Efficient spectral domain technique for the frequency locking analysis of nonlinear oscillators

After discussing an implementation of the harmonic balance technique that enables the efficient determination of the limit cycles for a nonlinear autonomous dynamical system, we consider the frequency locking of a set of oscillators that is studied by means of a proper extension of the aforementioned approach. Harmonic balance is also used for the numerical computation of the Floquet exponents and eigenvectors of the frequency locked limit cycle, thus enabling the assessment of its stability properties. The proposed technique is applied to the study of the frequency locking properties of a set of coupled Chua’s oscillators as a function of several parameters

Two-Point Versus Multipartite Entanglement in Quantum Phase Transitions

We analyze correlations between subsystems for an extended Hubbard model exactly solvable in one dimension, which exhibits a rich structure of quantum phase transitions (QPTs). The T=0 phase diagram is exactly reproduced by studying singularities of single-site entanglement. It is shown how comparison of the latter quantity and quantum mutual information allows one to recognize whether two-point or shared quantum correlations are responsible for each of the occurring QPTs. The method works in principle for any number D of degrees of freedom per site. As a by-product, we are providing a benchmark for direct measures of bipartite entanglement; in particular, here we discuss the role of negativity at the transition.Comment: 4 pages, 2 figures, 1 tabl

Effects of tin phosphate nanosheet addition on proton-conducting properties of sulfonated poly(ether sulfone) membranes

Organic/inorganic composite membranes were prepared by dispersing nanosheets of layered tin phosphate hydrate [Sn(HPO4)2·nH2O (SnP)] in sulfonated poly(ether sulfone) (SPES) at SnP contents of 0–40 vol.%. The stabilities and proton conductivities of SPES/SnP nanosheet (SnP-NS) composite membraneswere investigated and comparedwith those of SPES/SnP particle (SnP-P) composite membranes. The chemical stabilities as evaluated by thermogravimetry, differential thermal analysis, and diffuse reflectance Fourier-transform infrared spectroscopy were improved in both composite membranes. The improvement in the structural stability of SPES/SnP-NS composite membranes was more evident than that in SPES/SnP-P. The results suggest that exfoliation of SnP increases the area of the SPES–SnP interface and extends the connectivity of the network of hydrogen bonds. A composite membrane containing 10 vol.% SnP-NS (SPES/SnP-NS10vol.%) showed a high conductivity of 5.9×10−2 S cm−1 at 150 °C under saturated water vapor pressure. Although less water was present in SPES/SnP-NS10vol.% than in SPES/SnP-P10vol.% or pure SPES, the conductivity of SnP-NS10vol.% was the highest among these samples at 130 °C under a high relative humidity (RH). However at a low RH, the proton-conducting property was not improved by changing the composition of the SnP-NS. These results suggest that the hydrogen-bond network operates effectively for proton conduction at a high RH, but at a low RH, the network fails to conduct as a result of a decrease in water content accompanied by structural stabilization

Memcomputing NP-complete problems in polynomial time using polynomial resources and collective states

Memcomputing is a novel non-Turing paradigm of computation that uses interacting memory cells (memprocessors for short) to store and process information on the same physical platform. It was recently proven mathematically that memcomputing machines have the same computational power of nondeterministic Turing machines. Therefore, they can solve NP-complete problems in polynomial time and, using the appropriate architecture, with resources that only grow polynomially with the input size. The reason for this computational power stems from properties inspired by the brain and shared by any universal memcomputing machine, in particular intrinsic parallelism and information overhead, namely, the capability of compressing information in the collective state of the memprocessor network. We show an experimental demonstration of an actual memcomputing architecture that solves the NP-complete version of the subset sum problem in only one step and is composed of a number of memprocessors that scales linearly with the size of the problem. We have fabricated this architecture using standard microelectronic technology so that it can be easily realized in any laboratory setting. Although the particular machine presented here is eventually limited by noise—and will thus require error-correcting codes to scale to an arbitrary number of memprocessors—it represents the first proof of concept of a machine capable of working with the collective state of interacting memory cells, unlike the present-day single-state machines built using the von Neumann architecture

Microstructural and morphological properties of homoepitaxial (001)ZnTe layers investigated by x-ray diffuse scattering

The microstructural and morphological properties of homoepitaxial (001)ZnTe layers are investigated by x-ray diffuse scattering. High resolution reciprocal space maps recorded close to the ZnTe (004) Bragg peak show different diffuse scattering features. One kind of cross-shaped diffuse scattering streaks along directions can be attributed to stacking faults within the epilayers. Another kind of cross-shaped streaks inclined at an angle of about 80deg with respect to the in-plane direction arises from the morphology of the epilayers. (abridged version

Dynamic computing random access memory

The present von Neumann computing paradigm involves a significant amount of information transfer between a central processing unit and memory, with concomitant limitations in the actual execution speed. However, it has been recently argued that a different form of computation, dubbed memcomputing (Di Ventra and Pershin 2013 Nat. Phys. 9 200–2) and inspired by the operation of our brain, can resolve the intrinsic limitations of present day architectures by allowing for computing and storing of information on the same physicalplatform. Here we show a simple and practical realization of memcomputing that utilizes easy-to-build memcapacitive systems. We name this architecture dynamic computing random access memory (DCRAM). We show that DCRAM provides massively-parallel and polymorphic digital logic, namely it allows for different logic operations with the same architecture, by varying only the control signals. In addition, by taking into account realistic parameters, its energy expenditures can be as low as a few fJ per operation. DCRAM is fully compatible with CMOS technology, can be realized with current fabrication facilities, and therefore can really serve as an alternative to the present computing technology