Miracle-dz/UniFAR-FTUs huggingface dataset:
Edgeless-GNN: Unsupervised Inductive Edgeless Network Embedding

—We study the problem of embedding edgeless nodes such as users who newly enter the underlying network, while using graph neural networks (GNNs) widely studied for effective representation learning of graphs thanks to its highly expressive capability via message passing. Our study is motivated by the fact that existing GNNs cannot be adopted for our problem since message passing to such edgeless nodes having no connections is impossible. To tackle this challenge, we propose Edgeless-GNN , a new framework that enables GNNs to generate node embeddings even for edgeless nodes through unsupervised inductive learning . Speciﬁcally, we start by constructing a k -nearest neighbor graph ( k NNG) based on the similarity of node attributes to replace the GNN’s computation graph deﬁned by the neighborhood-based aggregation of each node. As our main contributions, the known network structure is used to train model parameters, while a new loss function is established using energy-based learning in such a way that our model learns the network structure. For the edgeless nodes, we inductively infer embeddings for the edgeless nodes by using edges via k NNG construction as a computation graph. By evaluating the performance of various downstream machine learning (ML) tasks, we empirically demonstrate that Edgeless-GNN consistently outperforms state-of-the-art methods of inductive network embedding. Moreover, our ﬁndings corroborate the effectiveness of Edgeless-GNN in judiciously combining the replaced computation graph with our newly designed loss. Our framework is GNN-model-agnostic; thus, GNN models can be appropriately chosen according to ones’ needs and ML tasks.
Updatable Learned Index with Precise Positions

Index plays an essential role in modern database engines to accelerate the query processing. The new paradigm of "learned index" has significantly changed the way of designing index structures in DBMS. The key insight is that indexes could be regarded as learned models that predict the position of a lookup key in the dataset. While such studies show promising results in both lookup time and index size, they cannot efficiently support update operations. Although recent studies have proposed some preliminary approaches to support update, they are at the cost of scarifying the lookup performance as they suffer from the overheads brought by imprecise predictions in the leaf nodes. In this paper, we propose LIPP, a brand new framework of learned index to address such issues. Similar with state-of-the-art learned index structures, LIPP is able to support all kinds of index operations, namely lookup query, range query, insert, delete, update and bulkload. Meanwhile, we overcome the limitations of previous studies by properly extending the tree structure when dealing with update operations so as to eliminate the deviation of location predicted by the models in the leaf nodes. Moreover, we further propose a dynamic adjustment strategy to ensure that the height of the tree index is tightly bounded and provide comprehensive theoretical analysis to illustrate it. We conduct an extensive set of experiments on several real-life and synthetic datasets. The results demonstrate that our method consistently outperforms state-of-the-art solutions, achieving by up to 4X for a broader class of workloads with different index operations.
Updater-Extractor Architecture for Inductive World State Representations

Developing NLP models traditionally involves two stages - training and application. Retention of information acquired after training (at application time) is architecturally limited by the size of the model's context window (in the case of transformers), or by the practical difficulties associated with long sequences (in the case of RNNs). In this paper, we propose a novel transformer-based Updater-Extractor architecture and a training procedure that can work with sequences of arbitrary length and refine its knowledge about the world based on linguistic inputs. We explicitly train the model to incorporate incoming information into its world state representation, obtaining strong inductive generalization and the ability to handle extremely long-range dependencies. We prove a lemma that provides a theoretical basis for our approach. The result also provides insight into success and failure modes of models trained with variants of Truncated Back-Propagation Through Time (such as Transformer XL). Empirically, we investigate the model performance on three different tasks, demonstrating its promise. This preprint is still a work in progress. At present, we focused on easily interpretable tasks, leaving the application of the proposed ideas to practical NLP applications for the future.
Non-Markovian open quantum system approach to the early Universe: Damping of gravitational waves by matter

M. Zarei,1, 2, 3, 4, ∗ N. Bartolo,3, 4, 5, † D. Bertacca,3, ‡ S. Matarrese,3, 4, 5, 6, § and A. Ricciardone3, 4, ¶ Department of Physics, Isfahan University of Technology, Isfahan 84156-83111, Iran ICRANet-Isfahan, Isfahan University of Technology, 84156-83111, Iran Dipartimento di Fisica e Astronomia “Galileo Galilei” Universita‘ di Padova, 35131 Padova, Italy INFN, Sezione di Padova, 35131 Padova, Italy INAF Osservatorio Astronomico di Padova, I-35122 Padova, Italy Gran Sasso Science Institute, I-67100 L’Aquila, Italy (Dated: September 21, 2021)
Self-supervised Multi-view Stereo via Effective Co-Segmentation and Data-Augmentation

Recent studies have witnessed that self-supervised methods based on view synthesis obtain clear progress on multi-view stereo (MVS). However, existing methods rely on the assumption that the corresponding points among different views share the same color, which may not always be true in practice. This may lead to unreliable self-supervised signal and harm the final reconstruction performance. To address the issue, we propose a framework integrated with more reliable supervision guided by semantic co-segmentation and data-augmentation. Specially, we excavate mutual semantic from multi-view images to guide the semantic consistency. And we devise effective data-augmentation mechanism which ensures the transformation robustness by treating the prediction of regular samples as pseudo ground truth to regularize the prediction of augmented samples. Experimental results on DTU dataset show that our proposed methods achieve the state-of-the-art performance among unsupervised methods, and even compete on par with supervised methods. Furthermore, extensive experiments on Tanks&Temples dataset demonstrate the effective generalization ability of the proposed method.
Building a Swedish Open-Domain Conversational Language Model

We present on-going work of evaluating the, to our knowledge, first large generative language model trained to converse in Swedish, using data from the online discussion forum Flashback. We conduct a human evaluation pilot study that indicates the model is often able to respond to conversations in both a human-like and informative manner, on a diverse set of topics. While data from online forums can be useful to build conversational systems, we reflect on the negative consequences that incautious application might have, and the need for taking active measures to safeguard against them.
Generalized second fluctuation-dissipation theorem in the nonequilibrium steady state: Theory and applications

In this paper, we derive a generalized second fluctuation-dissipation theorem (FDT) for stochastic dynamical systems in the steady state. The established theory is built upon the Mori-type generalized Langevin equation for stochastic dynamical systems and only uses the properties of the Kolmogorov operator. The new second FDT expresses the memory kernel of the generalized Langevin equation as the correlation function of the fluctuation force plus an additional term. In particular, we show that for nonequilibrium states such as heat transport between two thermostats with different temperatures, the classical second FDT is valid even when the exact form of the steady state distribution is unknown. The obtained theoretical results enable us to construct a data-driven nanoscale fluctuating heat conduction model based on the second FDT. We numerically verify that the new model of heat transfer yields better predictions than the Green-Kubo formula for systems far from the equilibrium.
TransWiC at SemEval-2021 Task 2: Transformer-based Multilingual and Cross-lingual Word-in-Context Disambiguation

Identifying whether a word carries the same meaning or different meaning in two contexts is an important research area in natural language processing which plays a significant role in many applications such as question answering, document summarisation, information retrieval and information extraction. Most of the previous work in this area rely on language-specific resources making it difficult to generalise across languages. Considering this limitation, our approach to SemEval-2021 Task 2 is based only on pretrained transformer models and does not use any language-specific processing and resources. Despite that, our best model achieves 0.90 accuracy for English-English subtask which is very compatible compared to the best result of the subtask; 0.93 accuracy. Our approach also achieves satisfactory results in other monolingual and cross-lingual language pairs as well.
Transcriptomic analysis identifies upregulation of secreted phosphoprotein 1 in silicotic rats

Silicosis is caused by exposure to crystalline silica and the molecular mechanism of silicotic fibrosis remains unclear. Therefore, the present study investigated the mRNA profiles of rats exposed to crystalline silica. RNA-sequencing techniques were used to observe differential expression of mRNAs in silicotic rats induced by chronic inhalation of crystalline silica particulates. Prediction of mRNA functions and signaling pathways was conducted using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. Certain differentially expressed mRNAs were verified in lung tissue of silicotic rats by quantitative polymerase chain reaction (qPCR). Secreted phosphoprotein 1 (SPP1) was measured in serum from silicosis patients, lungs of silicotic rats and NR8383 macrophages treated with silica. A total of 1,338 mRNAs were revealed to be differentially expressed in silicotic rat lungs, including 912 upregulated and 426 downregulated mRNAs. In GO analysis of significant changes in mRNAs, the most affected processes were the defense response, extracellular space and chemokine activity in terms of biological process, cellular component and molecular function. In KEGG pathway analysis, dysregulated mRNAs were involved in systemic lupus erythematosus, staphylococcus aureus infection, complement and coagulation cascades, alcoholism and pertussis. qPCR demonstrated that expression of Spp1, Mmp12, Ccl7, Defb5, Fabp4 and Slc26a4 was increased in silicotic rats, while Lpo, Itln1, Lcn2 and Dlk1 expression was decreased. It was also found that SPP1 was increased in serum from silicosis patients, silicotic rats and silica-treated NR8383 macrophages. The expression of mRNAs was altered significantly in silicotic rats, which suggested that certain genes are novel targets for the diagnosis and treatment of silicosis.
Expression levels of VEGF-C and VEGFR-3 in renal cell carcinoma and their association with lymph node metastasis

Renal cell carcinoma (RCC) is the most common form of kidney cancer. Vascular endothelial growth factor-C (VEGF-C) and its receptor, VEGFR-3, are involved in lymphangiogenesis. The aim of the present study was to investigate the expression levels of VEGF-C and VEGFR-3 in RCC, and their association with lymphatic vessel density (LVD) and lymph node metastasis. The mRNA expression levels of VEGF-C in 40 RCC tissues and 10 normal renal tissues were determined by reverse transcription-semiquantitative PCR. The differential expression of VEGF-C and VEGFR-3 was examined by immunohistochemistry. Using an anti-D2-40 antibody as a lymphatic marker, the morphology and structure of lymphatic vessels in tissues was examined, and the LVD was calculated. VEGF-C mRNA expression in RCC tissues was higher than that in normal renal tissues, and VEGF-C mRNA expression in the lymph node metastasis group was higher than that in the non-lymph node metastasis group. The positive expression rate of VEGF-C and VEGFR-3 in RCC tissues was significantly higher than that in normal renal tissues. VEGF-C expression in the lymph node metastasis group was significantly higher than that in the non-lymph node metastasis group, and the positive expression of VEGF-C was associated with the clinical staging of RCC. In addition, there was a correlation between VEGF-C and VEGFR-3 expression in tumor cells. The LVD around the tumor was higher than that in the center of the tumor tissues and normal renal tissues, and it was closely associated with lymphatic invasion and lymph node metastasis. Overall, the current findings demonstrated that the VEGF-C/VEGFR-3 signaling pathway promoted lymphangiogenesis around the tumor and provided an approach for tumor lymphatic invasion and lymph node metastasis. Therefore, VEGFC and VEGFR-3 expression may serve an important role in the initiation and development of RCC.
Human Laryngeal Mucus from the Vocal Folds: Rheological Characterization by Particle Tracking Microrheology and Oscillatory Shear Rheology

Mucus consistency affects voice physiology and is connected to voice disorders. Nevertheless, the rheological characteristics of human laryngeal mucus from the vocal folds remain unknown. Knowledge about mucus viscoelasticity enables fabrication of artificial mucus with natural properties, more realistic ex-vivo experiments and promotes a better understanding and improved treatment of dysphonia with regard to mucus consistency. We studied human laryngeal mucus samples from the vocal folds with two complementary approaches: 19 samples were successfully applied to particle tracking microrheology (PTM) and five additional samples to oscillatory shear rheology (OSR). Mucus was collected by experienced laryngologists from patients together with demographic data. The analysis of the viscoelasticity revealed diversity among the investigated mucus samples according to their rigidity (absolute G′ and G″). Moreover some samples revealed throughout solid-like character (G′ > G″), whereas some underwent a change from solid-like to liquid-like (G′ < G″). This led to a subdivision into three groups. We assume that the reason for the differences is a variation in the hydration level of the mucus, which affects the mucin concentration and network formation factors of the mucin mesh. The demographic data could not be correlated to the differences, except for the smoking behavior. Mucus of predominant liquid-like character was associated with current smokers.
Comparison between the Arrhenius model and the radial basis function neural network (RBFNN) model for predicting quality changes of frozen shrimp (Solenocera melantho)

ABSTRACT Changes in quality indices [total volatile base nitrogen (TVB-N), salt extractable protein (SEP), hypoxanthine (Hx), K-value, sensory assessment (SA), and electrical conductivity (EC)] for shrimp (Solenocera melantho) stored at −28, −20, and −12°C for 112 days were investigated in this study. The Arrhenius model and the radial basis function neural network (RBFNN) model were established to predict changes in the quality of shrimp during storage. Quality of shrimp stored at −12°C changed more quickly during 56–112 days, but those stored at −28°C deteriorated slowly during the entire storage period. Additionally, the indicators SEP, EC, and SA all fitted to the Arrhenius model well (relative errors within ±10%), but this model did not perform well in the prediction of K-value, Hx, and TVB-N on some days. However, the RBFNN model showed excellent accuracy for all indicators (relative errors within ±0.5%). The RBFNN model performed better than the Arrhenius model in predicting the quality of shrimp stored at −28°C to −12°C.
AbhemC encoding porphobilinogen deaminase plays an important role in chlorophyll biosynthesis and function in albino Ananas comosus var. bracteatus leaves

Background The chimeric leaves of Ananas comosus var. bracteatus are composed of normal green parts (Grs) and albino white parts (Whs). Although the underlying mechanism of albinism in A. comosus var. bracteatus leaves is not fully understood, it is likely associated with the chlorophyll (Chl) biosynthesis. In this biosynthetic process, porphobilinogen deaminase (PBGD) plays a crucial role by catalyzing the conversion of porphobilinogen (PBG) to uroporphyrinogen III (Urogen III). Therefore, its encoding gene AbhemC was investigated here in association with Chl biosynthesis and albinism in chimeric A. comosus var. bracteatus leaves. Methods The Chl content, main Chl biosynthesis precursor content, and main enzyme activity were determined and compared between the Whs and Grs of A. comosus var. bracteatus leaves. In addition, AbhemC was cloned and its transcriptional expression and prokaryotic protein expression were analyzed. Furthermore, RNAi-mediated silencing of AbhemC was produced and assessed in tobacco plants. Results The concentration of Chl a and Chl b in the Grs was significantly higher than that in the Whs, respectively. Additionally, the content of the Chl biosynthesis precursor Urogen III decreased significantly in the Whs compared with the Grs. Thus, the transition of PBG to Urogen III may be the first rate-limiting step leading to albinism in the chimeric leaves of A. comosus var. bracteatus. The gene AbhemC comprised 1,135 bp and was encoded into a protein with 371 amino acids; phylogenetically, AbhemC was most closely related to hemC of pineapple. Prokaryotic expression and in vitro enzyme activity analysis showed that the cloned mRNA sequence of AbhemC was successfully integrated and had PBGD activity. Compared with control plants, transgenic tobacco leaves with pFGC5941-AbhemC-RNAi vector were substantially less green with significantly reduced hemC expression and Chl content, as well as reduced PBGD enzyme activity and significantly decreased content of Chl biosynthesis precursors from Urogen III onwards. Our results suggest that the absence of hemC expression reduces the enzyme activity of PBGD, which blocks the transition of PBG to Urogen III, and in turn suppresses Chl synthesis leading to the pale-green leaf color. Therefore, we suggest that AbhemC plays an important role in Chl synthesis and may be an important factor in the albinism of A. comosus var. bracteatus leaves.
Ameliorative effect of apelin-13 against renal complications in L-NAME-induced preeclampsia in rats

Pre-eclampsia (PE) accompanying acute liver and kidney injury has remained a master cause of both fetal and maternal mortality and morbidity. Vasoactive mediators, oxidative stress and inflammatory imbalanceshave an important role in PE pathogenesis. Apelin is an adipokine that improves endothelial dysfunction; has anti-inflammatory and antioxidant effects; moreover, its level reduced during PE. This study aimed to explore the effects of apelin-13 administration on preeclampsia-associated renal dysfunction and proteinuria. Thirty-three pregnant female rats were divided into three groups; group: 1 (normal pregnant rats), group: 2 (preeclamptic rats); where rats were injected subcutaneously with 75 mg L-NAME/ kg body weight/day beginning from 9th to 20th day of pregnancy andgroup 3 (apelin-13 treated preeclamptic rats); In which L-NAME-induced preeclamptic rats were subcutaneously injected with 6 × 10−8 mol apelin-13/kg body weight/twice daily starting from 6th to 20th day of pregnancy. In all groups, mean arterial blood pressure, total urine protein, serum urea, creatinine, nitric oxide (NO), endothelin-1 (ET-1), interleukin–6 (IL-6) and malondialdhyde (MDA) were measured. Histopathological examination of kidney tissues was also done. preeclamptic rats showed significantly increased mean arterial blood pressure, total urine proteins, serum urea, creatinine, ET-1, IL-6, and MDA, but revealed a significantly decreased serum NO level. On the other hand, apelin treatment significantly improved these parameters together with amelioration of kidney histoarchitecture in the treated group. In conclusion, apelin may be a potentially curative candidate for prohibiting kidney damage and have a therapeutic benefit in PE rat models.
Magnetically separable sulfated zirconia as highly active acidic catalysts for selective synthesis of ethyl levulinate from furfuryl alcohol

Magnetically separable sulfated zirconia catalysts were prepared by a two-step approach. Coating of zirconia around the particles helps to increase the number of sites needed for sulfate ion loading and hence enhances the acidity of the catalyst. Different molar concentrations of chlorosulfonic acid were used for sulfonation. The prepared catalysts were used for selective synthesis of ethyl levulinate using renewable substrates: furfuryl alcohol and ethanol. Ethyl levulinate has many applications in different industries including as a potential blending component in biodiesel. The catalyst could be easily separated by the use of a magnet. The influence of different parameters was investigated to reach the optimum yield of ethyl levulinate. Detailed kinetics were established for scaling up purposes. The catalyst is robust and reusable.
Consequences of Diabetes Mellitus in Bone Health: Traditional Review

The diabetes mellitus (DM) pandemic was mostly related to the growing incidence of osteoporosis worldwide. Thus, DM-induced bone fragility was recently reported as a diabetic complication. This disorder needs to be identified and diagnosed early and adequately to avoid more symptoms and impairments. Bone weight is lowered and the risk of fractures rises in type 1 diabetes mellitus (T1DM). However, type 2 diabetes mellitus (T2DM) will increase bone density per se because of the elevated chance of fracturing. This indicates that bone consistency plays an important part in the pathogenesis of diseases. This research is aimed at defining the function of advanced glycation end-products (AGEs), micro-architectural changes, and altered bone turnover. The risk of fracture can be varied by drugs used for treating DM. Thiazolidinedione exacerbates bone degradation, for example, which raises the risk of fractures, particularly in older females. In contrast, metformin and sulfonylureas appeared to have no adverse effects on bone health and could guard against fragility. Evaluating bone mineral density (BMD) and other risk factors may aid in developing tailor-made recovery plans as part of the diagnostic process. Increased osteoporosis awareness is important, considering the increasing and older population of DM patients.
Dietary effects of different organic acids on growth and nutrient digestibility of broiler

An experiment was conducted to determine the effect of organic acids (acetic, butyric, citric and formic acids) on growth and nutrient digestibility of broilers. One hundred fifty (150) day old Hubbard chicks were used. There were five dietary treatments such viz Diet 1 as control contained no organic acid, diets 2, 3, 4, and 5 contained 0.25% acetic, butyric, citric and formic acids, respectively. Each treatment was replicated three times having 10 birds arranged in completely randomized design (CRD). Feed and water were given ad libitum . Feeding of organic acid diets lasted for 7 weeks starting from the second week. At the starter phase, formic acid improved live weight. Feed and water intakes were significantly (P 0.05). Crude protein and ether extracts digestibility were improved by all the organic acids (P<0.05). It is therefore concluded that 0.25% formic acid could be added to broiler diets. Bang. J. Anim. Sci. 2016. 45 (2): 10-17