Formalizing the Expertise of the Assembly Language Programmer

A novel compiler strategy for generating high quality code is described. The quality of the code results from reimplementing the program in the target language using knowledge of the program's behavior. The research is a first step towards formalizing the expertise of the assembly language programmer. The ultimate goal is to formalize code generation and implementation techniques in the same way that parsing and code generation techniques have been formalized. An experimental code generator based on the reimplementation strategy will be constructed. The code generator will provide a framework for analyzing the costs, applicability, and effectiveness of various implementation techniques. Several common code generation problems will be studied. Code written by experienced programmers and code generated by a conventional optimizing compiler will provide standards of comparison.MIT Artificial Intelligence Laborator

Domain Re-Modulation for Few-Shot Generative Domain Adaptation

In this study, we delve into the task of few-shot Generative Domain Adaptation (GDA), which involves transferring a pre-trained generator from one domain to a new domain using only a few reference images. Inspired by the way human brains acquire knowledge in new domains, we present an innovative generator structure called Domain Re-Modulation (DoRM). DoRM not only meets the criteria of high quality, large synthesis diversity, and cross-domain consistency, which were achieved by previous research in GDA, but also incorporates memory and domain association, akin to how human brains operate. Specifically, DoRM freezes the source generator and introduces new mapping and affine modules (M&A modules) to capture the attributes of the target domain during GDA. This process resembles the formation of new synapses in human brains. Consequently, a linearly combinable domain shift occurs in the style space. By incorporating multiple new M&A modules, the generator gains the capability to perform high-fidelity multi-domain and hybrid-domain generation. Moreover, to maintain cross-domain consistency more effectively, we introduce a similarity-based structure loss. This loss aligns the auto-correlation map of the target image with its corresponding auto-correlation map of the source image during training. Through extensive experiments, we demonstrate the superior performance of our DoRM and similarity-based structure loss in few-shot GDA, both quantitatively and qualitatively. The code will be available at https://github.com/wuyi2020/DoRM.Comment: Under Revie

TASKers: A Whole-System Generator for Benchmarking Real-Time-System Analyses

Implementation-based benchmarking of timing and schedulability analyses requires system code that can be executed on real hardware and has defined properties, for example, known worst-case execution times (WCETs) of tasks. Traditional approaches for creating benchmarks with such characteristics often result in implementations that do not resemble real-world systems, either due to work only being simulated by means of busy waiting, or because tasks have no control-flow dependencies between each other. In this paper, we address this problem with TASKers, a generator that constructs realistic benchmark systems with predefined properties. To achieve this, TASKers composes patterns of real-world programs to generate tasks that produce known outputs and exhibit preconfigured WCETs when being executed with certain inputs. Using this knowledge during the generation process, TASKers is able to specifically introduce inter-task control-flow dependencies by mapping the output of one task to the input of another

Secret Key Cryptosystem based on Non-Systematic Polar Codes

Polar codes are a new class of error correcting linear block codes, whose generator matrix is specified by the knowledge of transmission channel parameters, code length and code dimension. Moreover, regarding computational security, it is assumed that an attacker with a restricted processing power has unlimited access to the transmission media. Therefore, the attacker can construct the generator matrix of polar codes, especially in the case of Binary Erasure Channels, on which this matrix can be easily constructed. In this paper, we introduce a novel method to keep the generator matrix of polar codes in secret in a way that the attacker cannot access the required information to decode the intended polar code. With the help of this method, a secret key cryptosystem is proposed based on non-systematic polar codes. In fact, the main objective of this study is to achieve an acceptable level of security and reliability through taking advantage of the special properties of polar codes. The analyses revealed that our scheme resists the typical attacks on the secret key cryptosystems based on linear block codes. In addition, by employing some efficient methods, the key length of the proposed scheme is decreased compared to that of the previous cryptosystems. Moreover, this scheme enjoys other advantages including high code rate, and proper error performance as well

Data-Free Knowledge Distillation Using Adversarially Perturbed OpenGL Shader Images

Knowledge distillation (KD) has been a popular and effective method for model compression. One important assumption of KD is that the original training dataset is always available. However, this is not always the case due to privacy concerns and more. In recent years, "data-free" KD has emerged as a growing research topic which focuses on the scenario of performing KD when no data is provided. Many methods rely on a generator network to synthesize examples for distillation (which can be difficult to train) and can frequently produce images that are visually similar to the original dataset, which raises questions surrounding whether privacy is completely preserved. In this work, we propose a new approach to data-free KD that utilizes unnatural OpenGL images, combined with large amounts of data augmentation and adversarial attacks, to train a student network. We demonstrate that our approach achieves state-of-the-art results for a variety of datasets/networks and is more stable than existing generator-based data-free KD methods. Source code will be available in the future

HELAC-Onia: an automatic matrix element generator for heavy quarkonium physics

By the virtues of the Dyson-Schwinger equations, we upgrade the published code \mtt{HELAC} to be capable to calculate the heavy quarkonium helicity amplitudes in the framework of NRQCD factorization, which we dub \mtt{HELAC-Onia}. We rewrote the original \mtt{HELAC} to make the new program be able to calculate helicity amplitudes of multi P-wave quarkonium states production at hadron colliders and electron-positron colliders by including new P-wave off-shell currents. Therefore, besides the high efficiencies in computation of multi-leg processes within the Standard Model, \mtt{HELAC-Onia} is also sufficiently numerical stable in dealing with P-wave quarkonia (e.g. $h_{c,b},\chi_{c,b}$) and P-wave color-octet intermediate states. To the best of our knowledge, it is a first general-purpose automatic quarkonium matrix elements generator based on recursion relations on the market.Comment: Published version. 24 pages,1 figure, 7 tables, HELAC-Onia is available from http://helac-phegas.web.cern.ch/helac-phega

SeaNet -- Towards A Knowledge Graph Based Autonomic Management of Software Defined Networks

Automatic network management driven by Artificial Intelligent technologies has been heatedly discussed over decades. However, current reports mainly focus on theoretic proposals and architecture designs, works on practical implementations on real-life networks are yet to appear. This paper proposes our effort toward the implementation of knowledge graph driven approach for autonomic network management in software defined networks (SDNs), termed as SeaNet. Driven by the ToCo ontology, SeaNet is reprogrammed based on Mininet (a SDN emulator). It consists three core components, a knowledge graph generator, a SPARQL engine, and a network management API. The knowledge graph generator represents the knowledge in the telecommunication network management tasks into formally represented ontology driven model. Expert experience and network management rules can be formalized into knowledge graph and by automatically inferenced by SPARQL engine, Network management API is able to packet technology-specific details and expose technology-independent interfaces to users. The Experiments are carried out to evaluate proposed work by comparing with a commercial SDN controller Ryu implemented by the same language Python. The evaluation results show that SeaNet is considerably faster in most circumstances than Ryu and the SeaNet code is significantly more compact. Benefit from RDF reasoning, SeaNet is able to achieve O(1) time complexity on different scales of the knowledge graph while the traditional database can achieve O(nlogn) at its best. With the developed network management API, SeaNet enables researchers to develop semantic-intelligent applications on their own SDNs