126 research outputs found
Thermal evolution of spin excitations in honeycomb Ising antiferromagnetic FePSe3
We use elastic and inelastic neutron scattering (INS) to study the
antiferromagnetic (AF) phase transitions and spin excitations in the
two-dimensional (2D) zig-zag antiferromagnet FePSe. By determining the
magnetic order parameter across the AF phase transition, we conclude that the
AF phase transition in FePSe is first-order in nature. In addition, our INS
measurements reveal that the spin waves in the AF ordered state have a large
easy-axis magnetic anisotropy gap, consistent with an Ising Hamiltonian, and
possible biquadratic magnetic exchange interactions. On warming across ,
we find that dispersive spin excitations associated with three-fold rotational
symmetric AF fluctuations change into FM spin fluctuations above . These
results suggest that the first-order AF phase transition in FePSe may arise
from the competition between symmetric AF and symmetric FM spin
fluctuations around , in place of a conventional second-order AF phase
transition
Category-Specific CNN for Visual-aware CTR Prediction at JD.com
As one of the largest B2C e-commerce platforms in China, JD com also powers a
leading advertising system, serving millions of advertisers with fingertip
connection to hundreds of millions of customers. In our system, as well as most
e-commerce scenarios, ads are displayed with images.This makes visual-aware
Click Through Rate (CTR) prediction of crucial importance to both business
effectiveness and user experience. Existing algorithms usually extract visual
features using off-the-shelf Convolutional Neural Networks (CNNs) and late fuse
the visual and non-visual features for the finally predicted CTR. Despite being
extensively studied, this field still face two key challenges. First, although
encouraging progress has been made in offline studies, applying CNNs in real
systems remains non-trivial, due to the strict requirements for efficient
end-to-end training and low-latency online serving. Second, the off-the-shelf
CNNs and late fusion architectures are suboptimal. Specifically, off-the-shelf
CNNs were designed for classification thus never take categories as input
features. While in e-commerce, categories are precisely labeled and contain
abundant visual priors that will help the visual modeling. Unaware of the ad
category, these CNNs may extract some unnecessary category-unrelated features,
wasting CNN's limited expression ability. To overcome the two challenges, we
propose Category-specific CNN (CSCNN) specially for CTR prediction. CSCNN early
incorporates the category knowledge with a light-weighted attention-module on
each convolutional layer. This enables CSCNN to extract expressive
category-specific visual patterns that benefit the CTR prediction. Offline
experiments on benchmark and a 10 billion scale real production dataset from
JD, together with an Online A/B test show that CSCNN outperforms all compared
state-of-the-art algorithms
Single-shot spatial instability and electric control of polariton condensates at room temperature
In planar microcavities, the transverse-electric and transverse-magnetic
(TE-TM) mode splitting of cavity photons arises due to their different
penetration into the Bragg mirrors and can result in optical spin-orbit
coupling (SOC). In this work, we find that in a liquid crystal (LC) microcavity
filled with perovskite microplates, the pronounced TE-TM splitting gives rise
to a strong SOC that leads to the spatial instability of microcavity polariton
condensates under single-shot excitation. Spatially varying hole burning and
mode competition occurs between polarization components leading to different
condensate profiles from shot to shot. The single-shot polariton condensates
become stable when the SOC vanishes as the TE and TM modes are spectrally well
separated from each other, which can be achieved by application of an electric
field to our LC microcavity with electrically tunable anisotropy. Our findings
are well reproduced and traced back to their physical origin by our detailed
numerical simulations. With the electrical manipulation our work reveals how
the shot-to-shot spatial instability of spatial polariton profiles can be
engineered in anisotropic microcavities at room temperature, which will benefit
the development of stable polariton-based optoeletronic and light-emitting
devices
New insights into bacterial mechanisms and potential intestinal epithelial cell therapeutic targets of inflammatory bowel disease
The global incidence of inflammatory bowel disease (IBD) has increased rapidly in recent years, but its exact etiology remains unclear. In the past decade, IBD has been reported to be associated with dysbiosis of gut microbiota. Although not yet proven to be a cause or consequence of IBD, the common hypothesis is that at least some alterations in the microbiome are protective or pathogenic. Furthermore, intestinal epithelial cells (IECs) serve as a protective physical barrier for gut microbiota, essential for maintaining intestinal homeostasis and actively contributes to the mucosal immune system. Thus, dysregulation within the intestinal epithelium increases intestinal permeability, promotes the entry of bacteria, toxins, and macromolecules, and disrupts intestinal immune homeostasis, all of which are associated with the clinical course of IBD. This article presents a selective overview of recent studies on bacterial mechanisms that may be protective or promotive of IBD in biological models. Moreover, we summarize and discuss the recent discovery of key modulators and signaling pathways in the IECs that could serve as potential IBD therapeutic targets. Understanding the role of the IECs in the pathogenesis of IBD may help improve the understanding of the inflammatory process and the identification of potential therapeutic targets to help ameliorate this increasingly common disease
Phytoplankton community structure in the Western Subarctic Gyre of the Pacific Ocean during summer determined by a combined approach of HPLC-pigment CHEMTAX and metabarcoding sequencing
The Western Subarctic Gyre (WSG) is a cyclonic upwelling gyre in the northwest subarctic Pacific, which is a region with a high concentration of nutrients but low chlorophyll. We investigated the community structure and spatial distribution of phytoplankton in this area by using HPLC-pigment CHEMTAX (a chemotaxonomy program) and metabarcoding sequencing during the summer of 2021. The phytoplankton community showed significant differences between the two methods. The CHEMTAX analyses identified eight major marine phytoplankton assemblages. Cryptophytes were the major contributors (24.96%) to the total Chl a, followed by pelagophytes, prymnesiophytes, diatoms, and chlorophytes. The eukaryotic phytoplankton OTUs obtained by metabarcoding were categorized into 149 species in 96 genera of 6 major groups (diatoms, prymnesiophytes, pelagophytes, chlorophytes, cryptophytes, and dinoflagellates). Dinoflagellates were the most abundant group, accounting for 44.74% of the total OTUs obtained, followed by cryptophytes and pelagophytes. Sixteen out of the 97 identified species were annotated as harmful algal species, and Heterocapsa rotundata, Karlodinium veneficum, and Aureococcus anophagefferens were assigned to the abundant group (i.e., at least 0.1% of the total reads). Nutrients were more important in shaping the phytoplankton community than temperature and salinity. The 24 stations were divided into southern and northern regions along 44°N according to the k-means method, with the former being dominated by high Chl a and low nutrients. Although different phytoplankton assemblages analyzed by the two methods showed various relationships with environmental factors, a common feature was that the dinoflagellate proportion showed a significantly negative correlation with low nutrients and a positive correlation with Chl a
Skywork: A More Open Bilingual Foundation Model
In this technical report, we present Skywork-13B, a family of large language
models (LLMs) trained on a corpus of over 3.2 trillion tokens drawn from both
English and Chinese texts. This bilingual foundation model is the most
extensively trained and openly published LLMs of comparable size to date. We
introduce a two-stage training methodology using a segmented corpus, targeting
general purpose training and then domain-specific enhancement training,
respectively. We show that our model not only excels on popular benchmarks, but
also achieves \emph{state of the art} performance in Chinese language modeling
on diverse domains. Furthermore, we propose a novel leakage detection method,
demonstrating that test data contamination is a pressing issue warranting
further investigation by the LLM community. To spur future research, we release
Skywork-13B along with checkpoints obtained during intermediate stages of the
training process. We are also releasing part of our SkyPile corpus, a
collection of over 150 billion tokens of web text, which is the largest high
quality open Chinese pre-training corpus to date. We hope Skywork-13B and our
open corpus will serve as a valuable open-source resource to democratize access
to high-quality LLMs
Application of FGD-BCEL loss function in segmenting temporal lobes on localized CT images for radiotherapy
ObjectivesThe aim of this study was to find a new loss function to automatically segment temporal lobes on localized CT images for radiotherapy with more accuracy and a solution to dealing with the classification of class-imbalanced samples in temporal lobe segmentation.MethodsLocalized CT images for radiotherapy of 70 patients with nasopharyngeal carcinoma were selected. Radiation oncologists sketched mask maps. The dataset was randomly divided into the training set (n = 49), the validation set (n = 7), and the test set (n = 14). The training set was expanded by rotation, flipping, zooming, and shearing, and the models were evaluated using Dice similarity coefficient (DSC), Jaccard similarity coefficient (JSC), positive predictive value (PPV), sensitivity (SE), and Hausdorff distance (HD). This study presented an improved loss function, focal generalized Dice-binary cross-entropy loss (FGD-BCEL), and compared it with four other loss functions, Dice loss (DL), generalized Dice loss (GDL), Tversky loss (TL), and focal Tversky loss (FTL), using the U-Net model framework.ResultsWith the U-Net model based on FGD-BCEL, the DSC, JSC, PPV, SE, and HD were 0.87 ± 0.11, 0.78 ± 0.11, 0.90 ± 0.10, 0.87 ± 0.13, and 4.11 ± 0.75, respectively. Except for the SE, all the other evaluation metric values of the temporal lobes segmented by the FGD-BCEL-based U-Net model were improved compared to the DL, GDL, TL, and FTL loss function-based U-Net models. Moreover, the FGD-BCEL-based U-Net model was morphologically more similar to the mask maps. The over- and under-segmentation was lessened, and it effectively segmented the tiny structures in the upper and lower poles of the temporal lobe with a limited number of samples.ConclusionsFor the segmentation of the temporal lobe on localized CT images for radiotherapy, the U-Net model based on the FGD-BCEL can meet the basic clinical requirements and effectively reduce the over- and under-segmentation compared with the U-Net models based on the other four loss functions. However, there still exists some over- and under-segmentation in the results, and further improvement is needed
Reversible Non-Volatile Electronic Switching in a Near Room Temperature van der Waals Ferromagnet
The ability to reversibly toggle between two distinct states in a
non-volatile method is important for information storage applications. Such
devices have been realized for phase-change materials, which utilizes local
heating methods to toggle between a crystalline and an amorphous state with
distinct electrical properties. To expand such kind of switching between two
topologically distinct phases requires non-volatile switching between two
crystalline phases with distinct symmetries. Here we report the observation of
reversible and non-volatile switching between two stable and closely-related
crystal structures with remarkably distinct electronic structures in the near
room temperature van der Waals ferromagnet FeGeTe. From a
combination of characterization techniques we show that the switching is
enabled by the ordering and disordering of an Fe site vacancy that results in
distinct crystalline symmetries of the two phases that can be controlled by a
thermal annealing and quenching method. Furthermore, from symmetry analysis as
well as first principle calculations, we provide understanding of the key
distinction in the observed electronic structures of the two phases:
topological nodal lines compatible with the preserved global inversion symmetry
in the site-disordered phase, and flat bands resulting from quantum destructive
interference on a bipartite crystaline lattice formed by the presence of the
site order as well as the lifting of the topological degeneracy due to the
broken inversion symmetry in the site-ordered phase. Our work not only reveals
a rich variety of quantum phases emergent in the metallic van der Waals
ferromagnets due to the presence of site ordering, but also demonstrates the
potential of these highly tunable two-dimensional magnets for memory and
spintronics applications
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