745 research outputs found
Linear Scaling Calculations of Excitation Energies with Active-Space Particle-Particle Random Phase Approximation
We developed an efficient active-space particle-particle random phase
approximation (ppRPA) approach to calculate accurate charge-neutral excitation
energies of molecular systems. The active-space ppRPA approach constrains both
indexes in particle and hole pairs in the ppRPA matrix, which only selects
frontier orbitals with dominant contributions to low-lying excitation energies.
It employs the truncation in both orbital indexes in the particle-particle and
the hole-hole spaces. The resulting matrix, the eigenvalues of which are
excitation energies, has a dimension that is independent of the size of the
systems. The computational effort for the excitation energy calculation,
therefore, scales linearly with system size and is negligible compared with the
ground-state calculation of the (N-2)-electron system, where N is the electron
number of the molecule. With the active space consisting of 30 occupied and 30
virtual orbitals, the active-space ppRPA approach predicts excitation energies
of valence, charge-transfer, Rydberg, double and diradical excitations with the
mean absolute errors (MAEs) smaller than 0.03 eV compared with the full-space
ppRPA results. As a side product, we also applied the active-space ppRPA
approach in the renormalized singles (RS) T-matrix approach. Combining the
non-interacting pair approximation that approximates the contribution to the
self-energy outside the active space, the active-space
@PBE approach predicts accurate absolute and
relative core-level binding energies with the MAE around 1.58 eV and 0.3 eV,
respectively. The developed linear scaling calculation of excitation energies
is promising for applications to large and complex systems
Improving Question Generation with Multi-level Content Planning
This paper addresses the problem of generating questions from a given context
and an answer, specifically focusing on questions that require multi-hop
reasoning across an extended context. Previous studies have suggested that key
phrase selection is essential for question generation (QG), yet it is still
challenging to connect such disjointed phrases into meaningful questions,
particularly for long context. To mitigate this issue, we propose MultiFactor,
a novel QG framework based on multi-level content planning. Specifically,
MultiFactor includes two components: FA-model, which simultaneously selects key
phrases and generates full answers, and Q-model which takes the generated full
answer as an additional input to generate questions. Here, full answer
generation is introduced to connect the short answer with the selected key
phrases, thus forming an answer-aware summary to facilitate QG. Both FA-model
and Q-model are formalized as simple-yet-effective Phrase-Enhanced
Transformers, our joint model for phrase selection and text generation.
Experimental results show that our method outperforms strong baselines on two
popular QG datasets. Our code is available at
https://github.com/zeaver/MultiFactor.Comment: Camera-ready. Accepted by EMNLP 2023 Finding
Diversify Question Generation with Retrieval-Augmented Style Transfer
Given a textual passage and an answer, humans are able to ask questions with
various expressions, but this ability is still challenging for most question
generation (QG) systems. Existing solutions mainly focus on the internal
knowledge within the given passage or the semantic word space for diverse
content planning. These methods, however, have not considered the potential of
external knowledge for expression diversity. To bridge this gap, we propose
RAST, a framework for Retrieval-Augmented Style Transfer, where the objective
is to utilize the style of diverse templates for question generation. For
training RAST, we develop a novel Reinforcement Learning (RL) based approach
that maximizes a weighted combination of diversity reward and consistency
reward. Here, the consistency reward is computed by a Question-Answering (QA)
model, whereas the diversity reward measures how much the final output mimics
the retrieved template. Experimental results show that our method outperforms
previous diversity-driven baselines on diversity while being comparable in
terms of consistency scores. Our code is available at
https://github.com/gouqi666/RAST.Comment: EMNLP2023 camera-read
Local Geometric Distortions Resilient Watermarking Scheme Based on Symmetry
As an efficient watermark attack method, geometric distortions destroy the
synchronization between watermark encoder and decoder. And the local geometric
distortion is a famous challenge in the watermark field. Although a lot of
geometric distortions resilient watermarking schemes have been proposed, few of
them perform well against local geometric distortion like random bending attack
(RBA). To address this problem, this paper proposes a novel watermark
synchronization process and the corresponding watermarking scheme. In our
scheme, the watermark bits are represented by random patterns. The message is
encoded to get a watermark unit, and the watermark unit is flipped to generate
a symmetrical watermark. Then the symmetrical watermark is embedded into the
spatial domain of the host image in an additive way. In watermark extraction,
we first get the theoretically mean-square error minimized estimation of the
watermark. Then the auto-convolution function is applied to this estimation to
detect the symmetry and get a watermark units map. According to this map, the
watermark can be accurately synchronized, and then the extraction can be done.
Experimental results demonstrate the excellent robustness of the proposed
watermarking scheme to local geometric distortions, global geometric
distortions, common image processing operations, and some kinds of combined
attacks
DeAR: A Deep-learning-based Audio Re-recording Resilient Watermarking
Audio watermarking is widely used for leaking source tracing. The robustness
of the watermark determines the traceability of the algorithm. With the
development of digital technology, audio re-recording (AR) has become an
efficient and covert means to steal secrets. AR process could drastically
destroy the watermark signal while preserving the original information. This
puts forward a new requirement for audio watermarking at this stage, that is,
to be robust to AR distortions. Unfortunately, none of the existing algorithms
can effectively resist AR attacks due to the complexity of the AR process. To
address this limitation, this paper proposes DeAR, a deep-learning-based audio
re-recording resistant watermarking. Inspired by DNN-based image watermarking,
we pioneer a deep learning framework for audio carriers, based on which the
watermark signal can be effectively embedded and extracted. Meanwhile, in order
to resist the AR attack, we delicately analyze the distortions that occurred in
the AR process and design the corresponding distortion layer to cooperate with
the proposed watermarking framework. Extensive experiments show that the
proposed algorithm can resist not only common electronic channel distortions
but also AR distortions. Under the premise of high-quality embedding
(SNR=25.86dB), in the case of a common re-recording distance (20cm), the
algorithm can effectively achieve an average bit recovery accuracy of 98.55%.Comment: Accepted by AAAI202
Hydrogels enable negative pressure in water for efficient heat utilization and transfer
Metastable water in negative pressure can provide giant passive driving
pressure up to several megapascals for efficient evaporation-driven flow,
however, the practical applications with negative pressure are rare due to the
challenges of generating and maintaining large negative pressure. In this work,
we report a novel structure with thin hydrogel films as evaporation surfaces
and robust porous substrates as the supports, and obtain a high negative
pressure of -1.61 MPa through water evaporation. Molecular dynamics simulations
elucidate the essential role of strong interaction between water molecules and
polymer chains in generating the negative pressure. With such a large negative
pressure, we demonstrate a streaming potential generator that spontaneously
converts environmental energy into electricity and outputs a voltage of 1.06 V.
Moreover, we propose a "negative pressure heat pipe" for the first time, which
achieves a high heat transfer density of 11.2 kW cm-2 with a flow length of 1
m, showing the potential of negative pressure in efficient heat utilization and
transfer.Comment: 43 pages, 18 figure
Physics-Informed Optical Kernel Regression Using Complex-valued Neural Fields
Lithography is fundamental to integrated circuit fabrication, necessitating
large computation overhead. The advancement of machine learning (ML)-based
lithography models alleviates the trade-offs between manufacturing process
expense and capability. However, all previous methods regard the lithography
system as an image-to-image black box mapping, utilizing network parameters to
learn by rote mappings from massive mask-to-aerial or mask-to-resist image
pairs, resulting in poor generalization capability. In this paper, we propose a
new ML-based paradigm disassembling the rigorous lithographic model into
non-parametric mask operations and learned optical kernels containing
determinant source, pupil, and lithography information. By optimizing
complex-valued neural fields to perform optical kernel regression from
coordinates, our method can accurately restore lithography system using a
small-scale training dataset with fewer parameters, demonstrating superior
generalization capability as well. Experiments show that our framework can use
31% of parameters while achieving 69 smaller mean squared error with
1.3 higher throughput than the state-of-the-art.Comment: Accepted by DAC2
Examination on the wear process of polyformaldehyde gears under dry and lubricated conditions
This study presents the results of detailed wear process examination on polyformaldehyde gears under both dry and lubricated conditions. A multi-purpose durability test rig was employed to study the wear performance of polyformaldehyde gear pairs. The wear behaviors of polyformaldehyde gears under dry and oil-lubricated operating conditions were characterized via measurements of gear tooth surface micro-topography and tooth profile deviation. Under the dry running condition, a hump and a gully appear on the tooth surface in the pitch line area of the driving gear and the driven wheel, respectively. The largest amount of wear was observed around the tooth root of the driving gear. However, the gear tooth wear pattern with lubrication is different from that under the dry running condition
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