China became the world's largest apple producer, accounting for over 50% of the global planting area and output. The increasing urbanization has led to a decline in agricultural workers and higher labor costs, pro...
详细信息
Programmers frequently consult API documentation to learn how to use libraries, both those included with a programming language and those offered by third parties. Beginner programmers also have this need but struggle...
详细信息
ISBN:
(纸本)9798400712166
Programmers frequently consult API documentation to learn how to use libraries, both those included with a programming language and those offered by third parties. Beginner programmers also have this need but struggle to browse professional documentation systems, which are aimed at experienced programmers. Educators sometimes try to patch this problem by writing simplified, ad hoc educational documents as a surrogate for a documentation system. This paper presents Judicious, an API documentation system explicitly designed for novice programmers. It allows retrieving the documentation for one name at a time;offers a clear and distinctive visual representation of functions and constants;gradually presents more information such as types, optional and variable-length parameters for functions;highlights functions with side effects;and instantaneously generates documentation also for functions defined in student code. Judicious's design builds on prior research in the learning sciences and programming languages. The gradual disclosing of information matches the progression of increasingly larger subsets of programming languages. The diagrammatic representation, the clear distinction between functions and constants, and the pinpointing of side effects aim to address known novice misconceptions. The system is integrated into a code editor and is publicly available as a web platform.
The Column Subset Selection (CSS) problem has been widely studied in dimensionality reduction and feature selection. The goal of the CSS problem is to output a submatrix S, consisting of k columns from an n × d i...
The Column Subset Selection (CSS) problem has been widely studied in dimensionality reduction and feature selection. The goal of the CSS problem is to output a submatrix S, consisting of k columns from an n × d input matrix A that minimizes the residual error A − SS†A2F, where S† is the Moore-Penrose inverse matrix of S. Many previous approximation algorithms have non-linear running times in both n and d, while the existing linear-time algorithms have a relatively larger approximation ratios. Additionally, the local search algorithms in existing results for solving the CSS problem are heuristic. To achieve linear running time while maintaining better approximation using a local search strategy, we propose a local search-based approximation algorithm for the CSS problem with exactly k columns selected. A key challenge in achieving linear running time with the local search strategy is how to avoid exhaustive enumerations of candidate columns for constructing swap pairs in each local search step. To address this issue, we propose a two-step mixed sampling method that reduces the number of enumerations for swap pair construction from O(dk) to k in linear time. Although the two-step mixed sampling method reduces the search space of local search strategy, bounding the residual error after swaps is a non-trivial task. To estimate the changes in residual error after swaps, we propose a matched swap pair construction method to bound the approximation loss, ensuring a constant probability of loss reduction in each local search step. In expectation, these techniques enable us to obtain the local search algorithm for the CSS problem with theoretical guarantees, where a 53(k + 1)-approximate solution can be obtained in linear running time O(ndk4 log k). Empirical experiments show that our proposed algorithm achieves better quality and time compared to previous algorithms on both small and large datasets. Moreover, it is at least 10 times faster than state-of-the-art algorithms a
Transformer models, such as BERT, GPT, and ViT, have been applied to a wide range of areas in recent years, due to their efficacy. In order to improve the training efficiency of Transformer models, different distribut...
Transformer models, such as BERT, GPT, and ViT, have been applied to a wide range of areas in recent years, due to their efficacy. In order to improve the training efficiency of Transformer models, different distributed training approaches have been proposed, like Megatron-LM [8]. However, when multi-dimensional parallelism strategies are considered, due to the complexity, existing works can not harmonize the different strategies well enough to obtain a globally optimal solution. In this paper, we propose a parallelism strategy searching algorithm PTIP, which generates operator-level parallelism strategies consisting of three schemes: data parallelism, tensor parallelism, and pipeline parallelism. PTIP abstracts these three parallelism schemes simultaneously into an auxiliary graph, reformulates the searching problem into a mixed-integer programming (MIP) problem, and uses a MIP solver to obtain a high-quality multi-dimensional strategy. Experiments conducted on Transformers demonstrate that PTIP obtains 13.9% − 24.7% performance improvement compared to Megatron-LM [8].
Surface wave spectra estimation from Synthetic Aperture Radar (SAR) data has been a subject of many studies. In the open ocean, where high-resolution Wave Mode data capture predominately swell waves, various inverse m...
详细信息
This paper introduces a novel framework designed to achieve a high compression ratio in Split Learning (SL) scenarios where resource-constrained devices are involved in large-scale model training. Our investigations d...
详细信息
Federated learning (FL) has recently become a hot research topic, in which Byzantine robustness, communication efficiency and privacy preservation are three important aspects. However, the tension among these three as...
详细信息
Lattice-based Post-Quantum Cryptography (PQC) can effectively resist the quantum threat to blockchain's underlying cryptographic algorithms. Blockchain node decryption is one of the most commonly used cryptographi...
详细信息
Traveling is an integral part of our lifestyle. Irrespective of the situation or requirement, one cannot totally avoid traveling. People often sit for long hours in seats with limited legroom during travel. These seat...
详细信息
Graph Neural Networks (GNNs) as an emerging technique have shown excellent performance in a variety of fields, such as social networks and recommendation systems. However, GNNs may have to overcome privacy concerns as...
详细信息
暂无评论