Active Learning With Complementary Sampling for Instructing Class-Biased Multi-Label Text Emotion Classification

High-quality corpora have been very scarce for the text emotion research. Existing corpora with multi-label emotion annotations have been either too small or too class-biased to properly support a supervised emotion learning. In this paper, we propose a novel active learning method for efficiently instructing the human annotations for a less-biased and high-quality multi-label emotion corpus. Specifically, to compensate annotation for the minority-class examples, we propose a complementary sampling strategy based on unlabeled resources by measuring a probabilistic distance between the expected emotion label distribution in a temporary corpus and an uniform distribution. Qualitative evaluations are also given to the unlabeled examples, in which we evaluate the model uncertainties for multi-label emotion predictions, their syntactic representativeness for the other unlabeled examples, and their diverseness to the labeled examples, for a high-quality sampling. Through active learning, a supervised emotion classifier gets progressively improved by learning from these new examples. Experiment results suggest that by following these sampling strategies we can develop a corpus of high-quality examples with significantly relieved bias for emotion classes. Compared to the learning procedures based on traditional active learning algorithms, our learning procedure indicates the most efficient learning curve and estimates the best multi-label emotion predictions

Optimized Compilation of Aggregated Instructions for Realistic Quantum Computers

Recent developments in engineering and algorithms have made real-world applications in quantum computing possible in the near future. Existing quantum programming languages and compilers use a quantum assembly language composed of 1- and 2-qubit (quantum bit) gates. Quantum compiler frameworks translate this quantum assembly to electric signals (called control pulses) that implement the specified computation on specific physical devices. However, there is a mismatch between the operations defined by the 1- and 2-qubit logical ISA and their underlying physical implementation, so the current practice of directly translating logical instructions into control pulses results in inefficient, high-latency programs. To address this inefficiency, we propose a universal quantum compilation methodology that aggregates multiple logical operations into larger units that manipulate up to 10 qubits at a time. Our methodology then optimizes these aggregates by (1) finding commutative intermediate operations that result in more efficient schedules and (2) creating custom control pulses optimized for the aggregate (instead of individual 1- and 2-qubit operations). Compared to the standard gate-based compilation, the proposed approach realizes a deeper vertical integration of high-level quantum software and low-level, physical quantum hardware. We evaluate our approach on important near-term quantum applications on simulations of superconducting quantum architectures. Our proposed approach provides a mean speedup of $5\times$, with a maximum of $10\times$. Because latency directly affects the feasibility of quantum computation, our results not only improve performance but also have the potential to enable quantum computation sooner than otherwise possible.Comment: 13 pages, to apper in ASPLO

Optimal Synthesis of Stabilizer Codes via MaxSAT

Quantum Error Correction (QEC) codes are crucial for achieving fault-tolerant quantum computing in the long term. However, efficiently implementing these codes on hardware poses significant challenges, including hardware connectivity matching, efficient circuit scheduling, and fault-tolerance enforcement. In this study, we present an optimal synthesizer that stitches generic stabilizer codes onto diverse hardware structures via MaxSAT. Our evaluation demonstrates (1) the capability of our approach to be applied for various codes and devices and (2) the consistently better efficiency than the best prior heuristic approaches that only target specific QEC codes. By bridging the gap between high-level QEC code design and low-level hardware constraints, this work paves the way toward achieving long-term fault-tolerant quantum computing goals

Partial Compilation of Variational Algorithms for Noisy Intermediate-Scale Quantum Machines

Quantum computing is on the cusp of reality with Noisy Intermediate-Scale Quantum (NISQ) machines currently under development and testing. Some of the most promising algorithms for these machines are variational algorithms that employ classical optimization coupled with quantum hardware to evaluate the quality of each candidate solution. Recent work used GRadient Descent Pulse Engineering (GRAPE) to translate quantum programs into highly optimized machine control pulses, resulting in a significant reduction in the execution time of programs. This is critical, as quantum machines can barely support the execution of short programs before failing. However, GRAPE suffers from high compilation latency, which is untenable in variational algorithms since compilation is interleaved with computation. We propose two strategies for partial compilation, exploiting the structure of variational circuits to pre-compile optimal pulses for specific blocks of gates. Our results indicate significant pulse speedups ranging from 1.5x-3x in typical benchmarks, with only a small fraction of the compilation latency of GRAPE.Comment: Appearing in the 52nd Annual IEEE/ACM International Symposium on Microarchitecture (MICRO-52), October 12-16, 2019, Columbus, OH, US

Compilation, Optimization and Verification of Near-Term Quantum Computing

Quantum computing has recently transitioned from a theoretical prediction to a nascent technology. Nevertheless, there is still a gap between the computational ability and reliability of current QIP technologies and the requirements of the first useful quantum computing applications. It will require tremendous efforts and investment to solve the engineering and research challenges to close the gap. Because of the uncertainties and difficulties in relying on hardware breakthroughs, it will also be crucial to close the gap using higher-level quantum optimizations and software-hardware co-design, which could maximally utilize noisy devices and potentially provide an accelerated pathway to real-world quantum computing applications. This thesis develops methods to explore the vast design space for highly optimized and reliable quantum computing architectures and software and focuses on the compilation, verification, and error correction aspects of the quantum computing stack. Specifically, the three main chapters in this thesis discuss, respectively, a direct-to-pulse compiler, a protocol to fault-tolerantly prepare approximate GKP (Gottesman-Kitave-Preskill) qubit states, and a verification framework that helps quantum programmers write provably correct compiler components through formal verification. Though touching many aspects of the stack and based on different technologies in physics and computer science, the majority of this thesis can be connected by a main theme --- improving the efficiency of existing quantum computing stacks by breaking the abstraction between layers in the quantum computing stack

Application of Joint Inversion of Different Electrode Arrays in Ancient Mausoleum Detection

Electrical resistivity tomography is a popular geophysical method and has been applied in shallow exploration, involving hydrology, archaeology, and geology, in recent years. To enhance the resolution of electrical resistivity tomography and deal with complex geological settings, we propose the weighted combined inversion of different electrode arrays based on the Jacobian matrix, and then, taking Wenner and dipole-dipole datasets as examples, test its effectiveness on synthetic models and a field case of detecting ancient mausoleum. The results show that the resolution of the weighted combined inversion results is superior to that of a single electrode array in transverse and longitudinal directions, and in the field case, it is demonstrated that the weighted combined inversion algorithm can alleviate the inherent defects of U-shaped electrode array, reduce the ambiguity of inversion, and better constrain the width of the mausoleum

Shape It up! An Experimental Study of Fitness App Usage on Exercise Effectiveness

This study provides insights into how IT influences individual behavior change by identifying the mediating role of user’s regulatory-focus in the effects of distinguished IT usage motivations on exercise performance. It also explores the effect of users’ mobile fitness App usage on their exercise effectiveness in both physical and mental perspectives. based on ARCS Motivational Model, participants are randomly assigned to different groups to examine the role of App functions and their interactive effects through a field experiment. We believe this study will extend Keller\u27s ARCS Motivational Model and Regulatory Focus Theory into the context of IT fitness theoretically and help users to obtain a better understanding of the functional usage of IT fitness App practically