privacy of legacy recommender systems, but the existing solutions in the literature can protect either items or ratings only. In this paper, we propose a recommender system that protects both user's items and ratings. For this, we develop novel matrix factorization algorithms under local differential privacy (LDP). In a recommender system with LDP, individual users randomize their data themselves to satisfy differential privacy and send the perturbed data to the recommender. Then, the recommender computes aggregates of the perturbed data. This framework ensures that both user's items and ratings remain private from the recommender. However, applying LDP to matrix factorization typically raises utility issues with i) high dimensionality due to a large number of items and ii) iterative estimation algorithms. To tackle these technical challenges, we adopt dimensionality reduction technique and a novel binary mechanism based on sampling. We additionally introduce a factor that stabilizes the perturbed gradients. With MovieLens and LibimSeTi datasets, we evaluate recommendation accuracy of our recommender system and demonstrate that our algorithm performs better than the existing differentially private gradient descent algorithm for matrix factorization under stronger privacy requirements.

A Bi-Model Based RNN Semantic Frame Parsing Model for Intent Detection and Slot Filling

Abstract: Intent detection and slot filling are two main tasks for building a spoken language understanding(SLU) system. Multiple deep learning based models have demonstrated good results on these tasks . The most effective algorithms are based on the structures of sequence to sequence models (or “encoder-decoder” models), and generate the intents and semantic tags either using separate models. Most of the previous studies, however, either treat the intent detection and slot filling as two separate parallel tasks, or use a sequence to sequence model to generate both semantic tags and intent. None of the approaches consider the cross-impact between the intent detection task and the slot filling task. In this paper, new Bi-model based RNN semantic frame parsing network structures are designed to perform the intent detection and slot filling tasks jointly, by considering their cross-impact to each other using two correlated bidirectional LSTMs (BLSTM). Our Bi-model structure with a decoder achieves state-of-art result on the benchmark ATIS data, with about 0.5% intent accuracy improvement and 0.9 % slot filling improvement.

Time-asymmetric loop around an exceptional point over the full optical communications band.

Abstract: Topological operations around exceptional points1–8—time-varying system configurations associated with non-Hermitian singularities—have been proposed as a robust approach to achieving far-reaching open-system dynamics, as demonstrated in highly dissipative microwave transmission3 and cryogenic optomechanical oscillator4 experiments In stark contrast to conventional systems based on closed-system Hermitian dynamics, environmental interferences at exceptional points are dynamically engaged with their internal coupling properties to create rotational stimuli in fictitious-parameter domains, resulting in chiral systems that exhibit various anomalous physical phenomena9–16 To achieve new wave properties and concomitant device architectures to control them, realizations of such systems in application-abundant technological areas, including communications and signal processing systems, are the next step However, it is currently unclear whether non-Hermitian interaction schemes can be configured in robust technological platforms for further device engineering Here we experimentally demonstrate a robust silicon photonic structure with photonic modes that transmit through time-asymmetric loops around an exceptional point in the optical domain The proposed structure consists of two coupled silicon-channel waveguides and a slab-waveguide leakage-radiation sink that precisely control the required non-Hermitian Hamiltonian experienced by the photonic modes The fabricated devices generate time-asymmetric light transmission over an extremely broad spectral band covering the entire optical telecommunications window (wavelengths between 126 and 1675 micrometres) Thus, we take a step towards broadband on-chip optical devices based on non-Hermitian topological dynamics by using a semiconductor platform with controllable optoelectronic properties, and towards several potential practical applications, such as on-chip optical isolators and non-reciprocal mode converters Our results further suggest the technological relevance of non-Hermitian wave dynamics in various other branches of physics, such as acoustics, condensed-matter physics and quantum mechanics Time-asymmetric light transmission over the entire optical communications band is achieved using a silicon photonic structure with photonic modes that dynamically encircle an exceptional point in the optical domain