Disorder-free sputtering method on graphene

Deposition of various materials onto graphene without causing any disorder is highly desirable for graphene applications. Especially, sputtering is a versatile technique to deposit various metals and insulators for spintronics, and indium tin oxide to make transparent devices. However, the sputtering process causes damage to graphene because of high energy sputtered atoms. By flipping the substrate and using a high Ar pressure, we demonstrate that the level of damage to graphene can be reduced or eliminated in dc, rf, and reactive sputtering processes

Detection of Fall Risk Behaviors in Patients with Severe Mobility Issues Using FMCW Radar: Sitting Up and Sitting on the Side of the Bed

This study aimed to detect fall risk behaviors using radar—a non-contact sensor—to prevent falling accidents, which are one of the most fatal problems faced by older adults. Hospitals and nursing homes often have patients who cannot move alone without caregivers. In this context, the process of a patient sitting up from a lying-down position shortly before standing up has been observed as a fall risk behavior. This study added movement information as a new characteristic feature to the range and velocity information used in conventional radar-based behavior recognition studies. Performance comparisons confirmed that the addition of movement information performs excellently in detecting risk situations. Furthermore, a bidirectional long short-term memory model was trained using a feature to predict risk situations. The model exhibited accuracy, recall, and precision rates of 93.84%, 88.57%, and 99.07%, respectively. Additionally, its performance in detecting fall risk behavior was verified by conducting experiments involving continuous behaviors

Duo: Software Defined Intrusion Tolerant System Using Dual Cluster

An intrusion tolerant system (ITS) is a network security system that is composed of redundant virtual servers that are online only in a short time window, called exposure time. The servers are periodically recovered to their clean state, and any infected servers are refreshed again, so attackers have insufficient time to succeed in breaking into the servers. However, there is a conflicting interest in determining exposure time, short for security and long for performance. In other words, the short exposure time can increase security but requires more servers to run in order to process requests in a timely manner. In this paper, we propose Duo, an ITS incorporated in SDN, which can reduce exposure time without consuming computing resources. In Duo, there are two types of servers: some servers with long exposure time (White server) and others with short exposure time (Gray server). Then, Duo classifies traffic into benign and suspicious with the help of SDN/NFV technology that also allows dynamically forwarding the classified traffic to White and Gray servers, respectively, based on the classification result. By reducing exposure time of a set of servers, Duo can decrease exposure time on average. We have implemented the prototype of Duo and evaluated its performance in a realistic environment

Ambipolar bistable switching effect of graphene

Reproducible current hysteresis is observed in graphene with a back gate structure in a two-terminal configuration. By applying a back gate bias to tune the Fermi level, an opposite sequence of switching with the different charge carriers, holes and electrons, is found. The charging and discharging effect is proposed to explain this ambipolar bistable hysteretic switching. To confirm this hypothesis, one-level transport model simulations including charging effect are performed and the results are consistent with our experimental data. Methods of improving the ON/OFF ratio of graphene resistive switching are suggested

Extremely large magnetoresistance in few-layer graphene/boron-nitride heterostructures

Understanding magnetoresistance, the change in electrical resistance upon an external magnetic field, at the atomic level is of great interest both fundamentally and technologically. Graphene and other two-dimensional layered materials provide an unprecedented opportunity to explore magnetoresistance at its nascent stage of structural formation. Here, we report an extremely large local magnetoresistance of ~ 2,000% at 400 K and a non-local magnetoresistance of > 90,000% in 9 T at 300 K in few-layer graphene/boron-nitride heterostructures. The local magnetoresistance is understood to arise from large differential transport parameters, such as the carrier mobility, across various layers of few-layer graphene upon a normal magnetic field, whereas the non-local magnetoresistance is due to the magnetic field induced Ettingshausen-Nernst effect. Non-local magnetoresistance suggests the possibility of a graphene based gate tunable thermal switch. In addition, our results demonstrate that graphene heterostructures may be promising for magnetic field sensing applications

Tunneling characteristics of graphene

Negative differential conductance and tunneling characteristics of two-terminal graphene devices are observed before and after electric breakdown, respectively. The former is caused by the strong scattering under a high E-field, and the latter is due to the appearance of a tunneling barrier in graphene channel induced by a structural transformation from crystalline graphene to disordered graphene because of the breakdown. Using Raman spectroscopy and imaging, the presence of non-uniform disordered graphene is confirmed. A memory switching effect of 100000% ON/OFF ratio is demonstrated in the tunneling regime which can be employed in various applications

Efficient Privacy-Preserving Matrix Factorization via Fully Homomorphic Encryption

Recommendation systems become popular in our daily life. It is well known that the more the release of users’ personal data, the better the quality of recommendation. However, such services raise serious privacy concerns for users. In this paper, focusing on matrix factorization-based recommendation systems, we propose the first privacy-preserving matrix factorization using fully homomorphic encryption. On inputs of encrypted users\u27 ratings, our protocol performs matrix factorization over the encrypted data and returns encrypted outputs so that the recommendation system knows nothing on rating values and resulting user/item profiles. It provides a way to obfuscate the number and list of items a user rated without harming the accuracy of recommendation, and additionally protects recommender\u27s tuning parameters for business benefit and allows the recommender to optimize the parameters for quality of service. To overcome performance degradation caused by the use of fully homomorphic encryption, we introduce a novel data structure to perform computations over encrypted vectors, which are essential operations for matrix factorization, through secure 2-party computation in part. With the data structure, the proposed protocol requires dozens of times less computation cost over those of previous works. Our experiments on a personal computer with 3.4 GHz 6-cores 64 GB RAM show that the proposed protocol runs in 1.5 minutes per iteration. It is more efficient than Nikolaenko et al.\u27s work proposed in CCS 2013, in which it took about 170 minutes on two servers with 1.9 GHz 16-cores 128 GB RAM

Extremely long quasiparticle spin lifetimes in superconducting aluminium using MgO tunnel spin injectors

There has been an intense search in recent years for long-lived spin-polarized carriers for spintronic and quantum-computing devices. Here we report that spin polarized quasi-particles in superconducting aluminum layers have surprisingly long spin-lifetimes, nearly a million times longer than in their normal state. The lifetime is determined from the suppression of the aluminum's superconductivity resulting from the accumulation of spin polarized carriers in the aluminum layer using tunnel spin injectors. A Hanle effect, observed in the presence of small in-plane orthogonal fields, is shown to be quantitatively consistent with the presence of long-lived spin polarized quasi-particles. Our experiments show that the superconducting state can be significantly modified by small electric currents, much smaller than the critical current, which is potentially useful for devices involving superconducting qubits

An embedding technique to determine ττ backgrounds in proton-proton collision data

An embedding technique is presented to estimate standard model tau tau backgrounds from data with minimal simulation input. In the data, the muons are removed from reconstructed mu mu events and replaced with simulated tau leptons with the same kinematic properties. In this way, a set of hybrid events is obtained that does not rely on simulation except for the decay of the tau leptons. The challenges in describing the underlying event or the production of associated jets in the simulation are avoided. The technique described in this paper was developed for CMS. Its validation and the inherent uncertainties are also discussed. The demonstration of the performance of the technique is based on a sample of proton-proton collisions collected by CMS in 2017 at root s = 13 TeV corresponding to an integrated luminosity of 41.5 fb(-1).Peer reviewe