Efficient quantum arithmetic circuits are commonly found in numerous quantum algorithms of practical significance. To date, the logarithmic-depth quantum adders includes a constant coefficient k ≥ 2 while achieving t...
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Efficient quantum arithmetic circuits are commonly found in numerous quantum algorithms of practical significance. To date, the logarithmic-depth quantum adders includes a constant coefficient k ≥ 2 while achieving the Toffoli-Depth of \(k\log {}n + \mathcal {O}(1)\). In this work, 160 alternative compositions of the carry-propagation structure are comprehensively explored to determine the optimal depth structure for a quantum adder. By extensively studying these structures, it is shown that an exact Toffoli-Depth of \(\log {}n + \mathcal {O}(1)\) is achievable. This presents a reduction of Toffoli-Depth by almost \(50\%\) compared to the best known quantum adder circuits presented to date. We demonstrate a further possible design by incorporating a different expansion of propagate and generate forms, as well as an extension of the modular framework. Our paper elaborates on these designs, supported by detailed theoretical analyses and simulation-based studies, firmly substantiating our claims of optimality within all possible configurations outlined in this work. The results also mirror similar improvements, recently reported in classical adder circuit complexity.
In this article, we focus on the communication costs of three symmetric matrix computations: i) multiplying a matrix with its transpose, known as a symmetric rank-k update (SYRK) ii) adding the result of the multiplic...
In this article, we focus on the communication costs of three symmetric matrix computations: i) multiplying a matrix with its transpose, known as a symmetric rank-k update (SYRK) ii) adding the result of the multiplication of a matrix with the transpose of another matrix and the transpose of that result, known as a symmetric rank-2k update (SYR2K) iii) performing matrix multiplication with a symmetric input matrix (SYMM). All three computations appear in the Level 3 Basic Linear Algebra Subroutines (BLAS) and have wide use in applications involving symmetric matrices. We establish communication lower bounds for these kernels using sequential and distributed-memory parallel computational models, and we show that our bounds are tight by presenting communication-optimal algorithms for each setting. Our lower bound proofs rely on applying a geometric inequality for symmetric computations and analytically solving constrained nonlinear optimization problems. The symmetric matrix and its corresponding computations are accessed and performed according to a triangular block partitioning scheme in the optimal algorithms.
DeepFake, an artificial intelligence technology that can automatically synthesize facial forgeries, has recently attracted worldwide attention. While DeepFakes can be entertaining, they can also be used to spread fals...
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DeepFake, an artificial intelligence technology that can automatically synthesize facial forgeries, has recently attracted worldwide attention. While DeepFakes can be entertaining, they can also be used to spread falsified information or be weaponized as cognition warfare. Forensic researchers have been dedicated to designing defensive algorithms to combat such disinformation. However, attacking technologies have been developed to make DeepFake products more aggressive. For example, by launching anti-forensics and adversarial attacks, DeepFakes can be disguised as authentic media to evade forensic detectors. However, such manipulations often sacrifice image quality for satisfactory undetectability. To address this issue, we propose a method to generate a novel adversarial sharpening mask for launching black-box anti-forensics attacks. Unlike many existing methods, our approach injects perturbations that allow DeepFakes to achieve high anti-forensics performance while maintaining pleasant sharpening visual effects. Experimental evaluations demonstrate that our method successfully disrupts state-of-the-art DeepFake detectors. Moreover, compared to images processed by existing DeepFake anti-forensics methods, our method's quality of anti-forensics DeepFakes rendered is significantly improved. Our code is available at https://***/fb-reps/HQ-AF_GAN.
This book constitutes the proceedings of the 4th International Conference on Social Informatics, SocInfo 2012, held in Lausanne, Switzerland, in December 2012. The 21 full papers, 18 short papers included in this vol...
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ISBN:
(数字)9783642353864
ISBN:
(纸本)9783642353857
This book constitutes the proceedings of the 4th International Conference on Social Informatics, SocInfo 2012, held in Lausanne, Switzerland, in December 2012.
The 21 full papers, 18 short papers included in this volume were carefully reviewed and selected from 61 submissions. The papers are organized in topical sections named: social choice mechanisms in the e-society,computational models of social phenomena, social simulation, web mining and its social interpretations, algorithms and protocols inspired by human societies, socio-economic systems and applications, trust, privacy, risk and security in social contexts.
Automated code completion, aiming at generating subsequent tokens from unfinished code, has significantly benefited from recent progress in pre-trained Large Language Models (LLMs). However, these models often suffer ...
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Automated code completion, aiming at generating subsequent tokens from unfinished code, has significantly benefited from recent progress in pre-trained Large Language Models (LLMs). However, these models often suffer from coherence issues and hallucinations when dealing with complex code logic or extrapolating beyond their training data. Existing Retrieval Augmented Generation (RAG) techniques partially address these issues by retrieving relevant code with a separate encoding model where the retrieved snippet serves as contextual reference for code completion. However, their retrieval scope is subject to a singular perspective defined by the encoding model, which largely overlooks the complexity and diversity inherent in code semantics. To address this limitation, we propose ProCC, a code completion framework leveraging prompt engineering and the contextual multi-armed bandits algorithm to flexibly incorporate and adapt to multiple perspectives of code. ProCC first employs a prompt-based multi-retriever system which crafts prompt templates to elicit LLM knowledge to understand code semantics with multiple retrieval perspectives. Then, it adopts the adaptive retrieval selection algorithm to incorporate code similarity into the decision-making process to determine the most suitable retrieval perspective for the LLM to complete the code. Experimental results demonstrate that ProCC outperforms a widely-studied code completion technique RepoCoder by 7.92% on the public benchmark CCEval, 3.19% in HumanEval-Infilling, 2.80% on our collected open-source benchmark suite, and 4.48% on the private-domain benchmark suite collected from Kuaishou Technology in terms of Exact Match. ProCC also allows augmenting fine-tuned techniques in a plug-and-play manner, yielding an averaged 6.5% improvement over the fine-tuned model.
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