4,274 research outputs found
Quantum variational embedding for ground-state energy problems: sum of squares and cluster selection
We introduce a sum-of-squares SDP hierarchy approximating the ground-state
energy from below for quantum many-body problems, with a natural quantum
embedding interpretation. We establish the connections between our approach and
other variational methods for lower bounds, including the variational
embedding, the RDM method in quantum chemistry, and the Anderson bounds.
Additionally, inspired by the quantum information theory, we propose efficient
strategies for optimizing cluster selection to tighten SDP relaxations while
staying within a computational budget. Numerical experiments are presented to
demonstrate the effectiveness of our strategy. As a byproduct of our
investigation, we find that quantum entanglement has the potential to capture
the underlying graph of the many-body Hamiltonian
Far Infrared Radiation Property of Elbaite/Alumina Composite Materials
Far infrared materials have been prepared by precipitation method using natural elbaite powder as raw materials, which belongs to tourmaline group. The chemical formula of elbaite is Na(Al, Li)3Al6B3Si6O27(O, OH, F)4. X-ray powder diffraction (XRD) shows that elbaite and alumina in composite material has good crystal form. In addition, XRD results indicate the formation of alumina crystallites show that alumina powder exists as nano-meter particles on the surface of elbaite powder. It can be calculated the particles diameter of Al2O3 is 47.86nm. The maximum infrared radiation rate of tourmaline/alumina composite materials is 0.89 when the ratio of alumina in elbaite powder is 20%. The infrared radiation rate has been increased by 0.03, compared with single elbaite. It shows that the infrared radiation rate of the composite materials is higher than any of a single component. Two reasons are attributed to the improve of the rate of far infrared radiation: alumina powder exists as nano-meter particles and different materials will increase the absorption peak and the vibration intensity in FTIR spectra
Cognitive assisted living ambient system: a survey
The demographic change towards an aging population is creating a significant impact and introducing drastic challenges to our society. We therefore need to find ways to assist older people to stay independently and prevent social isolation of these population. Information and Communication Technologies (ICT) provide various solutions to help older adults to improve their quality of life, stay healthier, and live independently for a time. Ambient Assisted Living (AAL) is a field to investigate innovative technologies to provide assistance as well as healthcare and rehabilitation to impaired seniors. The paper provides a review of research background and technologies of AAL
Large Language Models are Superpositions of All Characters: Attaining Arbitrary Role-play via Self-Alignment
Considerable efforts have been invested in augmenting the role-playing
proficiency of open-source large language models (LLMs) by emulating
proprietary counterparts. Nevertheless, we posit that LLMs inherently harbor
role-play capabilities, owing to the extensive knowledge of characters and
potential dialogues ingrained in their vast training corpora. Thus, in this
study, we introduce Ditto, a self-alignment method for role-play. Ditto
capitalizes on character knowledge, encouraging an instruction-following LLM to
simulate role-play dialogues as a variant of reading comprehension. This method
creates a role-play training set comprising 4,000 characters, surpassing the
scale of currently available datasets by tenfold regarding the number of roles.
Subsequently, we fine-tune the LLM using this self-generated dataset to augment
its role-playing capabilities. Upon evaluating our meticulously constructed and
reproducible role-play benchmark and the roleplay subset of MT-Bench, Ditto, in
various parameter scales, consistently maintains a consistent role identity and
provides accurate role-specific knowledge in multi-turn role-play
conversations. Notably, it outperforms all open-source role-play baselines,
showcasing performance levels comparable to advanced proprietary chatbots.
Furthermore, we present the first comprehensive cross-supervision alignment
experiment in the role-play domain, revealing that the intrinsic capabilities
of LLMs confine the knowledge within role-play. Meanwhile, the role-play styles
can be easily acquired with the guidance of smaller models. We open-source
related resources at https://github.com/OFA-Sys/Ditto
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