Miracle-dz/UniFAR-FTUs huggingface dataset:
Single- and two-particle observables in the Emery model: a dynamical mean-field perspective

We compare the dynamical mean-field descriptions of the single-band Hubbard model and the three-band Emery model at the one- and two-particle level for parameters relevant to high-Tc superconductors. We show that even within dynamical mean-field theory, accounting solely for temporal fluctuations, the intrinsic multi-orbital nature of the Emery model introduces effective non-local correlations. These lead to a non-Curie-like temperature-dependence of the magnetic susceptibility, also seen in nuclear magnetic resonance experiments in the pseudogap regime by M. Avramovska, et al. [Journal of Superconductivity and Novel Magnetism 33, 2621 (2020)]. We demonstrate the agreement of our results with these experiments for a large range of dopings and trace back the effective non-local correlations to an emerging oxygen-copper singlet by analyzing a minimal finite-size cluster model. Despite this correct description of the hallmark of the pseudogap at the two-particle level, i.e., the drop in the Knight shift of nuclear magnetic resonance, dynamical mean-field theory fails to properly describe the spectral properties of the pseudogap.
Knowledge Graph Construction in Power Distribution Networks

In this paper, we propose a method for knowledge graph construction in power distribution networks. This method leverages entity features, which involve their semantic, phonetic, and syntactic characteristics, in both the knowledge graph of distribution network and the dispatching texts. An enhanced model based on Convolutional Neural Network, is utilized for effectively matching dispatch text entities with those in the knowledge graph. The effectiveness of this model is evaluated through experiments in real-world power distribution dispatch scenarios. The results indicate that, compared with the baselines, the proposed model excels in linking a variety of entity types, demonstrating high overall accuracy in power distribution knowledge graph construction task.
Alternating patterns in commutator monomials

Considering commutator monomials of the non-commutative associative variables $X_1,\ldots,X_n$; we determine the maximal possible number of alternating associative monomials in their noncommutative polynomial expansions. This is achieved by replacing generating functions with polytope sequences, which turn out to be finitely generated in some sense.
Never Lost in the Middle: Improving Large Language Models via Attention Strengthening Question Answering

While large language models (LLMs) are equipped with longer text input capabilities than before, they are struggling to seek correct information in long contexts. The"lost in the middle"problem challenges most LLMs, referring to the dramatic decline in accuracy when correct information is located in the middle. To overcome this crucial issue, this paper proposes to enhance the information searching and reflection ability of LLMs in long contexts via specially designed tasks called Attention Strengthening Multi-doc QA (ASM QA). Following these tasks, our model excels in focusing more precisely on the desired information. Experimental results show substantial improvement in Multi-doc QA and other benchmarks, superior to state-of-the-art models by 13.7% absolute gain in shuffled settings, by 21.5% in passage retrieval task. We release our model, Ziya-Reader to promote related research in the community.
The Safety Shell: an Architecture to Handle Functional Insufficiencies in Automated Driving

To enable highly automated vehicles where the driver is no longer a safety backup, the vehicle must deal with various Functional Insufficiencies (FIs). Thus-far, there is no widely accepted functional architecture that maximizes the availability of autonomy and ensures safety in complex vehicle operational design domains. In this paper, we present a survey of existing methods that strive to prevent or handle FIs. We observe that current design-time methods of preventing FIs lack completeness guarantees. Complementary solutions for on-line handling cannot suitably increase safety without seriously impacting availability of journey continuing autonomous functionality. To fill this gap, we propose the Safety Shell, a scalable multi-channel architecture and arbitration design, built upon preexisting functional safety redundant channel architectures. We compare this novel approach to existing architectures using numerical case studies. The results show that the Safety Shell architecture allows the automated vehicle to be as safe or safer compared to alternatives, while simultaneously improving availability of vehicle autonomy, thereby increasing the possible coverage of on-line functional insufficiency handling.
Optimal Placement of Capacitor in Distribution System using Particle Swarm Optimization

In power systems, the incorporation of capacitors offers a wide range of established advantages. These benefits encompass the enhancement of the systems power factor, optimization of voltage profiles, increased capacity for current flow through cables and transformers, and the mitigation of losses attributed to the compensation of reactive power components. Different techniques have been applied to enhance the performance of the distribution system by reducing line losses. This paper focuses on reducing line losses through the optimal placement and sizing of capacitors. Optimal capacitor placement is analysed using load flow analysis with the Newton Raphson method. The placement of capacitor optimization is related to the sensitivity of the buses, which depends on the loss sensitivity factor. The optimal capacitor size is determined using Particle Swarm Optimization (PSO). The analysis is conducted using the IEEE 14 bus system in MATLAB. The results reveal that placing capacitors at the most sensitive bus locations leads to a significant reduction in line losses. Additionally, the optimal capacitor size has a substantial impact on improving the voltage profile and the power loss is reduced by 21.02 percent through the proposed method.
New Challenges in Accounting and Finance

Introduction There is an ongoing debate if integrated reporting should be mandatory or voluntary, involving regulatory agencies and capital market authorities, such as the International Accounting Standards Board (IASB), the Financial Accounting Standards Board (FASB), the International Organization of Securities Commissions (IOSCO), the Johannesburg Stock Exchange (JSE), and the Global Reporting Initiative (GRI) (Baboukardos & Rimmel, 2016; Barth, Cahan, Chen, & Venter, 2017). The International Integrated Reporting Council (IIRC) is one of the leading initiatives that promote the implementation of ‘integrated thinking’ and the disclosure of integrated Debora Ximenes de Aragao Vale Castelo, Valcemiro Nossa*, Fábio Moraes da Costa, Danilo Soares Monte-Mor
A metric for characterizing the arm nonuse workspace in poststroke individuals using a robot arm

An overreliance on the less-affected limb for functional tasks at the expense of the paretic limb and in spite of recovered capacity is an often-observed phenomenon in survivors of hemispheric stroke. The difference between capacity for use and actual spontaneous use is referred to as arm nonuse. Obtaining an ecologically valid evaluation of arm nonuse is challenging because it requires the observation of spontaneous arm choice for different tasks, which can easily be influenced by instructions, presumed expectations, and awareness that one is being tested. To better quantify arm nonuse, we developed the bimanual arm reaching test with a robot (BARTR) for quantitatively assessing arm nonuse in chronic stroke survivors. The BARTR is an instrument that uses a robot arm as a means of remote and unbiased data collection of nuanced spatial data for clinical evaluations of arm nonuse. This approach shows promise for determining the efficacy of interventions designed to reduce paretic arm nonuse and enhance functional recovery after stroke. We show that the BARTR satisfies the criteria of an appropriate metric for neurorehabilitative contexts: It is valid, reliable, and simple to use. Description Interacting with a robot can quantify otherwise hard-to-measure clinical metrics.
Insights into spinach domestication from genomes sequences of two wild spinach progenitors, Spinacia turkestanica and S. tetrandra

Cultivated spinach (Spinacia oleracea) is a dioecious species (with male and female flowers on separate individuals). We report high-quality genome assemblies for its two closest wild relatives, S. turkestanica and S. tetrandra, which are also dioecious, to study the genetics of spinach domestication. Using a combination of genomic approaches, we assembled genome sequences of both these species, and analysed them in comparison to the previously assembled S. oleracea genome. These species diverged approximately 6.3 million years ago (Mya), while cultivated spinach split from S. turkestanica (its probable direct progenitor) 0.8 Mya. A common feature of all three species is that all six chromosomes include very large gene-poor, repeat-rich regions. In S. oleracea, these correspond with pericentromeric regions with very low recombination rates in both male and female genetic maps, and we infer that the similar pericentromeric regions in the wild species also recombine rarely. Although these regions include a low proportion of Spinacia genes, many genes are nevertheless within them, and they must be considered when analyzing selection during domestication. As a first approach to the difficult question of detecting genes involved in spinach domestication, we characterized 282 structural variants (SVs) whose frequencies are higher in a set of spinach accessions than in the wild species, suggesting that they mark genome regions that have been selected during domestication. These regions include genes associated with leaf margin type and flowering time. We also describe evidence that the downy mildew resistance loci of cultivated spinach are derived from introgression from both wild spinach species.
Investigating egocentric tuning in hippocampal CA1 neurons using simulated data

Navigation requires integrating sensory information with a stable schema to create a dynamic map of an animal’s position using egocentric and allocentric coordinate systems. In the hippocampus, place cells encode allocentric space, but their firing rates may also exhibit directional tuning within egocentric or allocentric reference frames. We compared experimental and simulated data to assess the prevalence of tuning to egocentric bearing (EB) among hippocampal cells in rats foraging in an open field. Using established procedures, we confirmed egocentric modulation of place cell activity in recorded data; however, simulated data revealed a high false positive rate. When we accounted for false positives by comparing with shuffled data that retain correlations between the animal’s direction and position, only a very low number of hippocampal neurons appeared modulated by EB. Our study highlights biases affecting false positive rates and provides insights into the challenges of identifying egocentric modulation in hippocampal neurons.
Exploring the Mosaic-like Tissue Architecture of Kidney Diseases Using Relation Equivariant Graph Neural Networks on Spatially Resolved Transcriptomics

Emerging spatially resolved transcriptomics (SRT) technologies provide unprecedented opportunities to discover the spatial patterns of gene expression at the cellular or tissue levels. Currently, most existing computational tools on SRT are designed and tested on the ribbon-like brain cortex. Their present expressive power often makes it challenging to identify highly heterogeneous mosaic-like tissue architectures, such as tissues from kidney diseases. This demands heightened precision in discerning the cellular and morphological changes within renal tubules and their interstitial niches. We present an empowered graph deep learning framework, REGNN (Relation Equivariant Graph Neural Networks), for SRT data analyses on heterogeneous tissue structures. To increase expressive power in the SRT lattice using graph modeling, the proposed REGNN integrates equivariance to handle the rotational and translational symmetries of the spatial space, and Positional Encoding (PE) to identify and strengthen the relative spatial relations of the nodes uniformly distributed in the lattice. Our study finds that REGNN outperforms existing computational tools in identifying inherent mosaic-like heterogenous tissue architectures in kidney samples sourced from different kidney diseases using the 10X Visium platform. In case studies on acute kidney injury and chronic kidney diseases, the results identified by REGNN are also validated by experienced nephrology physicians. This proposed framework explores the expression patterns of highly heterogeneous tissues with an enhanced graph deep learning model, and paves the way to pinpoint underlying pathological mechanisms that contribute to the progression of complex diseases. REGNN is publicly available at https://github.com/Mraina99/REGNN.
The mitochondrial deoxyguanosine kinase is required for female fertility in mice

Mitochondrial homeostasis plays a pivotal role in oocyte maturation and embryonic development. Deoxyguanosine kinase (DGUOK) is a nucleoside kinase that salvages purine nucleoside in the mitochondria and is critical for mitochondrial DNA replication and homeostasis in non-proliferating cells. Dguok loss-of-function mutations and deletions lead to hepatocerebral mitochondrial DNA deletion syndrome. However, its potential role in reproduction remains largely unknown. We found that Dguok knockout results in female infertility. Mechanistically, the deficiency of DGUOK hinders ovarian development and oocyte maturation. Moreover, Dguok deficiency in oocytes caused a significant reduction in mitochondrial DNA and abnormal mitochondrial dynamics, and impaired germinal vesicle breakdown. Only few DGUOK-deficient oocytes were able to extrude the first polar body during in vitro maturation, and these oocytes showed irregular chromosome arrangement and different spindle lengths. In addition, Dguok deficiency elevated reactive oxygen species and accelerated apoptosis in oocytes. Our findings reveal novel physiological roles for the mitochondrial nucleoside salvage pathway in oocyte maturation and implicate that DGUOK is a potential marker for the diagnosis of female infertility.
MoveR: an R package for easy processing and analysis of animal video-tracking data

Animal movement and behavior are critical to understanding ecological and evolutionary processes. Recent years have witnessed an increase in methodological and technological innovations in video-tracking solutions for phenotyping animal behavior. Although these advances enable the collection of high-resolution data describing the movement of multiple individuals, analyzing and interpreting them remains challenging due to their complexity, heterogeneity, and noisiness. Here, we introduce MoveR, an R package for importing, filtering, visualizing, and analyzing data from common video-tracking solutions. MoveR includes flexible tools for polishing data, removing tracking artifacts, subsetting and plotting individual paths, and computing different movement and behavior metrics. Metadata
Behavioral adaptation to changing energy constraints via altered frequency of movement selection

Animal behavior is strongly constrained by energy consumption. A natural manipulation which provides insight into this constraint is development, where an animal must adapt its movement to a changing energy landscape as its body grows. Unlike many other animals, for fish it is relatively easy to estimate the energy consumed by their movements via fluid mechanics. Here we simulated the fluid mechanics of >100,000 experimentally-recorded movement bouts from larval zebrafish across different ages and fluid conditions as they hunted Paramecia. We find that these fish adapt to their changing relationship with the fluid environment as they grow by adjusting the frequency with which they select different types of movements, so that more expensive movements are chosen less often. This strategy was preserved when fish were raised in an unnaturally viscous environment. This work suggests a general principle by which animals could minimize energy consumption in the face of changing energy costs over development.
Enhanced prefrontal nicotinic signaling as evidence of active compensation in Alzheimer’s disease models

Background Cognitive reserve allows for active compensation during neuropathology. Here, we examine electrophysiological evidence for active compensation in Alzheimer’s disease (AD) focusing on the cholinergic signalling pathways in the prefrontal cortex. These pathways are vulnerable to neuropathology in AD and its preclinical models and are essential for attention and executive function. Methods We functionally interrogate aspects of the cholinergic system ex vivo, in brain slices of prefrontal cortex from two preclinical models: a compound transgenic AD mouse that permits optogenetically-triggered release of endogenous acetylcholine and a transgenic AD rat that closely recapitulates the human trajectory of AD. We then tested the impact of therapeutic interventions to further amplify the compensated responses and preserve the typical kinetic profile of cholinergic signaling. Results In two AD models, we find a potentially-compensatory upregulation of functional cholinergic responses above non-transgenic controls after onset of pathology. To identify the locus of this enhanced cholinergic signal, we dissect key pre- and post-synaptic components with pharmacological strategies. We identify a significant and selective increase in post-synaptic nicotinic receptor signalling on prefrontal cortical neurons. To probe the additional impact of therapeutic intervention on the adapted circuit, we test cholinergic and nicotinic-selective pro-cognitive treatments. The inhibition of acetylcholinesterase further enhances endogenous cholinergic responses but greatly distorts their kinetics. Positive allosteric modulation of nicotinic receptors, by contrast, enhances endogenous cholinergic responses and retains their rapid kinetics. Conclusions We demonstrate that functional nicotinic compensation occurs within the prefrontal cortex in two AD models. Promisingly, this compensated nicotinic signal can be boosted while preserving its rapid kinetic signature. Taken together, we conclude that cognitive reserve mechanisms act selectively within the prefrontal cholinergic pathway and reveal a new approach for cognitive treatment in AD neuropathology.
Text Mining of User Comments Based on Deep Learning

Against the backdrop of the big data era, textual data has experienced an explosive growth trend, necessitating the development of an efficient methodological framework for extracting valuable information from texts. This paper aims to apply text mining techniques to the domain of laptop sales, specifically focusing on mining user comments about laptops to identify their primary concerns when purchasing different laptop brands. The research is divided into three main parts: data acquisition and preprocessing, sentiment analysis, and Latent Dirichlet Allocation (LDA) topic modeling. The study concludes with a summary of the research findings. To begin, data related to laptop comments from JD.com, a leading e-commerce platform, is scraped using Python web scraping techniques. This is followed by a series of text preprocessing tasks, including data cleaning, text tokenization, stop word removal, frequency-based word statistics, and text vector representation. These steps lay a solid foundation for subsequent empirical analysis using models. Furthermore, LDA topic modeling is employed to extract topics from user comments specifically related to the Asus laptop brand. The results of the LDA topic extraction reveal that ASUS laptop users primarily discuss three main aspects: hardware configuration, customer service, and after-sales support, and overall performance and appearance. Finally, the paper concludes by summarizing the research findings and providing some recommendations that can be useful for laptop manufacturers, JD.com, and potential consumers.
All outputs in CentAUR are protected by Intellectual Property

This work presents two schemes of measuring the linear and angular kinematics of a rigid body using a kinematically redundant array of triple-axis accelerometers with potential applications in biomechanics. A novel angular velocity estimation algorithm is proposed and evaluated that can compensate for angular velocity errors using measurements of the direction of gravity. Analysis and discussion of optimal sensor array characteristics are provided. A damped 2 axis pendulum was used to excite all 6 DoF of the a suspended accelerometer array through determined complex motion and is the basis of both simulation and experimental studies. The relationship between accuracy and sensor redundancy is investigated for arrays of up to 100 triple axis (300 accelerometer axes) accelerometers in simulation and 10 equivalent sensors (30 accelerometer axes) in the laboratory test rig. The paper also reports on the sensor calibration techniques and hardware implementation.
Plate-kinematics and crustal dynamics of circum-Caribbean arc-continent interactions: Tectonic controls on basin development in Proto-Caribbean margins

The American margins of the Caribbean comprise basins and accreted terranes recording a polyphase tectonic history. Plate kinematic models and reconstructions back to the Jurassic show that Mesozoic separation of the Americas produced passive margins that were overridden diachronously from west to east by allochthonous Caribbean plate–related arc and oceanic complexes. P-T-t and structural data, sedimentary provenance, and basin-subsidence studies constrain this history. Caribbean lithosphere is Pacifi c-derived and was engulfed between the Americas during their westward drift as the Atlantic Ocean opened. This began ca. 120 Ma with development of a west-dipping Benioff zone between Central America and the northern Andes, now marked by the Guatemalan and Cuban sutures in North America and by the northern Colombian and Venezuelan “sutures” of South America, persisting today as the Lesser Antilles subduction zone. Most Caribbean high-pressure metamorphic complexes originated at this subduction zone, which probably formed by arc-polarity reversal at an earlier west-facing Inter-American Arc and was probably caused by westward acceleration of the Americas. The mainly 90 Ma Caribbean basalts were extruded onto preexisting Caribbean crust ~30 m.y. later and are not causally linked to the reversal. The Great Caribbean Arc originated at this trench and evolved up to the present, acquiring the shape of the preexisting ProtoCaribbean Seaway. The uplift and cooling history of arc and forearc terranes, and history of basin opening and subsidence, can be tied to stages of Caribbean plate motion in a coherent, internally-consistent regional model that provides the basis for further studies.
Ring Structures, Resurgent Cauldron, and Ore Deposits in the Hokuroku Volcanic Field, Northern Akita, Japan*

Ring dikes, normal faults, central uplift, and slumping structure are considered to be the key factors of the submarine resurgent cauldron. In the east Odate area, there occurs a ring distribution of post-Kuroko dacites and epithermal ore deposits around the central Otaki quartz diorite. Vein-type and Kuroko deposits are distributed almost along the half of the ring. There also occur submarine slumping structures. The estimated sub- surface structure indicates the central uplifting and marginal depression in the inside of ring dacites. Those phe- nomena are interpreted as the fossil submarine resurgent cauldron in the Miocene age with the intermediate scale of about 8 kilometers in horizontal diameter, associated with the genesis of Kuroko deposits and epithermal veins. A structural model of the relationship between the processes of a cauldron formation and ore genesis is proposed.