379 research outputs found
Engineering Photon Delocalization in a Rabi Dimer with a Dissipative Bath
A Rabi dimer is used to model a recently reported circuit quantum
electrodynamics system composed of two coupled transmission-line resonators
with each coupled to one qubit. In this study, a phonon bath is adopted to
mimic the multimode micromechanical resonators and is coupled to the qubits in
the Rabi dimer. The dynamical behavior of the composite system is studied by
the Dirac-Frenkel time-dependent variational principle combined with the
multiple Davydov D ans\"{a}tze. Initially all the photons are pumped into
the left resonator, and the two qubits are in the down state coupled with the
phonon vacuum. In the strong qubit-photon coupling regime, the photon dynamics
can be engineered by tuning the qubit-bath coupling strength and
photon delocalization is achieved by increasing . In the absence of
dissipation, photons are localized in the initial resonator. Nevertheless, with
moderate qubit-bath coupling, photons are delocalized with quasiequilibration
of the photon population in two resonators at long times. In this case, high
frequency bath modes are activated by interacting with depolarized qubits. For
strong dissipation, photon delocalization is achieved via frequent
photon-hopping within two resonators and the qubits are suppressed in their
initial down state.Comment: 11 pages, 11 figure
Efficient RLHF: Reducing the Memory Usage of PPO
Reinforcement Learning with Human Feedback (RLHF) has revolutionized language
modeling by aligning models with human preferences. However, the RL stage,
Proximal Policy Optimization (PPO), requires over 3x the memory of Supervised
Fine-Tuning (SFT), making it infeasible to use for most practitioners. To
address this issue, we present a comprehensive analysis the memory usage,
performance, and training time of memory-savings techniques for PPO. We
introduce Hydra-RLHF by first integrating the SFT and Reward models and then
dynamically turning LoRA "off" during training. Our experiments show: 1. Using
LoRA during PPO reduces its memory usage to be smaller than SFT while improving
alignment across four public benchmarks, and 2. Hydra-PPO reduces the latency
per sample of LoRA-PPO by up to 65% while maintaining its performance. Our
results demonstrate that Hydra-PPO is a simple and promising solution for
enabling more widespread usage of RLHF
Noise reduction optimization of sound sensor based on a Conditional Generation Adversarial Network
To address the problems in the traditional speech signal noise elimination methods, such as the residual noise, poor real-time performance and narrow applications a new method is proposed to eliminate network voice noise based on deep learning of conditional generation adversarial network. In terms of the perceptual evaluation of speech quality (PESQ) and shorttime objective intelligibility measure (STOI) functions used as the loss function in the neural network, which were used as the loss function in the neural network, the flexibility of the whole network was optimized, and the training process of the model simplified. The experimental results indicate that, under the noisy environment, especially in a restaurant, the proposed noise reduction scheme improves the STOI score by 26.23% and PESQ score by 17.18%, respectively, compared with the traditional Wiener noise reduction algorithm. Therefore, the sound sensor\u27s noise reduction scheme through our approach has achieved a remarkable noise reduction effect, more useful information transmission, and stronger practicability
Adapting LLM Agents Through Communication
Recent advancements in large language models (LLMs) have shown potential for
human-like agents. To help these agents adapt to new tasks without extensive
human supervision, we propose the Learning through Communication (LTC)
paradigm, a novel training approach enabling LLM agents to improve continuously
through interactions with their environments and other agents. Recent
advancements in large language models (LLMs) have shown potential for
human-like agents. To help these agents adapt to new tasks without extensive
human supervision, we propose the Learning through Communication (LTC)
paradigm, a novel training approach enabling LLM agents to improve continuously
through interactions with their environments and other agents. Through
iterative exploration and PPO training, LTC empowers the agent to assimilate
short-term experiences into long-term memory. To optimize agent interactions
for task-specific learning, we introduce three structured communication
patterns: Monologue, Dialogue, and Analogue-tailored for common tasks such as
decision-making, knowledge-intensive reasoning, and numerical reasoning. We
evaluated LTC on three datasets: ALFWorld (decision-making), HotpotQA
(knowledge-intensive reasoning), and GSM8k (numerical reasoning). On ALFWorld,
it exceeds the instruction tuning baseline by 12% in success rate. On HotpotQA,
LTC surpasses the instruction-tuned LLaMA-7B agent by 5.1% in EM score, and it
outperforms the instruction-tuned 9x larger PaLM-62B agent by 0.6%. On GSM8k,
LTC outperforms the CoT-Tuning baseline by 3.6% in accuracy. The results
showcase the versatility and efficiency of the LTC approach across diverse
domains. We will open-source our code to promote further development of the
community.Comment: Preprin
Magnetic properties of undoped Cu2O fine powders with magnetic impurities and/or cation vacancies
Fine powders of micron- and submicron-sized particles of undoped Cu2O
semiconductor, with three different sizes and morphologies have been
synthesized by different chemical processes. These samples include nanospheres
200 nm in diameter, octahedra of size 1 micron, and polyhedra of size 800 nm.
They exhibit a wide spectrum of magnetic properties. At low temperature, T = 5
K, the octahedron sample is diamagnetic. The nanosphere is paramagnetic. The
other two polyhedron samples synthesized in different runs by the same process
are found to show different magnetic properties. One of them exhibits weak
ferromagnetism with T_C = 455 K and saturation magnetization, M_S = 0.19 emu/g
at T = 5 K, while the other is paramagnetic. The total magnetic moment
estimated from the detected impurity concentration of Fe, Co, and Ni, is too
small to account for the observed magnetism by one to two orders of magnitude.
Calculations by the density functional theory (DFT) reveal that cation
vacancies in the Cu2O lattice are one of the possible causes of induced
magnetic moments. The results further predict that the defect-induced magnetic
moments favour a ferromagnetically coupled ground state if the local
concentration of cation vacancies, n_C, exceeds 12.5%. This offers a possible
scenario to explain the observed magnetic properties. The limitations of the
investigations in the present work, in particular in the theoretical
calculations, are discussed and possible areas for further study are suggested.Comment: 20 pages, 5 figures 2 tables, submitted to J Phys Condense Matte
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