Heterogeneous metaprogramming is using a generally higherlevel host language to generate code in a lower-lever object language. Its appeal is taking advantage of the module system, higher-order functions, data types, ...
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ISBN:
(纸本)9783030994600;9783030994617
Heterogeneous metaprogramming is using a generally higherlevel host language to generate code in a lower-lever object language. Its appeal is taking advantage of the module system, higher-order functions, data types, type system and verification tools of the host language to quicker produce high-performant lower-level code with some correctness guarantees. We present two heterogeneous metaprogramming systems whose host language is OCaml and object language is C. the first relies on offshoring: treating a subset of (MetaOCaml-generated) OCaml as a different notation for (a subset of) C. the second embeds C in OCaml in tagless-final style. the systems have been used in several projects, including the generation of C supersets OpenCL and OpenMP. Generating C with some correctness guarantees is far less trivial than it may appear, with pitfalls abound. Not coincidentally, the most subtle ones accompany the introduction of variables into the code. Maintaining the offshoring system has traps of its own. We expound the pitfalls we have came across in our experience, and describe counter-measures.
Reinforcement learning has shown great potential to address long-horizon tasks. However, most existing work lacks the ability to reason about functional parts of objects and extract the task semantics to facilitate ro...
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ISBN:
(数字)9798350363173
ISBN:
(纸本)9798350363180
Reinforcement learning has shown great potential to address long-horizon tasks. However, most existing work lacks the ability to reason about functional parts of objects and extract the task semantics to facilitate robotic learning, making it difficult to achieve category-level generalization even on seen objects. To address these issues, we develop a temporal logic guided affordance learning framework for generalizable dexterous manipulations (TALD). In particular, TALD is equipped with affordance learning for predicting actionable information and LTL representation for understanding the task semantics and improving learning efficiency. We design a contact predictor from the affordance learning perspective to improve the generalization performance, which predicts the max-affordance point observation based on the 3D point clouds to guide the agent to manipulate the functional part of objects. And we exploit the LTL progression to construct a task-driven labeled POMDP to address the challenge of a non-Markovian reward function, and design a task module to extract the LTL representation by Transformer Encoder to improve the sampling efficiency and facilitate the robotic learning. We validate the proposed method in four articulated manipulation scenarios. the generalization performance corresponding to success rates and visualization effects show the effectiveness of TALD. Our project is available at: https://***/view/tald-0257/.
We study the relationship between two approaches to higher-order program verification: a semi-automated method using Dijkstra monads and a fully automated method using a higher-order fixpoint logic called HFL(Z). Alth...
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Neuromorphic computing attempts to mimic the neural architecture of human brain by delivering a non vonNeumann hardware which can run even the most complex artificial intelligence algorithms at extremely fast computat...
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ISBN:
(纸本)9781728187532
Neuromorphic computing attempts to mimic the neural architecture of human brain by delivering a non vonNeumann hardware which can run even the most complex artificial intelligence algorithms at extremely fast computational speeds at power requirement as low in order as few tens of watts just like the human brain does. Since the brain is a complex mesh of millions of neurons communicating via the synapses and spiking signals in between them, there is a requirement of a circuit based memory element which can play the role of these synapses in electronic circuits. the memristors withthere unique pinched hysteresis property have been proposed and modelled to act as these synapses. this paper introduces LTspice modelling of a simple artificial neural network with memristive synapses and training it for the universal gates-NOR and NAND by providing a mechanism for interpreting the compressed binary data generated by parametric LTspice simulations. the results show potential for application in many other crucial neuromorphic simulations and their numeric interpretation using the tool developed for Co-simulation of LTspice withthe open source programming language, Python.
In previous work we have presented lang-n-play, a functional language-oriented programming language with languages as first-class-citizens. Language definitions can be bound to variables, passed to and returned by fun...
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ISBN:
(纸本)9783030590246;9783030590253
In previous work we have presented lang-n-play, a functional language-oriented programming language with languages as first-class-citizens. Language definitions can be bound to variables, passed to and returned by functions, and can be modified at run-time before being used. lang-n-play programs are compiled and executed in the higher-order logicprogramming ***. In this paper, we describe our compilation methods, which highlight how the distinctive features of higher-order logicprogramming are a great fit in implementing a language-oriented programming language.
the use of pure functional languages for interactive applications, especially mobile applications and games, is still rare. Reasons include the lack of libraries and frameworks that implement necessary features, poor ...
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ISBN:
(纸本)9781450368117
the use of pure functional languages for interactive applications, especially mobile applications and games, is still rare. Reasons include the lack of libraries and frameworks that implement necessary features, poor integration with existing toolchains, and the lack of examples that demonstrate how to best structure large interactive applications in a way that is scalable in terms of performance and modularity. In this paper we identify three specific challenges that limit the application of functionalprogramming specifically to mobile apps and games: purity, compositionality, and abstraction. We discuss solutions to these problems, and propose a framework for mobile app programmingthat completely separates logic from IO, resulting in an architecture that is referentially transparent, modular, scalable, backend agnostic and trivial to test. We implement this proposal in FAWN, a collection of libraries that provide higher-level notions needed in commercial applications, like resource management, widgets, storing user preferences, audio playing, image rendering, and composable applications. We have verified the suitability of this approach by using it to build, in Haskell, six mobile games for iOS and Android.
Many transformation techniques developed for constraint logic programs, also known as constrained Horn clauses (CHCs), have found new useful applications in the field of program verification. In this paper, we work ou...
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the proceedings contain 27 papers. the topics discussed include: a novel approach for software requirement prioritization;a novel approach for software requirement prioritization based upon non functional requirements...
ISBN:
(纸本)9781728125244
the proceedings contain 27 papers. the topics discussed include: a novel approach for software requirement prioritization;a novel approach for software requirement prioritization based upon non functional requirements;evaluating the software quality non-functional requirement through a fuzzy logic-based model based on the ISO/IEC 25000 (SQuaRE) standard;when software engineering meets cybersecurity at the classroom;what is programming? putting all together, part II the cognitive skills associated;and exploring software design skills of students in different stages of their curriculum.
Near-data accelerators play an important role in satisfying the ever growing demand for compute resources. However, for an efficient integration of near-data computing resources into applications, a flexible programmi...
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ISBN:
(纸本)9781728152684
Near-data accelerators play an important role in satisfying the ever growing demand for compute resources. However, for an efficient integration of near-data computing resources into applications, a flexible programming model and suitable abstractions on the operating system level are required. this paper presents Metal FS, a framework that enables users and applications to orchestrate computations on a NVMe+FPGA near-data computing device through standard shell syntax, including the pipe operator. A user-space NVMe file system interface exposes the storage resources of the NVMe+FPGA accelerator. Computation pipelines expressed on the shell are mapped to pre-defined functional elements of a coarse-grained FPGA overlay, enabling data transformations to be performed in proximity to the data source. Overall, Metal FS greatly increases developer productivity for applications targeting near-data computing accelerators.
the most popular visual programming tools focus on procedural, object-oriented and event-based programming. this paper describes a boxes-and-wires functionalprogramming tool, aimed to be accessible to novice programm...
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