Showing papers by "Samsung published in 2017"

Continual Learning with Deep Generative Replay

Abstract: Attempts to train a comprehensive artificial intelligence capable of solving multiple tasks have been impeded by a chronic problem called catastrophic forgetting. Although simply replaying all previous data alleviates the problem, it requires large memory and even worse, often infeasible in real world applications where the access to past data is limited. Inspired by the generative nature of the hippocampus as a short-term memory system in primate brain, we propose the Deep Generative Replay, a novel framework with a cooperative dual model architecture consisting of a deep generative model (“generator”) and a task solving model (“solver”). With only these two models, training data for previous tasks can easily be sampled and interleaved with those for a new task. We test our methods in several sequential learning settings involving image classification tasks.

Flexible and Stretchable Energy Storage: Recent Advances and Future Perspectives

Abstract: Energy-storage technologies such as lithium-ion batteries and supercapacitors have become fundamental building blocks in modern society. Recently, the emerging direction toward the ever-growing market of flexible and wearable electronics has nourished progress in building multifunctional energy-storage systems that can be bent, folded, crumpled, and stretched while maintaining their electrochemical functions under deformation. Here, recent progress and well-developed strategies in research designed to accomplish flexible and stretchable lithium-ion batteries and supercapacitors are reviewed. The challenges of developing novel materials and configurations with tailored features, and in designing simple and large-scaled manufacturing methods that can be widely utilized are considered. Furthermore, the perspectives and opportunities for this emerging field of materials science and engineering are also discussed.

Highly stretchable polymer semiconductor films through the nanoconfinement effect

Abstract: Soft and conformable wearable electronics require stretchable semiconductors, but existing ones typically sacrifice charge transport mobility to achieve stretchability. We explore a concept based on the nanoconfinement of polymers to substantially improve the stretchability of polymer semiconductors, without affecting charge transport mobility. The increased polymer chain dynamics under nanoconfinement significantly reduces the modulus of the conjugated polymer and largely delays the onset of crack formation under strain. As a result, our fabricated semiconducting film can be stretched up to 100% strain without affecting mobility, retaining values comparable to that of amorphous silicon. The fully stretchable transistors exhibit high biaxial stretchability with minimal change in on current even when poked with a sharp object. We demonstrate a skinlike finger-wearable driver for a light-emitting diode.

Printable organometallic perovskite enables large-area, low-dose X-ray imaging

Abstract: Medical X-ray imaging procedures require digital flat detectors operating at low doses to reduce radiation health risks. Solution-processed organic-inorganic hybrid perovskites have characteristics that make them good candidates for the photoconductive layer of such sensitive detectors. However, such detectors have not yet been built on thin-film transistor arrays because it has been difficult to prepare thick perovskite films (more than a few hundred micrometres) over large areas (a detector is typically 50 centimetres by 50 centimetres). We report here an all-solution-based (in contrast to conventional vacuum processing) synthetic route to producing printable polycrystalline perovskites with sharply faceted large grains having morphologies and optoelectronic properties comparable to those of single crystals. High sensitivities of up to 11 microcoulombs per air KERMA of milligray per square centimetre (μC mGyair-1 cm-2) are achieved under irradiation with a 100-kilovolt bremsstrahlung source, which are at least one order of magnitude higher than the sensitivities achieved with currently used amorphous selenium or thallium-doped cesium iodide detectors. We demonstrate X-ray imaging in a conventional thin-film transistor substrate by embedding an 830-micrometre-thick perovskite film and an additional two interlayers of polymer/perovskite composites to provide conformal interfaces between perovskite films and electrodes that control dark currents and temporal charge carrier transportation. Such an all-solution-based perovskite detector could enable low-dose X-ray imaging, and could also be used in photoconductive devices for radiation imaging, sensing and energy harvesting.

Fermi Level Pinning at Electrical Metal Contacts of Monolayer Molybdenum Dichalcogenides.

Abstract: Electrical metal contacts to two-dimensional (2D) semiconducting transition metal dichalcogenides (TMDCs) are found to be the key bottleneck to the realization of high device performance due to strong Fermi level pinning and high contact resistances (Rc). Until now, Fermi level pinning of monolayer TMDCs has been reported only theoretically, although that of bulk TMDCs has been reported experimentally. Here, we report the experimental study on Fermi level pinning of monolayer MoS2 and MoTe2 by interpreting the thermionic emission results. We also quantitatively compared our results with the theoretical simulation results of the monolayer structure as well as the experimental results of the bulk structure. We measured the pinning factor S to be 0.11 and −0.07 for monolayer MoS2 and MoTe2, respectively, suggesting a much stronger Fermi level pinning effect, a Schottky barrier height (SBH) lower than that by theoretical prediction, and interestingly similar pinning energy levels between monolayer and bulk Mo...

Stacked nanosheet gate-all-around transistor to enable scaling beyond FinFET

Abstract: In this paper, for the first time we demonstrate that horizontally stacked gate-all-around (GAA) Nanosheet structure is a good candidate for the replacement of FinFET at the 5nm technology node and beyond. It offers increased W eff per active footprint and better performance compared to FinFET, and with a less complex patterning strategy, leveraging EUV lithography. Good electrostatics are reported at L g =12nm and aggressive 44/48nm CPP (Contacted Poly Pitch) ground rules. We demonstrate work function metal (WFM) replacement and multiple threshold voltages, compatible with aggressive sheet to sheet spacing for wide stacked sheets. Stiction of sheets in long-channel devices is eliminated. Dielectric isolation is shown on standard bulk substrate for sub-sheet leakage control. Wrap-around contact (WAC) is evaluated for extrinsic resistance reduction.

Coupling between oxygen redox and cation migration explains unusual electrochemistry in lithium-rich layered oxides

Abstract: Lithium-rich layered transition metal oxide positive electrodes offer access to anion redox at high potentials, thereby promising high energy densities for lithium-ion batteries. However, anion redox is also associated with several unfavorable electrochemical properties, such as open-circuit voltage hysteresis. Here we reveal that in Li1.17-x Ni0.21Co0.08Mn0.54O2, these properties arise from a strong coupling between anion redox and cation migration. We combine various X-ray spectroscopic, microscopic, and structural probes to show that partially reversible transition metal migration decreases the potential of the bulk oxygen redox couple by > 1 V, leading to a reordering in the anionic and cationic redox potentials during cycling. First principles calculations show that this is due to the drastic change in the local oxygen coordination environments associated with the transition metal migration. We propose that this mechanism is involved in stabilizing the oxygen redox couple, which we observe spectroscopically to persist for 500 charge/discharge cycles.

Millimeter-Wave 5G Antennas for Smartphones: Overview and Experimental Demonstration

Abstract: For the first time to the best of our knowledge, this paper provides an overview of millimeter-wave (mmWave) 5G antennas for cellular handsets. Practical design considerations and solutions related to the integration of mmWave phased-array antennas with beam switching capabilities are investigated in detail. To experimentally examine the proposed methodologies, two types of mesh-grid phased-array antennas featuring reconfigurable horizontal and vertical polarizations are designed, fabricated, and measured at the 60 GHz spectrum. Afterward the antennas are integrated with the rest of the 60 GHz RF and digital architecture to create integrated mmWave antenna modules and implemented within fully operating cellular handsets under plausible user scenarios. The effectiveness, current limitations, and required future research areas regarding the presented mmWave 5G antenna design technologies are studied using mmWave 5G system benchmarks.

Superflexible, high-efficiency perovskite solar cells utilizing graphene electrodes: towards future foldable power sources

Abstract: With rapid and brilliant progress in performance over recent years, perovskite solar cells have drawn increasing attention for portable power source applications. Their advantageous features such as high efficiency, low cost, light weight and flexibility should be maximized if a robust and reliable flexible transparent electrode is offered. Here we demonstrate highly efficient and reliable super flexible perovskite solar cells using graphene as a transparent electrode. The device performance reaches 16.8% with no hysteresis comparable to that of the counterpart fabricated on a flexible indium-tin-oxide electrode showing a maximum efficiency of 17.3%. The flexible devices also demonstrate superb stability against bending deformation, maintaining >90% of its original efficiency after 1000 bending cycles and 85% even after 5000 bending cycles with a bending radius of 2 mm. This overwhelming bending stability highlights that perovskite photovoltaics with graphene electrodes can pave the way for rollable and foldable photovoltaic applications.

Thermally Activated Delayed Fluorescence and Aggregation Induced Emission with Through-Space Charge Transfer.

Abstract: Emissive molecules comprising a donor and an acceptor bridged by 9,9-dimethylxanthene, were studied (XPT, XCT, and XtBuCT). The structures position the donor and acceptor with cofacial alignment at distances of 3.3–3.5 A wherein efficient spatial charge transfer can occur. The quantum yields were enhanced by excluding molecular oxygen and thermally activated delayed fluorescence with lifetimes on the order of microseconds was observed. Although the molecules displayed low quantum yields in solution, higher quantum yields were observed in the solid state. Crystal structures revealed π–π intramolecular interactions between a donor and an acceptor, however, the dominant intermolecular interactions were C—H···π, which likely restrict the molecular dynamics to create aggregation-induced enhanced emission. Organic light emitting devices using XPT and XtBuCT as dopants displayed electroluminescence external quantum efficiencies as high as 10%.

Neural Network-based Graph Embedding for Cross-Platform Binary Code Similarity Detection

Abstract: The problem of cross-platform binary code similarity detection aims at detecting whether two binary functions coming from different platforms are similar or not. It has many security applications, including plagiarism detection, malware detection, vulnerability search, etc. Existing approaches rely on approximate graph-matching algorithms, which are inevitably slow and sometimes inaccurate, and hard to adapt to a new task. To address these issues, in this work, we propose a novel neural network-based approach to compute the embedding, i.e., a numeric vector, based on the control flow graph of each binary function, then the similarity detection can be done efficiently by measuring the distance between the embeddings for two functions. We implement a prototype called Gemini. Our extensive evaluation shows that Gemini outperforms the state-of-the-art approaches by large margins with respect to similarity detection accuracy. Further, Gemini can speed up prior art's embedding generation time by 3 to 4 orders of magnitude and reduce the required training time from more than 1 week down to 30 minutes to 10 hours. Our real world case studies demonstrate that Gemini can identify significantly more vulnerable firmware images than the state-of-the-art, i.e., Genius. Our research showcases a successful application of deep learning on computer security problems.

ICDAR2017 Robust Reading Challenge on Multi-Lingual Scene Text Detection and Script Identification - RRC-MLT

Abstract: Text detection and recognition in a natural environment are key components of many applications, ranging from business card digitization to shop indexation in a street. This competition aims at assessing the ability of state-of-the-art methods to detect Multi-Lingual Text (MLT) in scene images, such as in contents gathered from the Internet media and in modern cities where multiple cultures live and communicate together. This competition is an extension of the Robust Reading Competition (RRC) which has been held since 2003 both in ICDAR and in an online context. The proposed competition is presented as a new challenge of the RRC. The dataset built for this challenge largely extends the previous RRC editions in many aspects: the multi-lingual text, the size of the dataset, the multi-oriented text, the wide variety of scenes. The dataset is comprised of 18,000 images which contain text belonging to 9 languages. The challenge is comprised of three tasks related to text detection and script classification. We have received a total of 16 participations from the research and industrial communities. This paper presents the dataset, the tasks and the findings of this RRC-MLT challenge.

DRISA: a DRAM-based Reconfigurable In-Situ Accelerator

Abstract: Data movement between the processing units and the memory in traditional von Neumann architecture is creating the “memory wall” problem. To bridge the gap, two approaches, the memory-rich processor (more on-chip memory) and the compute-capable memory (processing-in-memory) have been studied. However, the first one has strong computing capability but limited memory capacity/bandwidth, whereas the second one is the exact the opposite.To address the challenge, we propose DRISA, a DRAM-based Reconfigurable In-Situ Accelerator architecture, to provide both powerful computing capability and large memory capacity/bandwidth. DRISA is primarily composed of DRAM memory arrays, in which every memory bitline can perform bitwise Boolean logic operations (such as NOR). DRISA can be reconfigured to compute various functions with the combination of the functionally complete Boolean logic operations and the proposed hierarchical internal data movement designs. We further optimize DRISA to achieve high performance by simultaneously activating multiple rows and subarrays to provide massive parallelism, unblocking the internal data movement bottlenecks, and optimizing activation latency and energy. We explore four design options and present a comprehensive case study to demonstrate significant acceleration of convolutional neural networks. The experimental results show that DRISA can achieve 8.8× speedup and 1.2× better energy efficiency compared with ASICs, and 7.7× speedup and 15× better energy efficiency over GPUs with integer operations.CCS CONCEPTS• Hardware → Dynamic memory; • Computer systems organization → reconfigurable computing; Neural networks;

Simple Black-Box Adversarial Attacks on Deep Neural Networks

Abstract: Deep neural networks are powerful and popular learning models that achieve state-of-the-art pattern recognition performance on many computer vision, speech, and language processing tasks. However, these networks have also been shown susceptible to crafted adversarial perturbations which force misclassification of the inputs. Adversarial examples enable adversaries to subvert the expected system behavior leading to undesired consequences and could pose a security risk when these systems are deployed in the real world.,,,,,, In this work, we focus on deep convolutional neural networks and demonstrate that adversaries can easily craft adversarial examples even without any internal knowledge of the target network. Our attacks treat the network as an oracle (black-box) and only assume that the output of the network can be observed on the probed inputs. Our attacks utilize a novel local-search based technique to construct numerical approximation to the network gradient, which is then carefully used to construct a small set of pixels in an image to perturb. We demonstrate how this underlying idea can be adapted to achieve several strong notions of misclassification. The simplicity and effectiveness of our proposed schemes mean that they could serve as a litmus test for designing robust networks.

Short-channel field-effect transistors with 9-atom and 13-atom wide graphene nanoribbons

Abstract: Bottom-up synthesized graphene nanoribbons and graphene nanoribbon heterostructures have promising electronic properties for high-performance field-effect transistors and ultra-low power devices such as tunneling field-effect transistors. However, the short length and wide band gap of these graphene nanoribbons have prevented the fabrication of devices with the desired performance and switching behavior. Here, by fabricating short channel (L ch ~ 20 nm) devices with a thin, high-κ gate dielectric and a 9-atom wide (0.95 nm) armchair graphene nanoribbon as the channel material, we demonstrate field-effect transistors with high on-current (I on > 1 μA at V d = -1 V) and high I on /I off ~ 105 at room temperature. We find that the performance of these devices is limited by tunneling through the Schottky barrier at the contacts and we observe an increase in the transparency of the barrier by increasing the gate field near the contacts. Our results thus demonstrate successful fabrication of high-performance short-channel field-effect transistors with bottom-up synthesized armchair graphene nanoribbons.Graphene nanoribbons show promise for high-performance field-effect transistors, however they often suffer from short lengths and wide band gaps. Here, the authors use a bottom-up synthesis approach to fabricate 9- and 13-atom wide ribbons, enabling short-channel transistors with 105 on-off current ratio.

A deep learning framework for supporting the classification of breast lesions in ultrasound images.

Abstract: In this research, we exploited the deep learning framework to differentiate the distinctive types of lesions and nodules in breast acquired with ultrasound imaging. A biopsy-proven benchmarking dataset was built from 5151 patients cases containing a total of 7408 ultrasound breast images, representative of semi-automatically segmented lesions associated with masses. The dataset comprised 4254 benign and 3154 malignant lesions. The developed method includes histogram equalization, image cropping and margin augmentation. The GoogLeNet convolutionary neural network was trained to the database to differentiate benign and malignant tumors. The networks were trained on the data with augmentation and the data without augmentation. Both of them showed an area under the curve of over 0.9. The networks showed an accuracy of about 0.9 (90%), a sensitivity of 0.86 and a specificity of 0.96. Although target regions of interest (ROIs) were selected by radiologists, meaning that radiologists still have to point out the location of the ROI, the classification of malignant lesions showed promising results. If this method is used by radiologists in clinical situations it can classify malignant lesions in a short time and support the diagnosis of radiologists in discriminating malignant lesions. Therefore, the proposed method can work in tandem with human radiologists to improve performance, which is a fundamental purpose of computer-aided diagnosis.

Neural Network-based Graph Embedding for Cross-Platform Binary Code Similarity Detection

Abstract: The problem of cross-platform binary code similarity detection aims at detecting whether two binary functions coming from different platforms are similar or not. It has many security applications, including plagiarism detection, malware detection, vulnerability search, etc. Existing approaches rely on approximate graph matching algorithms, which are inevitably slow and sometimes inaccurate, and hard to adapt to a new task. To address these issues, in this work, we propose a novel neural network-based approach to compute the embedding, i.e., a numeric vector, based on the control flow graph of each binary function, then the similarity detection can be done efficiently by measuring the distance between the embeddings for two functions. We implement a prototype called Gemini. Our extensive evaluation shows that Gemini outperforms the state-of-the-art approaches by large margins with respect to similarity detection accuracy. Further, Gemini can speed up prior art's embedding generation time by 3 to 4 orders of magnitude and reduce the required training time from more than 1 week down to 30 minutes to 10 hours. Our real world case studies demonstrate that Gemini can identify significantly more vulnerable firmware images than the state-of-the-art, i.e., Genius. Our research showcases a successful application of deep learning on computer security problems.

VPGNet: Vanishing Point Guided Network for Lane and Road Marking Detection and Recognition

Abstract: In this paper, we propose a unified end-to-end trainable multi-task network that jointly handles lane and road marking detection and recognition that is guided by a vanishing point under adverse weather conditions. We tackle rainy and low illumination conditions, which have not been extensively studied until now due to clear challenges. For example, images taken under rainy days are subject to low illumination, while wet roads cause light reflection and distort the appearance of lane and road markings. At night, color distortion occurs under limited illumination. As a result, no benchmark dataset exists and only a few developed algorithms work under poor weather conditions. To address this shortcoming, we build up a lane and road marking benchmark which consists of about 20,000 images with 17 lane and road marking classes under four different scenarios: no rain, rain, heavy rain, and night. We train and evaluate several versions of the proposed multi-task network and validate the importance of each task. The resulting approach, VPGNet, can detect and classify lanes and road markings, and predict a vanishing point with a single forward pass. Experimental results show that our approach achieves high accuracy and robustness under various conditions in realtime (20 fps). The benchmark and the VPGNet model will be publicly available

Fused DNN: A Deep Neural Network Fusion Approach to Fast and Robust Pedestrian Detection

Abstract: We propose a deep neural network fusion architecture for fast and robust pedestrian detection. The proposed network fusion architecture allows for parallel processing of multiple networks for speed. A single shot deep convolutional network is trained as a object detector to generate all possible pedestrian candidates of different sizes and occlusions. This network outputs a large variety of pedestrian candidates to cover the majority of ground-truth pedestrians while also introducing a large number of false positives. Next, multiple deep neural networks are used in parallel for further refinement of these pedestrian candidates. We introduce a soft-rejection based network fusion method to fuse the soft metrics from all networks together to generate the final confidence scores. Our method performs better than existing state-of-the-arts, especially when detecting small-size and occluded pedestrians. Furthermore, we propose a method for integrating pixel-wise semantic segmentation network into the network fusion architecture as a reinforcement to the pedestrian detector. The approach outperforms state-of-the-art methods on most protocols on Caltech Pedestrian dataset, with significant boosts on several protocols. It is also faster than all other methods.

Boosting Power-Generating Performance of Triboelectric Nanogenerators via Artificial Control of Ferroelectric Polarization and Dielectric Properties

Abstract: Low output current represents a critical challenge that has interrupted the use of triboelectric nanogenerators (TNGs) in a wide range of applications as sustainable power sources. Many approaches (e.g., operation at high frequency, parallel stacks of individual devices, and hybridization with other energy harvesters) remain limited in solving the challenge of low output current from TNGs. Here, a nanocomposite material system having a superior surface charge density as a triboelectric active material is reported. The nanocomposite material consists of a high dielectric ceramic material, barium titanate, showing great charge-trapping capability, together with a ferroelectric copolymer matrix, Poly(vinylidenefluoride-co-trifluoroethylene) (P(VDF-TrFE)), with electrically manipulated polarization with strong triboelectric charge transfer characteristics. Based on a contact potential difference study showing that poled P(VDF-TrFE) has 18 times higher charge attracting properties, a fraction between two components is optimized. Boosting power-generating performance is achieved for 1130 V of output voltage and 1.5 mA of output current with this ferroelectric composite-based TNG, under 6 kgf of pushing force at 5 Hz. An enormously faster charging property than traditional polymer film-based TNGs is demonstrated in this study. Finally, the charging of a self-powering smartwatch with a charging management circuit system with no external power sources is demonstrated successfully.

Pattern Recognition Using Carbon Nanotube Synaptic Transistors with an Adjustable Weight Update Protocol

Abstract: Recent electronic applications require an efficient computing system that can perform data processing with limited energy consumption. Inspired by the massive parallelism of the human brain, a neuromorphic system (hardware neural network) may provide an efficient computing unit to perform such tasks as classification and recognition. However, the implementation of synaptic devices (i.e., the essential building blocks for emulating the functions of biological synapses) remains challenging due to their uncontrollable weight update protocol and corresponding uncertain effects on the operation of the system, which can lead to a bottleneck in the continuous design and optimization. Here, we demonstrate a synaptic transistor based on highly purified, preseparated 99% semiconducting carbon nanotubes, which can provide adjustable weight update linearity and variation margin. The pattern recognition efficacy is validated using a device-to-system level simulation framework. The enlarged margin rather than the linea...

VPGNet: Vanishing Point Guided Network for Lane and Road Marking Detection and Recognition

Abstract: In this paper, we propose a unified end-to-end trainable multi-task network that jointly handles lane and road marking detection and recognition that is guided by a vanishing point under adverse weather conditions We tackle rainy and low illumination conditions, which have not been extensively studied until now due to clear challenges For example, images taken under rainy days are subject to low illumination, while wet roads cause light reflection and distort the appearance of lane and road markings At night, color distortion occurs under limited illumination As a result, no benchmark dataset exists and only a few developed algorithms work under poor weather conditions To address this shortcoming, we build up a lane and road marking benchmark which consists of about 20,000 images with 17 lane and road marking classes under four different scenarios: no rain, rain, heavy rain, and night We train and evaluate several versions of the proposed multi-task network and validate the importance of each task The resulting approach, VPGNet, can detect and classify lanes and road markings, and predict a vanishing point with a single forward pass Experimental results show that our approach achieves high accuracy and robustness under various conditions in real-time (20 fps) The benchmark and the VPGNet model will be publicly available

Overview of Full-Dimension MIMO in LTE-Advanced Pro

Abstract: Multiple-input multiple-output (MIMO) systems with a large number of base station antennas, often called massive MIMO, have received much attention in academia and industry as a means to improve the spectral efficiency, energy efficiency, and processing complexity of next generation cellular systems. The mobile communication industry has initiated a feasibility study of massive MIMO systems to meet the increasing demand of future wireless systems. Field trials of the proof-of-concept systems have demonstrated the potential gain of the Full-Dimension MIMO (FD-MIMO), an official name for the MIMO enhancement in the 3rd generation partnership project (3GPP). 3GPP initiated standardization activity for the seamless integration of this technology into current 4G LTE systems. In this article, we provide an overview of FD-MIMO systems, with emphasis on the discussion and debate conducted on the standardization process of Release 13. We present key features for FD-MIMO systems, a summary of the major issues for the standardization and practical system design, and performance evaluations for typical FD-MIMO scenarios.

Generalizing DTW to the multi-dimensional case requires an adaptive approach

Abstract: In recent years Dynamic Time Warping (DTW) has emerged as the distance measure of choice for virtually all time series data mining applications. For example, virtually all applications that process data from wearable devices use DTW as a core sub-routine. This is the result of significant progress in improving DTW's efficiency, together with multiple empirical studies showing that DTW-based classifiers at least equal (and generally surpass) the accuracy of all their rivals across dozens of datasets. Thus far, most of the research has considered only the one-dimensional case, with practitioners generalizing to the multi-dimensional case in one of two ways, dependent or independent warping. In general, it appears the community believes either that the two ways are equivalent, or that the choice is irrelevant. In this work, we show that this is not the case. The two most commonly used multi-dimensional DTW methods can produce different classifications, and neither one dominates over the other. This seems to suggest that one should learn the best method for a particular application. However, we will show that this is not necessary; a simple, principled rule can be used on a case-by-case basis to predict which of the two methods we should trust at the time of classification. Our method allows us to ensure that classification results are at least as accurate as the better of the two rival methods, and, in many cases, our method is significantly more accurate. We demonstrate our ideas with the most extensive set of multi-dimensional time series classification experiments ever attempted.

A phase III randomised, double-blind, parallel-group study comparing SB4 with etanercept reference product in patients with active rheumatoid arthritis despite methotrexate therapy

Abstract: Objectives To compare the efficacy and safety of SB4 (an etanercept biosimilar) with reference product etanercept (ETN) in patients with moderate to severe rheumatoid arthritis (RA) despite methotrexate (MTX) therapy. Methods This is a phase III, randomised, double-blind, parallel-group, multicentre study with a 24-week primary endpoint. Patients with moderate to severe RA despite MTX treatment were randomised to receive weekly dose of 50 mg of subcutaneous SB4 or ETN. The primary endpoint was the American College of Rheumatology 20% (ACR20) response at week 24. Other efficacy endpoints as well as safety, immunogenicity and pharmacokinetic parameters were also measured. Results 596 patients were randomised to either SB4 (N=299) or ETN (N=297). The ACR20 response rate at week 24 in the per-protocol set was 78.1% for SB4 and 80.3% for ETN. The 95% CI of the adjusted treatment difference was −9.41% to 4.98%, which is completely contained within the predefined equivalence margin of −15% to 15%, indicating therapeutic equivalence between SB4 and ETN. Other efficacy endpoints and pharmacokinetic endpoints were comparable. The incidence of treatment-emergent adverse events was comparable (55.2% vs 58.2%), and the incidence of antidrug antibody development up to week 24 was lower in SB4 compared with ETN (0.7% vs 13.1%). Conclusions SB4 was shown to be equivalent with ETN in terms of efficacy at week 24. SB4 was well tolerated with a lower immunogenicity profile. The safety profile of SB4 was comparable with that of ETN. Trial registration numbers NCT01895309, EudraCT 2012-005026-30.

Vertical memory devices and methods of manufacturing the same

Abstract: A vertical memory device includes a channel, gate lines, and a cutting pattern, respectively, on a substrate. The channel extends in a first direction substantially perpendicular to an upper surface of the substrate. The gate linesare spaced apart from each other in the first direction. Each of the gate lines surrounds the channel and extends in a second direction substantially parallel to the upper surface of the substrate. The cutting pattern includes a first cutting portion extending in the first direction and cutting the gate lines, and a second cutting portion crossing the first cutting portion and merged with the first cutting portion.

Continual Learning with Deep Generative Replay

Abstract: Attempts to train a comprehensive artificial intelligence capable of solving multiple tasks have been impeded by a chronic problem called catastrophic forgetting. Although simply replaying all previous data alleviates the problem, it requires large memory and even worse, often infeasible in real world applications where the access to past data is limited. Inspired by the generative nature of hippocampus as a short-term memory system in primate brain, we propose the Deep Generative Replay, a novel framework with a cooperative dual model architecture consisting of a deep generative model ("generator") and a task solving model ("solver"). With only these two models, training data for previous tasks can easily be sampled and interleaved with those for a new task. We test our methods in several sequential learning settings involving image classification tasks.

SmartAuth: User-Centered Authorization for the Internet of Things

Abstract: Internet of Things (IoT) platforms often require users to grant permissions to third-party apps, such as the ability to control a lock. Unfortunately, because few users act based upon, or even comprehend, permission screens, malicious or careless apps can become overprivileged by requesting unneeded permissions. To meet the IoT’s unique security demands, such as cross-device, context-based, and automatic operations, we present a new design that supports user-centric, semantic-based “smart” authorization. Our technique, called SmartAuth, automatically collects security-relevant information from an IoT app’s description, code and annotations, and generates an authorization user interface to bridge the gap between the functionalities explained to the user and the operations the app actually performs. Through the interface, security policies can be generated and enforced by enhancing existing platforms. To address the unique challenges in IoT app authorization, where states of multiple devices are used to determine the operations that can happen on other devices, we devise new technologies that link a device’s context (e.g., a humidity sensor in a bath room) to an activity’s semantics (e.g., taking a bath) using natural language processing and program analysis. We evaluate SmartAuth through user studies, finding participants who use SmartAuth are significantly more likely to avoid overprivileged apps.