322 research outputs found
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
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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
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