Magnitude Improvement In Efficiency Research Articles

Parity-time symmetry enabled ultra-efficient nonlinear optical signal processing

Nonlinear optical signal processing (NOSP) has the potential to significantly improve the throughput, flexibility, and cost-efficiency of optical communication networks by exploiting the intrinsically ultrafast optical nonlinear wave mixing. It can support digital signal processing speeds of up to terabits per second, far exceeding the line rate of the electronic counterpart. In NOSP, high-intensity light fields are used to generate nonlinear optical responses, which can be used to process optical signals. Great efforts have been devoted to developing new materials and structures for NOSP. However, one of the challenges in implementing NOSP is the requirement of high-intensity light fields, which is difficult to generate and maintain. This has been a major roadblock to realize practical NOSP systems for high-speed, high-capacity optical communications. Here, we propose using a parity-time (PT) symmetric microresonator system to significantly enhance the light intensity and support high-speed operation by relieving the bandwidth-efficiency limit imposed on conventional single resonator systems. The design concept is the co-existence of a PT symmetry broken regime for a narrow-linewidth pump wave and near-exceptional point operation for broadband signal and idler waves. This enables us to achieve a new NOSP system with two orders of magnitude improvement in efficiency compared to a single resonator. With a highly nonlinear AlGaAs-on-Insulator platform, we demonstrate an NOSP at a data rate approaching 40 gigabits per second with a record low pump power of one milliwatt. These findings pave the way for the development of fully chip-scale NOSP devices with pump light sources integrated together, potentially leading to a wide range of applications in optical communication networks and classical or quantum computation. The combination of PT symmetry and NOSP may also open up opportunities for amplification, detection, and sensing, where response speed and efficiency are equally important.

AI4OPT: AI Institute for Advances in Optimization

AbstractThis article is a short introduction to AI4OPT, the NSF AI Institute for Advances in Optimization. AI4OPT fuses AI and optimization, inspired by societal challenges in supply chains, energy systems, chip design and manufacturing, and sustainable food systems. By combining machine learning and mathematical optimization, AI4OPT strives to develop AI‐assisted optimization systems that bring orders of magnitude improvements in efficiency, perform accurate uncertainty quantification, and address challenges in resiliency and sustainability. AI4OPT also applies its “teaching the teachers” philosophy to provide longitudinal educational pathways in AI for engineering.

Practical NTRU Signcryption in the Standard Model.

Based on the NTRU trapdoor used in NIST's Falcon, a signcryption scheme following the sign-then-encrypt paradigm is constructed. The existing partitioning technique based on Waters hash over the lattice can not complete the security reduction in the standard model for the signature part due to the "partiality" of the pre-image generated with the NTRU trapdoor. To address this, a variant of Waters hash over small integers is proposed and, the probability of the successful reduction is analyzed. The resulting signcryption achieves existential unforgeability under the adaptive chosen-message attacks. By utilizing the uniqueness of the secret and the noise in an NTRU instance, the tag used in encryption is eliminated. Furthermore, a method to construct tamper-sensitive lattice public key encryption is proposed. This approach implants the ciphertext-sensitive information into the lattice public key encryption and binds it to the encrypted information. The malleability to the public key ciphertext triggers the change of the message-signature pair so that the IND-CCA2 security of the entire ciphertext can be guaranteed by the signature for the message. Thanks to the rational design and the efficiency of the NTRU trapdoor, the computational overhead of the proposed scheme is reduced significantly compared to the existing lattice-based signcryption scheme, reaching orders of magnitude improvement in efficiency. The experiment shows that the proposed scheme is efficient.

An Improved Method for Estimating the Velocity Field of Coronal Propagating Disturbances

The solar corona is host to a continuous flow of propagating disturbances (PD). These are continuous and ubiquitous across broad regions of the corona, including the quiet Sun. The aim of this article is to present an improved, efficient method to create velocity vector field maps based on the direction and magnitude of the PD as observed in time series of extreme ultraviolet (EUV) images. The method presented here is for use with the Atmospheric Imaging Assembly (AIA)/Solar Dynamics Observatory (SDO) EUV channels and takes as input approx 2 hours of images at the highest 12 s cadence. Data from a region near disc centre is extracted, and a process called time normalisation is applied to the co-aligned data. Following noise reduction using à trous decomposition, the PD are effectively revealed. A modified Lucas–Kanade algorithm is then used to map the velocity field. The method described here runs comfortably on a desktop computer in a few minutes and offers an order of magnitude improvement in efficiency compared to a previous implementation. As applied to a region of the quiet Sun, we find that the velocity field describes a mosaic of cells of coherent outwardly-diverging PD flows of typical size 50 to 100'' (36 to 72 Mm). The flows originate from points and narrow corridors in the cell centres and end in the narrow boundaries between cells. Visual comparison with ultraviolet AIA images shows that the flow sources are correlated with the bright photospheric supergranular network boundaries. Assuming that the PD follow the local magnetic field, the velocity flow field is a proxy for the plane-of-sky distribution of the coronal magnetic field, and therefore the maps offer a unique insight into the topology of the corona. These are particularly valuable for quiet Sun regions where the appearance of structures in EUV images is hard to interpret.

Enhanced Overall Water Splitting by a Zirconium‐Doped TaON‐Based Photocatalyst

AbstractSolar‐powered one‐step‐excitation overall water splitting (OWS) using semiconducting materials is a simple means of achieving scalable and sustainable hydrogen production. While tantalum oxynitride (TaON) is one of the few photocatalysts capable of promoting OWS via single‐step visible‐light excitation, the efficiency of this process remains extremely poor. The present work employed 15 nm amorphous Ta2O5⋅3.3 H2O nanoparticles as a new precursor together with Zr doping and an optimized nitridation duration to synthesize a TaON‐based photocatalyst with reduced particle sizes and low defect densities. Upon loading with Ru/Cr2O3/IrO2 cocatalysts, this material exhibited stoichiometric water splitting into hydrogen and oxygen, with an order of magnitude improvement in efficiency. Our findings demonstrate the importance of inventing/selecting the appropriate synthetic precursor and of defect control for fabricating active OWS photocatalysts.

Enhanced Overall Water Splitting by a Zirconium-Doped TaON-Based Photocatalyst.

Solar-powered one-step-excitation overall water splitting (OWS) using semiconducting materials is a simple means of achieving scalable and sustainable hydrogen production. While tantalum oxynitride (TaON) is one of the few photocatalysts capable of promoting OWS via single-step visible-light excitation, the efficiency of this process remains extremely poor. The present work employed 15 nm amorphous Ta2 O5 ⋅3.3 H2 O nanoparticles as a new precursor together with Zr doping and an optimized nitridation duration to synthesize a TaON-based photocatalyst with reduced particle sizes and low defect densities. Upon loading with Ru/Cr2 O3 /IrO2 cocatalysts, this material exhibited stoichiometric water splitting into hydrogen and oxygen, with an order of magnitude improvement in efficiency. Our findings demonstrate the importance of inventing/selecting the appropriate synthetic precursor and of defect control for fabricating active OWS photocatalysts.

Informed Proposals for Local MCMC in Discrete Spaces

There is a lack of methodological results to design efficient Markov chain Monte Carlo ( MCMC ) algorithms for statistical models with discrete-valued high-dimensional parameters. Motivated by this consideration, we propose a simple framework for the design of informed MCMC proposals (i.e., Metropolis–Hastings proposal distributions that appropriately incorporate local information about the target) which is naturally applicable to discrete spaces. Using Peskun-type comparisons of Markov kernels, we explicitly characterize the class of asymptotically optimal proposal distributions under this framework, which we refer to as locally balanced proposals. The resulting algorithms are straightforward to implement in discrete spaces and provide orders of magnitude improvements in efficiency compared to alternative MCMC schemes, including discrete versions of Hamiltonian Monte Carlo. Simulations are performed with both simulated and real datasets, including a detailed application to Bayesian record linkage. A direct connection with gradient-based MCMC suggests that locally balanced proposals can be seen as a natural way to extend the latter to discrete spaces. Supplementary materials for this article are available online.

Multiphysics Modeling of Gas Diffusion Electrodes for CO2 Reduction

One of the main challenges towards achieving high efficiency solar fuel generators performing carbon dioxide reduction is the mass transport limitations in traditional aqueous systems. Typically, the current density for liquid phase carbon dioxide reduction is limited to approximately 10 mA/cm2 due to concentration polarization near the cathode surface.1This can be overcome by introducing a gas diffusion electrode, which allows vapor phase carbon dioxide to be fed directly to the catalyst, significantly decreasing the diffusion length. Experiments have shown almost two orders of magnitude improvement in efficiency for vapor-fed devices compared to traditional aqueous systems.2,3 This improvement is most likely due to a higher surface area in the porous electrode and an increased diffusivity of gas phase CO2. For this work, we have developed a multiphysics model for gas diffusion electrodes that simulates species transport in gas phase, liquid phase and solid phase, charge transport, and (electro)chemical reaction kinetics. The model will focus on understanding transport of species in and out of the catalyst layer, and investigate the effects of gas diffusion electrode properties such as thickness, porosity and hydrophobicity on cell performance. Acknowledgements This material is based upon work performed by the Joint Center for Artificial Photosynthesis, a DOE Energy Innovation Hub, supported through the Office of Science of the U.S. Department of Energy under Award Number DE-SC0004993. References Singh, M. R.; Clark, E. L.; Bell, A. T., Physical Chemistry Chemical Physics, 2015, 17(29), 18924-36.Verma, S.; Lu, X.; Ma, S.; Masel, R. I.; Kenis, P. J. A., Physical Chemistry Chemical Physics, 2015, 18, 7075-7084.Kim, B.; Hillman, F.; Ariyoshi, M.; Fujikawa, S.; Kenis, P. J. A., Journal of Power Sources, 2016, 312, 192-198.

Command-based importance sampling for statistical model checking

Statistical model checking avoids the exponential growth of states of numerical model checking, but rare properties are costly to verify. Importance sampling can reduce the cost if good importance sampling distributions can be found efficiently.Our approach uses a tractable cross-entropy minimisation algorithm to find an optimal parametrised importance sampling distribution. In contrast to previous work, our algorithm uses a naturally defined low dimensional vector to specify the distribution, thus avoiding an explicit representation of a transition matrix. Our parametrisation leads to a unique optimum and is shown to produce many orders of magnitude improvement in efficiency on various models. In this work we link the existence of optimal importance sampling distributions to logical properties and show how our parametrisation affects this link. We also motivate and present simple algorithms to create the initial distribution necessary for cross-entropy minimisation. Finally, we discuss the open challenge of defining error bounds with importance sampling and describe how our optimal parametrised distributions may be used to infer qualitative confidence.

The Q100 Database Processing Unit

The Q100 uses hardware specialization to improve the energy efficiency of analytic database applications. The proposed accelerators are called database processing units. DPUs are analogous to GPUs, but where GPUs target graphics applications, DPUs target analytic database workloads. This article demonstrates a proof of concept design, called the Q100, which provides one to two orders of magnitude improvement in efficiency over single- and multithreaded software database management systems. The Q100 exploits the innate structure of the workload, viewing the data in terms of tables and columns rather than as an unstructured array of bytes, to more efficiently move and manipulate database content. Because a large proportion of a chip's energy is spent delivering data to the computation engines, this approach both improves overall energy efficiency and complements other specialized computation engines.

Plasma channel flows: Electro-fluid dynamic jets

The present work builds on the success of a dielectric barrier discharge driven plasma channel by exploring an electrode configuration that directly actuates the bulk fluid minimizing jet impingement and viscosity related losses. Influence of several electrical and physical parameters including electrode materials are experimentally investigated. Results indicate significant variation of performance with these parameters and suggest that in comparison to surface dielectric barrier plasma actuator driven flows, at least an order of magnitude improvement in efficiency is possible. The jet produced from this plasma channel configuration allows greater versatility for applications in boundary layer flow control and internal flows.

Highly efficient terahertz pulse generation by optical rectification in stoichiometric and cryo-cooled congruent lithium niobate

We report, and review in detail, experiments resulting in a record 3.7% optical-to-terahertz (THz) conversion efficiency by optical rectification (OR) in cryogenically cooled congruent lithium niobate (cLN) using a near-optimal 680 fs pump pulse at 1030 nm. In addition, we report a record conversion efficiency of 1.7% at room temperature using stoichiometric lithium niobate (sLN) which results in 21.8 μJ of THz energy from a 1.2 mJ optical pulse. Electro-optical sampling measurements reveal the THz pulses to be single-cycle and centered at 0.45 THz. The experimentally measured efficiency, THz waveform, and THz spectrum are in good agreement with theoretical calculations. Finally, spatial beam profile measurements are also provided. To our knowledge, these results represent an order of magnitude improvement in efficiency of THz generation by OR in lithium niobate over previous results.

Codesign Tradeoffs for High-Performance, Low-Power Linear Algebra Architectures

As technology is reaching physical limits, reducing power consumption is a key issue on our path to sustained performance. In this paper, we study fundamental tradeoffs and limits in efficiency (as measured in energy per operation) that can be achieved for an important class of kernels, namely the level-3 Basic Linear Algebra Subprograms (BLAS). It is well-accepted that specialization is the key to efficiency. This paper establishes a baseline by studying GEneral Matrix-matrix Multiplication (GEMM) on a variety of custom and general-purpose CPU and GPU architectures. Our analysis shows that orders of magnitude improvements in efficiency are possible with relatively simple customizations and fine-tuning of memory hierarchy configurations. We argue that these customizations can be generalized to perform other representative linear algebra operations. In addition to exposing the sources of inefficiencies in current CPUs and GPUs, our results show our prototype Linear Algebra Processor (LAP) implementing Double-precision GEMM (DGEMM) can achieve 600 GFLOPS while consuming less than 25 Watts in standard 45 nm technology, which is up to 50 × more energy efficient than cutting-edge CPUs.

Optimal deployment of eventually-serializable data services

Providing consistent and fault-tolerant distributed object services is among the fundamental problems in distributed computing. To achieve fault-tolerance and to increase throughput, objects are replicated at different networked nodes. However, replication induces significant communication costs to maintain replica consistency. Eventually-Serializable Data Service (ESDS) has been proposed to reduce these costs and enable fast operations on data, while still providing guarantees that the replicated data will eventually be consistent. This paper reconsiders the deployment phase of ESDS, in which a particular implementation of communicating software components must be mapped onto a physical architecture. This deployment aims at minimizing the overall communication costs, while satisfying the constraints imposed by the protocol. Both MIP (Mixed Integer Programming) and CP (Constraint Programming) models are presented and applied to realistic ESDS instances. The experimental results indicate that both models can find optimal solutions and prove optimality. The CP model, however, provides orders of magnitude improvements in efficiency. The limitations of the MIP model and the critical aspects of the CP model are discussed. Symmetry breaking and parallel computing are also shown to bring significant benefits.

Three-dimensional coupled-mode model of propagation and scattering around a conical oceanic seamount

Motivated by interesting anomalies observed in recent transmission experiments across the Kermit Seamount in the Pacific, a numerically efficient three-dimensional (3D) propagation and scattering model has been developed based on 3D coupled mode theory for axisymmetric bathymetry. The 3D coupled mode approach applied here is based on the same spectral representation of the field as earlier models presented by Athanassoulis and Propathopoulous [J. Acoust. Soc. Am. 100 (1996)] and Tarudakis [J. Comp. Acoust. 4 (1996)]. However, the earlier formulations were severely limited in terms of frequency, size, and geometry of the seamount, the seabed composition, and the distance of the source from the seamount, and are totally inadequate for modeling the Kermit experiment. By introducing a number of changes in the numerical formulation and using a standard normal mode model (CSNAP) for determining the fundamental modal solutions and coupling coefficients, orders of magnitude improvement in efficiency and fidelity have been achieved, allowing for realistic propagation and scattering scenarios to be modeled, including effects of seamount roughness, realistic sedimentary structure. By comparing to traditional n-by-2D coupled mode results, the model is used to demonstrate and investigate the surprisingly strong significance of 3D effects associated with propagation across oceanic seamounts such as Kermit, reaching up to the depth of the SOFAR channel. [Work supported by the US Office of Naval Research, Code 321OA.]

Solvation Model Based on Order Parameters and a Fast Sampling Method for the Calculation of the Solvation Free Energies of Peptides

An analytical solvation model is proposed as a function of an order parameter, which represents the local arrangement of water molecules in the first solvation shell of peptide atoms. The model is combined with a fast sampling method, rotational isomeric state Monte Carlo, to sample efficiently the torsional degrees of freedom on a peptide backbone. This order parameter solvation model is shown to reproduce without ad hoc fitting parameters the solvation free energies of single amino acids and tripeptides with slightly better accuracy than the generalized Born model but with several orders of magnitude improvement in efficiency. This method is a potential candidate for efficiently and accurately tackling some important issues in biophysical chemistry that are related to solvation, for example, protein folding, ligand binding, etc. Our results also present fundamental new insights into solvation. Specifically, the local water geometry, represented in this work by a properly defined order parameter, carries the majority, if not all, of the energetic information of solvation, including solute-solvent interactions and solvent reorganization in the presence of the solute.

Neighbor list collision-driven molecular dynamics simulation for nonspherical hard particles.II. Applications to ellipses and ellipsoids

We apply the algorithm presented in the first part of this series of papers to systems of hard ellipses and ellipsoids. The theoretical machinery needed to treat such particles, including the overlap potentials, is developed in full detail. We describe an algorithm for predicting the time of collision for two moving ellipses or ellipsoids. We present performance results for our implementation of the algorithm, demonstrating that for dense systems of very aspherical ellipsoids the novel techniques of using neighbor lists and bounding sphere complexes, offer as much as two orders of magnitude improvement in efficiency over direct adaptations of traditional event-driven molecular dynamics algorithms. The practical utility of the algorithm is demonstrated by presenting several interesting physical applications, including the generation of jammed packings inside spherical containers, the study of contact force chains in jammed packings, and melting the densest-known equilibrium crystals of prolate spheroids.

Neighbor list collision-driven molecular dynamics simulation for nonspherical hard particles.: II. Applications to ellipses and ellipsoids

We apply the algorithm presented in the first part of this series of papers to systems of hard ellipses and ellipsoids. The theoretical machinery needed to treat such particles, including the overlap potentials, is developed in full detail. We describe an algorithm for predicting the time of collision for two moving ellipses or ellipsoids. We present performance results for our implementation of the algorithm, demonstrating that for dense systems of very aspherical ellipsoids the novel techniques of using neighbor lists and bounding sphere complexes, offer as much as two orders of magnitude improvement in efficiency over direct adaptations of traditional event-driven molecular dynamics algorithms. The practical utility of the algorithm is demonstrated by presenting several interesting physical applications, including the generation of jammed packings inside spherical containers, the study of contact force chains in jammed packings, and melting the densest-known equilibrium crystals of prolate spheroids.

An occupancy model for image retrieval and similarity evaluation

The analysis of visual information often involves the manipulation of enormous volumes of data. If some tolerance is allowed in the results, orders of magnitude improvement in efficiency can be achieved in such analysis by appropriate selective processing, without necessarily considering all the data features. To guarantee that the error introduced does not exceed the allowed limit, a certain minimum proportion of the data must be involved in the analysis. This proportion cannot be determined arbitrarily. It should be chosen based on some formal methods, with a consideration of the error inherent in the data. This paper presents some techniques for improving the retrieval efficiency in image-based information systems, with performance guarantees on the reliability of results. Using the statistical theory of occupancy, it develops a model for the formal selection of the minimal subset of image features to be involved in histogram-based similarity evaluation. This guarantees that decisions based on the minimum proportion are always the same as (or close to) the one that would have been reached by considering all the features. Results on real and simulated data show the performance of the model on speedup, robustness, scalability, and performance guarantees.

Redundancy elimination with a lexicographic solved form

Detection of redundant linear constraints is important for a variety of applications including linear programming, variable elimination, canonical forms, and convex hull algorithms to name a few. Systematic semantic methods (i.e. optimization) exist to identify all redundant constraints but they are generally computationally expensive. It is thus desirable to identify syntactic criteria to reduce the need for semantic methods. This paper presents new syntactic criteria to detect redundant and non-redundant constraints. The results exploit a lexicographic solved form for linear programming (originally proposed to detect implicit equalities) which makes it possible to define syntactic criteria much stronger than those proposed in the past. In particular, one of our results shows that a system in a lexicographic solved form with n constraints and m non-basic variables has at most min(2n-1,n+m) redundant variables. In addition, the paper presents new syntactic criteria to reduce the size of the constraint system before further applications of syntactic and semantic methods. These theoretical results have been integrated in a complete system to detect redundant linear constraints. Experimental evaluation shows that our syntactic criteria decide the redundancy of about 90 constraints and may produce orders of magnitude improvement in efficiency over previous work whose criteria were deciding between 24 and 56 increase with the size and with the sparsity of the constraint system and are especially dramatic when the number of variables is large compared to the number of constraints.