A CAD System for Exploring Neuromorphic Computing with Emerging Technologies
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Abstract not provided.
Abstract not provided.
A method of forming a three dimensional (3D) object is disclosed. The method may involve providing a volume of photo-curable resin contained within an optically transparent resin container, and simultaneously directing optical projections from an optical subsystem at a plurality of angles θ through the volume of photo-curable resin. The optical beams are directed about a z axis extending through the volume of photo-curable resin. Each of the projections is provided with a calculated 2D spatial intensity function which creates a 3D intensity map. The projections act over a fixed temporal exposure period, during which the net exposure dose is sufficient to cure select portions of the volume of photo-curable resin, and to leave other portions uncured, to form a desired 3D part.
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Abstract not provided.
A system for determining a light intensity field for use in manufacturing a 3D object from a volume of material. The system receives a 3D specification of a 3D geometry for the 3D object that specifies voxels within the volume that contain material that is to be part of the 3D object. The system employs a cost function for effectiveness of a light intensity field in manufacturing the 3D object. The cost function may be an adjoint of an Attenuated Radon Transform that models an energy dose that each voxel would receive during manufacture of the 3D object using the light intensity field. The system applies an optimization technique that employs the cost function to generate a measure of the effectiveness of possible light intensity fields and outputs an indication of a light intensity field that will be effective in manufacturing the 3D object.
An exemplary apparatus can provide radiation to a sample(s), which can include, for example, a radiation source arrangement configured to provide radiation, a beam splitter configured to split the radiation into (i) a first radiation, and (ii) a second radiation. An optical element can also be provided which, in operation, can, e.g., (a) receive the first radiation and the second radiation, (b) reflect the first radiation as a reference radiation, (c) provide the second radiation as illumination for the sample(s), (d) receive a resultant radiation from the sample(s) that can be based on the illumination from the second radiation, and (e) provide the reference radiation and the resultant radiation to be detected and used for interferometric imaging or spectroscopy.
High-performance computing (HPC) systems play a critical role in facilitating scientific discoveries. Their scale and complexity (e.g., the number of computational units and software stack) continue to grow as new systems are expected to process increasingly more data and reduce computing time. However, with more processing elements, the probability that these systems will experience a random bit-flip error that corrupts a program's output also increases, which is often recognized as silent data corruption. Analyzing the resiliency of HPC applications in extreme-scale computing to silent data corruption is crucial but difficult. An HPC application often contains a large number of computation units that need to be tested, and error propagation caused by error corruption is complex and difficult to interpret. Here, to accommodate this challenge, we propose an interactive visualization system that helps HPC researchers understand the resiliency of HPC applications and compare their error propagation. Our system models an application's error propagation to study a program's resiliency by constructing and visualizing its fault tolerance boundary. Coordinating with multiple interactive designs, our system enables domain experts to efficiently explore the complicated spatial and temporal correlation between error propagations. At the end, the system integrated a nonmonotonic error propagation analysis with an adjustable graph propagation visualization to help domain experts examine the details of error propagation and answer such questions as why an error is mitigated or amplified by program execution.
Operating modern power grids with stability guarantees is markedly important. Typical methods for analyzing and certifying power grid stability are largely centralized relying on the ability of the system operator to gather network-wide information and accurately compute the system's eigenvalues. These methods are oftentimes not privacy-preserving and computationally burdensome. They are therefore, not well-suited to modern power grids where small-signal stability has to be evaluated timely, efficiently and in a privacy-preserving fashion. Herein, we introduce a distributed methodology for certifying small-signal stability of power grids and designing the local controllers. First, we analytically derive distributed conditions for network-wide stability that bus agents can inspect using local information. By leveraging these conditions, we then introduce a distributed control design algorithm (DCDA) that can guide the local control design so that stability of the interconnected system is guaranteed. The agents that adopt the proposed distributed algorithm are responsible for tuning their local controllers, producing their local control commands and ensuring that their local stability condition is met. The system operator is only responsible for verifying network-wide stability upon receiving affirmative responses from all agents and, announcing, that the overall system is stable. The proposed DCDA algorithm is numerically validated via simulations using the IEEE 39-bus system.
Methods, apparatus, and products are disclosed for reducing power consumption during execution of an application on a plurality of compute nodes that include: powering up, during compute node initialization, only a portion of computer memory of the compute node, including configuring an operating system for the compute node in the powered up portion of computer memory; receiving, by the operating system, an instruction to load an application for execution; allocating, by the operating system, additional portions of computer memory to the application for use during execution; powering up the additional portions of computer memory allocated for use by the application during execution; and loading, by the operating system, the application into the powered up additional portions of computer memory.
Security of cyber-physical systems (CPS) continues to pose new challenges due to the tight integration and operational complexity of the cyber and physical components. To address these challenges, this article presents a domain-aware, optimization-based approach to determine an effective defense strategy for CPS in an automated fashion—by emulating a strategic adversary in the loop that exploits system vulnerabilities, interconnection of the CPS, and the dynamics of the physical components. Our approach builds on an adversarial decision-making model based on a Markov Decision Process (MDP) that determines the optimal cyber (discrete) and physical (continuous) attack actions over a CPS attack graph. The defense planning problem is modeled as a non-zero-sum game between the adversary and defender. We use a model-free reinforcement learning method to solve the adversary’s problem as a function of the defense strategy. We then employ Bayesian optimization (BO) to find an approximate best-response for the defender to harden the network against the resulting adversary policy. This process is iterated multiple times to improve the strategy for both players. We demonstrate the effectiveness of our approach on a ransomware-inspired graph with a smart building system as the physical process. Numerical studies show that our method converges to a Nash equilibrium for various defender-specific costs of network hardening.
A method and system are disclosed for detecting memory chip failure in a computer memory system. The method comprises the steps of accessing user data from a set of user data chips, and testing the user data for errors using data from a set of system data chips. This testing is done by generating a sequence of check symbols from the user data, grouping the user data into a sequence of data symbols, and computing a specified sequence of syndromes. If all the syndromes are zero, the user data has no errors. If one of the syndromes is non-zero, then a set of discriminator expressions are computed, and used to determine whether a single or double symbol error has occurred. In the preferred embodiment, less than two full system data chips are used for testing and correcting the user data.
Purpose: Here, we present a physical and psychophysical characterization of a new clinical unit (named AcSelerate) for digital radiography based on a thick a-Se layer. We also compared images acquired with and without a software filter (named CRF) developed for reducing sharpness and noise of the images and making them similar to images coming from traditional computed radiography systems. Methods: The characterization was achieved in terms of physical figures of merit [modulation transfer function (MTF), noise power spectra (NPS), detective quantum efficiency (DQE)], and psychophysical parameters (contrast-detail analysis with an automatic reading of CDRAD images). We accomplished measurements with four standard beam conditions: RAQ3, RQA5, RQA7, and RQA9. Results: The system shows an excellent MTF (about 50% at the Nyquist frequency). The DQE is about 55% at 0.5 lp/mm and above 20% at the Nyquist frequency and is almost independent from exposure. The contrast-detail curves are comparable to some of the best published data for other systems devoted to imaging in general radiography. The CRF filter influences both the MTF and NPS, but it does lead to very small changes on DQE. Also the visibility of CDRAD details is basically unaltered, when the filter is activated. Conclusions: As normally happens with detector based on direct conversion, the system presents an excellent MTF. The improved efficiency caused by the thick layer allows getting good noise characteristics and DQE results better (about 10% on average) than many of the computed radiography (CR) systems and comparable to those obtained by the best systems for digital radiography available on the market.
Modeling hydrological fracture networks is a hallmark challenge in computational earth sciences. Accurately predicting critical features of fracture systems, e.g. percolation, can require solving large linear systems far beyond current or future high performance capabilities. Quantum computers can theoretically bypass the memory and speed constraints faced by classical approaches, however several technical issues must first be addressed. Chief amongst these difficulties is that such systems are often ill-conditioned, i.e. small changes in the system can produce large changes in the solution, which can slow down the performance of linear solving algorithms. We test several existing quantum techniques to improve the condition number, but find they are insufficient. We then introduce the inverse Laplacian preconditioner, which improves the scaling of the condition number of the system from O(N) to O($\sqrt{N}$) and admits a quantum implementation. These results are a critical first step in developing a quantum solver for fracture systems, both advancing the state of hydrological modeling and providing a novel real-world application for quantum linear systems algorithms.
A data protection system is provided that allows applications to access protected data in a way that restricts applications from outputting to unauthorized targets any unprotected data derived from the protected data and that ensures that the applications do not have access to a key that allows access to the unprotected data. The data protection system provides a policy server that may execute on a service node of a high performance computing system and a data encryption process that may execute on each compute node that is allocated to an application or batch job. The policy server maintains policies of entities specifying access control for protected data. The data encryption process generates a secure execution environment for an application process and interfaces with the policy server to retrieve keys for decrypting protected data in accordance with a policy, and it decrypts and provides the decrypted data to the application process.