Weak Scale Research Articles

Label embedding semantic-guided hashing

Hashing technologies have been widely used for information retrieval tasks due to their efficient retrieval and storage capabilities. Generally, most of the current supervised learning only utilizes labels to construct a binary similarity matrix of instance pairs and ignores the rich semantic information contained in the labels. Indeed, the reason why supervised hashing is better than unsupervised hashing is that the labels itself has strong discriminative information. Therefore, how to effectively explore the label information is one of the ways to improve the performance of retrieval tasks. In addition, existing hashing methods have the problems of high time consumption and weak scalability when facing large-scale data. To remedy these problems, in this paper, we present a flexible two-step label embedding hashing method named Label Embedding Semantic-Guided Hashing (LESGH). In the first step, LESGH leverages an asymmetric discrete learning framework to learn discriminative compact hash codes only from label information, and adds the constraints of bit-balance and bit-decorrelation to boost the quality of the hash code generation. In the second step, LESGH learns the hash projection function through the generated hash codes in the first step. Moreover, an effective and fast iterative discrete optimization algorithm is presented to solve the discrete problem instead of using the relaxation-based scheme. In doing so, we can not only simplify the optimization process, but also easily scale to large-scale data. We conduct several experiments on three public datasets, i.e., WIKI, MIRFlickr and NUS-WIDE, demonstrate that LESGH can improve the retrieval performance over the compared state-of-the-art baselines.

Large-scale ab initio simulation of light–matter interaction at the atomic scale in Fugaku

In the field of optical science, it is becoming increasingly important to observe and manipulate matter at the atomic scale using ultrashort pulsed light. For the first time, we have performed the ab initio simulation solving the Maxwell equation for light electromagnetic fields, the time-dependent Kohn-Sham equation for electrons, and the Newton equation for ions in extended systems. In the simulation, the most time-consuming parts were stencil and nonlocal pseudopotential operations on the electron orbitals as well as fast Fourier transforms for the electron density. Code optimization was thoroughly performed on the Fujitsu A64FX processor to achieve the highest performance. A simulation of amorphous SiO2 thin film composed of more than 10,000 atoms was performed using 27,648 nodes of the Fugaku supercomputer. The simulation achieved excellent time-to-solution with the performance close to the maximum possible value in view of the memory bandwidth bound, as well as excellent weak scalability.

A hybrid algorithm based on parareal and Schwarz waveform relaxation

<abstract><p>In this paper, we present a hybrid algorithm based on parareal and Schwarz waveform relaxation (SWR) for solving time dependent partial differential equations. The parallelism can be simultaneously realized in the time direction by using a parareal and in the space direction via SWR. We give a convergence analysis for the hybrid algorithm for a 1D model problem, the reaction-diffusion equation. Weak scaling of the algorithm in terms of both the number of space subdomains and the number of paralleled time intervals were investigated via theoretical analysis and numerical experiments.</p></abstract>

Evaluation the effectiveness of immersive VR-assisted rehabilitation in a child with chemotherapy-induced neurological complication in acute lymphoblastic leukemia

Rehabilitation therapy is considered as an actual and complex system of knowledge, in which the main task is the development and implementation of new methods of rehabilitation. In present time, the most perspective rehabilitation program is utilizing virtual reality. A report was made with the utilization of rehabilitation therapy with virtual reality in a child with chemotherapy-induced neurological disorders in acute lymphoblastic leukemia. The child performed a set of exercises using fully immersive virtual reality. Over the course of rehabilitation positive dynamics was observed, namely increased muscle strength in the injured limb from 3 to 5 scores according to the Medical Research Council Weakness Scale. There was improved balance on the Berg Balance Scale from 35 to 42. In addition, there were increased range of active movements, partly restored biomechanics of gait with increased velocity by 2 times. According to the results of testing the psycho-emotional state using the Luscher color test and the graphic technique «Cactus» by M.A. Panfilova, self-esteem, the desire to succeed and independence were improved, the level of auto-aggression was decreased. The results show that rehabilitation using fully immersive virtual reality is probably a perspective tool in addition to traditional rehabilitation. It improves the neurological and psycho-emotional state, raises motivation of patients, which, in turn, helps to increase the effectiveness of rehabilitation therapy and speeds up the rehabilitation process.

Light Higgsinos for electroweak naturalness in mirage-mediated high-scale supersymmetry

Mirage mediation realized in the Kachru-Kallosh-Linde-Trivedi (KKLT) flux compactification can naturally suppress the up-type Higgs soft mass at low energy scales. As a result, compared to the conventional scenarios, the degree of electroweak fine-tuning can be reduced further up to by a loop factor if the Higgsinos are much lighter than the heavy Higgs doublet. Interestingly, this feature holds even in high-scale supersymmetry as long as the gauge coupling unification, which is required as a prerequisite for mirage mediation, accommodates such light Higgsinos. Under the experimental constraints on the observed Higgs boson, it turns out that mirage mediation can exhibit low electroweak fine-tuning better than a few percent for stops between about 2 and 6 TeV, i.e., at the same level as in the weak scale supersymmetry, if the Higgsinos are around or below a few hundred GeV.

Supersymmetric particle and Higgs boson masses from the landscape: Dynamical versus spontaneous supersymmetry breaking

Perturbative supersymmetry breaking on the landscape of string vacua is expected to favor large soft terms as a power-law or log distribution, but tempered by an anthropic veto of inappropriate vacua or vacua leading to too large a value for the derived weak scale -- a violation of the atomic principle. Indeed, scans of such vacua yield a statistical prediction for light Higgs boson mass m_h~ 125 GeV with sparticles (save possibly light higgsinos) typically beyond LHC reach. In contrast, models of dynamical SUSY breaking (DSB) -- with a hidden sector gauge coupling g^2 scanned uniformly -- lead to gaugino condensation and a uniform distribution of soft parameters on a log scale. Then soft terms are expected to be distributed as $m_{\rm soft}^{-1}$ favoring small values. A scan of DSB soft terms generally leads to $m_h\ll 125$ GeV and sparticle masses usually below LHC limits. Thus, the DSB landscape scenario seems excluded from LHC search results. An alternative is that the exponential suppression of the weak scale is set anthropically on the landscape via the atomic principle.

Eulerian incompressible smoothed particle hydrodynamics on multiple GPUs

Recent advances in the development of Eulerian incompressible smoothed particle hydrodynamics (EISPH), such as high-order convergence and natural coupling with Lagrangian formulations, demonstrate its potential as a meshless alternative to traditional computational fluid dynamics (CFD) methods. This work aims to address one of the major outstanding limitations of EISPH, its relatively high computational cost, by providing an implementation that can be deployed on multiple graphics processing units (GPUs). To this end, a pre-existing multi-GPU version of the open-source Lagrangian weakly-compressible code DualSPHysics is converted to an EISPH formulation and integrated with an open-source multi-GPU multigrid solver (AmgX) to treat the pressure Poisson equation implicitly. The integration of AmgX within DualSPHysics presents a significant challenge, since AmgX is designed for distributed systems and therefore conflicts with the single-node shared memory design of the multi-GPU DualSPHysics code. The present implementation is validated against well-known test cases, showing excellent agreement with benchmark solutions and demonstrating second-order convergence. A detailed profiling and performance testing is also presented to investigate memory consumption and scaling characteristics. The results show approximately 87%–95% strong scaling efficiency and 92%–94% weak scaling efficiency in both 2D and 3D on up to four GPUs. Large spikes in memory consumption during the initialisation of the linear solver library are found to impede full utilisation of the device memory. Nevertheless, the present implementation is shown to permit problem sizes on the order of 69.3 million (2D) and 20.8 million (3D) particles on four GPUs (128 GB total device memory), which is beyond what has previously been reported for incompressible SPH on GPUs, where the PPE is treated implicitly, and demonstrates its potential as an alternative to traditional CFD methods.

Moving Multiscale Modelling to the Edge: Benchmarking and Load Optimization for Cellular Automata on Low Power Microcomputers

Numerical computations are usually associated with the High Performance Computing. Nevertheless, both industry and science tend to involve devices with lower power in computations. This is especially true when the data collecting devices are able to partially process them at place, thus increasing the system reliability. This paradigm is known as Edge Computing. In this paper, we propose the use of devices at the edge, with lower computing power, for multi-scale modelling calculations. A system was created, consisting of a high-power device—a two-processor workstation, 8 RaspberryPi 4B microcomputers and 8 NVidia Jetson Nano units, equipped with GPU processor. As a part of this research, benchmarking was performed, on the basis of which the computational capabilities of the devices were classified. Two parameters were considered: the number and performance of computing units (CPUs and GPUs) and the energy consumption of the loaded machines. Then, using the calculated weak scalability and energy consumption, a min–max-based load optimization algorithm was proposed. The system was tested in laboratory conditions, giving similar computation time with same power consumption for 24 physical workstation cores vs. 8x RaspberryPi 4B and 8x Jetson Nano. The work ends with a proposal to use this solution in industrial processes on example of hot rolling of flat products.

Сomplex rehabilitation of walking function in central paresis of the lower limb using neuroprosthetic technology

Disability after a stroke is most often associated with decreased patient activity due to walking disorder. In case of severe paresis, the function of the limb in the early stages of recovery is advisable to support with the help of partial prosthetics, using methods that provide functional stimulation. The complex treatment of central paresis includes botulinum therapy, which is due to the high incidence of spasticity.
 Aim: of the study: evaluation of the efficacy of functional electrical stimulation in a complex of multimodal effects in restoring movement in stroke patients with lower limb paresis.
 Materials and methods: Stimulation and neuroprosthetic methods (Bioness L300) were used as the main therapeutic program of rehabilitation treatment for patients with motor disorders of the lower limb after an ischemic stroke (n = 70). The median period of stroke was 21 [11; 47] days, median age 54 [42; 65] years. Scales and questionnaires were used to assess the loss of strength and volume of movement disorders, spasticity, walking speed and self-care (Medical Research Council Weakness Scale (1981, MRC), Barthel Index (1965), Modified Ashworth Scale (MAS), 10-meter test).
 Results: The effectiveness of personalized therapy with the use of a neuroprothesis (Bioness L300) in patients in the acute and early recovery period of ischemic stroke was shown. The additional use of botulinum toxin made it possible to eliminate the inhibitory effect of spasticity, which expanded the possibility of using intensive methods of physical rehabilitation. There was an increase in daily activity, walking speed, and the level of self-care of patients in 90 % of cases.
 Conclusion: The use of a complex of stimulating personalized techniques in the acute period of a stroke is justified and safe. The functional electrical stimulation helps to overcome power paresis, increases the overall level of physical activity of patients after a stroke, motivation for recovery and improves the quality of life. (1 figure, 2 tables, bibliography: 9 refs)

Parallel finite volume simulation of the spherical shell dynamo with pseudo-vacuum magnetic boundary conditions

In this paper, we study the parallel simulation of the magnetohydrodynamic (MHD) dynamo in a rapidly rotating spherical shell with pseudo-vacuum magnetic boundary conditions. A second-order finite volume scheme based on a collocated quasi-uniform cubed-sphere grid is applied to the spatial discretization of the MHD dynamo equations. To ensure the solenoidal condition of the magnetic field, we adopt a widely-used approach whereby a pseudo-pressure is introduced into the induction equation. The temporal integration is split by a second-order approximate factorization approach, resulting in two linear algebraic systems both solved by a preconditioned Krylov subspace iterative method. A multi-level restricted additive Schwarz preconditioner based on domain decomposition and multigrid method is then designed to improve the efficiency and scalability. Accurate numerical solutions of two benchmark cases are obtained with our code, comparable to the existing local method results. Several large-scale tests performed on the Sunway TaihuLight supercomputer show good strong and weak scalabilities and a noticeable improvement from the multi-level preconditioner with up to 10368 processor cores.

Nonperturbative effects in the Standard Model with gauged 1-form symmetry

We study the Standard Model with gauged {mathrm{mathbb{Z}}}_{N=2,3,6}^{(1)} subgroups of its {mathrm{mathbb{Z}}}_6^{(1)} 1-form global symmetry, making the gauge group frac{mathrm{SU}(3)times mathrm{SU}(2)times mathrm{U}(1)}{{mathrm{mathbb{Z}}}_N} . We show that, on a finite {mathbbm{T}}^3 , there are self-dual instantons of fractional topological charge. They mediate baryon- and lepton-number violating processes. We compare their amplitudes to the ones due to the usual BPST-instantons. We find that the small hypercharge coupling suppresses the fractional-instanton contribution, unless the torus size is taken sub-Planckian, or extra matter is added above the weak scale. We also discuss these results in light of the cosmological bounds on the torus size.

Parallel assignment of flow directions over flat surfaces in massive digital elevation models

The assignment of flow directions in flat regions needs to be treated with special algorithms to obtain hydrologically correct flow patterns. Based on the sequential algorithm and the three-step parallel framework, we propose a parallel algorithm for assigning the hydrologically correct flow directions in flat regions. The proposed parallel algorithm assigns pre-divided tiles to multiple consumer processes, which construct local graphs that encode the geodesic distance information among tile border flat cells and higher terrain or lower terrain. Based on the local graphs in all tiles, the producer process constructs the global graph and computes the global geodesic distances of tile border flat cells from higher terrain and lower terrain. The consumer processes then compute the final geodesic distances in each tile and assign the hydrologically correct flow directions. Four experiments are conducted to evaluate the performance of our proposed algorithm. The speed-up ratios are greater than 3 when the number of consumer processes is greater than 9. When the number of consumer processes reaches 20, the average strong scaling efficiency is around 40%. The weak scaling efficiency is lower than 20% when the number of consumer processes is greater than 5. Compared with the sequential algorithm that is simpler to implement, the proposed parallel algorithm requires less memory on consumer processes than the sequential algorithm and can process massive digital elevation models that cannot be successfully processed using the sequential algorithm. It fills the void in the processing pipeline of automatic drainage network extraction based on the three-step parallel framework and enables parallel implementation of the entire processing pipeline possible, which should substantially improve the attractiveness of the three-step parallel framework.

Variational, stable, and self-consistent coupling of 3D electromagnetics to 1D transmission lines in the time domain

This work presents a new multiscale method for coupling the 3D Maxwell's equations to the 1D telegrapher's equations. While Maxwell's equations are appropriate for modeling complex electromagnetics in arbitrary-geometry domains, simulation cost for many applications (e.g. pulsed power) can be dramatically reduced by representing less complex transmission line regions of the domain with a 1D model. By assuming a transverse electromagnetic (TEM) ansatz for the solution in a transmission line region, we reduce the Maxwell's equations to the telegrapher's equations. We propose a self-consistent finite element formulation of the fully coupled system that uses boundary integrals to couple between the 3D and 1D domains and supports arbitrary unstructured 3D meshes. Additionally, by using a Lagrange multiplier to enforce continuity at the coupling interface, we allow for an absorbing boundary condition to also be applied to non-TEM modes on this boundary. We demonstrate that this feature reduces non-physical reflection and ringing of non-TEM modes off of the coupling boundary. By employing implicit time integration, we ensure a stable coupling, and we introduce an efficient method for solving the resulting linear systems. We demonstrate the accuracy of the new method on two verification problems, a transient O-wave in a rectilinear prism and a steady-state problem in a coaxial geometry, and show the efficiency and weak scalability of our implementation on a cold test of the Z-machine MITL and post-hole convolute.

Weak scale as a trigger

Does the value of the Higgs mass parameter affect the expectation value of local operators in the Standard Model? For essentially all local operators the answer to this question is "no", and this is one of the avatars of the hierarchy problem: nothing is "triggered" when the Higgs mass parameter crosses zero. In this letter, we explore settings in which Higgs mass parameters $can$ act as a "trigger" for some local operators ${\cal O}_T$. In the Standard Model, this happens for ${\cal O}_T = {\rm Tr} (G \tilde G)$. We also introduce a "type-0" two Higgs doublet model, with a $Z_4$ symmetry, for which ${\cal O}_T = H_1 H_2$ is triggered by the Higgs masses, demanding the existence of new Higgs states necessarily comparable to or lighter than the weak scale, with no wiggle room to decouple them whatsoever. Surprisingly, this model is not yet entirely excluded by collider searches, and will be incisively probed by the high-luminosity run of the LHC, as well as future Higgs factories. We also discuss a possibility for using this trigger to explain the origin of the weak scale, invoking a landscape of extremely light, weakly interacting scalars $\phi_i$, with a coupling to ${\cal O}_T$ needed to make it possible to find vacua with small enough cosmological constant. The weak scale trigger links the tuning of the Higgs mass to that of the cosmological constant, while coherent oscillations of the $\phi_i$ can constitute dark matter.

A scalable solver for a stochastic, hybrid cellular automaton model of personalized breast cancer therapy.

Mathematical modeling and simulation is a promising approach to personalized cancer medicine. Yet, the complexity, heterogeneity and multi-scale nature of cancer pose significant computational challenges. Coupling discrete cell-based models with continuous models using hybrid cellular automata (CA) is a powerful approach for mimicking biological complexity and describing the dynamical exchange of information across different scales. However, when clinically relevant cancer portions are taken into account, such models become computationally very expensive. While efficient parallelization techniques for continuous models exist, their coupling with discrete models, particularly CA, necessitates more elaborate solutions. Building upon FEniCS, a popular and powerful scientific computing platform for solving partial differential equations, we developed parallel algorithms to link stochastic CA with differential equations (https://bitbucket.org/HTasken/cansim). The algorithms minimize the communication between processes that share CA neighborhood values while also allowing for reproducibility during stochastic updates. We demonstrated the potential of our solution on a complex hybrid cellular automaton model of breast cancer treated with combination chemotherapy. On a single-core processor, we obtained nearly linear scaling with an increasing problem size, whereas weak parallel scaling showed moderate growth in solving time relative to increase in problem size. Finally, we applied the algorithm to a problem that is 500 times larger than previous work, allowing us to run personalized therapy simulations based on heterogeneous cell density and tumor perfusion conditions estimated from magnetic resonance imaging data on an unprecedented scale.

GR-Athena++: Puncture Evolutions on Vertex-centered Oct-tree Adaptive Mesh Refinement

Abstract Numerical relativity is central to the investigation of astrophysical sources in the dynamical and strong-field gravity regime, such as binary black hole and neutron star coalescences. Current challenges set by gravitational-wave and multimessenger astronomy call for highly performant and scalable codes on modern massively parallel architectures. We present GR-Athena++, a general-relativistic, high-order, vertex-centered solver that extends the oct-tree, adaptive mesh refinement capabilities of the astrophysical (radiation) magnetohydrodynamics code Athena++. To simulate dynamical spacetimes, GR-Athena++ uses the Z4c evolution scheme of numerical relativity coupled to the moving puncture gauge. We demonstrate stable and accurate binary black hole merger evolutions via extensive convergence testing, cross-code validation, and verification against state-of-the-art effective-one-body waveforms. GR-Athena++ leverages the task-based parallelism paradigm of Athena++ to achieve excellent scalability. We measure strong-scaling efficiencies above 95% for up to ∼1.2 × 104 CPUs and excellent weak scaling is shown up to ∼105 CPUs in a production binary black hole setup with adaptive mesh refinement. GR-Athena++ thus allows for the robust simulation of compact binary coalescences and offers a viable path toward numerical relativity at exascale.

Multidimensional Assessment of Polish Farm Household Financial Security by TOPSIS and Generalised Distance Measure

Purpose: Financial security is a multidimensional term, with its level is depending, in subjective and objective terms, on many socio-demographic conditions. This article aims to assess Polish farm households’ financial security by means of a combined measure. Design/Methodology/Approach: The study was based on non-identified (raw) individual data originating from the Social Diagnosis Study conducted by the Social Monitoring Council in 2015. Due to non-metric nature of many simple criteria, in order to construct a combined measure of the farm - household financial security level, a Generalised Distance Measure (GDM) was used along with TOPSIS. Findings: Farm households are classified by types and profiles that have been developed of households featuring the highest and the lowest level of financial security. The study revealed significant differences among farm households in terms of their financial security level in both, subjective and objective terms. Practical Implications: This research is interesting for methodological reasons, since a single methodology for studying the level of financial security and identifying its determinants has yet to be developed. The problem is not just the multidimensional character of the phenomenon and the lack of an unequivocally defined set of variables characterising it. The choice of appropriate statistical methods is also an important consideration, e.g., for constructing a single synthetic financial security indicator and dedicated software. Originality/value: To the authors’ knowledge, no one has hitherto made any studies concerning the multi-aspect assessment of the household financial security level based on a combined feature, the construction of which takes account of objective criteria (quantified on strong measurement scales) as well as subjective ones (measured on weak scales).

All-electron real-time and imaginary-time time-dependent density functional theory within a numeric atom-centered basis function framework.

Real-time time-dependent density functional theory (RT-TDDFT) is an attractive tool to model quantum dynamics by real-time propagation without the linear response approximation. Sharing the same technical framework of RT-TDDFT, imaginary-time time-dependent density functional theory (it-TDDFT) is a recently developed robust-convergence ground state method. Presented here are high-precision all-electron RT-TDDFT and it-TDDFT implementations within a numerical atom-centered orbital (NAO) basis function framework in the FHI-aims code. We discuss the theoretical background and technical choices in our implementation. First, RT-TDDFT results are validated against linear-response TDDFT results. Specifically, we analyze the NAO basis sets' convergence for Thiel's test set of small molecules and confirm the importance of the augmentation basis functions for adequate convergence. Adopting a velocity-gauge formalism, we next demonstrate applications for systems with periodic boundary conditions. Taking advantage of the all-electron full-potential implementation, we present applications for core level spectra. For it-TDDFT, we confirm that within the all-electron NAO formalism, it-TDDFT can successfully converge systems that are difficult to converge in the standard self-consistent field method. We finally benchmark our implementation for systems up to ∼500 atoms. The implementation exhibits almost linear weak and strong scaling behavior.

High-Performance Computing Probabilistic Fracture Mechanics Implementation for Gas Turbine Rotor Disks on Distributed Architectures Including Graphics Processing Units

Abstract We present an efficient Monte Carlo-based probabilistic fracture mechanics simulation implementation for heterogeneous high-performance computing (HPC) architectures including central processing units (CPUs) and graphics processing units (GPUs). The specific application focuses on large heavy-duty gas turbine rotor components for the energy sector. A reliable probabilistic risk quantification requires the simulation of millions to billions of Monte Carlo (MC) samples. We apply a modified Runge–Kutta algorithm in order to solve numerically the fatigue crack growth for this large number of cracks for varying initial crack sizes, locations, material, and service conditions. This compute intensive simulation has already been demonstrated to perform efficiently and scalable on parallel and distributed HPC architectures including hundreds of CPUs utilizing the message passing interface (MPI) paradigm. In this work, we go a step further and include GPUs in our parallelization strategy. We develop a load distribution scheme to share one or more GPUs on compute nodes distributed over a network. We detail technical challenges and solution strategies in performing the simulations on GPUs efficiently. We show that the key computation of the modified Runge–Kutta integration step speeds up over two orders of magnitude on a typical GPU compared to a single threaded CPU. This is supported by our use of GPU textures for efficient interpolation of multidimensional tables utilized in the implementation. We demonstrate weak and strong scaling of our GPU implementation, i.e., that we can efficiently utilize a large number of GPUs/CPUs in order to solve for more MC samples, or reduce the computational turn-around time, respectively. On seven different GPUs spanning four generations, the presented probabilistic fracture mechanics simulation tool ProbFM achieves a speed-up ranging from 16.4× to 47.4× compared to single threaded CPU implementation.

Tree cutting approach for domain partitioning on forest-of-octrees-based block-structured static adaptive mesh refinement with lattice Boltzmann method

The aerodynamics simulation code based on the lattice Boltzmann method (LBM) using forest-of-octrees-based block-structured adaptive mesh refinement (AMR) with temporary-fixed refinement was implemented, and its performance was evaluated on GPU-based supercomputers. Although the Space-Filling-Curve-based (SFC) domain partitioning algorithm for the octree-based AMR has been widely used on conventional CPU-based supercomputers, accelerated computation on GPU-based supercomputers revealed a bottleneck due to costly halo data communication. Our new tree cutting approach adopts a hybrid domain partitioning with the coarse structured block decomposition and the SFC partitioning in each block. This hybrid approach improved the locality and the topology of the partitioned sub-domains and reduced the amount of the halo communication to one-third of the original SFC approach. In the strong scaling test, the code achieved maximum ×1.82 speedup at the performance of 2207 MLUPS (mega-lattice update per second) on 128 GPUs (NVIDIA® Tesla® V100). In the weak scaling test, the code achieved 9620 MLUPS at 128 GPUs with 4.473 billion grid points, while keeping the parallel efficiency of 93.4% from 8 to 128 GPUs.