Miracle-dz/UniFAR-FTUs huggingface dataset:
Selection of Maternity Roosts by Evening Bats (Nycticeius humeralis) in a Riparian Forest at the Northern Edge of Their Range

Flexibility in resource selection by a species at the edge of its geographic distribution is a key factor in determining the chance of successful range expansion. The evening bat (Nycticeius humeralis) is a medium-sized vespertilionid that is widely distributed over a span of 17 degrees of latitude, from the Gulf of Mexico northward to the Great Lakes in North America. In the core of their range, evening bats prefer mature and commonly available trees that are located close to water sources as maternity roosts. However, data are lacking on roost selection by this wide-ranging species in the periphery of its distribution. In this study, we examined roost selection of the evening bat at the northern edge of its continental range. We radio tracked 44 evening bats in Michigan, United States, and identified 33 maternity trees. We collected variables at individual tree, plot, and landscape scales and compared roost and randomly selected trees. Although evening bats preferred roosting in Fraxinus (ash), we found that the evening bat was a generalist in its choice of roost genera, as long as the tree receives sufficient solar exposure, which is presumably important at the cool, northern edge of its range. At the landscape level, evening bats favored roosts surrounded by a less dense canopy, which would provide easy access for a flying bat and allow considerable solar radiation to strike the tree for additional warmth. We also found that evening bats preferred forest sections with more forest-farmland edges. We suspected that evening bats at the northern edge of their range show less preference in roost selection than evening bats in the core of their range. This flexibility in roost selection could allow the evening bat to expand further north if summers become warmer and other tree-roosting species decline due to white-nose syndrome, thus providing competitive release.
A generative-based design methodology to enable the democratisation of 3D printing

Abstract 3D printing technologies, such as material extrusion (MEX), hold the potential to revolutionise manufacturing by providing individuals without traditional manufacturing capabilities with powerful and affordable resources. However, widespread adoption is impeded by the lack of user-friendly design tools due to the necessity of domain-specific expertise in computer-aided design (CAD) software and the overwhelming level of design freedom afforded by the MEX process. To overcome these barriers and facilitate the democratisation of design (DoD), this article introduces an innovative, generative-based design (GBD) methodology aimed at enabling non-technical users to create functional components independently. The novelty of this methodology lies in its capacity to simplify complex design tasks, making them more accessible to non-designers. The proposed methodology was tested in the design of a load-bearing part, yielding a functional component within two design iterations. A comparative analysis with the conventional CAD-based process revealed that the GBD methodology enables the DoD, reflected in a 68% reduction in design activities and a decrease in design difficulty of 62% in requisite know-how and a 55% in understanding. Through the creation and implementation of this methodology, the article demonstrates a pioneering integration of state-of-the-art techniques of generative design with design repositories enabling effective co-design with non-designers.
( Baby ) Talk to Me : The Social Context of Infant-Directed Speech and Its Effects on Early Language Acquisition

Since the mid-20th century, scientists have observed unique features in speech, facial expression, and content directed to infants and toddlers in comparison to speech directed to adults. Whereas much research has studied the characteristics of so-called infant-directed speech and speculated about its significance for language learning, research directly testing these ideas has been more limited until recently. Studies now suggest that infant-directed speech (a) promotes infant attention to language, (b) fosters social interaction between infants and caregivers, and (c) informs infants about various aspects of their native language by heightening distinctions relative to the speech addressed to adults. New developments focusing on the social role of infant-directed conversational interactions highlight the importance of caregiver responsiveness to the infant. Building a communicative foundation even prior to the time language emerges is crucial for fostering language development.
A Framework for Uniformly Analyze and Mitigate Radiation-effects on FPGAs for Aerospace

This paper describes an architecture-modulable FPGA framework comprising synthesis, mapping, place and route, and bitstream analysis and mitigation for circuits mapped on FPGAs suitable for aerospace applications. The framework has several benefits, including analysis of soft-error effects and comparison between different vendors and parts, compatibility with commercial and radiation-hardened FPGA, and the ability to individuate single point of failure and embeds different mitigation strategies such as Triple Modular Redundancy (TMR) and Single Event Transient (SET) filtering at the synthesis or the place and route level. In this work, we provide the description of the framework and the radiation-effects analysis and mitigation on a set of benchmark circuits implemented using the most recent FPGA devices families for aerospace such as AMD Xilinx Ultrascale, NanoXplore NG-Medium, Microchip Radiation-Tolerant ProASIC3, and Radiation-Tolerant G4. Finally, we present a comparative analysis of a benchmark circuit's performance and radiation sensitivity mapped on the different FPGA device manufacturers.
Multi-Task CTC for Joint Handwriting Recognition and Character Bounding Box Prediction

Deep learning based models continue to push the frontier of OCR and Handwritten Text Recognition (HTR) both in terms of increasing accuracy and minimizing the burden of data annotation. For example, the CTC loss [3] allows for line-level training without the explicit spatial positions of the characters and words. Other recent works have further reduced the annotation effort by training systems on entire paragraphs or pages of text without line-level annotations. While these techniques have greatly increased accuracy and significantly reduced the data collection burden, useful outputs beyond just the Unicode character prediction have been lost. Because these systems have fewer explicit segmentation steps, they do not produce character-level or word-level bounding boxes. As a result, downstream applications such as highlighting, redaction tools, and basic document correction, are not provided and must be obtained with other methods. In this work we propose a novel technique to augment existing CTC-based OCR/HTR methods to output other attributes, such as character bounding boxes, without any added inference computation overhead or changes to the neural network. We demonstrate our technique for two types of tasks: fully supervised and self-supervised. We achieve state-of-the-art results on the CASIA-HWDB for the fully supervised task of character bounding box prediction. We then explore a self-supervised method to learning character bounding boxes without manually created annotations and demonstrate promising results.
Existence of Global Solutions for a Class of Abstract Second-Order Nonlocal Cauchy Problem with Impulsive Conditions in Banach Spaces

ABSTRACT In this paper, we are concerned with a class of abstract second-order nonlocal Cauchy problem with impulsive conditions in Banach spaces. First, we study the existence of mild solutions for a class of second-order nonlocal Cauchy problem with impulsive conditions in Banach spaces on an interval [0,a]. Later, we study a couple of cases where we can establish the existence of global solutions for a class of abstract second-order nonlocal Cauchy problem with impulsive conditions in Banach spaces. We apply our theory to study the existence of solutions for impulsive partial differential equations.
Holistic RISC-V Virtualization: CVA6-based SoC

This work describes our efforts to provide a holistic hardware RISC-V virtualization SoC based on the CVA6 core. At the core level, we implemented hardware support for virtualization through the ratified Hypervisor instruction set architecture (ISA) extension version 1.0. At the system level, we are working on providing reference open-source IPs for two non-ISA components needed to build a virtualization-aware platform: (i) the advanced interrupt architecture (AIA) to enable hardware support for interrupt virtualization; (ii) the input/output memory management unit (IOMMU) to protect memory accesses from direct memory access (DMA) devices. All these IPs will be open and freely available to the RISC-V community under permissive open-source licenses.
Energy and Performance Improvements for Convolutional Accelerators Using Lightweight Address Translation Support

The growing demand for deep learning applications has led to the design and development of several hardware accelerators to increase performance and energy efficiency. In particular, convolutional accelerators are among those receiving the most attention due to their applicability in many fields. Another aspect that is gaining increasing attention is the use of a shared virtual address space between processor and accelerators. It can provide several advantages such as programmability and security. The use of a shared address space relies on a time-consuming IOMMU to satisfy address translation requests. In this work, we analyze convolutional workloads in convolutional accelerators, identifying the sensitivity of performance to IOMMU activity. Additionally, based on the analysis done on convolutional workloads, we propose the use of dedicated accelerator registers (Translation Registers) to reduce costly IOMMU accesses. Translation Registers allow reducing execution time by about 20% and the energy consumption related to address translation up to about 55%.
Adverse childhood experiences, the risk of pregnancy complications and adverse pregnancy outcomes: a systematic review and meta-analysis

Background Adverse childhood experiences (ACEs) have a profound negative impact on health. However, the strength of the association between ACEs and pregnancy complications and adverse pregnancy outcomes is not well quantified or understood. Objective To conduct a systematic review and meta-analysis of the association between ACEs and risk of pregnancy complications and adverse pregnancy outcomes. Search strategy A comprehensive search was conducted using PubMed, Embase, CINAHL, PsycINFO, ClinicalTrials.gov and Google scholar up to July 2022. Data collection and analysis Two reviewers independently conducted the screening and quality appraisal using a validated tool. Meta-analysis using the quality-effects model on the reported odds ratio (OR) was conducted. Heterogeneity and inconsistency were examined using the I2 statistics. Results 32 studies from 1508 met a priori inclusion criteria for systematic review, with 21 included in the meta-analysis. Pooled analyses showed that exposure to ACEs increased the risk of pregnancy complications (OR 1.37, 95% CI 1.20 to 1.57) and adverse pregnancy outcomes (OR 1.31, 95% CI 1.17 to 1.47). In sub-group analysis, maternal ACEs were associated with gestational diabetes mellitus (OR 1.39, 95% CI 1.11 to 1.74), antenatal depression (OR 1.59, 95% CI 1.15 to 2.20), low offspring birth weight (OR 1.27, 95% CI 1.02 to 1.47), and preterm delivery (OR 1.41, 95% CI 1.16 to 1.71). Conclusion The results suggest that exposure to ACEs increases the risk of pregnancy complications and adverse pregnancy outcomes. Preventive strategies, screening and trauma-informed care need to be examined to improve maternal and child health.
Learning to Sketch with Deep Q Networks and Demonstrated Strokes

Doodling is a useful and common intelligent skill that people can learn and master. In this work, we propose a two-stage learning framework to teach a machine to doodle in a simulated painting environment via Stroke Demonstration and deep Q-learning (SDQ). The developed system, Doodle-SDQ, generates a sequence of pen actions to reproduce a reference drawing and mimics the behavior of human painters. In the first stage, it learns to draw simple strokes by imitating in supervised fashion from a set of strokeaction pairs collected from artist paintings. In the second stage, it is challenged to draw real and more complex doodles without ground truth actions; thus, it is trained with Qlearning. Our experiments confirm that (1) doodling can be learned without direct stepby- step action supervision and (2) pretraining with stroke demonstration via supervised learning is important to improve performance. We further show that Doodle-SDQ is effective at producing plausible drawings in different media types, including sketch and watercolor.
Is it a persistent ailment for the city? Urban resident perceptions and attitudes toward informal employment in tourism

ABSTRACT Informal employment in tourism is an important component of urban economies. A mixed-method approach using a questionnaire survey combined with in-depth interviews was used to analyze urban resident perceptions and attitudes toward tourism informal employment in Beijing, while considering the residents as both hosts and consumers. The results showed that: (1) resident perceptions toward tourism informal entrepreneurs are positive and negative, and the latter is comparatively stronger. (2) Favorable evaluations positively influence retention willingness and regulate management willingness. (3) Negative evaluations negatively influence retention willingness whist positively influencing regulative management willingness. (4) Retention willingness negatively influences regulative management willingness. (5) Consumption experience moderates the relationship between retention willingness and regulative management willingness. This study extends the theoretical research on informal employment and informs decision-making for effective city management.
GGPA: A General Graph Processing Architecture with Flexible Execution Paradigm

Currently, graph data are particularly common in various fields, and graph algorithms are increasingly widely used. However, due to the features of graph datasets, such as sparsity, the acceleration of graph algorithms with traditional architectures is faced with great challenges. In terms of domain-specific architecture (DSA), accelerators are mostly designed for a specific graph algorithm because of the different features of different graph algorithms. There is still a great need for a general graph algorithmic processing architecture. In this work, we propose a parallel General Graph Processing Architecture, GGPA. As a general graph computing architecture, GGPA can support multiple graph algorithms, realize parallel processing and fully explore the parallelism through a unique and effective subgraph partitioning method. GGPA implements flexibility at the execution paradigm level. During algorithm iteration, GGPA dynamically selects the execution paradigm by analyzing vertex update conditions to achieve the best performance. We verify GGPA at the CPU level and architecture simulator level, and experimental results show that GGPA achieves 1.01x to 5.86x speedup compared to other related start-of-the-art work.
Nonlinear dynamics from discrete time two-player status-seeking games

We study the dynamics of two-player status-seeking games where moves are made simultaneously in discrete time. For such games, each player's utility function will depend on both non-positional goods and positional goods (the latter entering into "status"). In order to understand the dynamics of such games over time, we sample a variety of different general utility functions, such as CES, composite log-Cobb-Douglas, and King-Plosser-Rebelo utility functions (and their various simplifications). For the various cases considered, we determine asymptotic dynamics of the two-player game, demonstrating the existence of stable equilibria, periodic orbits, or chaos, and we show that the emergent dynamics will depend strongly on the utility functions employed. For periodic orbits, we provide bifurcation diagrams to show the existence or non-existence of period doubling or chaos resulting from bifurcations due to parameter shifts. In cases where multiple feasible solution branches exist at each iteration, we consider both cases where deterministic or random selection criteria are employed to select the branch used, the latter resulting in a type of stochastic game.
Laimydorus zehsaziani n. sp., a new member of the genus from northwest of Iran, with additional data on Labronema vulvapapillatum (Meyl, 1954) Loof & Grootaert, 1981 (Dorylaimida: Dorylaimidae)

Abstract A new species belonging to the genus Laimydorus, and a known species of the genus Labronema, collected from the rhizosphere of grassland plants in West Azarbaijan province, Iran, are presented. The new species is characterized by its body length (1.56–1.81 mm in females and 1.39–1.57 mm in males), lip region offset by weak constriction and wider than adjacent body (11–14 μm), odontostyle 17–20 μm long, neck length 312–434 μm, vulva longitudinal (V = 42.2–46.5), uterus well-developed and with Z-differentiations, tail long (219–237 μm, c = 6.9–8.3, c´ = 8.2–10) in females, short and convex-conoid with blunt terminus (19–22 μm, c = 68–72.5, c´= 0.77–0.84) in males, spicules 40–45 μm long, and 18–20 contiguous ventromedian supplements with hiatus. Molecular analyses, using Bayesian inference based on D2-D3 expansion segments of the 28S rRNA (LSU), placed the new species in a clade comprising representatives of the genera Nevadanema, Dorylaimus, Labronema, Calcaridorylaimus, Mesodorylaimus, and Prodorylaimus, which all belong to Dorylaimidae. In addition, new data (molecular, morphometrics, images, and geographic distribution) are presented for the Iranian population of Labronema vulvapapillatum.
OntG-Bart: Ontology-Infused Clinical Abstractive Summarization

Automating the process of clinical text summarization could save clinicians' reading time and reduce their fatigue, acknowledging the necessity of human professionals in the loop. This paper addresses clinical text summarization, aiming to incorporate ontology concept relationships via a Graph Neural Network (GNN) into the summarization process. Specifically, we propose a model, extending Bart's encoder-decoder framework with GNN encoder and multi-head attentional layers for decoder, producing ontology-aware summaries. This GNN interacts with the textual encoder, influencing their mutual representations. The model's effectiveness is validated on two real-world radiology datasets. We also present an ablation study to elucidate the impact of varied graph configurations and an error analysis aimed at pinpointing potential areas for future improvements.
Relational Wide Column Store: a new Big Data paradigm

Driven by Big Data constraints, several new database paradigms have emerged, one of them is the Wide Column Store (WCS) paradigm. Current WCS solutions are relatively mature, but they have many drawbacks. This project aims to provide a WCS alternative by combining the WCS paradigm with the Relational DataBase Management System (RDBMS) architecture, which is called: the Relational Wide Column Store paradigm. Early results show a 31 % performance increase with read queries and relatively solid writing performances. This early-stage paper is encouraging and demonstrates that there is still room for improvements with RDBMS systems.
Phase-aware System-Side Sampling for HPC

HPC compute centers always benefit from better insights into the application mix running on their systems. We present a low-overhead statistical sampling tool running in the background on the system side, which can capture application compute phases. Our tool leverages eBPF (extended Berkeley Packet Filter) from modern Linux kernels to extract phase information provided by developers via instrumentation or instruction pointers. We outline how this tool can be integrated into the monitoring framework DCDB, and we show resulting performance insights.
Establishing Ac/Ds Starter Lines for Large-Scale Transposon-Tagged Mutagenesis in Tomato

Tomato (Solanum lycopersicum), a model system for ripening of fleshy fruits, has ∼40,000 genes predicted by in silico homology-based annotation. However, functional validation is lacking for most annotated tomato genes. Among the strategies for functional annotations, transposon-tagged mutagenesis is the most powerful approach. Transposon- tagged genes can be functionally validated by phenotyping and activation tagging. However, the lack of a robust in planta transformation system precludes large-scale transposon- mutagenesis of tomato. To overcome this limitation, we developed two sets of starter lines in tomato, each carrying maize transposon element (Ds) and transposase (Ac), respectively. The Ds and Ac lines were crossed to allow the Ac-mediated transposition of the Ds in the F1 generation. In the F2 generation, the location of excised Ds at new sites in the tomato genome was monitored. The Ds transposition was interspersed on different chromosomes of the tomato, indicating unlinked transposition of the Ds. The analysis of DNA sequences flanking Ds showed random integration of Ds in intergenic, genes, and the promoter region of the genome. Our study paves the way for the generation of large-scale transposon-tagged tomato lines using Ac/Ds starter lines and provides a potential tool for the functional validation of genes in tomato. Short summary We developed two sets of starter lines in tomato, carrying maize transposon element (Ds) and transposase (Ac), to enable large-scale transposon-mutagenesis, facilitating functional validation of tomato genes and for generating an insertional mutant resource in tomato.
Some Practical Analyses of the Judgment Documents of Labor Litigations for Social Conflicts and Similar Cases

We report two applications of the analysis of the judgment documents of labor litigations. The disputes listed in the judgment documents provide a very good foundation for recommending similar cases with explanations. For this narrowly focused “similarity”, we could achieve a 70% accuracy in our recommendations. Analyzing and learning about the disputes that the litigants argued in their cases help the lawyers, the social workers, and ordinary people to know more about and improve their society.