Electrochemical Synthesis of Gamma Manganese Dioxide Mediated by Cerium

This research project aims to investigate and propose a novel method for producing gamma manganese dioxide. By using cerium (Ce3+/4+) as an mediator, a circulation system was established to solve the problem of discontinuous reactions in industrial gamma manganese dioxide production. By studying different types of reactors, electrodes, electrolytes, and electrochemical parameters, an energy efficiency that is competitive to the currently commercialized process was achieved. The use of XRD, XPS, Raman, FTIR, SEM and other characterization methods proved that the manganese dioxide produced by this project meets the requirement of commercial gamma manganese dioxid

Optimizing Quantum Programs against Decoherence: Delaying Qubits into Quantum Superposition

Quantum computing technology has reached a second renaissance in the last decade. However, in the NISQ era pointed out by John Preskill in 2018, quantum noise and decoherence, which affect the accuracy and execution effect of quantum programs, cannot be ignored and corrected by the near future NISQ computers. In order to let users more easily write quantum programs, the compiler and runtime system should consider underlying quantum hardware features such as decoherence. To address the challenges posed by decoherence, in this paper, we propose and prototype QLifeReducer to minimize the qubit lifetime in the input OpenQASM program by delaying qubits into quantum superposition. QLifeReducer includes three core modules, i.e.,the parser, parallelism analyzer and transformer. It introduces the layered bundle format to express the quantum program, where a set of parallelizable quantum operations is packaged into a bundle. We evaluate quantum programs before and after transformed by QLifeReducer on both real IBM Q 5 Tenerife and the self-developed simulator. The experimental results show that QLifeReducer reduces the error rate of a quantum program when executed on IBMQ 5 Tenerife by 11%; and can reduce the longest qubit lifetime as well as average qubit lifetime by more than 20% on most quantum workloads.Comment: To appear in TASE2019 - the 13th International Symposium on Theoretical Aspects of Software Engineering (submitted on Jan 25, 2019, and this is camera-ready version

Branchy-GNN: a Device-Edge Co-Inference Framework for Efficient Point Cloud Processing

The recent advancements of three-dimensional (3D) data acquisition devices have spurred a new breed of applications that rely on point cloud data processing. However, processing a large volume of point cloud data brings a significant workload on resource-constrained mobile devices, prohibiting from unleashing their full potentials. Built upon the emerging paradigm of device-edge co-inference, where an edge device extracts and transmits the intermediate feature to an edge server for further processing, we propose Branchy-GNN for efficient graph neural network (GNN) based point cloud processing by leveraging edge computing platforms. In order to reduce the on-device computational cost, the Branchy-GNN adds branch networks for early exiting. Besides, it employs learning-based joint source-channel coding (JSCC) for the intermediate feature compression to reduce the communication overhead. Our experimental results demonstrate that the proposed Branchy-GNN secures a significant latency reduction compared with several benchmark methods

CODAR: A Contextual Duration-Aware Qubit Mapping for Various NISQ Devices

Quantum computing devices in the NISQ era share common features and challenges like limited connectivity between qubits. Since two-qubit gates are allowed on limited qubit pairs, quantum compilers must transform original quantum programs to fit the hardware constraints. Previous works on qubit mapping assume different gates have the same execution duration, which limits them to explore the parallelism from the program. To address this drawback, we propose a Multi-architecture Adaptive Quantum Abstract Machine (maQAM) and a COntext-sensitive and Duration-Aware Remapping algorithm (CODAR). The CODAR remapper is aware of gate duration difference and program context, enabling it to extract more parallelism from programs and speed up the quantum programs by 1.23 in simulation on average in different architectures and maintain the fidelity of circuits when running on Origin Quantum noisy simulator.Comment: arXiv admin note: substantial text overlap with arXiv:2001.0688

Consistent and Truthful Interpretation with Fourier Analysis

For many interdisciplinary fields, ML interpretations need to be consistent with what-if scenarios related to the current case, i.e., if one factor changes, how does the model react? Although the attribution methods are supported by the elegant axiomatic systems, they mainly focus on individual inputs, and are generally inconsistent. To support what-if scenarios, we introduce a new notion called truthful interpretation, and apply Fourier analysis of Boolean functions to get rigorous guarantees. Experimental results show that for neighborhoods with various radii, our method achieves 2x - 50x lower interpretation error compared with the other methods

Knowledge-enhanced Iterative Instruction Generation and Reasoning for Knowledge Base Question Answering

Multi-hop Knowledge Base Question Answering(KBQA) aims to find the answer entity in a knowledge base which is several hops from the topic entity mentioned in the question. Existing Retrieval-based approaches first generate instructions from the question and then use them to guide the multi-hop reasoning on the knowledge graph. As the instructions are fixed during the whole reasoning procedure and the knowledge graph is not considered in instruction generation, the model cannot revise its mistake once it predicts an intermediate entity incorrectly. To handle this, we propose KBIGER(Knowledge Base Iterative Instruction GEnerating and Reasoning), a novel and efficient approach to generate the instructions dynamically with the help of reasoning graph. Instead of generating all the instructions before reasoning, we take the (k-1)-th reasoning graph into consideration to build the k-th instruction. In this way, the model could check the prediction from the graph and generate new instructions to revise the incorrect prediction of intermediate entities. We do experiments on two multi-hop KBQA benchmarks and outperform the existing approaches, becoming the new-state-of-the-art. Further experiments show our method does detect the incorrect prediction of intermediate entities and has the ability to revise such errors.Comment: Accepted by NLPCC 2022(oral

A high-performance, low power and memory-efficient VLD for MPEG applications

An extremely important area that has enabled or will enable many of the digital video services and applications such as VideoCD, DVD, DVC, HDTV, video conferencing, and DSS is digital video compression. The great success of digital video compression is mainly because of two factors. The state of the art in very large scale integrated circuit (VLSI) and a considerable body of knowledge accumulated over the last several decades in applying video compression algorithms such as discrete cosine transform (DCT), motion estimation (ME), motion compensation (MC) and entropy coding techniques. The MPEG (Moving Pictures Expert Group) standard reflects the second factor. In this thesis, MPEG standards are discussed thoroughly and interpreted, and a VLSI chip implementation (CMOS 0.35μ technology and 3 layer metal) of a variable length decoder (VLD) for MPEG applications is developed. The VLD developed here achieves high performance by using a parallel and pipeline architecture. Furthermore, MPEG bitstream patterns are carefully analyzed in order to drastically improve VLD memory efficiency. Finally, a special clock scheme is applied to reduce the chip's power consumption

Entanglement-Assisted Absorption Spectroscopy

Spectroscopy is an important tool for probing the properties of materials, chemicals and biological samples. We design a practical transmitter-receiver system that exploits entanglement to achieve a provable quantum advantage over all spectroscopic schemes based on classical sources. To probe the absorption spectra, modelled as pattern of transmissivities among different frequency modes, we employ broad-band signal-idler pairs in two-mode squeezed vacuum states. At the receiver side, we apply photodetection after optical parametric amplification. Finally, we perform a maximal-likehihood decision test on the measurement results, achieving orders-of-magnitude-lower error probability than the optimum classical systems in various examples, including `wine-tasting' and `drug-testing' where real molecules are considered. In detecting the presence of an absorption line, our quantum scheme achieves the optimum performance allowed by quantum mechanics. The quantum advantage in our system is robust against noise and loss, which makes near-term experimental demonstration possible