273 research outputs found
Slidephononics: Tailoring Thermal Transport Properties by van der Waals Sliding
By interlayer sliding in van der Waals (vdW) materials, the switching
electric polarization of ultrathin ferroelectric materials leads to the widely
studied slidetronics. In this work, we report that such sliding can further
tailor anharmonic effects and hence thermal transport properties due to the
changed intrinsic coupling between atomic layers. And we propose an
unprecedented concept dubbed as slidephononics, where the phonons and
associated physical properties can be controlled by varying the intrinsic
stacking configurations of slidetronic vdW materials. Based on the
state-of-the-art first-principles calculations, it is demonstrated that the
thermal conductivity of boron nitride (BN) bilayers can be significantly
modulated (by up to four times) along the sliding pathways. Detailed analysis
reveals that the variation of thermal conductivities can be attributed to the
tunable (de-)coupling of the out-of-plane acoustic phonon branches with the
other phonon modes, which is induced by the interlayer charge transfer. Such
strongly modulated thermal conductivity via interlayer sliding in vdW materials
paves the way to engineer thermal management materials in emerging vdW
electronic devices, which would shed light on future studies of slidephononics
Modeling relation paths for knowledge base completion via joint adversarial training
Knowledge Base Completion (KBC), which aims at determining the missing
relations between entity pairs, has received increasing attention in recent
years. Most existing KBC methods focus on either embedding the Knowledge Base
(KB) into a specific semantic space or leveraging the joint probability of
Random Walks (RWs) on multi-hop paths. Only a few unified models take both
semantic and path-related features into consideration with adequacy. In this
paper, we propose a novel method to explore the intrinsic relationship between
the single relation (i.e. 1-hop path) and multi-hop paths between paired
entities. We use Hierarchical Attention Networks (HANs) to select important
relations in multi-hop paths and encode them into low-dimensional vectors. By
treating relations and multi-hop paths as two different input sources, we use a
feature extractor, which is shared by two downstream components (i.e. relation
classifier and source discriminator), to capture shared/similar information
between them. By joint adversarial training, we encourage our model to extract
features from the multi-hop paths which are representative for relation
completion. We apply the trained model (except for the source discriminator) to
several large-scale KBs for relation completion. Experimental results show that
our method outperforms existing path information-based approaches. Since each
sub-module of our model can be well interpreted, our model can be applied to a
large number of relation learning tasks.Comment: Accepted by Knowledge-Based System
Total knee arthroplasty and physical therapy for arthropathy in alkaptonuria: A 4-year follow-up case report
IntroductionAlkaptonuria is a rare autosomal recessive metabolic disorder which leads to accumulation of homogentisic acid in the body.Case PresentationWe report a rare case of an alkaptonuria-related knee arthritis who underwent left total knee arthroplasty and received postoperative systematic physical therapy in a 57-year-old male patient. The patient has suffered from bilateral knee pain for over 4 years. The patient developed melanin pigmentation on the skin of the whole body, especially on the face and auricle. He self-reported that fresh urine was normal color but after standing overnight, the color deepened to black or soy color. He underwent routine urine examination for many times, but no obvious abnormality was found. The patient has suffered from low back pain for more than 20 years. He had been considered for lumbar disc herniation and ankylosing spondylitis after many in-hospital visits. After symptomatic medication, there was no obvious relief. We followed the patient for 4 years after surgery.ResultThe patient presented with pain relief and enhanced range of motion at the 4-year follow-up. The improvements of daily living and the pain relief suggest that the surgery is appropriate for this rare disease.ConclusionIt is rare that the knee pain is diagnosed as alkaptonuria. After total knee arthroplasty and physical therapy, the patient had a good outcome. This case provides experience for the diagnosis and treatment of alkaptonuria-related knee arthritis
Coordinated Reasoning for Cross-Lingual Knowledge Graph Alignment
Existing entity alignment methods mainly vary on the choices of encoding the
knowledge graph, but they typically use the same decoding method, which
independently chooses the local optimal match for each source entity. This
decoding method may not only cause the "many-to-one" problem but also neglect
the coordinated nature of this task, that is, each alignment decision may
highly correlate to the other decisions. In this paper, we introduce two
coordinated reasoning methods, i.e., the Easy-to-Hard decoding strategy and
joint entity alignment algorithm. Specifically, the Easy-to-Hard strategy first
retrieves the model-confident alignments from the predicted results and then
incorporates them as additional knowledge to resolve the remaining
model-uncertain alignments. To achieve this, we further propose an enhanced
alignment model that is built on the current state-of-the-art baseline. In
addition, to address the many-to-one problem, we propose to jointly predict
entity alignments so that the one-to-one constraint can be naturally
incorporated into the alignment prediction. Experimental results show that our
model achieves the state-of-the-art performance and our reasoning methods can
also significantly improve existing baselines.Comment: in AAAI 202
Flat electronic band structure and anisotropic optical, mechanical, and thermoelectric properties of two-dimensional fullerene networks
Nanoclusters like fullerenes as the unit to build intriguing two-dimensional
topological structures is of great challenge. Here we propose three bridged
fullerene monolayers and comprehensively investigate the novel fullerene
monolayer as synthesized experimentally Zheng et al.,[Nature 606, 507-510
(2022)] by state of the art first principles calculations. Our results show
that alpha-C60-2D has a direct bandgap of 1.49 eV owing to a flat conduction
band bottom close to the experimental value, the optical linear dichroism with
strong absorption in long-wave ultraviolet region, a small anisotropic Youngs
modulus, the large hole mobility, and the ultrahigh Seebeck coefficient at
middle low temperatures. Moreover, Li ions are found to migrate easily along
the X path in alpha-C60-2D. It is unveiled that the anisotropic optical,
mechanical, electrical, and thermoelectric properties of alpha-C60-2D originate
from the asymmetric bridging arrangements between C60 clusters. Our study
promises potential applications of monolayer fullerene networks in diverse
fields
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
Discover, Explanation, Improvement: Automatic Slice Detection Framework for Natural Language Processing
Current natural language processing (NLP) models such as BERT and RoBERTa
have achieved high overall performance, but they often make systematic errors
due to bias or certain difficult features to learn. Thus research on slice
detection models (SDM) which automatically identifies underperforming groups of
datapoints has gradually caught more attention, which aims at both
understanding model behaviors and providing insights for future model training
and designing. However, there is little systematic research on SDM and
quantitative evaluation of its assessment for NLP models. Our paper fills this
gap by proposing "Discover, Explanation, Improvement" framework that discovers
coherent and underperforming groups of datapoints and unites datapoints of each
slice under human-understandable concepts; it also provides comprehensive
evaluation tasks and the corresponding quantitative metrics, which enable
convenient comparison for future works. Results show that our framework can
accurately select error-prone datapoints with informative semantic features
that summarize error patterns, based on which it directly boosts model
performance by an average of 2.85 points based on trained models without tuning
any parameters across multiple datasets.Comment: 15 pages, 5 figure
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