Miracle-dz/UniFAR-FTUs huggingface dataset:
Redistributing and Re-Stylizing Features for Training a Fast Photorealistic Stylizer

Style transfer studies can be categorized into two types—artistic and photorealistic. The high-speed transfer has been well-studied for artistic styles but remains challenging for photorealistic styles. To guarantee semantic accuracy and style faithfulness, prior photorealistic style transfer techniques often rely on intensive feature matching, hierarchical stylization, and complex auxiliary smoothing. Such high design complexity severely limits the space of transfer speed improvement. In this paper, we propose to accelerate the transfer through a single-level stylization without complex auxiliary smoothing. We design a two-stage "stylization and re-stylization" training pipeline to enhance style faithfulness. The stylization/re-stylization stage consists of two core steps: feature aggregation and redistribution. A new type of layers, Feature Aggregation (FA) layers, is proposed to gradually aggregate multi-scale style features into content features at each spatial location. A Spatially coherent Content-style Preserving (SCP) loss at feature map level is then used to preserve semantic accuracy. The SCP loss provides effective guidance on redistributing the aggregated features between locations to enforce spatial coherence of style-sensitive content semantic. Experimental results show that compared to previous competitive methods, our method reduces at least 72% run time while achieving better image synthesis quality based on both subjective and objective evaluation metrics. Ablation studies validate the major contribution of our proposed SCP loss and re-stylization to the quality of our synthesized images.
VEGF treatment could significantly improve cardiac function by inducing myocardial collateral vessel development and inhibiting myocardial apoptosis by decreasing the expression levels of tumor necrosis factor (TnF)-α and Bax. a decrease in ecM degradation

Myocardial infarction (Mi) is a leading cause of mortality due to progression to ventricular arrhythmias (Vas) or heart failure (HF). cardiac remodeling at the infarct border zone (iBZ) is the primary contributor for Vas or HF. Therefore, genes involved in iBZ remodeling may be potential targets for the treatment of Mi, but the mechanism remains unclear. The present study aimed to explain the molecular mechanisms of iBZ remodeling based on the roles of long non-coding rnas (lncrnas). after downloading mirna (GSe76592) and mrna/lncrna (GSe52313) datasets from the Gene expression omnibus database, 23 differentially expressed mirnas (deMs), 2,563 genes (deGs) and 168 lncrnas (DELs) were identified between IBZ samples of MI mice and sham controls. a total of 483 deGs were predicted to be regulated by 23 deMs, among which itgam, Met and TnF belonged to hub genes after five topological parameters were calculated for genes in the protein-protein interaction network. These hub genes-associated deMs (mmu-mir-181a, mmu-mir-762) can also interact with six dels (Gm15832, Gas5, Gm6634, Pvt1, Gm14636 and a330023F24rik) to constitute the competing endogenous rna (cerna) axes. Furthermore, a co-expression network was constructed based on the co-expression pairs between 44 dels and 297 deGs, in which Pvt1 and Bst1 were overlapped with the cerna network. Thus, Bst1-associated cerna (Pvt1-mmu-mir-181a-Bst1) and co-expression (Pvt-Bst1) axes were also pivotal for Mi. accordingly, Pvt1 may be a crucial lncRNA for modification of cardiac remodeling in the iBZ after Mi and may function by acting as a cerna for mir-181a to regulate TnF/Met/itgam/Bst1 or by co-expressing with Bst1.
Construct Convolutional Neural Networks Using Low-yield Binary Memristor Crossbars

—Recent years, a number of multi-level memristor-based neural networks have been proposed, these architectures use multi-level memristors to complete analog vector-matrix multiplication calculations which to improve computing efﬁ-ciency, and also increase power consumption and implementation complexity. In addition, low yield of multi-level memristor crossbars is still a major issue in memristor-based neuromorphic computing. In this paper, we proposed a method which constructs CNNs by using ternary neural networks on binary memristor crossbars, and a training strategy which can hold on the high neural network accuracy is utilized under low-yield conditions. To demonstrate the performance of the training strategy, two neural networks architectures are applied for simulation experiments. Extensive experiments show that convolutional neural networks can still obtain more than 95% accuracy even at 80% yield.
Increased expression of FAK isoforms as potential cancer biomarkers in ovarian cancer

Focal adhesion kinase (FAK) is a non-receptor tyrosine kinase that is expressed in most human cell types (example: Epithelial cells, fibroblasts and endothelial), it serves a key role in the control of cell survival, proliferation and motility. The abnormal expression of FAK has been associated with poor prognosis in cancer, including ovarian cancer. However, although FAK isoforms with specific molecular and functional properties have been characterized, there are a limited number of published studies that examine FAK isoforms in ovarian cancer. The aim of the present study was to analyze the expression level of FAK and its isoforms in ovarian cancer. The expression of FAK kinase and focal adhesion targeting (FAT) domains was determined with immunohistochemistry in healthy ovary, and serous and mucinous cystadenoma, borderline tumor and carcinoma samples. Additionally, the expression of FAK and its isoforms were investigated in three ovarian cancer-derived cell lines with western blotting and reverse transcription-semi-quantitative polymerase chain reaction. An increased expression of FAK kinase domain was determined in serous tumor samples and was associated with advancement of the lesion. FAK kinase domain expression was moderate-to-low in mucinous tumor samples. The expression of the FAK FAT domain in tumor samples was reduced, compared with healthy ovary samples; however, the FAT domain was localized to the cellular nucleus. Expression of alternative transcripts FAK°, FAK28,6 and FAK28 was determined in all three cell lines investigated. In conclusion, FAK kinase and FAT domains are differentially expressed among ovarian tumor types. These results indicated the presence of at least two isoforms of FAK (FAK and the putative FAK-related non-kinase) in tumor tissue, which is supported by the cells producing at least three FAK alternative transcripts. These results may support the use of FAK and its isoforms as biomarkers for ovarian cancer.
A Simplified Deep Network Architecture on Optic Cup and Disc Segmentation

Glaucoma is caused by damaged optic nerves, and can lead to permanent vision loss. The cup-to-disk ratio (CDR) is a key criterion for glaucoma diagnosis, therefore an accurate automatic segmentation of the optic disc (OD) and optic cup (OC) in retinal fundus images has become a major research topic. However, with deeper deep learning models being used to complete a fundus segmentation, the segmentation results remain acceptable only on certain datasets. In this research, a lightweight deep-learning encoder–decoder architecture, which adopts polar coordinate transformation and histogram equalization to simplify the learning complexity, is proposed for the simultaneous segmentation of OD and OC. The proposed model employs an encoder–decoder architecture consisting of a feature extraction module and a multi-output module to both simplify the model and to adjust the result through different outputs and multilabel loss functions. Experiment results demonstrate that the proposed approach outperforms other deep network models on OD and OC segmentation over the datasets REFUGE, MESSIDOR, and RIM-ONE, which contain images captured from cameras with various specifications. In addition, better results on glaucoma screening through CDR calculation were obtained on the REFUGE dataset with the proposed method.
Changes of Sugar Content and Sucrose Metabolizing Enzyme Activities During Fleshy Taproot Development in Radish(Raphanus sativus L.)

Several physiological changes during the development of radish fleshy taproot were studied u- sing the advanced inbred lines of different ratio of root to shoot(R/S). The results showed that sink activity patterns of taproot were much similar among the four lines. The contents of carbohydrates including soluble to- tal sugar, sucrose and fructose decreased at the beginning of taproot enlarging, and to the minimum at the stage of the primary cortex split. The sucrose content of the low R/S line NAU-LR-04 was higher than those of three high R/S lines at all stages, while the fructose content was lower in NAU-LR-04. Changes of activities of sucrose synthase(SuSy)were similar to sink activities in all lines, and the activity of SuSy in low R/S line was higher than those of high R/S lines. It can be inferred that the SuSy may play an important role in the su- crose level regulation and sink activity control during radish fleshy taproot thickening.
REDISCOVERY OF THE WOOD-EATING COCKROACH CRYPTOCERCUS PRIMARIUS (DICTYOPTERA: CRYPTOCERCIDAE) IN CHINA, WITH NOTES ON ECOLOGY AND DISTRIBUTION

The wood eating cockroach Cryptocercus primarius, previously recorded only from Sichuan Province, is reported from two areas in the Hengduan Mountain s of northwestern Yunnan Province, China. Survival of this species, critical to systematic and evolutionary studies, depends on preservation of old growth fores ts in which the natural processes of tree death and decay are not compromised by human removal of forest litter for fuel.
Homeoproteins and the molecular basis of neuronal morphological plasticity

The co‐culturing of embryonic neurons and astrocytes derived from distinct brain regions has demonstrated that neuronal morphology and polarity can be influenced through region‐specific neuro‐astroglial interactions. In an attempt to search for region‐specific factors capable of modulating neuronal morphologies a new strategy has been developed, aimed at antagonizing homeoprotein activity which demonstrated that homeoproteins control the morphological traits of post‐mitotic neurons. In the course of the latter experiments we have observed that homeodomains and homeoproteins translocate across biological membranes and gain access to the cytoplasm and nucleus of live cells where they can interfere with the transcriptional activity of endogeneous homeoproteins. We identified the third helix of the homeodomain as the shortest structure necessary and sufficient for internalization and used it to address pharmacological substances inside the cells, in particular peptidic and phosphopeptidic antagonists of distinct signalling pathways. The internalization of full‐length homeoproteins led to the investigation of the possibility that homeoproteins are secreted. It was shown that Engrailed gains access to a compartment compatible with secretion in vivo and in vitro and is secreted in vitro, thus possibly acting as a putative polypeptidic messenger. Finally, to further understand the mode of action of homeoproteins, an original gene trap strategy was developed to identify genes in the genetic pathway of homeogenes and we have identified Bullous Pemphigoid Antigen 1 as a candidate Engrailed target. Copyright © 1999 John Wiley & Sons, Ltd.
Discovering biomedical causality by a generative Bayesian causal network under uncertainty

With the rapid development of biomedical technology, discovering causality from genes and human physiological and pathological characteristics has become a hot but challenge spot over the past decades. Due to the increment of the amount of biomedical data, discovering causality from observed data becomes more and more difficult to search this large body of knowledge in a meaningful manner. To address the issues in existing causality discovering models, we introduce a generative Bayesian causal network that combines neural network to explicitly characterize these unique causal-effect relationships as a variable number of nodes and links. Particularly, a basic skeleton is generated for node selection to reduce the network size by minimizing the maximum mean discrepancy among variables. In addition, a causal generative neural network model is presented to construct causal network with cause-effect scores between variables. Empirical evaluations on two publicly available biomedical datasets and four synthetic datasets suggest our approach significantly outperforms the state-of-the-art methods in discovering causal relationships among biomedical variables.
The substitutability of brands

Ehrenberg (1988, for example) has shown that comparatively few purchasers of a product category are 100% loyal to a particular brand. Most consumers show multi-brand purchasing over a sequence of shopping opportunities, choosing within a small “repertoire” of available brands. Ehrenberg explains this in terms of the functional similarities of brands within a product category. Usually, they have near-identical physical formulations and perform identical tasks. The consumer typically exchanges one brand for another because the benefits gained from one are directly substitutable with those provided by others within the repertoire. Ehrenberg is equally known for his approach to theory formulation, resolutely opposing premature theory-building, and favoring instead the detailed observation of buyer behavior and the empirical determination of patterns within the data, especially recurrences of brand choice regularities. Theory can, and should, come later (Ehrenberg, 1993). However, Ehrenberg’s assumptions about, and explanations of, the patterns he identifies can be evaluated only if there is a theoretical account of consumer choice that elucidates the meaning of his findings. His work, and that of his colleagues, collaborators and other researchers, spans several decades: it is surely time to move on now to the theory-building phase of this research program.
A note on estimation of $\alpha$-stable CARMA processes sampled at low frequencies

In this paper, we investigate estimators for symmetric $\alpha$-stable CARMA processes sampled equidistantly. Simulation studies suggest that the Whittle estimator and the estimator presented in Garc\'ia et al. (2011) are consistent estimators for the parameters of stable CARMA processes. For CARMA processes with finite second moments it is well-known that the Whittle estimator is consistent and asymptotically normally distributed. Therefore, in the light-tailed setting the properties of the Whittle estimator for CARMA processes are similar to those of the Whittle estimator for ARMA processes. However, in the present paper we prove that, in general, the Whittle estimator for symmetric $\alpha$-stable CARMA processes sampled at low frequencies is not consistent and highlight why simulation studies suggest something else. Thus, in contrast to the light-tailed setting the properties of the Whittle estimator for heavy-tailed ARMA processes can not be transferred to heavy-tailed CARMA processes. We elaborate as well that the estimator presented in Garc\'ia et al. (2011) faces the same problems. However, the Whittle estimator for stable CAR(1) processes is consistent.
"Is it a Qoincidence?": A First Step Towards Understanding and Characterizing the QAnon Movement on Voat.co

Conspiracy theories, and suspicion in general, define us as human beings. Our suspicion and tendency to create conspiracy theories have always been with the human race, powered by our evolutionary drive to survive. Although this evolutionary drive to survive is helpful, it can often become extreme and lead to "apophenia. " Apophenia refers to the notion of connecting previously unconnected ideas and theories. Unlike learning, apophenia refers to a cognitive, paranoid disorder due to the unreality of the connections they make. Social networks allow people to connect in many ways. Besides communicating with a distant family member and sharing funny memes with friends, people also use social networks to share their paranoid, unrealistic ideas that may cause panic, harm democracies, and gather other unsuspecting followers. In this work, we focus on characterizing the QAnon movement on this http URL. QAnon is a conspiracy theory that supports the idea that powerful politicians, aristocrats, and celebrities are closely engaged in a pedophile ring. At the same time, many governments are controlled by the "puppet masters" where the democratically elected officials serve as a fake showroom of democracy. Voat, a 5-year-old news aggregator, captured the interest of many journalists because of the often hateful content its users' post. Hence, we collect data from seventeen QAnon related subverses to characterize the narrative around QAnon, detect the most prominent topics of discussion, and showcase how the different topics and terms used in QAnon related subverses are interconnected.
On a distinguished family of random variables
and Painlevé equations

A family of random variables $\mathbf{X}(s)$, depending on a real parameter $s>-\frac{1}{2}$, appears in the asymptotics of the joint moments of characteristic polynomials of random unitary matrices and their derivatives, in the ergodic decomposition of the Hua-Pickrell measures and conjecturally in the asymptotics of the joint moments of Hardy's function and its derivative. Our first main result establishes a connection between the characteristic function of $\mathbf{X}(s)$ and the $\sigma$-Painleve III' equation in the full range of parameter values $s>-\frac{1}{2}$. Our second main result gives the first explicit expression for the density and all the complex moments of the absolute value of $\mathbf{X}(s)$ for integer values of $s$. As a corollary we obtain that the principal value sum of the inverse points of the sine point process is distributed as a Cauchy random variable. Finally, we establish an analogous connection to another special case of the $\sigma$-Painleve III' equation for the Laplace transform of the sum of the inverse points of the Bessel point process.
Self-Supervised Annotation of Seismic Images Using Latent Space Factorization

Annotating seismic data is expensive, laborious and subjective due to the number of years required for seismic interpreters to attain proficiency in interpretation. In this paper, we develop a framework to automate annotating pixels of a seismic image to delineate geological structural elements given image-level labels assigned to each image. Our framework factorizes the latent space of a deep encoder-decoder network by projecting the latent space to learned sub-spaces. Using constraints in the pixel space, the seismic image is further factorized to reveal confidence values on pixels associated with the geological element of interest. Details of the annotated image are provided for analysis and qualitative comparison is made with similar frameworks.
Ideal Kerker scattering by homogeneous spheres: the role of gain or loss

We investigate how the optical gain or loss (characterized by isotropic complex refractive indexes) influence the ideal Kerker scattering of exactly zero backward scattering. It was previously shown that, for non-magnetic homogeneous spheres with incident plane waves, either gain or loss prohibit ideal Kerker scattering, provided that only electric and magnetic multipoles of a specific order are present and contributions from other multipoles can all be made precisely zero. Here we reveal that, when two multipoles of a fixed order are perfectly matched in terms of both phase and magnitude, multipoles of at least the next two orders cannot possibly be tuned to be all precisely zero or even perfectly matched, and consequently cannot directly produce ideal Kerker scattering. Moreover, we further demonstrate that, when multipoles of different orders are simultaneously taken into consideration, loss or gain can serve as helpful rather than harmful contributing factors, for the elimination of backward scattering.
Constraining the Jet Launching Time of GRB 170817A by Utilizing the Baryon Loading

The observed delay of GRB 170817A relative to GW170817 provides significant information about gamma-ray burst (GRB) physics, and is subject to intense debate. In this Letter, we present an approach for discussing the major source of this time delay. First, we use the structured jet model to fit the X-ray/optical/radio afterglows of GRB 170817A together with superluminal motion measured using Very Long Baseline Interferometry. Our structured jet is modeled with angle-dependent energy and baryon loading. It is found that our model well fits the afterglows of GRB 170817A. Then, the baryon loading in the jet is inferred based on our fitting results. By comparing the baryon loading to the mass outflow in different stages, we infer that the time lag of the jet launch relative to the merger is less than hundreds or tens of milliseconds. It suggests that the time delay of GRB 170817A relative to GW170817 is defined mostly by the spreading time of the jet propagating to its dissipation radius.
Task-specific Objectives of Pre-trained Language Models for Dialogue Adaptation

Pre-trained Language Models (PrLMs) have been widely used as backbones in lots of Natural Language Processing (NLP) tasks. The common process of utilizing PrLMs is first pre-training on large-scale general corpora with task-independent LM training objectives, then fine-tuning on task datasets with task-specific training objectives. Pre-training in a task-independent way enables the models to learn language representations, which is universal to some extent, but fails to capture crucial task-specific features in the meantime. This will lead to an incompatibility between pre-training and fine-tuning. To address this issue, we introduce task-specific pre-training on in-domain task-related corpora with task-specific objectives. This procedure is placed between the original two stages to enhance the model understanding capacity of specific tasks. In this work, we focus on Dialogue-related Natural Language Processing (DrNLP) tasks and design a Dialogue-Adaptive Pre-training Objective (DAPO) based on some important qualities for assessing dialogues which are usually ignored by general LM pre-training objectives. PrLMs with DAPO on a large in-domain dialogue corpus are then fine-tuned for downstream DrNLP tasks. Experimental results show that models with DAPO surpass those with general LM pre-training objectives and other strong baselines on downstream DrNLP tasks.
Physics-Informed Neural Networks for Nonhomogeneous Material Identification in Elasticity Imaging

We apply Physics-Informed Neural Networks (PINNs) for solving identification problems of nonhomogeneous materials. We focus on the problem with a background in elasticity imaging, where one seeks to identify the nonhomogeneous mechanical properties of soft tissue based on the full-field displacement measurements under quasi-static loading. In our model, we apply two independent neural networks, one for approximating the solution of the corresponding forward problem, and the other for approximating the unknown material parameter field. As a proof of concept, we validate our model on a prototypical plane strain problem for incompressible hyperelastic tissue. The results show that the PINNs are effective in accurately recovering the unknown distribution of mechanical properties. By employing two neural networks in our model, we extend the capability of material identification of PINNs to include nonhomogeneous material parameter fields, which enables more flexibility of PINNs in representing complex material properties.