183 research outputs found
Non-adiabatic Dynamics in a Continuous Circularly Polarized Laser Field with Floquet Phase-space Surface Hopping
Non-adiabatic chemical reactions involving continuous circularly polarized
light (cw CPL) have not attracted as much attention as dynamics in
unpolarized/linearly polarized light. However, including circularly (in
contrast to linearly) polarized light allows one to effectively introduce a
complex-valued time-dependent Hamiltonian, which offers a new path for control
or exploration through the introduction of Berry forces. Here, we investigate
several inexpensive semiclassical approaches for modeling such nonadiabatic
dynamics in the presence of a time-dependent complex-valued Hamiltonian,
beginning with a straightforward instantaneous adiabatic fewest-switches
surface hopping (IA-FSSH) approach (where the electronic states depend on
position and time), continuing to a standard Floquet fewest switches surface
hopping (F-FSSH) approach (where the electronic states depend on position and
frequency), and ending with an exotic Floquet phase-space surface hopping
(F-PSSH) approach (where the electronic states depend on position, frequency,
and momentum). Using a set of model systems with time-dependent complex-valued
Hamiltonians, we show that the Floquet phase-space adiabats are the optimal
choice of basis as far as accounting for Berry phase effects and delivering
accuracy. Thus, the F-PSSH algorithm sets the stage for modeling nonadiabatic
dynamics under strong externally pumped circular polarization in the future.Comment: 40 pages, 4 figure
Recitation-Augmented Language Models
We propose a new paradigm to help Large Language Models (LLMs) generate more
accurate factual knowledge without retrieving from an external corpus, called
RECITation-augmented gEneration (RECITE). Different from retrieval-augmented
language models that retrieve relevant documents before generating the outputs,
given an input, RECITE first recites one or several relevant passages from
LLMs' own memory via sampling, and then produces the final answers. We show
that RECITE is a powerful paradigm for knowledge-intensive NLP tasks.
Specifically, we show that by utilizing recitation as the intermediate step, a
recite-and-answer scheme can achieve new state-of-the-art performance in
various closed-book question answering (CBQA) tasks. In experiments, we verify
the effectiveness of RECITE on three pre-trained models (PaLM, UL2, and OPT)
and three CBQA tasks (Natural Questions, TriviaQA, and HotpotQA)
Correlation Between Phase Competition and the Nucleation of a Griffiths Phase in (La1-yPry)0.7Ca0.3Mn16/18O3
Detailed analyses of the temperature-dependent zero field ac susceptibility
of prototypical phase-separated (La1-yPry)0.7Ca0.3Mn16/18O3, 0 < y < 1, reveal
features consistent with the presence of a Griffiths phase (GP), viz., an
inverse susceptibility characterized by power law with 0.05 < lamda < 0.33 as y
decreases towards yc < 0.85. Beyond yc = 0.85, the GP is suppressed. These
data, combined with previous neutron diffraction measurements, enable a phase
diagram summarizing the evolution of the GP with composition to be constructed
for this system; in particular, it shows that the disorder relevant for the
establishment of such a phase is linked closely to the relative volume
fractions of the phase separated antiferromagnetic and ferromagnetic
components, even when the recently estimated double exchange (DE) linked
percolation threshold is exceeded. The influence of electron-phonon coupling
can also be seen through oxygen isotope effects.Comment: 4 page
TEMPERA: Test-Time Prompting via Reinforcement Learning
Careful prompt design is critical to the use of large language models in
zero-shot or few-shot learning. As a consequence, there is a growing interest
in automated methods to design optimal prompts. In this work, we propose
Test-time Prompt Editing using Reinforcement learning (TEMPERA). In contrast to
prior prompt generation methods, TEMPERA can efficiently leverage prior
knowledge, is adaptive to different queries and provides an interpretable
prompt for every query. To achieve this, we design a novel action space that
allows flexible editing of the initial prompts covering a wide set of
commonly-used components like instructions, few-shot exemplars, and
verbalizers. The proposed method achieves significant gains compared with
recent SoTA approaches like prompt tuning, AutoPrompt, and RLPrompt, across a
variety of tasks including sentiment analysis, topic classification, natural
language inference, and reading comprehension. Our method achieves 5.33x on
average improvement in sample efficiency when compared to the traditional
fine-tuning methods
Integration of Small-Scale Compressed Air Energy Storage with Wind Generation for Flexible Household Power Supply
Universal Self-Consistency for Large Language Model Generation
Self-consistency with chain-of-thought prompting (CoT) has demonstrated
remarkable performance gains on various challenging tasks, by utilizing
multiple reasoning paths sampled from large language models (LLMs). However,
self-consistency relies on the answer extraction process to aggregate multiple
solutions, which is not applicable to free-form answers. In this work, we
propose Universal Self-Consistency (USC), which leverages LLMs themselves to
select the most consistent answer among multiple candidates. We evaluate USC on
a variety of benchmarks, including mathematical reasoning, code generation,
long-context summarization, and open-ended question answering. On open-ended
generation tasks where the original self-consistency method is not applicable,
USC effectively utilizes multiple samples and improves the performance. For
mathematical reasoning, USC matches the standard self-consistency performance
without requiring the answer formats to be similar. Finally, without access to
execution results, USC also matches the execution-based voting performance on
code generation
FreshLLMs: Refreshing Large Language Models with Search Engine Augmentation
Most large language models (LLMs) are trained once and never updated; thus,
they lack the ability to dynamically adapt to our ever-changing world. In this
work, we perform a detailed study of the factuality of LLM-generated text in
the context of answering questions that test current world knowledge.
Specifically, we introduce FreshQA, a novel dynamic QA benchmark encompassing a
diverse range of question and answer types, including questions that require
fast-changing world knowledge as well as questions with false premises that
need to be debunked. We benchmark a diverse array of both closed and
open-source LLMs under a two-mode evaluation procedure that allows us to
measure both correctness and hallucination. Through human evaluations involving
more than 50K judgments, we shed light on limitations of these models and
demonstrate significant room for improvement: for instance, all models
(regardless of model size) struggle on questions that involve fast-changing
knowledge and false premises. Motivated by these results, we present
FreshPrompt, a simple few-shot prompting method that substantially boosts the
performance of an LLM on FreshQA by incorporating relevant and up-to-date
information retrieved from a search engine into the prompt. Our experiments
show that FreshPrompt outperforms both competing search engine-augmented
prompting methods such as Self-Ask (Press et al., 2022) as well as commercial
systems such as Perplexity.AI. Further analysis of FreshPrompt reveals that
both the number of retrieved evidences and their order play a key role in
influencing the correctness of LLM-generated answers. Additionally, instructing
the LLM to generate concise and direct answers helps reduce hallucination
compared to encouraging more verbose answers. To facilitate future work, we
release FreshQA at github.com/freshllms/freshqa and commit to updating it at
regular intervals.Comment: Preprint, 26 pages, 10 figures, 5 tables; Added FreshEva
Inter-patient ECG heartbeat classification for arrhythmia classification: a new approach of multi-layer perceptron with weight capsule and sequence-to-sequence combination
Objective: The objective of this research is to construct a method to alleviate the problem of sample imbalance in classification, especially for arrhythmia classification. This approach can improve the performance of the model without using data enhancement.Methods: In this study, we have developed a new Multi-layer Perceptron (MLP) block and have used a Weight Capsule (WCapsule) network with MLP combined with sequence-to-sequence (Seq2Seq) network to classify arrhythmias. Our work is based on the MIT-BIH arrhythmia database, the original electrocardiogram (ECG) data is classified according to the criteria recommended by the American Association for Medical Instrumentation (AAMI). Also, our method’s performance is further evaluated.Results: The proposed model is evaluated using the inter-patient paradigm. Our proposed method shows an accuracy (ACC) of 99.88% under sample imbalance. For Class N, sensitivity (SEN) is 99.79%, positive predictive value (PPV) is 99.90%, and specificity (SPEC) is 99.19%. For Class S, SEN is 97.66%, PPV is 96.14%, and SPEC is 99.85%. For Class V, SEN is 99.97%, PPV is 99.07%, and SPEC is 99.94%. For Class F, SEN is 97.94%, PPV is 98.70%, and SPEC is 99.99%. When using only half of the training sample, our method shows that the SEN of Class N and V is 0.97% and 5.27% higher than the traditional machine learning algorithm.Conclusion: The proposed method combines MLP, weight capsule network with Seq2seq network, effectively addresses the problem of sample imbalance in arrhythmia classification, and produces good performance. Our method also shows promising potential in less samples
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