An algorithm is presented for analyzing the computational process on a user's computer device based on the sings of a cryptojacker, i.e. performing hidden illegal cryptomining. The algorithm is based on a sequenti...
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Moretti’s "distant reading" is regarded as a pioneer and an exemplar of computational literary studies. "Distant reading" puts forward a strict boundary between hermeneutics and quantitative analy...
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A physical system that retrieves information from the past and acts on it appropriately can efficiently solve difficult combinatorial-optimization problems.
A physical system that retrieves information from the past and acts on it appropriately can efficiently solve difficult combinatorial-optimization problems.
Modelling soft-robot deformations induced by actuators and interactions with the surrounding environment can enable full uptake of embodied intelligence. This Technical Review provides a concise guide to modelling app...
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Modelling soft-robot deformations induced by actuators and interactions with the surrounding environment can enable full uptake of embodied intelligence. This Technical Review provides a concise guide to modelling approaches and computational strategies that can lead to model-informed design of embodied intelligent robots. Embodied intelligence (intelligence that requires and leverages a physical body) is a well-known paradigm in soft robotics, but its mathematical description and consequent computational modelling remain elusive, with a need for models that can be used for design and control purposes. We argue that filling this gap will enable full uptake of embodied intelligence in soft robots. We provide a concise guide to the main mathematical modelling approaches, and consequent computational modelling strategies, that can be used to describe soft robots and their physical interactions with the surrounding environment, including fluid and solid media. We aim to convey the challenges and opportunities within the context of modelling the physical interactions underpinning embodied intelligence. We emphasize that interdisciplinary work is required, especially in the context of fully coupled robot-environment interaction modelling. Promoting this dialogue across disciplines is a necessary step to further advance the field of soft robotics.
We present the results of a Lattice QCD computation of pion generalized parton distribution (GPD), employing perturbative matching up to next-to-next-to-leading order (NNLO). The computations are based on an ensemble ...
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Multi-variate linear regression is one of the most popular supervised machine learning algorithms that is used to find a linear relation between independent variables and a single dependent variable. It has been succe...
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ISBN:
(纸本)9798331527341
Multi-variate linear regression is one of the most popular supervised machine learning algorithms that is used to find a linear relation between independent variables and a single dependent variable. It has been successfully utilized in predicting dependent variables in many applications. Due to the wide adoption of machine learning algorithms, researchers have been developing ever more efficient implementations of such algorithms. Hardware implementations can offer high computational power and parallel processing. Field Programmable Gate Arrays (FPGAs) offer a very attractive hardware implementation option. FPGAs offer a flexible architecture that allows for algorithm modifications as needed. They can outperform GPUs and CPUs in terms of speed and power efficiency. In this paper, we propose a resource-efficient hardware implementation of a multi-variate linear regression training circuit on FPGA using Verilog HDL. This design leverages the available Block RAMs (BRAMs) on the FPGA board to store the data samples before feeding them to the algorithm. It is parametrizable in terms of both the number of data samples and the number of features in the data. However, a re-synthesis of the design is required when the number of features changes. We tested the design using a 200-training data set with 10 features and the time needed to produce the coefficients was 2.101 milliseconds. We synthesized the design using a low-cost FPGA board with a Xilinx xc7a100t-3-fgg484 FPGA device. The design only utilized 4% of the available registers, 8% of the available Logic Units, 5% of the available block RAMs, and 10% of the available DSP48E1 blocks. The estimated average power used was 130.84 milliwatts. A Python implementation of the same algorithm took 11.5921 milliseconds to compute the results using an Intel Core i7-10510U Processor with an average power of 15 W. Hence, the hardware implementation achieved a 5.5x reduction in computation time and a 115x reduction in power consumpt
We study the connection between discrete Morse theory and persistent homology in the context of shape reconstruction methods. Specifically, we consider the construction of Wrap complexes, introduced by Edelsbrunner as...
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Optoelectronic synapses have been attracting significant attention due to their important role in visual information processing. In this work, we fabricate an all-organic optoelectronic synaptic device with a double h...
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Optoelectronic synapses have been attracting significant attention due to their important role in visual information processing. In this work, we fabricate an all-organic optoelectronic synaptic device with a double heterojunction structure of PEDOT:PSS/poly(vinylidene fluoridetrifluoroethylene) (P(VDF-TrFE))/copper (II) phthalocyanine (CuPc) by a simple preparation process. The introduction of a dielectric P(VDF-TrFE) layer between PEDOT:PSS and CuPc layers benefits the trapping of charge carriers and slows down the electron-hole recombination rate. This two-terminal optoelectronic device is successfully applied to simulate synaptic functions of biological synapses by using optical pulses of 660 nm, including paired-pulse facilitation, spike-duration dependent plasticity, spike-rate dependent plasticity, spike-number dependent plasticity, and learning-experience behavior. Furthermore, the key characteristics of a nociceptor and the optical logic function of the "AND" and "OR" operations are also emulated. This work illustrates the potential of such device for constructing neuromorphic computing systems at the physical level.
This paper deals with the synthesis of four-bar linkage symmetrical coupler curves having Ball’s point on each flank. These are synthesized for driving a jerk free internal Geneva mechanism for a given locking to mov...
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Big Data is data whose shape and volume are rising with the passage of time and innovations in technology. This increase will give birth to more uncertain and complex situations, which will then be difficult to proper...
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Big Data is data whose shape and volume are rising with the passage of time and innovations in technology. This increase will give birth to more uncertain and complex situations, which will then be difficult to properly analyze and manage. Various devices are interconnected with each other, which communicate different types of information. This information is used for different purposes. A huge volume of data is produced, and the storage becomes larger. computational modeling is the tool that helps analyze, process, and manage the data to extract useful information. The modern industry's challenge is to incorporate knowledge into Big Data applications to deal with distinguishing difficulties in computational models. The techniques and models are delivered with guides to help analysts quickly fit models to information insights. The decision support system is a strong system that plays a significant role in shaping Big Data for sustaining efficiency and performance. Decision-making through computational modeling is also a powerful mechanism for supporting efficient tools for managing Big Data for influential use. Keeping in view the issues of modern-day industry, the proposed study has been considered to present decision-making and computational modeling of Big Data for sustaining influential usage. The existing state-of-the-art literature is presented in an organized way to analyze the currently available research.
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