Miracle-dz/UniFAR-FTUs huggingface dataset:
Splitting Tuples of Mismatched Entities

There has been a host of work on entity resolution (ER), to identify tuples that refer to the same entity. This paper studies the inverse of ER, to identify tuples to which distinct real-world entities are matched by mistake, and split such tuples into a set of tuples, one for each entity. We formulate the tuple splitting problem. We propose a scheme to decide what tuples to split and what tuples to correct without splitting, fix errors/assign attribute values to the split tuples, and impute missing values. The scheme introduces a class of rules, which embed predicates for aligning entities across relations and knowledge graphs G, assessing correlation between attributes, and extracting data from G. It unifies logic deduction, correlation models, and data extraction by chasing the data with the rules. We train machine learning models to assess attribute correlation and predict missing values. We develop algorithms for the tuple splitting scheme. Using real-life data, we empirically verify that the scheme is efficient and accurate, with F-measure 0.92 on average.
IT-SBA: An Improved Timer-based Scalable Broadcast Algorithm for Wireless Ad-hoc Networks

Minimizing number of forwarding stations is the prime objective of any broadcast protocol of wireless ad-hoc networks. Although existing proactive protocols show better performance than reactive protocols in minimizing number of forwarding stations but these are non-scalable than reactive protocols because proactive protocols use more network overhead (extended neighbor information) and message overhead (appended forward list) which consume more network resources. Timer mechanism based reactive protocols are more scalable than self-pruning and enhance the network performance but these are not more effective due to probabilistic selection of timer value. In this paper, we propose a reactive broadcast protocol named IT-SBA that uses a deterministic selection of timer value and completes the broadcast by less number of forwarding stations in expense of broadcast delay. Simulation results show that IT-SBA outperforms existing reactive protocols viz. flooding, self-pruning, SBA, E-SBA for both sparse and dense networks.
European High-Dimensional Option Pricing using Backward Stochastic Differential Equation-Based Convolutional Neural Network

Options, as financial derivatives, play a crucial role in hedging against financial risks in global markets. The pricing of options has been a topic of extensive research, particularly for high-dimensional options. Traditional methods such as the binomial tree and finite difference methods are impractical for solving high-dimensional options due to the curse of dimensionality. Additionally, simulation-based methods like Monte Carlo is highly dependent on variance, posing challenges in accurately pricing high-dimensional options. In recent years, a method with a backward stochastic differential equation (BSDE) -based deep neural networks (DNNs) approach called the Deep BSDE method has shown a promising result on solving a 100-dimensional European option. This approach addresses the limitations of traditional methods. However, the Deep BSDE method utilizes a sequence of feedforward networks (FNNs) that neglects the temporal information of underlying assets price dynamics, and the number of parameters depends on the number of discretization time steps. In this paper, we propose an alternative network, namely the convolutional neural network (CNN) to overcome these problems. We demonstrate that by employing this network, we can price high-dimensional options with higher accuracy and reduced computational time. Our results show that our network performs up to 2.9 times faster than the sequence of FNNs used in the Deep BSDE method.
Lemo: A Cache-Enhanced Learned Optimizer for Concurrent Queries

With the expansion of modern database services, multi-user access has become a crucial feature in various practical application scenarios, including enterprise applications and e-commerce platforms. However, if multiple users submit queries within a short time frame, it can result in potential issues such as redundant computation and query concurrency. Unfortunately, most existing multi-query optimization methods, which aim to enhance query processing efficiency, have not adequately addressed these two problems, especially in the setting where multiple queries are being executed concurrently. To this end, we propose a novel method named Lemo for the multi-query optimization problem. Specifically, we propose a novel value network to predict latencies of concurrent queries as the foundation model for query plan generation. Furthermore, we introduce a shared buffer manager component to cache the intermediate results of sub-queries. The shared buffer manager applies a novel replacement policy to maintain the cached buffer with the objective of maximizing the opportunity for the reuse of the cached sub-queries. Based on the shared buffer, our proposed value network can incorporate the cached results into cost estimation to further guide Lemo in generating query plans, thus avoiding redundant computation. Lemo has been integrated into PostgreSQL and experiments conducted on real datasets with PostgreSQL show that it outperforms all the baselines in efficiency.
MirrorKV: An Efficient Key-Value Store on Hybrid Cloud Storage with Balanced Performance of Compaction and Querying

LSM-based key-value stores have been leveraged in many state-of-the-art data-intensive applications as storage engines. As data volume scales up, a cost-efficient approach is to deploy these applications on hybrid cloud storage with hot/cold separation, which splits the LSM-tree into two parts and thus brings new challenges on how to split and how to close the significant performance gap between these two parts. Existing LSM-tree key-value stores mainly focus on the optimizations of local storage, which incurs sub-optimal performance when directly applied to hybrid storage. In this paper, we present MirrorKV for efficient compaction and querying on hybrid cloud storage. First, based on the capacities of fast and slow cloud storage, MirrorKV vertically separates hot/cold data of different levels stored in different cloud storage with different compaction mechanisms. To avoid compaction in slow storage being the bottleneck of the write path, MirrorKV proposes a novel virtual split to only compact the metadata during the compaction, which postpones the actual compaction until it reaches deep enough levels. Second, to reduce accessing slow storage during querying, MirrorKV horizontally separates keys and values into two mirrored LSM-trees to differentiate caching priorities; the maintained tree structures preserve the data locality for efficient sequential reading without incurring the overhead of the traditional key-value separation solutions. Finally, MirrorKV leverages cached data to guide the compaction where the hot data is retained in the fast storage while the cold data is compacted to deeper levels in slow storage. Compared with RocksDB-cloud, MirrorKV achieves 2.4× higher random insertion throughput, 29% higher random read throughput, and 99% less compaction time.
Explainability in NLP model: Detection of Covid-19 Twitter Fake News

Fake news has found fertile ground on social media. A global health crisis such as COVID-19 further helps propagate fake news on social media. Much research has been done to develop AI systems that classify news as real or fake. However, there is a growing concern about trust in these AI systems. To this end, we attempt to improve the trustworthiness of AI text classification systems. We use tools to explore data, explain feature extraction techniques, interpret the ML models implemented, and explain the decision-making progress of AI systems. In this study, we compared five ML classifiers for our experiments: Naive Bayes, Support Vector Machines (SVMs), Logistic Regression, Decision Tree, and Random Forest. The models were trained on 10700 tweets containing 5,600 real and 5,100 fake tweets related to COVID-19. In comparison, the SVMs model performance was the best, with a detection accuracy of 0.93 and F1 scores of 0.94 and 0.93 for real and fake news, respectively. Global and local explanations are included to understand the overall model behavior, ensuring transparency and fostering confidence in AI users. We have chosen the SVMs model for the explanation section as it was the best model in this study.
An isotope dilution-liquid chromatography-tandem mass spectrometry (ID-LC-MS/MS)-based candidate reference measurement procedure for the quantification of carbamazepine in human serum and plasma

Abstract Objectives An isotope dilution liquid chromatography-tandem mass spectrometry (ID-LC-MS/MS)-based candidate reference measurement procedure (RMP) was developed and validated to accurately measure serum and plasma concentrations of carbamazepine. Methods Quantitative nuclear magnetic resonance (qNMR) spectroscopy was used to determine the absolute content of the reference material, ensuring its traceability to SI units. The separation of carbamazepine from potential interferences, whether known or unknown, was achieved using a C18 column. A protein precipitation protocol followed by a high dilution step was established for sample preparation. Assay validation and determination of measurement uncertainty were performed in accordance with the guidelines of the Clinical and Laboratory Standards Institute, the International Conference on Harmonization (ICH), and the Guide to the Expression of Uncertainty in Measurement (GUM). In order to demonstrate equivalence to the already existing RMP a method comparison study was performed. Results The RMP was proven to be highly selective and specific with no evidence of a matrix effect, allowing for quantification of carbamazepine within the range of 0.800–18.0 μg/mL. Intermediate precision and repeatability (n=60 measurements) was found to be <1.6 % and <1.3 % over all concentration levels and independent from the matrix. The relative mean bias ranged from −0.1 to 0.6 % for native serum and from −0.3 to −0.1 % for Li-heparin plasma levels. The measurement uncertainties for single measurements and target value assignment were found to be <1.8 % and <1.3 %, respectively. Method comparison showed a good agreement between the Joint Committee of Traceability in Laboratory Medicine (JCTLM) listed RMP and the candidate RMP resulting in a Passing–Bablok regression equation with a slope of 1.01 and an intercept of −0.01. The bias in the patient cohort was found to be 0.9 %. Conclusions We present a novel LC-MS/MS-based candidate RMP for carbamazepine in human serum and plasma which provides a traceable and reliable platform for the standardization of routine assays and evaluation of clinically relevant samples.
Estimating the potential distribution of yellow spotted stink bug (Erthesina fullo) using ecological niche models

Invasive insect species threaten the productivity of ecosystems worldwide, and ecological niche models can be used to predict distributions of invaders and guide management efforts. Ecological niche models can also aid monitoring for invasive species that are globally distributed. One such species is the yellow spotted stink bug (Erthesina fullo Thunberg), a polyphagous pest native to Asia that has established in Europe and South America and threatens specialty crops. Here, we used ecological niche models to predict the potential distribution of E. fullo, and created a website to display predictions. We show that E. fullo has peak occurrence probability in areas with annual mean temperatures around 20°C, and that the occurrence probability increases as maximum monthly temperature reaches up to 38°C. The likelihood of occurrence decreased as annual precipitation increased, but increased with greater precipitation in the wettest and driest months. This suggests E. fullo is most suited to regions that are warm and dry and where most precipitation occurs across only a few months, such as southern North America, central and southern South America, southern Europe, southern Africa, and central and eastern Australia. Given that E. fullo is a highly mobile hitchhiking insect that travels through cargo and other containers to new areas, the potential spread of this species into new regions should be carefully monitored.
Structural variation discovery in wheat using PacBio high-fidelity sequencing

Background Structural variations (SVs) pervade plant genomes and contribute substantially to the phenotypic diversity. However, most SVs were ineffectively assayed because of their complex nature and the limitations of early genomic technologies. The recent advance in third-generation sequencing, particularly the PacBio high-fidelity (HiFi) sequencing technology, produces highly accurate long-reads and offers an unprecedented opportunity to characterize SVs’ structure and functionality. As HiFi sequencing is relatively new to population genomics, it is imperative to evaluate and optimize HiFi sequencing based SV detection before applying the technology at scale. Results We sequenced wheat genomes using HiFi reads, followed by a comprehensive evaluation of mainstream long-read aligners and SV callers in SV detection. The results showed that the accuracy of deletion discovery is markedly influenced by callers, which account for 87.73% of the variance, while both aligners (38.25%) and callers (49.32%) contributed substantially to the accuracy variance for insertions. Among the aligners, Winnowmap2 and NGMLR excelled in detecting deletions and insertions, respectively. For SV callers, SVIM achieved the best performance. We demonstrated that combining the aligners and callers mentioned above is optimal for SV detection. Furthermore, we evaluated the effect of sequencing depth on the accuracy of SV detection, showing that low-coverage HiFi sequencing is sufficiently robust for high-quality SV discovery. Conclusions This study thoroughly evaluated SV discovery approaches using HiFi reads, establishing optimal workflows to investigate structural variations in the wheat genome. The notable accuracy of SV discovery from low-coverage HiFi sequencing indicates that skim HiFi sequencing is effective and preferable to characterize SVs at the population level. This study will help advance SV discovery and decipher the biological functions of SVs in wheat and many other plants.
Genetic structure of khon mueang populations along a historical yuan migration route in northern thailand

The genetic structure and diversity of the Khon Mueang, who constitute the majority of the current northern Thai populations, is poorly understood. In present study, 433 unrelated individuals from 10 Khon Mueang villages, located in different geographic areas along historical Yuan migration route, were analyzed using the mtDNA hypervariable region (HVR) 1 and 17 Y chromosome short tandem repeats (Y-STRs) as markers. The studied populations from the Chiang Mai-Lamphun basin showed the evidence of demographic expansion and gene flow process in this area. Genetic structure of the geographically diverse Khon Mueang was driven by geography, while genetic differentiation of Chiang Mai-Lamphun populations was shaped by genetic exchange with the neighbouring populations in the area. Contrasting patterns of mtDNA and Y chromosome variations, influenced by sex-bias rates of migration and admixture, suggests that male and female Khon Mueang do not have identical demographic histories.
Restoring Protein Glycosylation with GlycoShape

During the past few years, we have been witnessing a revolution in structural biology. Leveraging on technological and computational advances, scientists can now resolve biomolecular structures at the atomistic level of detail by cryogenic electron microscopy (cryo-EM) and predict 3D structures from sequence alone by machine learning (ML). One technique often supports the other to provide the view of atoms in molecules required to capture the function of molecular machines. An example of the extraordinary impact of these advances on scientific discovery and on public health is given by how structural information supported the rapid development of COVID-19 vaccines based on the SARS-CoV-2 spike (S) glycoprotein. Yet, none of these new technologies can capture the details of the dense coat of glycans covering S, which is responsible for its natural, biologically active structure and function and ultimately for viral evasion. Indeed, glycosylation, the most abundant post-translational modification of proteins, is largely invisible through experimental structural biology and in turn it cannot be reproduced by ML, because of the lack of data to learn from. Molecular simulations through high-performance computing (HPC) can fill this crucial information gap, yet the computational resources, the users’ skills and the long timescales involved limit applications of molecular modelling to single study cases. To broaden access to structural information on glycans, here we introduce GlycoShape (https://glycoshape.org) an open access (OA) glycan structure database and toolbox designed to restore glycoproteins to their native functional form by supplementing the structural information available on proteins in public repositories, such as the RCSB PDB (www.rcsb.org) and AlphaFold Protein Structure Database (https://alphafold.ebi.ac.uk/), with the missing glycans derived from over 1 ms of cumulative sampling from molecular dynamics (MD) simulations. The GlycoShape Glycan Database (GDB) currently counts over 435 unique glycans principally covering the human glycome and with additional structures, fragments, and epitopes from other eukaryotic and prokaryotic organisms. The GDB feeds into Re-Glyco, a bespoke algorithm in GlycoShape designed to rapidly restore the natural glycosylation to protein 3D structures and to predict N-glycosylation occupancy, where unknown. Ultimately, integration of GlycoShape with other OA protein structure databases can provide a step-change in scientific discovery, from the structural and functional characterization of the active form of biomolecules, all the way down to pharmacological applications and drug discovery.
High-density sampling reveals volume growth in human tumours

In growing cell populations such as cancer, mutations can serve as markers that allow tracking the past evolution from current samples. The genomic analysis of bulk samples and samples from multiple regions have shed light on the evolutionary forces acting on tumours. However, little is known empirically on the spatio-temporal dynamics of tumour evolution. Here, we leverage published data from resected hepatocellular carcinomas, each with several hundred samples taken in two and three dimensions. Using spatial metrics of evolution, we find that tumour cells grow predominantly uniformly within the tumour volume instead of at the surface. We determine how mutations and cells are dispersed throughout the tumour and how cell death contributes to the overall tumour growth. Our methods shed light on the early evolution of tumours in vivo and can be applied to high-resolution data in the emerging field of spatial biology.
Maternal dietary deficiencies in folic acid or choline reduce primary neuron viability after exposure to hypoxia through increased levels of apoptosis

Stoke is the leading cause of death and disability globally. By addressing modifiable risk factors, particularly nutrition, the prevalence of stroke and its dire consequences can be mitigated. One-carbon (1C) metabolism is a critical biosynthetic process that is involved in neural tube closure, neuronal plasticity, and cellular proliferation in the developing embryo. Folic acid and choline are two active components of 1C metabolism, we have previously demonstrated that maternal dietary deficiencies in folic acid or choline worsen stroke outcomes in offspring. However, there is insufficient data to understand the neuronal mechanisms involved. We exposed embryonic neurons of offspring, whom mothers were on folic acid or choline deficient diets, to hypoxia conditions for 6 hours and return to normoxic conditions for 24 hours to model an ischemic stroke and reperfusion injury. To determine whether increased levels of either folic acid or choline can rescue reduced neuronal viability, we supplemented cell media with folic acid and choline prior to and after exposure to hypoxia. Our results suggest that maternal dietary deficiencies in either folic acid or choline during pregnancy negatively impacts offspring neuronal viability after hypoxia. Furthermore, increasing levels of folic acid or choline prior to and after hypoxia have a beneficial impact on neuronal viability. The findings contribute to our understanding of the intricate interplay between maternal dietary factors, 1C metabolism, and the outcome of offspring to hypoxic events, emphasizing the potential for nutritional interventions in mitigating adverse outcomes.
Learning of object-in-context sequences in freely-moving macaques

Flexible learning is a hallmark of primate cognition, which arises through interactions with changing environments. Studies of the neural basis for this flexibility are typically limited by laboratory settings that use minimal environmental cues and restrict interactions with the environment, including active sensing and exploration. To address this, we constructed a 3-D enclosure containing touchscreens on its walls, for studying cognition in freely moving macaques. To test flexible learning, two monkeys completed trials consisting of a regular sequence of object selections across four touchscreens. On each screen, the monkeys had to select by touching the sole correct object item (‘target’) among a set of four items, irrespective of their positions on the screen. Each item was the target on exactly one screen of the sequence, making correct performance conditioned on the spatiotemporal sequence rule across screens. Both monkeys successfully learned multiple 4-item sets (N=14 and 22 sets), totaling over 50 and 80 unique, conditional item-context memoranda, with no indication of capacity limits. The enclosure allowed freedom of movements leading up to and following the touchscreen interactions. To determine whether movement economy changed with learning, we reconstructed 3D position and movement dynamics using markerless tracking software and gyroscopic inertial measurements. Whereas general body positions remained consistent across repeated sequences, fine head movements varied as monkeys learned, within and across sequence sets, demonstrating learning set or “learning to learn”. These results demonstrate monkeys’ rapid, capacious, and flexible learning within an integrated, multisensory 3-D space. Furthermore, this approach enables the measurement of continuous behavior while ensuring precise experimental control and behavioral repetition of sequences over time. Overall, this approach harmonizes the design features that are needed for electrophysiological studies with tasks that showcase fully situated, flexible cognition.