Miracle-dz/UniFAR-FTUs huggingface dataset:
Renormalization of transition matrix elements of particle number operators due to strong electron correlation

Renormalization of non-magnetic and magnetic impurities due to electron double-occupancy prohibition is derived analytically by an improved Gutzwiller approximation. Non-magnetic impurities are effectively weakened by the same renormalization factor as that for the hopping amplitude, whereas magnetic impurities are strengthened by the square root of the spin-exchange renormalization factor, in contrast to results by the conventional Gutzwiller approximation. We demonstrate it by showing that transition matrix elements of number operators between assumed excited states and between an assumed ground state and excited states are renormalized differently than diagonal matrix elements. Deviation from such simple renormalization with a factor is also discussed. In addition, as a related calculation, we correct an error in treatment of the renormalization of charge interaction in the literature. Namely, terms from the second order of the transition matrix elements are strongly suppressed. Since all these results do not depend on the signs of impurity potential or the charge interaction parameter, they are valid both in attractive and repulsive cases.
Brain activity during pursuit and goal-conflict threat avoidance in major depressive disorder

Abstract Threat avoidance is a prominent symptom of affective disorders, yet its biological basis remains poorly understood. Here, we used a validated task, the Joystick Operated Runway Task (JORT), combined with fMRI, to explore whether abnormal function in neural circuits responsible for avoidance underlies these symptoms. Eighteen individuals with major depressive disorder (MDD) and 17 unaffected controls underwent the task, which involved using physical effort to avoid threatening stimuli, paired with mild electric shocks on certain trials. Activity during anticipation and avoidance of threats was explored and compared between groups. Anticipation of aversive stimuli was associated with significant activation in the dorsal anterior cingulate cortex, superior frontal gyrus, and striatum, while active avoidance of aversive stimuli was associated with activity in dorsal anterior cingulate cortex, insula, and prefrontal cortex. There were no significant group differences in neural activity or behavioral performance on the JORT; however, participants with depression reported more dread while being chased on the task. The JORT effectively identified neural systems involved in avoidance and anticipation of aversive stimuli. However, the absence of significant differences in behavioral performance and activation between depressed and non-depressed groups suggests that MDD is not associated with abnormal function in these networks. Future research should investigate the basis of passive avoidance in major depression. Further, the JORT should be explored in patients with anxiety disorders, where threat avoidance may be a more prominent characteristic of the disorder.
Steady periodic waves over a planar seabed: a global characterization

Abstract The problem of propagation of steady periodic waves over a planar seabed is faced through the definition of a suitable semi-analytical iterative scheme. The latter is capable of describing highly nonlinear waves in deep, intermediate and shallow water conditions. Comparisons with the existing fifth-order theories show that the proposed model is accurate in all the regimes of motion and that it does not present any of the limitations affecting the Stokes and cnoidal wave solutions. Further, it also provides a reliable approximation of the dynamics of maximum-amplitude waves. The definition of the iterative scheme is preceded by a detailed study of the geometrical wave parameters. This latter part is aimed at the definition of a global scaling for water waves valid in all the regimes of motion.
Associations between maternal lifetime stress and placental mitochondrial DNA mutations in an urban multi-ethnic cohort

Background: Disrupted placental functioning due to stress can have lifelong implications. Cumulative stress and trauma are likely to have lasting impacts on maternal physiological functioning and offspring development resulting in increased risk for later life complex disorders for which racial disparities exist. Methods: This study examines the association between maternal lifetime stress and placental mitochondrial DNA (mtDNA) mutational load in an urban multi-ethnic cohort. Maternal lifetime exposure to stressful events was assessed using the validated Life Stressor Checklist-Revised. Whole mtDNA sequencing was performed and mutations were determined for 365 placenta samples with complete exposure and covariate data. Multivariable regression was used to model maternal lifetime stress in relation to placental mtDNA mutational load. Racial/ethnic differences were examined by cross-product terms and contrast statements. Gene-wise analyses were conducted. Results: We identified 13,189 heteroplasmies (phred score >10000, MAF < 0.5, # of mutant reads > 1). Women experiencing increased psychosocial stress over their lifetime exhibited a higher number of total placental mitochondrial mutations ( β =0.23, 95% CI 0.03–0.42) and heteroplasmic ( β =0.18, 95% CI 0.05–0.31) but not homoplasmic ( β = −0.008, 95% CI −0.03 – 0.01) mutations; the strongest associations were observed among Black women and genes coding for NADH dehydrogenase and cytochrome c oxidase subunits. Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Disclosure Statement: The authors report no biomedical financial interests or potential conflicts of interest. Conclusions: Cumulative maternal lifetime stress is associated with a greater mitochondrial mutational load, particularly among Black women. The impact of racial/ethnic differences in mutational load on placental function directly impacting offspring development and/or leading to chronic disease disparities warrants further investigation.
Customer Lifetime Value Analysis Based on Machine Learning

Customer lifetime value (CLV) is a powerful tool to determine the value of customers and filter customers most likely to attrite or most likely to make their first purchase, especially for e-commerce companies. This article reviewed machine learning models in analyzing CLV and prospected some potential directions for future research. Data of 8099 samples were collected and analyzed through four kinds of machine learning methods: Linear Regression, Support Vector Machine, Random Forest, Neural Network. The correlations between features showed that CLV are generally affected by monthly premium auto, total claim amount, and coverage. Analysis through machine learning models has high precision and Random Forest performs best. CLV prediction and customer segmentation are vital in business field today. Marketers could take advantage of the huge amount of data and machine learning models to portrait customer behaviors. Collecting browsing and purchase histories is also beneficial for providing best offers to individual customers.
In-Place Updates in Tree-Encoded Bitmaps

The Tree-Encoded Bitmap (TEB) is a tree-based bitmap compression scheme that maps runs in a bitmap to leaf nodes in a binary tree. Currently, TEBs perform updates using an auxiliary differential data structure. However, consulting this additional data structure at every read introduces both memory and read overheads. To mitigate the shortcomings of differential updates, we propose algorithms to update TEBs in place. To that end, we classify the updates that can occur in a TEB into two types: run-forming and run-breaking. Run-forming updates correspond to leaf nodes at the lowest level of the binary tree. All other updates are run-breaking. Each type of update requires different handling. Through experimentation with synthetic data, we determined that in-place run-forming updates are 2-3 × faster than differential updates, while run-breaking updates cannot be efficiently performed in place. Therefore, we propose a hybrid solution that performs run-forming updates in place and stores run-breaking updates in a differential data structure. Our experiments with synthetic data show that our hybrid solution performs updates faster than the differential approach. For example, for a workload where 20% of the updates are run forming, our hybrid solution is 69% faster on average. Artifact Availability: The source code is available at https://github.com/marcellus-saputra/Thuja.
Crack Detection and Localization based on Spatio-Temporal Data using Residual Networks

Damage detection in materials and structures plays a critical role in engineering and science applications like structural health monitoring. A particular challenge is presented by micro-scale cracks, which are imperceptible to the naked eye or in images, but may ultimately evolve into larger, potentially dangerous cracks. In this work, we propose spatio-temporal pattern recognition techniques to enable the detection of such imperceptible micro-cracks. In order to make these cracks detectable, we generate seismic waves on the surface area of interest and monitor how cracks interfere with the spatial propagation of the wave over time. On the resulting propagation image series we then apply segmentation techniques using deep encoder-decoder CNNs to predict the location of cracks, which otherwise could not be directly observed. Our solution is evaluated through extensive experiments on highly-realistic finite element simulations, which were developed by domain experts.
Mechanical properties and thermal analysis of graphene nanoplatelets reinforced polyimine composites

Abstract The polymer with imine bonds (C═N) synthesized by condensation of aldehydes and amines was called polyimine. Graphene nanoplatelets (GNPs) were blended into polyimine by imine dynamic chemistry, and GNPs/polyimine (GNPs-P) composites were fabricated by heat-pressing. A series of thermal and mechanical properties have been tested for the matrix and GNPs-P composites. Thermogravimetric analyzer showed that the GNPs were able to improve the thermal stability of the GNPs-P composites. From the test of mechanical properties, GNPs-P composite with 0.5 wt% GNPs was superior to the matrix in bending and tensile properties. The bending and tensile strengths were 92.65 and 73.05 MPa, with an improvement of 18% and 5%. GNPs-P composites with 1 wt% GNPs showed the most significant advancement in impact properties, reaching an impact strength of 11.745 kJ·m−2 with a gain of 21.6%. Cross-sectional observations using scanning electron microscope proved that the GNPs-P composites have brittle fractures. A small number of GNPs could synergize with the matrix by bridging the cracks, creating a crack diffusion resistance and a load transfer reinforcement effect, which improved the mechanical properties of the GNPs-P composites.
Spin-Lattice Relaxation of Coupled Metal-Radical Spin-Dimers in Proteins: Application to Fe -Cofactor (QA

The spin-lattice relaxation times (T1) for the reduced quinone acceptors QA . and QB . , and the intermediate pheophytin acceptor . , were measured in native photosynthetic reaction centers (RC) containing a high spin Fe (S 2) and in RCs in which Fe was replaced by diamagnetic Zn . From these data, the contribution of the Fe to the spin-lattice relaxation of the cofactors was determined. To relate the spin-lattice relaxation rate to the spin-spin interaction between the Fe and the cofactors, we developed a spin-dimer model that takes into account the zero field splitting and the rhombicity of the Fe ion. The relaxation mechanism of the spin-dimer involves a two-phonon process that couples the fast relaxing Fe spin to the cofactor spin. The process is analogous to the one proposed by R. Orbach (Proc. R. Soc. A. (Lond.). 264:458–484) for rare earth ions. The spin-spin interactions are, in general, composed of exchange and dipolar contributions. For the spin dimers studied in this work the exchange interaction, Jo, is predominant. The values of Jo for QA . Fe2 , QB . Fe2 , and . Fe2 were determined to be (in kelvin) 0.58, 0.92, and 1.3 10 , respectively. The Jo of the various cofactors (obtained in this work and those of others) could be fitted with the relation exp( Jd), where d is the distance between cofactor spins and J had a value of (0.66-0.86) Å . The relation between Jo and the matrix element Vij 2 involved in electron transfer rates is discussed.
Requirement for NF- (cid:107) B in osteoclast and B-cell development

NF- (cid:107) B is a family of related, dimeric transcription factors that are readily activated in cells by signals associated with stress or pathogens. These factors are critical to host defense, as demonstrated previously with mice deficient in individual subunits of NF- (cid:107) B. We have generated mice deficient in both the p50 and p52 subunits of NF- (cid:107) B to reveal critical functions that may be shared by these two highly homologous proteins. We now demonstrate that unlike the respective single knockout mice, the p50/p52 double knockout mice fail to generate mature osteoclasts and B cells, apparently because of defects that track with these lineages in adoptive transfer experiments. Furthermore, these mice present markedly impaired thymic and splenic architectures and impaired macrophage functions. The blocks in osteoclast and B-cell maturation were unexpected. Lack of mature osteoclasts caused severe osteopetrosis, a family of diseases characterized by impaired osteoclastic bone resorption. These findings now establish critical roles for NF- (cid:107) B in development and expand its repertoire of roles in the physiology of differentiated hematopoietic cells.
Constructing a Knowledge Graph from Indian Legal Domain Corpus

While being an important pillar of human society, legal domain consists of large corpora of complex documents about different aspects such as laws or court judgements. In recent years, knowledge graphs have become a prominent solution to represent such complex information in semantically rich machine readable manner allowing access to other AI powered downstream applications. In this work, we aim to construct a reliable knowledge graph from Legal domain corpus that may be utilized by researchers and the application developers working in legal domain.The source dataset chosen is the Indian Legal Court Judgements and NyOn 1 (Nyaya Ontology) has been utilized for conceptualization. A framework that consists of entity extraction, relation extraction, triple construction is used to convert the legal text into RDF triples. The knowledge graph thus built has been quantitatively evaluated over a small random sample with reasonable results.
FAIR Knowledge Graph Construction from Text, an Approach Applied to Fictional Novels

A Knowledge Graph (KG) is a form of structured human knowledge depicting relations between entities, destined to reflect cognition and human-level intelligence. Large and openly available knowledge graphs (KGs) like DBpedia, YAGO, WikiData are universal cross-domain knowledge bases and are also accessible within the Linked Open Data (LOD) cloud, according to the FAIR principles that make data findable, accessible, interoperable and reusable. This work aims at proposing a methodological approach to construct domain-oriented knowledge graphs by parsing natural language content to extract simple triple-based sentences that summarize the analyzed text. The triples coded in RDF are in the form of subject, predicate, and object. The goal is to generate a KG that, through the main identified concepts, can be navigable and linked to the existing KGs to be automatically found and usable on the Web LOD cloud.
Open Information Extraction on German Wikipedia Texts (short paper)

Knowledge Graphs are becoming a fundamental building block for semantic search and voice assistants. This paper deals with the automated Knowledge Graph Construction from unstructured data. Predomi-nantly, the focus is on Open Information Extraction (Open IE), an unsupervised learning approach that attempts to extract triples from plain text independent of their domain. Hence, it is the first step towards automated Knowledge Graph Construction. Previous work mainly applied Open IE to English texts. In this paper, the focus is on German texts. Due to the lack of German Open Information Extraction datasets, a dataset on the basis of Wikipedia is created. Two Open Information Extraction Systems for German are introduced. Finally, the performance of the systems are evaluated.
Scene Separation & Data Selection: Temporal Segmentation Algorithm for Real-time Video Stream Analysis

We present 2SDS (Scene Separation and Data Selection algorithm), a temporal segmentation algorithm used in real-time video stream interpretation. It complements CNN-based models to make use of temporal information in videos. 2SDS can detect the change between scenes in a video stream by com-paring the image difference between two frames. It separates a video into segments (scenes), and by combining itself with a CNN model, 2SDS can select the optimal result for each scene. In this paper, we will be discussing some basic methods and concepts behind 2SDS, as well as presenting some preliminary experiment results regarding 2SDS. During these experiments, 2SDS has achieved an overall accuracy of over 90%.
Generating Domain-Specific Knowledge Graphs: Challenges with Open Information Extraction

Knowledge Graphs (KGs) are a popular way to structure and represent knowledge in a machine-readable way. While KGs serve as the foundation for many applications, the automatic construction of these KGs from texts is a challenging task where Open Information Extraction techniques are prominently leveraged. In this paper, we focus on generating a domain-specific knowledge graph based on art-historic texts from a digitized text collection. We describe the combined use and adaption of existing open information extraction methods to build an art-historic KG that can facilitate data exploration for domain experts. We discuss the challenges that were faced at each step and present detailed error analysis to identify the limitations of existing methods when working with domain-specific corpora.
Equivariant Graph Neural Network for Crystalline Materials (Invited Paper)

Materials generation is an essential task in material science that aims to discover new materials. While most of the existing models have shown interesting results in simulation, they struggle to produce new original and stable materials. This paper discusses the salient properties required for material generation and studies the difficulties related to material pattern repetition, which impacts the stability of the generated structures.
Distributive Training can Improve Neural Network Performance based on RL-CNN Architecture

Recent studies of reinforced learning have suggested a distributive method of rewarding, which can be used to improve the performance of the deep Q-learning networks for such tasks as image classification, item recognition and movement simulation. This article uses the same method for more basic neural network types (RL + CNN more specifically) to conclude, whether this approach in machine learning systems can improve the results without the need for profound architectural changes or test and dev data cleansing. The same technique can then be used to improve the AI-SIEM threat detection system.
An Autosomal Dominant Cerebellar Ataxia Linked to Chromosome 16q22.1 Is Associated with a Single-Nucleotide Substitution in the 5 (cid:1) Untranslated Region of the Gene Encoding a Protein with Spectrin Repeat and Rho Guanine-Nucleotide Exchange-Factor Domains Autosomal dominant cerebellar ataxia

(ADCA) is a group of heterogeneous neurodegenerative disorders. By positional cloning, we have identiﬁed the gene strongly associated with a form of degenerative ataxia (chromosome 16q22.1–linked ADCA) that clinically shows progressive pure cerebellar ataxia. Detailed examination by use of audiogram suggested that sensorineural hearing impairment may be associated with ataxia in our families. After restricting the candidate region in chromosome 16q22.1 by haplotype analysis, we found that all patients from 52 unrelated Japanese families harbor a heterozygous C r T single-nucleotide substitution, 16 nt upstream of the putative translation initiation site of the gene for a hypothetical protein DKFZP434I216, which we have called “puratrophin-1” (Purkinje cell atrophy associated protein-1). The full-length puratrophin-1 mRNA had an open reading frame of 3,576 nt, predicted to contain important domains, including the spectrin repeat and the guanine-nucleotide exchange factor (GEF) for Rho GTPases, followed by the Dbl-homologous domain, which indicates the role of puratrophin-1 in intracellular signaling and actin dynamics at the Golgi apparatus. Puratrophin-1—normally expressed in a wide range of cells, including epithelial hair cells in the cochlea—was aggregated in Purkinje cells of the chromosome 16q22.1–linked ADCA brains. Consistent with the protein prediction data of puratrophin-1, the Golgi-apparatus membrane protein and spectrin also formed aggregates in Purkinje cells. The present study high-lights the importance of the 5 (cid:1) untranslated region (UTR) in identiﬁcation of genes of human disease, suggests that a single-nucleotide substitution in the 5 (cid:1) UTR could be associated with protein aggregation, and indicates that the GEF protein is with cerebellar degeneration in humans. reduced luciferase activity; the wild-type 7 These data suggest that the C r T in
Property C for ODE and applications

An overview of the authors results is given. Property C for ODE is defioned, It is proved that the pair of Sturm-Liouville operators has property C. This property is applied to many inverse problems. Some well-known results, such as uniqueness of the recovery of the potential from scattering data, from the spectral function and from two spectra are proved in a new short way based on property C. Many new results are obtained. In particular, it is proved that a compactly supported potential can be uniquely recovered from the phase shift of s-wave alone, known at all energies, without knowledge of bound states and norming constants. The fixed-energy phase shifts known at some values of the angular momentum determine a compactly supported potential uniquely. Mixed data inverse problems are studied. Analysis of the Newton-Sabatier procedure is given. It is shown that this procedure is a parameter-fitting procedure rather than an inversion method. Other results on inverse problems are obtained.
Applications of Cyber-Physical System: A Literature Review

Cyber-Physical System (CPS), a new generation of digital system, mainly focuses on complex interdependencies and integration between cyberspace and physical world. A CPS is composed of highly-integrated computation, communication, control, and physical elements. CPS is currently of interest in academia, industry, and government. However, a systematic and extensive review of research on CPS is not available. As such, this paper conducts an expanded literature review on applications of CPS by examining existing literatures from 2012 to 2017 in Scopus database. Specifically, 77 papers studying applications of CPS are grouped into 10 categories and reviewed. The contents of the papers in each research category are summarized. The challenges and trends of research on CPS are illustrated as well.