Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient Spin-Orbit Torque Generation in Semiconducting WTe2 with Hopping Transport.

Spin-orbit torques (SOTs) from transition metal dichalcogenide systems (TMDs) in conjunction with ferromagnetic materials are recently found to be attractive in spintronics for their versatile features. However, most of the previously studied crystalline TMDs are prepared by mechanical exfoliation, which limits their potentials for industrial applications. Here, we show that amorphous WTe2 heterostructures deposited by magnetron sputtering possess a sizable damping-like SOT efficiency of ξDLWTe2 ≈ 0.20 and low damping constant of α = 0.009 ± 0.001. Only an extremely low critical switching current density of Jc≈ 7.05 × 109 A/m2 is required to achieve SOT-driven magnetization switching. The SOT efficiency is further proved to depend on the W and Te relative compositions in the co-sputtered W100-xTex samples, from which a sign change of ξDLWTe2 is observed. In addition, the electronic transport in amorphous WTe2 is found to be semiconducting and is governed by a hopping mechanism. With the above advantages and rich tunability, amorphous and semiconducting WTe2 serves as a unique SOT source for future spintronics applications.
Dynamics of k-essence in loop quantum cosmology *

In this paper, we study the dynamics of k-essence in loop quantum cosmology (LQC). The study indicates that the loop quantum gravity (LQG) effect plays a key role only in the early epoch of the universe and is diluted in the later stages. The fixed points in LQC are basically consistent with those in standard Friedmann-Robertson-Walker (FRW) cosmology. For most of the attractor solutions, the stability conditions in LQC are in agreement with those for the standard FRW universe. For some special fixed points, however, tighter constraints are imposed thanks to the LQG effect.
Comparisons of Graph Neural Networks on Cancer Classification Leveraging a Joint of Phenotypic and Genetic Features

Cancer is responsible for millions of deaths worldwide every year. Although significant progress hasbeen achieved in cancer medicine, many issues remain to be addressed for improving cancer therapy.Appropriate cancer patient stratification is the prerequisite for selecting appropriate treatment plan, ascancer patients are of known heterogeneous genetic make-ups and phenotypic differences. In thisstudy, built upon deep phenotypic characterizations extractable from Mayo Clinic electronic healthrecords (EHRs) and genetic test reports for a collection of cancer patients, we evaluated variousgraph neural networks (GNNs) leveraging a joint of phenotypic and genetic features for cancer typeclassification. Models were applied and fine-tuned on the Mayo Clinic cancer disease dataset. Theassessment was done through the reported accuracy, precision, recall, and F1 values as well as throughF1 scores based on the disease class. Per our evaluation results, GNNs on average outperformed thebaseline models with mean statistics always being higher that those of the baseline models (0.849 vs0.772 for accuracy, 0.858 vs 0.794 for precision, 0.843 vs 0.759 for recall, and 0.843 vs 0.855 for F1score). Among GNNs, ChebNet, GraphSAGE, and TAGCN showed the best performance, while GATshowed the worst. We applied and compared eight GNN models including AGNN, ChebNet, GAT,GCN, GIN, GraphSAGE, SGC, and TAGCN on the Mayo Clinic cancer disease dataset and assessedtheir performance as well as compared them with each other and with more conventional machinelearning models such as decision tree, gradient boosting, multi-layer perceptron, naive bayes, andrandom forest which we used as the baselines.
EAGER: Embedding-Assisted Entity Resolution for Knowledge Graphs

Entity Resolution (ER) is a constitutional part for integrating different knowledge graphs in order to identify entities referring to the same real-world object. A promising approach is the use of graph embeddings for ER in order to determine the similarity of entities based on the similarity of their graph neighborhood. The similarity computations for such embeddings translates to calculating the distance between them in the embedding space which is comparatively simple. However, previous work has shown that the use of graph embeddings alone is not sufficient to achieve high ER quality. We therefore propose a more comprehensive ER approach for knowledge graphs called EAGER (Embedding-Assisted Knowledge Graph Entity Resolution) to flexibly utilize both the similarity of graph embeddings and attribute values within a supervised machine learning approach. We evaluate our approach on 23 benchmark datasets with differently sized and structured knowledge graphs and use hypothesis tests to ensure statistical significance of our results. Furthermore we compare our approach with state-of-the-art ER solutions, where our approach yields competitive results for table-oriented ER problems and shallow knowledge graphs but much better results for deeper knowledge graphs.
Inductive Representation Learning in Temporal Networks via Causal Anonymous Walks

Temporal networks serve as abstractions of many real-world dynamic systems. These networks typically evolve according to certain laws, such as the law of triadic closure, which is universal in social networks. Inductive representation learning of temporal networks should be able to capture such laws and further be applied to systems that follow the same laws but have not been unseen during the training stage. Previous works in this area depend on either network node identities or rich edge attributes and typically fail to extract these laws. Here, we propose Causal Anonymous Walks (CAWs) to inductively represent a temporal network. CAWs are extracted by temporal random walks and work as automatic retrieval of temporal network motifs to represent network dynamics while avoiding the time-consuming selection and counting of those motifs. CAWs adopt a novel anonymization strategy that replaces node identities with the hitting counts of the nodes based on a set of sampled walks to keep the method inductive, and simultaneously establish the correlation between motifs. We further propose a neural-network model CAW-N to encode CAWs, and pair it with a CAW sampling strategy with constant memory and time cost to support online training and inference. CAW-N is evaluated to predict links over 6 real temporal networks and uniformly outperforms previous SOTA methods by averaged 10% AUC gain in the inductive setting. CAW-N also outperforms previous methods in 4 out of the 6 networks in the transductive setting.
Emission Properties of Periodic Fast Radio Bursts from the Motion of Magnetars: Testing Dynamical Models

Recent observations of the periodic fast radio burst source 180916.J0158+65 (FRB 180916) find small linear polarization position angle swings during and between bursts, with a burst activity window that becomes both narrower and earlier at higher frequencies. Although the observed chromatic activity window disfavors models of periodicity in FRB 180916 driven solely by the occultation of a neutron star by the optically thick wind from a stellar companion, the connection to theories where periodicity arises from the motion of a bursting magnetar remains unclear. In this Letter, we show how altitude-dependent radio emission from a magnetar, with bursts emitted from regions that are asymmetric with respect to the magnetic dipole axis, can lead to burst activity windows and polarization consistent with the recent observations. In particular, the fact that bursts arrive systematically earlier at higher frequencies disfavors theories where the FRB periodicity arises from forced precession of a magnetar by a companion or fallback disk, but is consistent with theories where periodicity originates from a slowly rotating or freely precessing magnetar. Several observational tests are proposed to verify/differentiate between the remaining theories, and pin down which theory explains the periodicity in FRB 180916.
MFFCN: Multi-layer Feature Fusion Convolution Network for Audio-visual Speech Enhancement

—The purpose of speech enhancement is to extract target speech signal from a mixture of sounds generated from several sources. Speech enhancement can potentially beneﬁt from the visual information from the target speaker, such as lip movement and facial expressions, because the visual aspect of speech is essentially unaffected by acoustic environment. In order to fuse audio and visual information, an audio-visual fusion strategy is proposed, which goes beyond simple feature concatenation and learns to automatically align the two modalities, leading to more powerful representation which increase intelligibility in noisy conditions. The proposed model fuses audio-visual features layer by layer, and feed these audio-visual features to each corresponding decoding layer. Experiment results show relative improvement from 6 % to 24 % on test sets over the audio modality alone, depending on audio noise level. Moreover, there is a signiﬁcant increase of PESQ from 1.21 to 2.06 in our -15dB
Quantum Computing with Superconducting Circuits in the Picosecond Regime

Daoquan Zhu, 2 Tuomas Jaako, Qiongyi He, 2, ∗ and Peter Rabl † State Key Laboratory for Mesoscopic Physics, School of Physics, Frontiers Science Center for Nano-optoelectronics & Collaborative Innovation Center of Quantum Matter, Peking University, Beijing 100871, China Collaborative Innovation Center of Extreme Optics, Shanxi University, Taiyuan, Shanxi 030006, China Vienna Center for Quantum Science and Technology, Atominstitut, TU Wien, 1040 Vienna, Austria (Dated: July 20, 2021)
Walk in Wild: An Ensemble Approach for Hostility Detection in Hindi Posts

As the reach of the internet increases, pejorative terms started flooding over social media platforms. This leads to the necessity of identifying hostile content on social media platforms. Identification of hostile contents on low-resource languages like Hindi poses different challenges due to its diverse syntactic structure compared to English. In this paper, we develop a simple ensemble based model on pre-trained mBERT and popular classification algorithms like Artificial Neural Network (ANN) and XGBoost for hostility detection in Hindi posts. We formulated this problem as binary classification (hostile and non-hostile class) and multi-label multi-class classification problem (for more fine-grained hostile classes). We received third overall rank in the competition and weighted F1-scores of ~0.969 and ~0.61 on the binary and multi-label multi-class classification tasks respectively.
Average Cyclicity for Elliptic Curves in Torsion Families

We prove asymptotic formulas for cyclicity of reductions of elliptic curves over the rationals in a family of curves having specified torsion. These results agree with established conditional results and with average results taken over larger families. As a key tool, we prove an analogue of a result of Vlăduţ that estimates the number of elliptic curves over a finite field with a specified torsion point and cyclic group structure.
Dynamic Normalization

Batch Normalization has become one of the essential components in CNN. It allows the network to use a higher learning rate and speed up training. And the network doesn't need to be initialized carefully. However, in our work, we find that a simple extension of BN can increase the performance of the network. First, we extend BN to adaptively generate scale and shift parameters for each mini-batch data, called DN-C (Batch-shared and Channel-wise). We use the statistical characteristics of mini-batch data ($E[X], Std[X]\in\mathbb{R}^{c}$) as the input of SC module. Then we extend BN to adaptively generate scale and shift parameters for each channel of each sample, called DN-B (Batch and Channel-wise). Our experiments show that DN-C model can't train normally, but DN-B model has very good robustness. In classification task, DN-B can improve the accuracy of the MobileNetV2 on ImageNet-100 more than 2% with only 0.6% additional Mult-Adds. In detection task, DN-B can improve the accuracy of the SSDLite on MS-COCO nearly 4% mAP with the same settings. Compared with BN, DN-B has stable performance when using higher learning rate or smaller batch size.
Spoofing attack detection in dynamic channels with imperfect CSI

Recently, channel state information (CSI) at the physicallayer has been utilized to detect spoofing attacks in wireless communications. However, due to hardware impairments and communication noise, the CSI cannot be estimated accurately, which significantly degrades the attack detection performance. Besides, the reliability of CSI based detection schemes is challenged by time-varying scenarios. To address these issues, we propose an adaptive Kalman based detection scheme. By utilizing the knowledge of the predicted channel we eliminate the channel estimation error, especially the random phase error which occurs due to the lack of synchronization between transmitter and receiver. Furthermore, we define a Kalman residual based test statistic for attack detection. Simulation results show that our proposed scheme makes the detection more robust at low signal-to-noise ratio (SNR) and in dynamic scenarios.
Rapid changes in DNA methylation associated with the initiation of reproduction in a small songbird

Abstract Species with a circannual life cycle need to match the timing of their life history events to the environment to maximize fitness. However, our understanding of how circannual traits such as timing of reproduction are regulated on a molecular level remains limited. Recent studies have implicated that epigenetic mechanisms can be an important part in the processes that regulate circannual traits. Here, we explore the role of DNA methylation in mediating reproductive timing in a seasonally breeding bird species, the great tit (Parus major), using genome‐wide DNA methylation data from individual females that were blood sampled repeatedly throughout the breeding season. We demonstrate rapid and directional changes in DNA methylation within the promoter region of several genes, including a key transcription factor (NR5A1) known from earlier studies to be involved in the initiation of timing of reproduction. Interestingly, the observed changes in DNA methylation at NR5A1 identified here are in line with earlier gene expression studies of reproduction in chicken, indicating that the observed shifts in DNA methylation at this gene can have a regulatory role. Our findings provide an important step towards elucidating the genomic mechanism that mediates seasonal timing of a key life history traits and provide support for the idea that epigenetic mechanisms may play an important role in circannual traits.
Molecular responses of legumes to abiotic stress: post-translational modifications of proteins and redox signaling

Abstract Legumes include several major crops that can fix atmospheric nitrogen in symbiotic root nodules, thus reducing the demand for nitrogen fertilizers and contributing to sustainable agriculture. Global change models predict increases in temperature and extreme weather conditions. This scenario might increase plant exposure to abiotic stresses and negatively affect crop production. Regulation of whole plant physiology and nitrogen fixation in legumes during abiotic stress is complex, and only a few mechanisms have been elucidated. Reactive oxygen species (ROS), reactive nitrogen species (RNS), and reactive sulfur species (RSS) are key players in the acclimation and stress tolerance mechanisms of plants. However, the specific redox-dependent signaling pathways are far from understood. One mechanism by which ROS, RNS, and RSS fulfil their signaling role is the post-translational modification (PTM) of proteins. Redox-based PTMs occur in the cysteine thiol group (oxidation, S-nitrosylation, S-glutathionylation, persulfidation), and also in methionine (oxidation), tyrosine (nitration), and lysine and arginine (carbonylation/glycation) residues. Unraveling PTM patterns under different types of stress and establishing the functional implications may give insight into the underlying mechanisms by which the plant and nodule respond to adverse conditions. Here, we review current knowledge on redox-based PTMs and their possible consequences in legume and nodule biology.
Artemisinin inhibits TLR4 signaling by targeting co‐receptor MD2 in microglial BV‐2 cells and prevents lipopolysaccharide‐induced blood–brain barrier leakage in mice

Artemisinin and its derivatives have been the frontline drugs for treating malaria. In addition to the antiparasitic effect, accumulating evidence shows that artemisinins can alleviate neuroinflammatory responses in the central nervous system (CNS). However, the precise mechanisms underlying their anti‐neuroinflammatory effects are unclear. Herein we attempted to delineate the molecule target of artemisinin in microglia. In vitro protein intrinsic fluorescence titrations and saturation transfer difference (STD)‐NMR showed the direct binding of artemisinin to Toll‐like receptor TLR4 co‐receptor MD2. Cellular thermal shift assay (CETSA) showed that artemisinin binding increased MD2 stability, which implies that artemisinin directly binds to MD2 in the cellular context. Artemisinin bound MD2 showed much less collapse during the molecular dynamic simulations, which supports the increased stability of MD2 upon artemisinin binding. Flow cytometry analysis showed artemisinin inhibited LPS‐induced TLR4 dimerization and endocytosis in microglial BV‐2 cells. Therefore, artemisinin was found to inhibit the TLR4‐JNK signaling axis and block LPS‐induced pro‐inflammatory factors nitric oxide, IL‐1β and TNF‐α in BV‐2 cells. Furthermore, artemisinin restored LPS‐induced decrease of junction proteins ZO‐1, Occludin and Claudin‐5 in primary brain microvessel endothelial cells, and attenuated LPS‐induced blood–brain barrier disruption in mice as assessed by Evans blue. In all, this study unambiguously adds MD2 as a direct binding target of artemisinin in its anti‐neuroinflammatory function. The results also suggest that artemisinin could be repurposed as a potential therapeutic intervention for inflammatory CNS diseases.
Removal of nitric oxide in a pulsed corona discharge reactor

Conversion of nitric oxide (NO) in a pulsed corona discharge reactor was investigated. Relative importance of each of the active species such as O, OH, HO 2 and O 3 produced by corona discharge was evaluated with respect to the oxidation of NO to NO 2 . Of those species, O 3 was found to be the most important one in the oxidation of NO. In order to reduce the energy required to convert NO, olefins (ethylene, propylene) were used as additive, and the scheme for the oxidation of NO promoted by the hydrocarbon was discussed. In the presence of hydrocarbon, ozone also played very important role in the reaction mechanism. The concentration of ozone was measured at the reactor outlet to verify the importance of ozone in the oxidation chemistry. Compared to ethylene, propylene gave much better performance in the conversion of NO at the same specific energy. When propylene was added to the gas stream at identical amount to initial NO x (300 ppm), 60% of NO could be converted with a specific energy of only 2.6 Wh/m 3 .
Genetic parameter estimation for beef bull semen attributes.

Improvements in bull reproductive performance are necessary to optimize efficiency of cattle production. Female fertility has been enhanced through assisted reproductive technologies as well as genetic selection; however, improving beef bull fertility has been largely ignored. Phenotypes routinely collected at bull semen collection facilities are believed to affect fertility and provide the phenotypes necessary for a genetic evaluation. The first objective of this study was to determine the significant fixed effects for modeling beef bull fertility using data from bull semen collection facilities. The second objective was to estimate variance components, heritabilities, repeatabilities, and correlations between beef bull semen attributes. Beef bull fertility phenotypes including volume (VOL), concentration (CONC), number of spermatozoa (NSP), initial motility (IMot), post-thaw motility (PTMot), three-hour post-thaw motility (3HRPTMot), percentage of normal spermatozoa (%NORM), primary abnormalities (PRIM), and secondary abnormalities (SEC) were obtained from two bull semen collection facilities. A total of 1,819 Angus bulls with 50,624 collection records were analyzed. Of the fixed class and covariate effects tested, the significant class effects were collection location and collection day within year and the significant covariate effects included age at collection, days since previous collection, and cumulative comprehensive climate index (CCI). For this study, the CCI was calculated for a 75-day period including the 61-day spermatogenesis cycle and 14-day epididymal transit time. The 75 days prior to collection accounted for the environmental stress a bull may have experienced over the course of development of the spermatozoa, which was more significant than CCI calculated for collection day or spermatogenesis start date. Pre-thaw beef bull semen traits had low heritability estimates of 0.11 ± 0.02 (VOL), 0.09 ± 0.02 (CONC), 0.08 ± 0.02 (NSP), and 0.12 ± 0.03 (IMot). Heritabilities of post-thaw beef bull semen attributes were more variable at 0.10 ± 0.02 (PTMot), 0.05 ± 0.04 (3HRPTMot), 0.10 ± 0.04 (%NORM), 0.03 ± 0.03 (PRIM), and 0.18 ± 0.04 (SEC). Correlations of breeding values for these traits with SC EPD is low. The low to moderate heritability estimates indicate genetic improvement can be made in beef bull semen quality traits if new tools are developed to augment the scrotal circumference EPD that are currently available within the industry.
Relationship between molecular characteristics of polymers and parameters of far-infrared spectra

Abstract Far-infrared (FIR) spectra of linear and cross-linked polymers have been obtained and analyzed. The relationship of the parameters of these spectra to the characteristics of macromolecules is established. The role of molecular interactions in the formation of potential barriers to libration of polar groups and the low-frequency skeletal motion of the backbone chain of macromolecules is shown. It was found that the chain part participating in low-frequency skeletal motion for polystyrene and poly(methyl methacrylate) is commensurable with the correlation part of the chain for these polymers (the Kuhn segment).
Synthesis and Characterization of Graphene Sheets as an Anode Material for Lithium-Ion Batteries

The graphene sheets were obtained using rapid thermal expansion in nitrogen atmosphere from graphene oxide sheets prepared from graphite powder through improved Hummers’ method. The structure and morphology of the graphene sheets as anode materials with different dimensions were characterized through X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), Scanning electron microscope (SEM). The electrochemical performances were evaluated in coin-type cells versus metallic lithium. The results showed that graphene as the two dimensional nanomaterials possessed more advantages in microstructure and better Li-ions intercalation performances than the traditional materials.
Twisted Domains in Smectic B2-Phase Under an External Electric Field

A simplified model is used to approximate the energies of observed structures in smectic B2- phase composed of bent-shaped molecules. Twisted, mixed (having alternating layers with twisted and uniform molecular structure) and anticlinic antiferroelectric structures coexist without an applied field having thus nearly the same energies. When an electric field is applied, mixed structure starts to be preferred. The first critical field compensates surface polar anchoring on e.g., upper sample surface and favorites partly unwound structure. The second critical field leads to transition to uniform synclinic ferroelectric structure. The behavior of the energies of B2-phase structures under an electric field permits the estimation of model parameters, like e.g., anchoring energies.