553 research outputs found
Numerical Solutions of Backward Stochastic Differential Equations: A Finite Transposition Method
In this note, we present a new numerical method for solving backward
stochastic differential equations. Our method can be viewed as an analogue of
the classical finite element method solving deterministic partial differential
equations.Comment: 4 page
Multipartite Quantum Correlation and Communication Complexities
The concepts of quantum correlation complexity and quantum communication
complexity were recently proposed to quantify the minimum amount of resources
needed in generating bipartite classical or quantum states in the single-shot
setting. The former is the minimum size of the initially shared state
on which local operations by the two parties (without communication) can
generate the target state , and the latter is the minimum amount of
communication needed when initially sharing nothing. In this paper, we
generalize these two concepts to multipartite cases, for both exact and
approximate state generation. Our results are summarized as follows. (1) For
multipartite pure states, the correlation complexity can be completely
characterized by local ranks of sybsystems. (2) We extend the notion of
PSD-rank of matrices to that of tensors, and use it to bound the quantum
correlation complexity for generating multipartite classical distributions. (3)
For generating multipartite mixed quantum states, communication complexity is
not always equal to correlation complexity (as opposed to bipartite case). But
they differ by at most a factor of 2. Generating a multipartite mixed quantum
state has the same communication complexity as generating its optimal
purification. But for correlation complexity of these two tasks can be
different (though still related by less than a factor of 2). (4) To generate a
bipartite classical distribution approximately, the quantum
communication complexity is completely characterized by the approximate
PSD-rank of . The quantum correlation complexity of approximately generating
multipartite pure states is bounded by approximate local ranks.Comment: 19 pages; some typos are correcte
Split Federated Learning: Speed up Model Training in Resource-Limited Wireless Networks
In this paper, we propose a novel distributed learning scheme, named
group-based split federated learning (GSFL), to speed up artificial
intelligence (AI) model training. Specifically, the GSFL operates in a
split-then-federated manner, which consists of three steps: 1) Model
distribution, in which the access point (AP) splits the AI models and
distributes the client-side models to clients; 2) Model training, in which each
client executes forward propagation and transmit the smashed data to the edge
server. The edge server executes forward and backward propagation and then
returns the gradient to the clients for updating local client-side models; and
3) Model aggregation, in which edge servers aggregate the server-side and
client-side models. Simulation results show that the GSFL outperforms vanilla
split learning and federated learning schemes in terms of overall training
latency while achieving satisfactory accuracy
AdapINT: A Flexible and Adaptive In-Band Network Telemetry System Based on Deep Reinforcement Learning
In-band Network Telemetry (INT) has emerged as a promising network
measurement technology. However, existing network telemetry systems lack the
flexibility to meet diverse telemetry requirements and are also difficult to
adapt to dynamic network environments. In this paper, we propose AdapINT, a
versatile and adaptive in-band network telemetry framework assisted by
dual-timescale probes, including long-period auxiliary probes (APs) and
short-period dynamic probes (DPs). Technically, the APs collect basic network
status information, which is used for the path planning of DPs. To achieve full
network coverage, we propose an auxiliary probes path deployment (APPD)
algorithm based on the Depth-First-Search (DFS). The DPs collect specific
network information for telemetry tasks. To ensure that the DPs can meet
diverse telemetry requirements and adapt to dynamic network environments, we
apply the deep reinforcement learning (DRL) technique and transfer learning
method to design the dynamic probes path deployment (DPPD) algorithm. The
evaluation results show that AdapINT can redesign the telemetry system
according to telemetry requirements and network environments. AdapINT can
reduce telemetry latency by 75\% in online games and video conferencing
scenarios. For overhead-aware networks, AdapINT can reduce control overheads by
34\% in cloud computing services.Comment: 14 pages, 19 figure
An Immune Detector-Based Method for the Diagnosis of Compound Faults in a Petrochemical Plant
Aiming at the serious overlap of traditional dimensionless indices in the diagnosis of compound faults in petrochemical plants, we use genetic programming to construct optimal indices for that purpose. In order to solve the problem of losing some useful fault feature information due to classification processing, during the generation of the dimensionless index immune detector, such as reduction and clustering, we propose an integrated diagnosis method using each dimensionless index immune detector. Simulation results show that this method has high diagnostic accuracy
Photoelectric Properties of DSSCs Sensitized by Phloxine B and Bromophenol Blue
Phloxine B and bromophenol blue as the sensitizers of dye-sensitized solar cells were investigated via UV-Vis spectra, FT-IR spectra, fluorescence spectra, and current-voltage characteristics. The frontier molecular orbital, vibration analysis, and the first hyperpolarizability were calculated with DFT/6-31G(d). The dipole moment, light harvesting efficiency (LHE), and larger absolute value of driving force of electron injection (ΔGinject) were also discussed. The calculated results were compared with the experimental results of phloxine B and bromophenol blue. It was found that, compared with bromophenol blue, bigger dipole moment of phloxine B results in larger open circuit voltage (Voc) according to the correlation between dipole moment and Voc. At the same time, for configuration of phloxine B, it has higher LHE and ΔGinject, which are helpful to enhance the abilities of absorbing sunlight and electron injection. Therefore, higher LHE and ΔGinject for phloxine B produced a larger value of Jsc
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