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This paper extends previous studies by the authors that aimed to describe the effect of apparent cross-link density (CLD) of the rubber polymer networks on the fracture mechanism caused by cut and chip (CC) wear of natural rubber (NR), demonstrating the positive effect of conventional vulcanization (CV). This work is focused on the determination of the effect of CLD while keeping constant the accelerator-to-sulfur ratio A/S = 0.2, typical for CV systems. For this ratio, different sulfur quantities were chosen, and the concentration of the accelerator N-tert-butyl-benzothiazole sulphonamide (TBBS) was calculated to achieve CLDs in a range from 35 to 524 μmol·cm–3. Standard analyses such as tensile tests, hardness, rebound resilience and DIN abrasion were performed. From these analyses, the optimum physical properties of the NR-based rubber were estimated to be in the CLD range of approximately 60 to 160 μmol·cm–3. A CC wear analysis was performed with an Instrumented cut and chip analyzer (ICCA) and it was found that the highest CC wear resistance of the NR is in the CLD range of 35 to 100 μmol·cm–3. Furthermore, the effect of straininduced crystallization (SIC) of NR on CC wear and its dependence on the CLD region was discussed. For the first time, we determine a CLD range for a CV system in which the material achieves both optimal mechanical properties and CC wear resistance. [ABSTRACT FROM AUTHOR]

Deep learning has brought great development to radar emitter identification technology. In addition, specific emitter identification (SEI), as a branch of radar emitter identification, has also benefited from it. However, the complexity of most deep learning algorithms makes it difficult to adapt to the requirements of the low power consumption and high-performance processing of SEI on embedded devices, so this article proposes solutions from the aspects of software and hardware. From the software side, we design a Transformer variant network, lightweight convolutional Transformer (LW-CT) that supports parameter sharing. Then, we cascade convolutional neural networks (CNNs) and the LW-CT to construct a one-dimensional-CNN-Transformer(1D-CNN-Transformer) lightweight neural network model that can capture the long-range dependencies of radar emitter signals and extract signal spatial domain features meanwhile. In terms of hardware, we design a low-power neural network accelerator based on an FPGA to complete the real-time recognition of radar emitter signals. The accelerator not only designs high-efficiency computing engines for the network, but also devises a reconfigurable buffer called "Ping-pong CBUF" and two-level pipeline architecture for the convolution layer for alleviating the bottleneck caused by the off-chip storage access bandwidth. Experimental results show that the algorithm can achieve a high recognition performance of SEI with a low calculation overhead. In addition, the hardware acceleration platform not only perfectly meets the requirements of the radar emitter recognition system for low power consumption and high-performance processing, but also outperforms the accelerators in other papers in terms of the energy efficiency ratio of Transformer layer processing. [ABSTRACT FROM AUTHOR]

Both kraft lignin phenol-formaldehyde (KLPF) and phenol-formaldehyde (PF) resins were synthesized and the curing kinetics of the resins was determined using thermal analysis data. The kinetic parameters were predicted using two popular model-free kinetic (MFK) methods: the advanced form of the Vyazovkin method and the Kissinger–Akahira–Sunose method. From the experimental and predicted values, the rate of kraft lignin phenol-formaldehyde curing was less compared to the phenol-formaldehyde resin. To increase the cure rate different cure additives were tried. Among these, only potassium carbonate (KC) showed a positive effect: An increase in additive concentration to 4% reduced the curing time to almost 50% as compared to pure KLPF resin at 160°C. Comparison of the predicted values from MFK calculations with isothermal experimental data showed that both MFK approaches were suitable to predict the curing characteristics. [ABSTRACT FROM AUTHOR]

Graph convolution networks (GCN) have demonstrated success in learning graph structures; however, they are limited in inductive tasks. Graph attention networks (GAT) were proposed to address the limitations of GCN and have shown high performance in graph-based tasks. Despite this success, GAT faces challenges in hardware acceleration, including: 1) The GAT algorithm has difficulty adapting to hardware; 2) challenges in efficiently implementing Sparse matrix multiplication (SPMM); and 3) complex addressing and pipeline stall issues due to irregular memory accesses. To this end, this paper proposed SH-GAT, an FPGA-based GAT accelerator that achieves more efficient GAT inference. The proposed approach employed several optimizations to enhance GAT performance. First, this work optimized the GAT algorithm using split weights and softmax approximation to make it more hardware-friendly. Second, a load-balanced SPMM kernel was designed to fully leverage potential parallelism and improve data throughput. Lastly, data preprocessing was performed by pre-fetching the source node and its neighbor nodes, effectively addressing pipeline stall and complexly addressing issues arising from irregular memory access. SH-GAT was evaluated on the Xilinx FPGA Alveo U280 accelerator card with three popular datasets. Compared to existing CPU, GPU, and state-of-the-art (SOTA) FPGA-based accelerators, SH-GAT can achieve speedup by up to 3283 × , 13 × , and 2.3 ×. [ABSTRACT FROM AUTHOR]

This paper conducts an extensive comparative study of state-of-the-art solutions for implementing the SHA-3 hash function. SHA-3, a pivotal component in modern cryptography, has spawned numerous implementations across diverse platforms and technologies. This research aims to provide valuable insights into selecting and optimizing Keccak SHA-3 implementations. Our study encompasses an in-depth analysis of hardware, software, and software–hardware (hybrid) solutions. We assess the strengths, weaknesses, and performance metrics of each approach. Critical factors, including computational efficiency, scalability, and flexibility, are evaluated across different use cases. We investigate how each implementation performs in terms of speed and resource utilization. This research aims to improve the knowledge of cryptographic systems, aiding in the informed design and deployment of efficient cryptographic solutions. By providing a comprehensive overview of SHA-3 implementations, this study offers a clear understanding of the available options and equips professionals and researchers with the necessary insights to make informed decisions in their cryptographic endeavors. [ABSTRACT FROM AUTHOR]

Runtime systems can significantly reduce the cognitive complexity of scientific applications, narrowing the gap between systems engineering and domain science in HPC. One of the most important angles in this is automating data migration in a cluster. Traditional approaches require the application developer to model communication explicitly, for example through MPI primitives. Celerity, a runtime system for accelerator clusters heavily inspired by the SYCL programming model, instead provides a purely declarative approach focused around access patterns. In addition to eliminating the need for explicit data transfer operations, it provides a basis for efficient and dynamic scheduling at runtime. However, it is currently only suitable for accessing array-like data from runtime-controlled tasks, while real programs often need to interact with opaque data local to each host, such as handles or database connections, and also need a defined way of transporting data into and out of the virtualised buffers of the runtime. In this paper, we introduce a graph-based approach and declarative API for expressing side-effect dependencies between tasks and moving data from the runtime context to the application space. [ABSTRACT FROM AUTHOR]

This paper presents high-performance computing efforts with FPGA for the accelerated pulsar/transient search for the square kilometre array (SKA). Case studies are presented from within SKA and pathfinder telescopes highlighting future opportunities. It reviews the scenario that has shifted from offline processing of the radio telescope data to digitizing several hundreds/thousands of antenna outputs over huge bandwidths, forming several hundreds of beams, and processing the data in the SKA real-time pulsar search pipelines. A brief account of the different architectures of the accelerators, primarily, the new generation field programmable gate array-based accelerators, showing their critical roles to achieve high-performance computing and in handling the enormous data volume problems of the SKA is presented here. It also presents power-performance efficiency of this emerging technology and presents potential future scenarios. [ABSTRACT FROM AUTHOR]

Precast concrete construction is frequently preferred all over the world because of its rapid production and low cost. Prefabricated structures are constructed by assembling, the structural elements that were produced in the factory, at the construction site. However, some connection types such as wet connection used in prefabricated buildings eliminate the rapid installation feature. The wet connections are the method of assembling with fresh concrete. In the wet connections, the setting time of concrete decreases the installation rapid of prefabricated structures. In the past years, various additives have been used to accelerate the setting time. However, these additives consisting of organic salts cause corrosion in the reinforcement. In recent years, the use of inorganic salts with low ions activity as the accelerator has become widespread. Rapid-setting concrete is suitable for producing precast concrete. This paper deals with the determination of optimal accelerator type and dosage for precast concrete. Sodium aluminate and Polycarboxylic ether-based chemicals were utilized as the accelerator to obtain rapid-setting concrete mixtures. Mechanical and durability tests were performed to analyze each chemical effect on the fresh and hardened properties of the concrete mixes. Polycarboxylic ether-based chemical is observed as a potential accelerator for precast concrete, especially for the connection concrete parts. With polycarboxylic ether-based chemicals, it is observed that rapid strength gain is achieved without much loss of final strength. Besides, polycarboxylic ether-based chemicals increase the workability of mixtures. However, it is observed that the workability feature is lost in a short time due to the rapid-setting feature. [ABSTRACT FROM AUTHOR]

FPGAs facilitate prototyping and debug, and recently accelerate full-stack simulations due to their rapid turnaround time (TAT). However, this TAT is restrictive in exhaustive design space explorations of parameterized RTL generators, especially DNN accelerators that unleash an explosive full-stack search space. This paper presents Quickloop, an efficient and scalable framework to enable FPGA-accelerated exploration. Quickloop first abstracts away the cumbersome flow of RTL generation, software stack, FPGA toolflow, workload execution and metrics extraction by wrapping these stages into isolated Quicksteps, featuring cascadability, scalability, and replay. Then, we analytically minimize the FPGA toolflow TAT via a novel, data-driven strategy that intelligently utilizes build fragments from previous iterations, enhancing the loop efficiency and simultaneously lowering the toolflow's compute utilization. Quickloop is built around the OpenAI Gym environment framework and thus supports drop-in regression and reinforcement learning explorations. With a Quickloop around a reference Berkeley's Gemmini DNN accelerator, we exhaustively explore its parameter space and discover complex performance patterns, based on full-stack simulation of Imagenet benchmarks as a workload. Compared to conventional FPGA toolflow, we further show that Quickloop effectively reduces episodal time by above 30%, as the episode approaches realistic lengths. • Machine Learning. • Accelerators. • Systolic Array. • Design Space Exploration. [ABSTRACT FROM AUTHOR]

Linear optics parameters are often estimated using turn-by-turn (TbT) data. In-plane beta functions, which are the most common measurement objectives, can be estimated from the amplitudes or phases of TbT data, providing an overall characterization of the linear lattice. In addition to estimating uncoupled Twiss parameters, TbT data can also be utilized for characterizing coupled linear motion. We investigate several methods for constructing a full normalization matrix at each beam position monitor (BPM). BPMs provide information on beam centroid transverse coordinates, but direct observation of transverse momenta is not available. To estimate momenta, one can use a pair of BPMs or fit data obtained from several BPMs. By using both coordinates and momenta, it is possible to fit the one-turn matrix (or its power) at each BPM, allowing for the computation of coupled Twiss parameters. Another approach involves the minimization of side peak amplitudes in the spectrum of normalized complex coordinates. Similarly, the normalization matrix can be estimated by fitting linear coupled invariants. In this study, we derive and utilize a special form of the normalization matrix, which remains non-singular in the zero coupling limit. Coupled Twiss parameters can be obtained from the normalization matrix. The paper presents the results of applying and comparing these methods to both modeled and measured VEPP-4M TbT data, demonstrating effective estimation of coupled Twiss parameters. [ABSTRACT FROM AUTHOR]

Liquid accelerating agents have the advantages of simple operation and fast construction, and have become indispensable admixtures in shotcrete. However, most liquid accelerating agents in the market at present contain alkali or fluorine, which adversely affect concrete and seriously threaten the physical and mental health of workers. Therefore, in view of the above deficiencies, it is necessary to develop a new type of alkali-free fluorine-free liquid accelerating agent. In this paper, the polyaluminum sulfate early strength alkali-free liquid accelerator is prepared using polymeric aluminum sulfate, diethanolamine, magnesium sulfate heptahydrate and nano-silica. The influence of this agent on the setting time of fresh cement paste and compressive strength of the corresponding cement mortar is determined. Thermogravimetric analysis curves, X-ray diffraction and scanning electron microscopy images are obtained to investigate the mechanism. Findings show that the initial setting time and the final setting time of cement paste are 2 min 30 s and 7 min 25 s. The compressive strengths of cement mortar cured for 1 d, 28 d and 90 d are 2.4 MPa, 52.2 MPa and 54.3 MPa respectively. Additionally, the corresponding flexural strengths are 3.4 MPa, 9.8 MPa, 11.8 MPa. When the mass rate of accelerator is 7%, the mechanical strengths of cement mortar are the highest. The additions of fly ash and blast furnace slag can affect the mechanical of cement mortar mixed with accelerator. When the mass ratio of the fly ash and blast furnace slag is 15%, the mechanical strengths of cement mortar reach the highest. Moreover, the hydration heat release rate of cement is increased by the accelerator and the corresponding time of hydration heat peak is decreased by the accelerator. The accelerator can decrease the amount of needle-like hydration products and improve the compactness. The mechanical strengths are improved by consuming a large amount of Ca(OH)2 and forming more compact hydration products. It is recommended that the optimum dosage range of the polyaluminum sulfate early strength alkali-free liquid accelerator is 7%. [ABSTRACT FROM AUTHOR]

• New experimental data on fragmentation of 200 MeV/u 40-Ar in copper. • Fine-resolution depth-profiles of nuclides induced by 200 MeV/u 40-Ar in copper. • FLUKA simulations of the experimental depth-profiles of the induced nuclides. • Comparison of the measured and simulated profiles. • Experimental data for development of physical models implemented in FLUKA. This work is a further contribution to our long-term experimental studies of activation- and fragmentation-related effects induced by high-energy heavy-ion irradiation in selected accelerator-construction materials. A stacked-foil copper target consisting of thin foils with different thicknesses was irradiated by 200 MeV/u 40Ar ions. Gamma spectra of the radiation emitted from the activated foils were measured and the nuclides created in the foils as a result of nuclear interaction between the 40Ar ions and the copper target nuclei were identified. The activities of all identified nuclides have been determined from the gamma spectra. The measured activity of each nuclide has been converted to the number of nuclei (of that nuclide) contained in the foil. Finally, the depth-profiles of the induced nuclides, i.e. the number of the induced nuclei as a function of the target depth, have been constructed and analyzed. The measured depth-profiles provide information on physical mechanisms of the high-energy heavy-ion fragmentation. The measured depth-profiles have been also compared with the simulated ones obtained with FLUKA2020. Keeping in mind complexity of the nucleus-nucleus interactions in the broad energy interval, the measured and simulated depth-profiles show a reasonable agreement. However, in some cases, discrepancies between the measured and the simulated depth-profiles have been observed. These cases are presented and discussed in the paper, as they can serve improvement of physical models implemented in the simulation codes. [ABSTRACT FROM AUTHOR]

The High Fluence Irradiation Facility at the University of Tokyo, which had provided a platform for dual-beam irradiation in Japan, was severely damaged by the 2011 earthquake. This paper reports a modification at this facility, i.e., installing a low-energy ion implanter instead of repairing the previous Van de Graaff accelerator. The platform of the dual-beam irradiation and/or s gas-ion beam irradiation was reconstructed, and the newly installed ion implanter was a stable, uniform, and high-density light element ion beam. The radiation-induced amorphization (RIA) behavior in Zr(Fe,Cr) 2 Laves phase was investigated in 180 keV-He+ beam irradiation to assess the performance of this ion implanter, and the irradiation temperature and dose-dependent RIA behavior were confirmed. Implying that this modification ensures the capability in terms of gas-ion beam irradiation and dual-beam irradiation in this facility, which will promote the research progress on radiation effects in nuclear material. [ABSTRACT FROM AUTHOR]

In recent decades, convolutional neural network (CNN) has become essential in many real-time applications due to its massive computational ability. But its use in portable devices is limited due to its high computation requirements. This paper proposes a novel One-Pass Processing Element (OPPE) to mitigate this limitation. The proposed OPPE removes redundant computations by eliminating those with zeros that leads to low area as well as low power consumption. The proposed OPPE model is evaluated with the help of VGG-16-based CNN accelerator. The proposed OPPE design reduces the number of four-input LUTs by 5.19%, 15.91%, 10.06% and 4.93% and the power consumption by 4.26%, 7.36%, 5.81% and 1.55% when compared with the conventional processing element (PE), activation gating PE, weight gating PE and zero gating PE, respectively. The proposed CNN accelerator design using OPPE achieves high throughput with less resource utilization. [ABSTRACT FROM AUTHOR]

In order to find a method to synthesize spinel solid solution at low temperature, in this paper, pseudo-boehmite and copper nitrate were used as raw materials, and citric acid was used as accelerator for the first time, the Cu 1−x Al 2.5 spinel solid solutions were synthesized by ball milling treatment of the solid mixture and then calcined at elevated temperatures. The obtained materials were characterized by X-ray diffraction (XRD), Brunner−Emmett−Teller (BET) measurements, H 2 -temperature programmed reduction (H 2 -TPR), infrared (IR) and thermogravimetric methods (TG), and the mechanism of citric acid promoting the synthesis of the spinel solid solution was suggested. The results show that an optimal amount of citric acid can significantly promote the formation of the spinel structure, thus substantially decreasing the synthetic temperature from the above 900 °C–700 °C. At 700 °C, the preparation without the accelerator resulted in small amount of the spinel solid solution, while the use of accelerator gave rise to 82.96% spinel solid solution. Importantly, the Cu 1−x Al 2.5 spinel solid solution prepared at 700 °C demonstrates the best sustained release catalytic performance, and the catalytic activity reached more than 90% and remained stable within 50 h. The findings of this report might be provide guidance for the solid phase preparation of catalysts at relatively lower temperatures, thus facilitating the commercialization of the sustained release catalyst system. For the preparation of Cu–Al spinel by ball milling, citric acid was used as an accelerator for the first time, effectively decreasing the synthetic temperature. [Display omitted] • Citric acid was used as an accelerator for the first time. • The synthetic temperature of Cu–Al spinel by was decreased. • Possible mechanism for such an acceleration has been suggested. • The obtained solid solution can be used as a sustained release catalyst. [ABSTRACT FROM AUTHOR]

The evolution of the incubator is retraced from the foundational research in the 1950s and 1960s of Research-on-Research—that explored the R&D process in a number of contexts to derive and test theories of organizational behavior. In this paper, we review the next phase of this evolution, starting with seminal incubation research in the early 1970s and, we reflect on the prolific growth of incubation research and practice, as well as new challenges that lie ahead for the next phase of incubation. The incubation field has expanded to include a wide range of models and a plethora of spin-off terminology that have been used interchangeably, with the emergence of the most recent model—the accelerator. Our historical critical review of the evolution of the incubation field is used to ask: in this current era of technological change, what can we learn from the past? We reflect on this question in an attempt to help direct researchers in their endeavors to contribute to the next generation of incubation research. This paper presents lessons learned from prior research and provides a range of indicative areas for further empirical studies at four levels of analysis. [ABSTRACT FROM AUTHOR]

The production of reactor-based medical isotopes is fragile, which has meant supply shortages from time to time. This paper reviews alternative production methods in the form of cyclotrons, linear accelerators and neutron generators. Finally, the status of the production of medical isotopes in China is described. [ABSTRACT FROM AUTHOR]

This paper aimed to identify what are the strategic benefits that the social incubation process offers for social enterprise. We conducted a case study with a Brazilian social incubator. Social enterprises that participated in the social incubation program were the analysis units. Among the eight strategic benefits found, three of them have never been previously indicated in the studied literature, therefore being a novelty herein highlighted to improve social enterprise's commercial and social missions. By understanding these benefits, social entrepreneurs can make better decisions about joining social incubation. Besides, managers of social incubators can understand the limitations inherent to the social incubation herein stressed, and, from this, improve the programs. Hence, this paper advances knowledge in the social entrepreneurship field. [ABSTRACT FROM AUTHOR]

Since off-chip DRAM access affects both performance and power consumption significantly, convolutional neural network (CNN) accelerators commonly aim to maximize data reuse in on-chip memory. By organizing the on-chip memory to multiple banks, we may hide off-chip DRAM access delay by prefetching data to unused banks during computation. When and where to prefetch data and how to reuse the feature map data between layers define the multi-bank on-chip memory management (MOMM) problem. In this paper, we propose compiler techniques to solve the MOMM problem with two different objectives: one is to minimize the off-chip memory access volume, and the other is to minimize the processing delay caused by unhidden DRAM accesses. By running CNN benchmarks on a cycle-level NPU simulator, we demonstrate the trade-off relation between two objectives. Compared with the baseline approach that does not reuse the feature map between layers, we could reduce the DRAM access volume and the processing delay up to 55.0 and 79.4 percent, respectively. Moreover, we extend the proposed techniques to consider layer fusion that aims to reuse feature maps between layers. Experiment results confirm the superiority of the proposed hybrid fusion technique to the per-layer processing technique and the pure fusion technique. [ABSTRACT FROM AUTHOR]

A particle beam-thin foil scattering model is updated within the context of parametrized relativistic quantum theory (pRQT). This paper focuses on the creation, annihilation, and detection of tachyons when a beam of particles scatters off a thin foil. Improved calculation procedures and recent data are used to update model calculations for a pion-proton system. [ABSTRACT FROM AUTHOR]

In this paper, a new match circuit of the capacitive divider is studied. Using the proposed match circuit, the signal measured by the capacitive divider will not be secondarily attenuated near the accelerator, and the secondary attenuation is done at the terminal end of the cable which is located in a shielding room with a good electromagnetic environment. So, the proposed circuit has a better anti-interference capability than the traditional terminal-end match circuit. The result of the theoretic analysis shows that the proposed match circuit is equivalent with the traditional terminal-end match circuit when the parameters of the circuit are well chosen. Rectangular pulse response of the proposed match circuit is tested based on an equivalent circuit. It shows that the proposed match circuit has a good pulse response characteristic for rectangular pulse with a rise time of 5 ns, and the output waveform of the match circuit has a flattop jitter less than 1%. The match circuit proposed in this paper is utilized to measure the diode voltage of the accelerator in our laboratory. It is found that the high-frequency interference on the output waveform measured by the new match circuit is much smaller than that measured by the traditional terminal-end match circuit, which proves that the match circuit proposed in this paper is an effective method for increasing the signal-to-noise ratio of capacitive divider for measurement of nanosecond-range HV pulse. [ABSTRACT FROM AUTHOR]

Convolutional neural network (CNN) is one of the most promising algorithms that outweighs other traditional methods in terms of accuracy in classification tasks. However, several CNNs, such as VGG, demand a huge computation in convolutional layers. Many accelerators implemented on powerful FPGAs have been introduced to address the problems. In this paper, we present a VGG-based accelerator which is optimized for a low-cost FPGA. In order to optimize the FPGA resource of logic element and memory, we propose a dedicated input buffer that maximizes the data reuse. In addition, we design a low resource processing engine with the optimal number of Multiply Accumulate (MAC) units. In the experiments, we use VGG16 model for inference to evaluate the performance of our accelerator and achieve a throughput of 38.8 GOPS at a clock speed of 150 MHz on Intel Cyclone V SX SoC. The experimental results show that our design is better than previous works in terms of resource efficiency. [ABSTRACT FROM AUTHOR]

In a fusion reactor, the plasma based on a deuterium-tritium reaction generates high energy neutron flux interacting with the materials of the plasma facing components. In order to study the irradiated behavior of these materials, the International Fusion Materials Irradiation Facility (IFMIF) was conceived to generate these fusion relevant neutrons through d -Li stripping source. IFMIF is presently in its Engineering Validation and Engineering Design Activities (EVEDA) phase under the Broader Approach agreement signed between EURATOM and Japanese Government in 2007. This agreement mandates the IFMIF/EVEDA project to validate the design of the different systems of IFMIF and to produce an integrated engineering design of IFMIF, together with the data necessary for future decisions on its construction and operation. While the Engineering Validation Activity (EVA) of the Lithium Target Facility and the Test Facility was completed by constructing prototypes, the EVA of the Accelerator Facility with the Linear IFMIF Prototype Accelerator (LIPAc) is still on-going at the Rokkasho Fusion Institute, Japan. This paper overviews the achievements in the previous phase ended on 31 March 2020 and the progress in the current phase to complete the commissioning of the LIPAc. [ABSTRACT FROM AUTHOR]

This paper contains a description of an original calculations method of electric field intensity and magnetic field induction which were produced by bunches assembling relativistic proton beams. The spatial distribution of electric charge density inside the bunch is described by the three-axial Gauss distribution. The method consists of component calculations of the electric field intensity at the frame of reference moving with the bunch and further, on the application of the Lorentz transformation to electromagnetic fields. As a result, we obtained values of components of electric field intensity and magnetic field induction at the rest frame of reference where could be located examined samples. Formula worked out with the method were discussed and gathered at the tables. Formula were applied to calculations of proton bunches accelerated in the LHC. The collected results were presented in the form of charts and discussed. Useful values of magnetic field induction and electric field intensity were very close to already published at the paper in which one-axial Gauss distribution was applied. The results contained in this paper confirmed possibility of using such particle bunches as sources of strong magnetic field pulses, useful among the others, in solid state physics, condensed phase physics and electronics as well. [ABSTRACT FROM AUTHOR]

In this paper, we report on the development and first deployment of a distributed optical fiber radiation sensor (DOFRS) for the online monitoring of radiation levels in the high-energy accelerator facilities of CERN. The DOFRS is composed of two basic parts: a set of suitably chosen radiation sensitive optical fibers (OFs), to be installed in the machine tunnel, and an optical time domain reflectometer (OTDR), to be installed in a radiation-free area. We carried out a calibration of the radiation response of a P-doped multimode OF under 60Co $\gamma $ -rays and in the mixed-field radiation environment of the CHARM facility at CERN. By performing OTDR measurements, we are able to probe the radiation-induced attenuation in the OF along its length and calculate the deposited radiation dose with a spatial resolution of 1 m. In this paper, we describe the main features associated with DOFRS implementation in the proton synchrotron booster (PSB) at CERN. We also report the first results obtained from the monitoring of PSB radiation levels since its recommissioning in April 2017. The performances and advantages of the DOFRS system are discussed. [ABSTRACT FROM AUTHOR]

This paper describes the latest high-current multi cusp type ion source developed at TRIUMF, which is capable of producing a negative hydrogen ion beam (H − ) of 60 mA of direct current at 140V and 90A arc. The results achieved to date including emittance measurements and filament lifetime issues are presented. The low current version of this ion source is suitable for medical cyclotrons as well as accelerators and the high current version is intended for producing large neutral hydrogen beams for fusion research. The description of the source magnetic configuration, the electron filter profile and the differential pumping techniques given in the paper will allow the building of an arc discharge H − ion source with similar properties. [ABSTRACT FROM AUTHOR]

Aiming at the linear bottleneck structure of MobileNetV2, we study the design scheme of the dedicated processor chip. Based on the Layer Fusion mode and the configurable block structure, we design a pipeline structure for bottleneck convolution as well as a corresponding analysis framework. Then, a design space is proposed accordingly, and a software simulator is used to traverse and compare the performance of various schemes in this space. We then find the rules for optimal parameter selection. The validity of the conclusion is verified by hardware behavior simulation. The study can help system chip designers to select or design a suitable MobileNetV2 processor IP design scheme based on their own resource constraints and performance requirements. The paper also provides some inspiration for future automatic processor design. [ABSTRACT FROM AUTHOR]

In recent years, machine learning (ML) techniques are proven to be powerful tools in various emerging applications. Traditionally, ML techniques are processed on general-purpose CPUs and GPUs, but their energy efficiencies are limited due to their excessive support for flexibility. As an efficient alternative to CPUs/GPUs, hardware accelerators are still limited as they often accommodate only a single ML technique (family). However, different problems may require different ML techniques, which implies that such accelerators may achieve poor learning accuracy or even be ineffective. In this paper, we present a polyvalent accelerator architecture integrated with multiple processing cores, called ParaML, which accommodates ten representative ML techniques, including $k$ -means, $k$ -nearest neighbors ($k$ -NN), naive Bayes (NB), support vector machine (SVM), linear regression (LR), classification tree (CT), deep neural network (DNN), learning vector quantization (LVQ), parzen window (PW), and principal component analysis (PCA). Benefited from our thorough analysis on computational primitives and locality properties of different ML techniques, the single-core ParaML can perform up to 1056 GOP/s (e.g., additions and multiplications) in an area of 3.51 mm2 and consumes 596 mW only, estimated by ICC and PrimeTime PX with post-synthesis netlist, respectively. Compared with the NVIDIA K20M GPU (28-nm process), the single-core ParaML (65-nm process) is $1.21\times $ faster, and can reduce the energy by $137.93\times $. We also compare the single-core ParaML with other accelerators. Compared with PRINS, single-core ParaML achieves $72.09\times $ and $2.57\times $ energy benefit for $k$ -NN and $k$ -means, respectively, and speeds up each query in $k$ -NN by $44.76\times $. Compared with EIE, the single-core ParaML achieves $5.02\times $ speedup and $4.97\times $ energy benefit with $11.62\times $ less area when evaluating with dense DNN. Compared with TPU, the single-core ParaML achieves $2.45\times $ better power efficiency (5647 Gop/W versus 2300 Gop/W) with $321.36\times $ less area. Compared to the single-core version, the 8-core ParaML will further improve the speedup up to $3.98\times $ with an area of 13.44 mm2 and a power of 2036 mW. [ABSTRACT FROM AUTHOR]

The increasing importance of inference algorithms, such as neural networks (NNs), principle component analysis (PCA), and singular value decomposition (SVD), etc., has led to the emergence of hardware accelerators to address power-performance tradeoffs in their implementation. Their large data sets make DRAM access the bottleneck for power and performance. Private SRAM scratch-pad memory is used to mitigate the DRAM access penalty but it is a limited resource in size and bandwidth. Thus, accelerator design is not just about computation, but also how data flow is scheduled across the memory hierarchy, including DRAM, scratch-pad SRAM, and datapath registers. Current accelerator design tools automate the generation of customized datapaths to improve performance, but have limited support for reducing DRAM/SRAM accesses during the computation. In this paper, we propose a memory-driven accelerator design methodology for large-scale inference applications, to maximize data access in the datapath and SRAM. We demonstrate its efficacy using several key kernels from large-scale inference applications. [ABSTRACT FROM AUTHOR]

In this paper, we have studied particle collision around a rotating acoustic black hole in 2 + 1 dimensions. This black hole is analog to a fluid flow in a draining bath tub with a sink. Center of mass energy for two-particle collision at the horizon of the rotating acoustic black hole is considered. There is a possibility of the two-mass collision to create infinite center of mass energy for certain fine tuning of the parameters of the theory. [ABSTRACT FROM AUTHOR]

It is well known that radio frequency breakdown is one of the main limitations in high frequency accelerators. Similarities have been detected between breakdowns in direct current vacuum gaps and those in superconducting radio frequency cavities. Therefore, cavity breakdowns due to electric field phenomena can be understood by studying direct current vacuum breakdowns. Significant irregularity of a surface and a variety of involved processes objectively stipulate a number of factors which may lead to a breakdown. In this paper, the effects of surface conditions, accelerator gradient, pulse length, and operating frequency on the breakdown have been studied by using COMSOL simulation package. It was found that the dependence of breakdown rate on accelerating gradient and pulse length follows scaling laws. Based on the time evolutions of electron density and the potential in cone-cylinder electrode configuration at the pressure of 0.1 Pa, the time scale of a vacuum breakdown has been established. It was also confirmed that the emission from an electrode surface can be regarded as a major factor leading to electrical breakdown in vacuum. The obtained results could be very useful in high-gradient accelerating structures. [ABSTRACT FROM AUTHOR]

Convolutional neural network's high computational complexity often obstructs its widespread adhibition in real-time and low-power applications. The existing software implementation solution cannot meet the demands of the CNN for computing performance and power consumption. The traditional FPGA-oriented CNN construction method has problems such as complicated process, long cycle and small optimization space. For these problems, according to the characteristics of CNN calculation pattern, this paper proposed a design and scheduling mechanism of convolutional neural network accelerator for cloud FPGA. By using for reference the design which based HLS technology, importing the cyclic cutting parameters and rearranging the convolution layer circularly, it constructed the network in a modular way, and extended parameters to further optimize the accelerator processing process. It summarized the scheduling scheme by analyzing the characteristics of system tasks and resources, and optimized its design from two aspects of control and data flow. In comparison with other existing works, the proposed design provided a solution with flexibility, low energy consumption, high energy efficiency and performance. The design also discussed the efficient universal scheduling scheme of the accelerator. Experimental results show that the accelerator can improve the computing speed and reduce the power consumption. [ABSTRACT FROM AUTHOR]

Triethanolamine (TEA) is a multifunctional organic additive, applied chiefly to a grinding aid and accelerator for cement. Its effect on cement paste in a sulfate-rich environment remains still unclear and elusive. In this paper, the expansion and mechanical performance of cement paste under external sulfate attack were measured. Thermodynamic model lent itself to estimate the correlation between triethanolamine and generated products content ahead of quantified phase analysis. The transformation of internal component was surveyed by multiple technologies of thermal gravimetric (TG), scanning electron microscope (SEM), X-ray diffraction (XRD), and nuclear magnetic resonance (NMR), while inductively coupled plasma (ICP) was applied to investigate the evolution of interstitial ion concentration. Furthermore, the changes of pore structure over immersion time were separately figured and tabulated by virtue of mercury intrusion porosimetry (MIP). The above analysis shows that when cement paste suffers sulfate attack, triethanolamine intensifies the formation of calcite and exacerbates the decalcification of C-S-H, promoting a large quantity of insoluble precipitation including mirabilite of abundant crystalline water which accounts essentially for severe damage. Determined after comprehensive evaluation, the addition of TEA is conducive to the emergence of physical sulfate attack and an increment of the total porosity and permeability, contributing to an extension of microcracks and a more fragile structure, ultimately makes cement paste appear as a reduction in strength and stiffness. • Triethanolamine correlates adversely with the resistance to sulfate attack of cement paste • The complex of sulfate and aluminum ions occupied by the TEA intensifies decalcification of C-S-H • Triethanolamine loosens internal structure and permeability paths inside cement paste within sulfate attack [ABSTRACT FROM AUTHOR]

This paper presents the potential use of eggshell powder (ESP) as green accelerator in ground palm oil fuel ash (GPOFA) concrete. Three categories of ESPs namely uncarbonised ESP (UC-ESP), carbonised ESP (C-ESP) and decarbonised ESP (DC-ESP) corresponding to no calcination and calcination at temperatures of 750 and 1000 °C, respectively, were prepared and studied. In addition, ESPs was used as cement replacement (0, 5, 10, 15, and 20 wt%) in GPOFA concrete and their performance were examined in term of reactivity, workability, heat hydration, compressive strength and microstructure performance. It was found that the presence of UC-ESP, C-ESP and DC-ESP can provide a low, medium and superior accelerator behaviour in the early age of GPOFA concrete. Overall, the GPOFA concrete combined with DC-ESP revealed an excellence SiO 2 -CaO reactivity, giving a twice compressive strength at early age and provides inductive hydration behaviour with low hydration temperature and significant improvement in dense gels formulation. Hence, this study provides a positive impact for rapid construction technology. As a conclusion, modified ESP is a highly potential as partial cement replacement and accelerator for concrete. • Eggshell powder (ESP) calcined at different temperatures are studies. • ESP is used as accelerator for palm oil fuel ash (POFA) concrete. • SiO 2 -CaO derived from GPOFA and calcined ESP provided an excellence accelerator in concrete. [ABSTRACT FROM AUTHOR]

Neural networks (NNs) have been demonstrated to be useful in a broad range of applications, such as image recognition, automatic translation, and advertisement recommendation. State-of-the-art NNs are known to be both computationally and memory intensive, due to the ever-increasing deep structure, i.e., multiple layers with massive neurons and connections (i.e., synapses). Sparse NNs have emerged as an effective solution to reduce the amount of computation and memory required. Though existing NN accelerators are able to efficiently process dense and regular networks, they cannot benefit from the reduction of synaptic weights. In this paper, we propose a novel accelerator, Cambricon-X, to exploit the sparsity and irregularity of NN models for increased efficiency. The proposed accelerator features a processing element (PE)-based architecture consisting of multiple PEs. An indexing module efficiently selects and transfers needed neurons to connected PEs with reduced bandwidth requirement, while each PE stores irregular and compressed synapses for local computation in an asynchronous fashion. With 16 PEs, our accelerator is able to achieve at most 544 GOP/s in a small form factor (6.38 mm2 and 954 mW at 65 nm). Experimental results over a number of representative sparse networks show that our accelerator achieves, on average, $7.23\times$ speedup and $6.43\times$ energy saving against the state-of-the-art NN accelerator. We further investigate possibilities of leveraging activation sparsity and multi-issue controller, which improve the efficiency of Cambricon-X. To ease the burden of programmers, we also propose a high efficient library-based programming environment for our accelerator. [ABSTRACT FROM AUTHOR]

By considering the information granulation in Granular Computing, the concept of the multi-granularity is important. It is mainly because different results of information granulation will imply different levels of granularity. Nevertheless, multi-granularity has been paid less attention to the problem of attribute reduction in rough set which is regarded as one of the most important mathematical tools in Granular Computing. Therefore, how to search the multi-granularity reduct will be mainly explored in this paper. Different from the previous studies which generate reduct by using one and only one granularity, multi-granularity reduct is actually a set of the reducts derived from multiple levels of granularity. A natural way for computing multi-granularity reduct is to repeat the process of searching reduct for each level of granularity. Obviously, such an approach is time-consuming. To fill such a gap, an acceleration strategy is introduced into the process of searching multi-granularity reduct. Our acceleration strategy can be respectively realized through considering two variations of granularity: 1) from a finer granularity to a coarser granularity; 2) from a coarser granularity to a finer granularity. Such two variations indicate that the reduct related to previous granularity may have guidance on the computation of reduct related to the present granularity. Consequently, two accelerators are designed for speeding up the process of finding multi-granularity reduct. The experimental results over 16 UCI data sets show that our accelerators can not only reduce the elapsed time of searching attributes significantly, but also select attributes which will not contribute to a poorer classification performance. This study suggests new trends concerning the problem of attribute reduction and the corresponding searching strategy. • The multi-granularity is introduced into attribute reduction. • An acceleration strategy is proposed to speed up the process of finding multi-granularity reduct. • Our approach can reduce the time consumption of finding reduct. [ABSTRACT FROM AUTHOR]

High Voltage Engineering has developed a high-current, light-ion 3.5 MV single-ended accelerator system to meet the stringent requirements on beam intensity and stability of the LUNA-MV project at Laboratori Nazionali del Gran Sasso (LNGS), L'Aquila, Italy. The accelerator has an all permanent magnet, 10 GHz ECR ion source to deliver intense beams of H (∼1 mA), He and C. The machine ensures energy stability below 10−5, terminal voltage ripple of 1.5 × 10−5 and uninterrupted operations time greater than 24 h as requested by LNGS. Various changes to the standard in-line Singletron accelerator were needed to satisfy these requirements. In this paper, we highlight design details about the accelerator and ECR source and present early performance results. [ABSTRACT FROM AUTHOR]

There are two approaches to improve the performance of Convolutional Neural Networks (CNNs): 1) accelerating computation and 2) reducing the amount of computation. The acceleration approaches take the advantage of CNN computing regularity which enables abundant fine-grained parallelisms in feature maps, neurons, and synapses. Alternatively, reducing computations leverages the intrinsic sparsity of CNN neurons and synapses. The sparsity represents as the computing "bubbles", i.e., zero or tiny-valued neurons and synapses. These bubbles can be removed to reduce the volume of computations. Although distinctly different from each other in principle, we find that the two types of approaches are not orthogonal to each other. Even worse, they may conflict to each other when working together. The conditional branches introduced by some bubble-removing mechanisms in the original computations destroy the regularity of deeply nested loops, thereby impairing the intrinsic parallelisms. Therefore, enabling the synergy between the two types of approaches is critical to arrive at superior performance. This paper proposed a relaxed synchronous computing architecture, FlexFlow-Pro, to fulfill this purpose. Compared with the state-of-the-art accelerators, the FlexFlow-Pro gains more than 2.5× performance on average and 2× energy efficiency. [ABSTRACT FROM AUTHOR]

In this paper, the authors' aim to explore the Hungarian startup ecosystem on both the demand side and the supply side. To achieve this, they conducted a survey reaching both startups and investors. In the survey, the authors observed fea - tures of Hungarian startup entrepreneurs such as gender, age, education, previous entrepreneurial experience, and their motivation for establishing startup companies. Furthermore, the authors focused on financing, location, target markets, employment, and operational difficulties. They also examined the attitudes of the startupers and of the ecosystem's other actors such as venture capital investors, incubator houses, accelerators, corporations and co-working spaces regarding various characteristics of the ecosystem. They deemed cooperation between the members of the ecosystem, international relations and the opportunity for startupers to start again after a failed startup to be the most important characteristics. They perceived the strongest characteristic of the Hungarian ecosystem to be social events (meetups and networking), while the most problematic: the opportunity to start again after failing a startup and the required level of entrepreneurial administration. [ABSTRACT FROM AUTHOR]

Hybrid computing platforms are now commonplace, featuring a large number of CPU cores and accelerators. This trend makes balancing computations between these heterogeneous resources performance critical. In this paper we propose aggregating several CPU cores in order to execute larger parallel tasks and improve load balancing between CPUs and accelerators. Additionally, we present our approach to exploit internal parallelism within tasks, by combining two runtime system schedulers: a global runtime system to schedule the main task graph and a local one to cope with internal task parallelism. We demonstrate the relevance of our approach in the context of the dense Cholesky factorization kernel implemented on top of the StarPU task-based runtime system. We present experimental results showing that our solution outperforms state-of-the-art implementations on two architectures: a heterogeneous CPU+GPU machine and the Intel Xeon Phi Knights Landing processor. [ABSTRACT FROM AUTHOR]

The Facility for Rare Isotope Beams (FRIB) Project has entered the phase of beam commissioning starting from the room-temperature front end and the superconducting linac segment of first 15 cryomodules. With the newly commissioned helium refrigeration system supplying 4.5 K liquid helium to the quarter-wave resonators and solenoids, the FRIB accelerator team achieved the sectional key performance parameters as designed ahead of schedule accelerating heavy ion beams above 20 MeV/u energy. Thus, FRIB accelerator becomes world's highest-energy heavy ion linear accelerator. We also validated machine protection and personnel protection systems that will be crucial to the next phase of commissioning. FRIB is on track towards a national user facility at the power frontier with a beam power two orders of magnitude higher than operating heavy-ion facilities. This paper summarizes the status of accelerator design, technology development, construction, commissioning as well as path to operations and upgrades. [ABSTRACT FROM AUTHOR]

A passive superconducting shield was proposed earlier to realize a high-field (3–4 T) septum magnet for the Future Circular Collider. This paper presents the experimental results of a potential shield material, an NbTi/Nb/Cu multilayer sheet. A cylindrical shield was constructed from two halves, each consisting of four layers with a total thickness of 3.2 mm, and inserted into the bore of a spare dipole corrector magnet of the Large Hadron Collider. At 4.2 K, up to about 3.1 T at the shield's surface, only a leakage field of 12.5 mT was measured inside the shield. This can be attributed to the misalignment of the two half cylinders, as confirmed by finite element simulations. With a better configuration, we estimate the shield's attenuation to be better than $\mathbf{{\text{4}}\times {\text{10}}^{-5}}$ , which is acceptable for the intended application. Above 3.1 T, the field penetrated smoothly. Below that limit, no flux jumps were observed even at the highest achievable ramp rate of more than 50 mT/s at the shield's surface. A “degaussing” cycle was used to eliminate the effects of the field trapped in the thick wall of the shield, which could otherwise distort the homogeneous field pattern at the extracted beam's position. At 1.9 K, the shield's performance was superior to that at 4.2 K, but it suffered from flux jumps. [ABSTRACT FROM AUTHOR]

The standard procedure for neutron reactor dosimetry is based on neutron irradiation of a target and its post-irradiation analysis by Gamma and/or X-ray spectrometry. Nowadays, the neutron spectra can be easily characterized for thermal and fast energies (respectively 0.025 eV and > 1~\MeV). In this paper we propose a new target and an innovating post-irradiation technique of analysis in order to detect the neutron spectra within the energy of 1 keV to 1 MeV. This paper will present the calculations performed for the selection of a suitable nuclear reaction and isotope, the results predicted by simulations, the irradiation campaign that is proposed, and the post-irradiation technique of analysis. [ABSTRACT FROM PUBLISHER]

The Tsetlin Machine (TM) is a machine learning algorithm based on an ensemble of Tsetlin Automata (TAs) that learns propositional logic expressions from Boolean input features. In this paper, the design and implementation of a Field Programmable Gate Array (FPGA) accelerator based on the Convolutional Tsetlin Machine (CTM) is presented. The accelerator performs classification of two pattern classes in 4 × 4 Boolean images with a 2 × 2 convolution window. Specifically, there are two separate TMs, one per class. Each TM comprises 40 propositional logic formulas, denoted as clauses, which are conjunctions of literals. Include/exclude actions from the TAs determine which literals are included in each clause. The accelerator supports full training, including random patch selection during convolution based on parallel reservoir sampling across all clauses. The design is implemented on a Xilinx Zynq XC7Z020 FPGA platform. With an operating clock speed of 40 MHz, the accelerator achieves a classification rate of 4.4 million images per second with an energy per classification of 0.6 μ J. The mean test accuracy is 99.9% when trained on the 2-dimensional Noisy XOR dataset with 40% noise in the training labels. To achieve this performance, which is on par with the original software implementation, Linear Feedback Shift Register (LFSR) random number generators of minimum 16 bits are required. The solution demonstrates the core principles of a CTM and can be scaled to operate on multi-class systems for larger images. • First fully functional Convolutional Tsetlin Machine (CTM) hardware accelerator. • The accelerator is self-contained and supports full training. • Random patch selection, based on parallel reservoir sampling across all clauses, during training. • Test accuracy matches the corresponding software implementation. • Prepared for scaling to support multi-class systems and classification of larger images. [ABSTRACT FROM AUTHOR]