Miracle-dz/UniFAR-FTUs huggingface dataset:
Approximate Bayesian computation and ecological niche models elucidate the demographic history and current fragmented population distribution of a Korean endemic shrub

Abstract Climatic fluctuations and geological events since the LGM are believed to have significantly impacted the population size, distribution, and mobility of many species that we observe today. In this paper, we determined the processes driving the phylogeographic structure of the Korean endemic white forsythia by combining the use of genome‐wide SNPs and predicting paleoclimatic habitats during the LGM (21 kya), Early Holocene (10 kya), Mid‐Holocene (6 kya), and Late Holocene (3 kya). Using a maximum of 1897 SNPs retrieved from 124 samples across nine wild populations, five environmental predictors, and the species' natural occurrence records, we aimed to infer the species' demographic history and reconstruct its possible paleodistributions with the use of approximate Bayesian computation and ecological niche models, respectively. Under this integrated framework, we found strong evidence for patterns of range shift and expansion, and population divergence events from the onset of the Holocene, resulting in the formation of its five distinct genetic units. The most highly supported model inferred that after the split of an ancestral population into the southern group and a larger central metapopulation lineage, the latter gave rise to the eastern and northern clusters, before finally dividing into two sub‐central groups. While the use of molecular data allowed us to identify and refine the (phylo)genetic relationships of the species' lineages and populations, the use of ecological data helped us infer a past LGM refugium and the directions of post‐glacial range dynamics. The time frames of these demographic events were shown to be congruent with climatic and geological events that affected the central Korean Peninsula during these periods. These findings gave us a better understanding of the consequences of past spatiotemporal factors that may have resulted in the current fragmented population distribution of this endangered plant.
Association of milk microbiome with bovine mastitis before and after antibiotic therapy

Background and Aim: Mastitis is recognized as the most common disease in cattle and causes economic losses in the dairy industry. A number of opportunistic bacterial taxa have been identified as causative agents for this disease. Conventionally, antibiotics are used to treat mastitis; however, most bacteria are resistant to the majority of antibiotics. This study aimed to use molecular methods to identify milk microbiome patterns characteristic of mastitis that can help in the early diagnosis of this disease and in the development of new treatment strategies. Materials and Methods: To evaluate the microbiome composition, we performed NGS sequencing of the 16S rRNA gene of the V3 region. Results: An increase in the abundance of the bacterial genera Hymenobacter and Lachnospiraceae NK4A136 group is associated with the development of subclinical and clinical mastitis in dairy cows. These bacteria can be added to the list of markers used to detect mastitis in cows. Furthermore, a decrease in the abundance of Ralstonia, Lachnospiraceae NK3A20 group, Acetitomaculum, Massilia, and Atopostipes in cows with mastitis may indicate their role in maintaining a healthy milk microbiome. Antibiotics reduced the levels of Streptococcus in milk compared to those in the healthy group and cows before antibiotic treatment. Antibiotic therapy also contributed to an increase in the abundance of beneficial bacteria of the genus Asticcacaulis. Conclusion: This study expands our understanding of the association between milk microbiota and mastitis.
Improving High School Students' Little Creativity by Creative Problem Solving Model In General Technology Course

Creativity is important in human life and how it is cultivated in the formal education system is a hot topic today. In order to accomplish the improvement of students' creativity level in a relatively short period of time, we chose little creativity as the object of our study. In this study, a quasi-experimental research method was used, in which the control class used traditional teaching methods and the experimental class used a new teaching framework developed based on the creative problem solving model for the class. Through comparison, we found that the control and experimental classes showed significant differences in the total score, flexibility and fluency of the tests, and the experimental class also showed significant differences in the originality of the tests.
Prevalence of Neuropathic Pain in Patients with Osteoarthritis

Resumo Objetivo O objetivo deste estudo foi determinar a prevalência de dor neuropática e caracterizar a qualidade de vida de pacientes com osteoartrite que consultaram um ambulatório de dor no sudoeste da Colômbia. Métodos Este foi um estudo transversal realizado por meio de entrevista telefônica. Foram incluídos participantes ≥18 anos de idade com diagnóstico de osteoartrite. O questionário Leeds Assessment of Neuropathic Symptoms and Signs (LANSS) foi utilizado para avaliação dos sintomas e sinais de dor neuropática e o Short Form-8 analisou a qualidade de vida. Resultados A taxa de resposta foi de 54,1% (46/85). A razão homem:mulher foi de 5:1, com média de idade de 72 ± 10 anos. A maioria dos participantes (91,3%) apresentava dor intensa. A prevalência de dor neuropática foi de 28,3% (intervalo de confiança [IC] de 95% = 15,99-43,46) e a prevalência de dor neuropática entre mulheres foi de 84,6% (IC 95% = 54,55-98,01). Disestesias e dor paroxística foram relatadas por 92,3% dos indivíduos com dor neuropática. Em relação à qualidade de vida, as limitações na prática de atividade física foram as mais significativas e relatadas por 63% dos indivíduos. Conclusão A dor neuropática foi prevalente e tinha impacto negativo na função física. Isso destaca a necessidade de estratégias terapêuticas direcionadas a vias específicas da dor neuropática em pacientes com osteoartrite. Abstract Objective The aim of this study was to determine the prevalence of neuropathic pain and characterize the quality of life of patients with osteoarthritis who consulted a pain clinic in Southwestern Colombia. Methods A cross-sectional study was conducted via telephone survey. Participants ≥18 years of age with a diagnosis of osteoarthritis were included. The LANSS questionnaire was used to evaluate symptoms and signs of neuropathic pain, and the Short Form-8 was used to evaluate quality of life. Results Response rate was 54.1% (46/85). The male-to-female ratio was 5:1, with an average age of 72 ± 10 years. Most participants (91.3%) had severe pain. The prevalence of neuropathic pain was 28.3% (95%CI = 15.99-43.46), and the prevalence of neuropathic pain amongst women was 84.6% (95%CI = 54.55-98.01). Dysesthesias and paroxysmal pain were present in 92.3% of individuals with neuropathic pain. Regarding quality of life, limitations in physical activity were the most significant, as 63% of individuals reported such limitations. Conclusion Neuropathic pain was found to be prevalent and had a negative impact on physical function, highlighting the need for therapeutic strategies targeted to specific neuropathic pain pathways in patients with osteoarthritis.
TEXTURE-BASED SEPARATION TO REFINE BUILDING MESHES

Abstract. In this paper, we present a texture-based separation approach to refine building meshes, which aims to address the challenges of detecting and isolating different objects in an indoor scene mesh. We propose a novel segmentation model based on the materials of the different parts of the scene. The proposed approach uses factorization-based texture segmentation to separate the different materials in the meshes and detect the edges on the segmented texture. To prevent large faces containing multiple materials from being segmented wrongly, the mesh is cut along the texture boundaries and new faces with a single material are created. Finally, we segment the new faces based on the material index and neighbouring faces using a region-growing algorithm. We evaluate the proposed approach on the Matterport indoor dataset, which shows that our approach performs well on detecting boundaries between distinct materials, but over-segments on complex shapes. Our proposed method improves the segmentation of large flat surfaces like posters or rugs.
Kajian Bakteri Heterotropik di Perairan Laut Lamalera

Peranan bakteri yang sangat vital pada ekosistem perairan laut adalah sebagai dekomposer dengan hasil dekomposisi unsur-unsur mineral yang essensial sebagai sumber nutrien pada rantai makanan. Penelitian produktivitas perairan Lamalera ditinjau dari aspek bakteriologis telah dilakukan pada bulan Juli 2011 dengan menggunakan Kapal Riset Baruna Jaya VIII. Penelitian ini bertujuan untuk mempelajari bakteri heterotropik di Perairan Lamalera dan hubungannya dengan kesuburan perairan lautnya. Metode Total Plate Count digunakan untuk menentukan kandungan bakteri heterotrofik, sedangkan produktivitas bakteri ditentukan dengan metode Acridine Orange Direct Count. Hasil penelitian menunjukkan bahwa kandungan bakteri heterotrofik pada permukaan perairan di Selat Flores lebih tinggi bila dibandingkan dengan Selat Bolling dan Selat Alor dengan rata-rata kandungannya 3.70 CFU x 102/ml. Sedangkan pada kedalaman termoklin dan di bawah termoklin Selat Alor paling tinggi kandungannya bila dibandingkan dengan Selat Flores dan Selat Bolling dengan rata-rata kandungannya yaitu 4.30 CFU x 102/ml dan 6.20 x 102 CFU/ml. Untuk kandungan produktivitas bakteri dalam bentuk biomass Carbon pada permukaan dan termoklin di perairan Selat Alor lebih tinggi bila dibandingkan dengan perairan Selat Flores dan Selat Bolling dengan nilai rata-rata 6.8 x 10-10grC/ml dan 5.3 x 10-10grC/ml. Berdasarkan hasil penelitian terhadap kandungan bakteri heterotrofik dan produktivitasnya, mengindikasikan bahwa perairan Selat Alor cenderung lebih tinggi bila dibandingkan dengan perairan Selat Bolling dan Selat Flores. Perbedaan jumlah kandungan bakteri ini dimungkinkan karena faktor musim dan lingkungan yang berpengaruh terhadap ekosistem perairan lautnya. Secara umum dalam penelitian ini dapat disimpulkan bahwa produktivitas perairan laut Lamalera relatif subur.
On the relationship of data mining-based sound and emotion in poetry

It is generally recognized that poetry is somehow related to emotion. Automatic mining of poetic emotions is hotly discussed in recent years. This paper demonstrates a descriptive analysis on a well-known sad poem When You Are Old in a new perspective of phonetics, aiming to figure out the relationship between sound and emotion in poetry. It is found that in this poem, alveolar is a salient group in place of articulation. Also, the frequency of monophthong and that of diphthong is significantly different. However, no significant results are found in voicing, manner of articulation, vowel duration, vowel height and vowel location. It is still possible that readers can recognize the poet’s suggested sentiment since the frequency of some phonemes are so statistically different that evokes readers emotion. Findings here could contribute to automatic emotion recognition, AI poetry therapy, machine translation, and automatically poem creation.
AESPA: Asynchronous Execution Scheme to Exploit Bank-Level Parallelism of Processing-in-Memory

This paper presents an asynchronous execution scheme to leverage the bank-level parallelism of near-bank processing-in-memory (PIM). We observe that performing memory operations underutilizes the parallelism of PIM computation because near-bank PIMs are designated to operate all banks synchronously. The all-bank computation can be delayed when one of the banks performs the basic memory commands, such as read/write requests and activation/precharge operations. We aim to mitigate the throughput degradation and especially focus on execution delay caused by activation/precharge operations. For all-bank execution accessing the same row of all banks, a large number of activation/precharge operations inevitably occur. Considering the timing parameter limiting the rate of row-open operations (tFAW), the throughput might decrease even further. To resolve this activation/precharge overhead, we propose AESPA, a new parallel execution scheme that operates banks asynchronously. AESPA is different from the previous synchronous execution in that (1) the compute command of AESPA targets a single bank, and (2) each processing unit computes data stored in multiple DRAM columns. By doing so, while one bank computes multiple DRAM columns, the memory controller issues activation/precharge or PIM compute commands to other banks. Thus, AESPA hides the activation latency of PIM computation and fully utilizes the aggregated bandwidth of the banks. For this, we modify hardware and software to support vector and matrix computation of previous near-bank PIM architectures. In particular, we change the matrix-vector multiplication based on an inner product to fit it on AESPA PIM. Previous matrix-vector multiplication requires data broadcasting and simultaneous computation across all processing units. By changing the matrix-vector multiplication method, AESPA PIM can transfer data to respective processing units and start computation asynchronously. As a result, the near-bank PIMs adopting AESPA achieve 33.5% and 59.5% speedup compared to two different state-of-the-art PIMs.CCS CONCEPTS• Computer systems organization → Parallel architectures.
Research on SVM-based RFID Indoor Location

Non-contact identification of objects can be achieved using radio frequency technology (RFID). This paper proposes a location method based on Support Vector Machine (SVM), which harnesses RFID technology to locate objects. By analysing the shortcomings of standard SVM algorithm in dealing with significantly misclassified samples and multi-classification problems, a fuzzy iterative OVO-FSVM algorithm is proposed. Combined with RFID hardware equipment, a RFID-based location recognition system was designed. The experimental results show that the proposed location system demonstrated an accuracy of 77.8%, which can meet the needs of system positioning.
UNICO: Unified Hardware Software Co-Optimization for Robust Neural Network Acceleration

Specialized hardware has become an indispensable component to deep neural network (DNN) acceleration. To keep up with the rapid evolution of neural networks, holistic and automated solutions for jointly optimizing both hardware (HW) architectures and software (SW) mapping have been studied. These studies face two major challenges. First, the combined HW-SW design space is vast, which hinders the finding of optimal or near-optimal designs. This issue is exacerbated for industrial cases when cycle accurate models are used for design evaluation in the joint optimization. Second, HW design is prone to overfitting to the input DNNs used in the HW-SW co-optimization. To address these issues, in this paper, we propose UNICO, an efficient Unified Co-Optimization framework with a novel Robustness metric for better HW generalization. Guided by a high-fidelity surrogate model, UNICO employs multi-objective Bayesian optimization to effectively explore the HW design space, and conducts adaptive, parallel and scalable software mapping search based on successive halving. To reduce HW overfitting, we propose a HW robustness metric by relating a HW configuration’s quality to its sensitivity in software mapping search, and quantitatively incorporate this metric to search for more robust HW design(s). We implement UNICO in open source accelerator platform, and compare it with the state-of-the-art solution HASCO. Experiments show that UNICO significantly outperforms HASCO; it finds design(s) with similar quality to HASCO up to 4× faster, and eventually converges to better and more robust designs. Finally, we deploy UNICO for optimizing an industrial accelerator, and show that it generates enhanced HW design(s) for key real-world DNNs.CCS CONCEPTS• Hardware → Emerging tools and methodologies.
Single-molecule imaging reveals a direct role of CTCF’s zinc fingers in SA interaction and cluster-dependent RNA recruitment

CTCF is a zinc finger protein associated with transcription regulation that also acts as a barrier factor for topologically associated domains (TADs) generated by cohesin via loop extrusion. These processes require different properties of CTCF-DNA interaction, and it is still unclear how CTCF’s structural features may modulate its diverse roles. Here, we employ single-molecule imaging to study both full-length CTCF and truncation mutants. We show that CTCF enriches at CTCF binding sites (CBSs), displaying a longer lifetime than observed previously. We demonstrate that the zinc finger domains mediate CTCF clustering and that clustering enables RNA recruitment, possibly creating a scaffold for interaction with RNA-binding proteins like cohesin’s subunit SA. We further reveal a direct recruitment and an increase of SA residence time by CTCF bound at CBSs, suggesting that CTCF-SA interactions are crucial for cohesin stability on chromatin at TAD borders. Furthermore, we establish a single-molecule transcription assay and show that although a transcribing polymerase can remove CTCF from CBSs, transcription is impaired. Our study shows that context-dependent nucleic acid binding determines the multifaceted CTCF roles in genome organization and transcription regulation.
Future direction for the deployment of deep learning artificial intelligence: Vision threatening disease detection in underserved communities during COVID-19

PURPOSE: Vision-threatening diseases (VTDs) are the leading causes of vision loss and blindness. Use of deep learning artificial intelligence (DLAI) in early detection and subspecialty referral is critical to saving vision years and maintaining quality of life. To address this, we propose a comprehensive community-based screening retinal approach that incorporates DLAI to mitigate disparities and address need in an underserved urban community. METHODS: We evaluated two DLAI software designed for 45° retinal image analysis. DLAI was deployed in clinical settings to triage cases for ophthalmic referrals. Functionality was evaluated to propose implementation in a community screening. RESULTS: Our community screenings have incorporated various imaging modalities to improve VTD pick up rate: nonmydriatic color retinal imaging (18%), fundus autofluorescence (AF) (23%), and ocular coherence tomography B and angiography scans (35%). Robotic teleconsultation increased follow-up reached 100%. In clinical settings, DLAI reduced image analysis time (EyeArt™ = under 38 s, SELENA+™ =10.6 s) and highlighted multiple VTDs. High concordance was observed between human graders and DLAI (k = 0.68 in the department of endocrinology and k = 1 in the emergency department). CONCLUSION: Integration of DLAI in our ocular screening protocol can be used to reach underserved communities, especially when traditional health-care access is limited.
GMX: Instruction Set Extensions for Fast, Scalable, and Efficient Genome Sequence Alignment

Sequence alignment remains a fundamental problem in computer science with practical applications ranging from pattern matching to computational biology. The ever-increasing volumes of genomic data produced by modern DNA sequencers motivate improved software and hardware sequence alignment accelerators that scale with longer sequence lengths and high error rates without losing accuracy. Furthermore, the wide variety of use cases requiring sequence alignment demands flexible and efficient solutions that can match or even outperform expensive application-specific accelerators.To address these challenges, we propose GMX, a set of ISA extensions that enable efficient sequence alignment computations based on dynamic programming (DP). GMX extensions provide the basic building-block operations to perform fast tile-wise computations of the DP matrix, reducing the memory footprint and allowing easy integration into widely-used algorithms and tools. Furthermore, we provide an efficient hardware implementation that integrates GMX extensions in a RISC-V-based edge system-on-chip (SoC). Compared to widely-used software implementations, our hardware-software co-design leveraging GMX extensions obtains speed-ups from 25–265×, scaling to megabyte-long sequences. Compared to domain-specific accelerators (DSA), we demonstrate that GMX-accelerated implementations demand significantly less memory bandwidth, requiring less area per processing element (PE). As a result, a single GMX-enabled core achieves a throughput per area between 0.35-0.52× that of state-of-the-art DSAs while being more flexible and reusing the core’s resources. Post-place-and-route results for a GMX-enhanced SoC in 22nm technology shows that GMX extensions only account for 1.7% of the overall area while consuming just 8.47mW. We conclude that GMX extensions represent versatile and scalable ISA additions to improve the performance of genome analysis tools and other use cases that require fast and efficient sequence alignment.CCS CONCEPTS• Computer systems organization Special purpose systems;• Hardware → Application specific→instruction set processors;• Applied computing → Genomics.
Reflection on Abstract Art through Kandinsky's Teaching: Reflection on Abstract Art through Reconstruction of Kandinsky's Teaching at the Bauhaus

Wassily Kandinsky is considered as one of the pioneers of abstract painting in Europe. In this paper, our goal is to develop a user interface that models Kandinsky's analytical drawing process within a computational context to advance research in artificial intelligence and the arts. A user first chooses a photograph from the ObjectNet3D database, objects of which are aligned with the 3D shapes. Using the 3D shape information, the software extracts 2D projection contours. Then, it performs corner detection and finds structural networks. Lastly, the software transforms the photograph into an abstract image based on a student example.
Assessing Storm Surge Multiscenarios Based on Ensemble Tropical Cyclone Forecasting

Ensemble forecasting is a promising tool to aid in making informed decisions against risks of coastal storm surges. Although tropical cyclone (TC) ensemble forecasts are commonly used in operational numerical weather prediction systems, their potential for disaster prediction has not been maximized. Here we present a novel, efficient, and practical method to utilize a large ensemble forecast of 1,000 members to analyze storm surge scenarios toward effective decision making such as evacuation planning and issuing surge warnings. We perform the simulation of TC Hagibis (2019) using the Japan Meteorological Agency's (JMA) nonhydrostatic model. The simulated atmospheric predictions were utilized as inputs for a statistical surge model named the Storm Surge Hazard Potential Index to estimate peak surge heights along the central coast of Japan. We show that Pareto‐optimized solutions from an ensemble storm surge forecast can describe potential worst (maximum) and optimum (minimum) storm surge scenarios. These solutions exemplify a diversity of trade‐off surge outcomes across diverse coastal locations, reflecting variations in coastal geometry, including bathymetry. For example, some of the Pareto‐optimized solutions that illustrate worst surge scenarios for inner bay locations are not necessarily accountable for bringing severe surge cases in open coasts. We further emphasize that an in‐depth evaluation of Pareto‐optimal solutions can shed light on how meteorological variables such as track, intensity, and size of TCs influence the worst and optimum surge scenarios, which is not clearly quantified in current multiscenario assessment methods such as those used by JMA/National Hurricane Center in the United States.