Miracle-dz/UniFAR-FTUs huggingface dataset:
Role of MicroRNAs and their Downstream Target Transcription Factors in Zebrafish Thrombopoiesis

Previous studies have shown that human platelets and megakaryocytes carry microRNAs suggesting their role in platelet function and megakaryocyte development, respectively. However, a comprehensive study on the microRNAs and their targets has not been undertaken. Zebrafish thrombocytes could be used as a model to study their role in megakaryocyte maturation and platelet function because thrombocytes have both megakaryocyte features and platelet properties. In our laboratory, we identified 15 microRNAs in thrombocytes using single-cell RNA sequencing. We knocked down each of these 15 microRNAs by the piggyback method and found knockdown of three microRNAs, mir-7148, let-7b, and mir-223 in adult zebrafish led to an increase in the percentage of thrombocytes. Functional thrombocyte analysis using plate tilt assay showed no modulatory effect of the three microRNAs on thrombocyte aggregation/agglutination. We also found enhanced thrombosis using arterial laser thrombosis assay in a group of zebrafish larvae after mir-7148, let-7b, and mir-223 knockdowns. These results suggested mir-7148, let-7b, and mir-223 are repressors for thrombocyte production. We then explored miRWalk database for let-7b downstream targets and then selected those that are expressed in thrombocytes, and from this list based on their role in differentiation selected 14 genes, rorca, tgif1, rfx1a, deaf1, zbtb18, mafba, cebpa, spi1a, spi1b, fhl3b, ikzf1, irf5, irf8, and lbx1b that encode transcriptional regulators. The qRT-PCR analysis of expression levels of the above genes following let-7b knockdown showed changes in the expression of 13 targets. We then studied the effect of the 13 targets on thrombocyte production and identified 5 genes, irf5, tgif1, irf8, cebpa, and rorca that showed thrombocytosis and one gene, ikzf1 that showed thrombocytopenia. Furthermore, we tested whether mir-223 regulates any of the above 13 transcription factors after mir-223 knockdown using qRT-PCR. Six of the 13 genes showed similar gene expression as observed with let-7b knockdown and 7 genes showed opposing results. Thus, our results suggested a possible regulatory network in common with both let-7b and mir-223. We also identified that tgif1, cebpa, ikzf1, irf5, irf8, and ikzf1 play a role in thrombopoiesis. Since the ikzf1 gene showed a differential expression profile in let-7b and mir-223 knockdowns but resulted in thrombocytopenia in ikzf1 knockdown in both adults and larvae we also studied an ikzf1 mutant and showed the mutant had thrombocytopenia. Taken together, these studies showed that thrombopoiesis is controlled by a network of transcription regulators that are regulated by multiple microRNAs in both positive and negative manner resulting in overall inhibition of thrombopoiesis.
Graph-based extractive text summarization method for Hausa text

Automatic text summarization is one of the most promising solutions to the ever-growing challenges of textual data as it produces a shorter version of the original document with fewer bytes, but the same information as the original document. Despite the advancements in automatic text summarization research, research involving the development of automatic text summarization methods for documents written in Hausa, a Chadic language widely spoken in West Africa by approximately 150,000,000 people as either their first or second language, is still in early stages of development. This study proposes a novel graph-based extractive single-document summarization method for Hausa text by modifying the existing PageRank algorithm using the normalized common bigrams count between adjacent sentences as the initial vertex score. The proposed method is evaluated using a primarily collected Hausa summarization evaluation dataset comprising of 113 Hausa news articles on ROUGE evaluation toolkits. The proposed approach outperformed the standard methods using the same datasets. It outperformed the TextRank method by 2.1%, LexRank by 12.3%, centroid-based method by 19.5%, and BM25 method by 17.4%.
Managing depression in older adults

Antidepressant medications are considered the firstline treatment for people suffering from major depression (Barry et al., 2012; National Health Service, 2019; National Institute of Aging, 2017). For older adults, however, low response rates or side effects of antidepressants have often been reported (Kok et al., 2012; Tham et al., 2016). Rates of non-adherence to antidepressants among older adults with depression are also high, ranging from 29 to 40% (Sirey et al., 2017). Many older adults perceive depression as a normal part of aging and believe that depression would pass with time by self-management, without the need for interventions (Nair et al., 2020). Psychotherapy is also recommended for older adults with depression, especially for those with mild to moderate depression (Kok & Reynolds, 2017). In a systematic review of community-based treatment for late-life depression, Frederick et al. (2007) showed that the effectiveness of group psychotherapy for late-life depression was mixed and recommended that a multifaceted approach that includes antidepressant medications and Introduction
A Syntactic Adaptive Problem Solver Learning Landscape Structures for Scheduling in Clinical Laboratory

This paper attempts to derive a mathematical formulation for real-practice clinical laboratory schedul-ing, and to present a syntactic adaptive problem solver by leveraging landscape structures. After formulating scheduling of medical tests as a distributed scheduling problem in heterogeneous, flexible job shop environment, we establish a mixed integer programming model to minimize mean test turn-around time. Preliminary landscape analysis sustains that these clinics-orientated scheduling instances are difficult to solve. The search difficulty motivates the search for an adaptive problem solver to reduce repetitive algorithm-tuning work, but with a guaranteed convergence. Yet, under a search strategy, relatedness from exploitation competence to landscape topology is not transparent. Under strategies that impose different-magnitude perturbations, we investigate changes in landscape struc-ture and find that disturbance amplitude, local-global optima connectivity, landscape's ruggedness and plateau size fairly predict strategies' efficacy. Medium-size instances of 100 tasks are easier un-der smaller-perturbation strategies that lead to smoother landscapes with smaller plateaus. For large-size instances of 200-500 tasks, existing strategies at hand, having either larger or smaller perturba-tions, face more rugged landscapes with larger plateaus that impede search. Our hypothesis that me-dium perturbations may generate smoother landscapes with smaller plateaus drives our design of this new strategy and its verification by experiments. Composite neighborhoods managed by meta-Lamarckian learning show beyond average performance, implying reliability when prior knowledge of landscape is unknown.
FusionDepth: Complement Self-Supervised Monocular Depth Estimation with Cost Volume

Multi-view stereo depth estimation based on cost volume usually works better than self-supervised monocular depth estimation except for moving objects and low-textured surfaces. So in this paper, we propose a multi-frame depth estimation framework which monocular depth can be refined continuously by multi-frame sequential constraints, leveraging a Bayesian fusion layer within several iterations. Both monocular and multi-view networks can be trained with no depth supervision. Our method also enhances the interpretability when combining monocular estimation with multi-view cost volume. Detailed experiments show that our method surpasses state-of-the-art unsupervised methods utilizing single or multiple frames at test time on KITTI benchmark.
Towards an Automatic Optimisation Model Generator Assisted with Generative Pre-trained Transformer

This article presents a framework for generating optimisation models using a pre-trained generative transformer. The framework involves specifying the features that the optimisation model should have and using a language model to generate an initial version of the model. The model is then tested and validated, and if it contains build errors, an automatic edition process is triggered. An experiment was performed using MiniZinc as the target language and two GPT-3.5 language models for generation and debugging. The results show that the use of language models for the generation of optimisation models is feasible, with some models satisfying the requested specifications, while others require further refinement. The study provides promising evidence for the use of language models in the modelling of optimisation problems and suggests avenues for future research.
ANALOGYKB: Unlocking Analogical Reasoning of Language Models with A Million-scale Knowledge Base

Analogical reasoning is a fundamental cognitive ability of humans. However, current language models (LMs) still struggle to achieve human-like performance in analogical reasoning tasks due to a lack of resources for model training. In this work, we address this gap by proposing ANALOGYKB, a million-scale analogy knowledge base (KB) derived from existing knowledge graphs (KGs). ANALOGYKB identifies two types of analogies from the KGs: 1) analogies of the same relations, which can be directly extracted from the KGs, and 2) analogies of analogous relations, which are identified with a selection and filtering pipeline enabled by large LMs (InstructGPT), followed by minor human efforts for data quality control. Evaluations on a series of datasets of two analogical reasoning tasks (analogy recognition and generation) demonstrate that ANALOGYKB successfully enables LMs to achieve much better results than previous state-of-the-art methods.
PAI at SemEval-2023 Task 2: A Universal System for Named Entity Recognition with External Entity Information

The MultiCoNER II task aims to detect complex, ambiguous, and fine-grained named entities in low-context situations and noisy scenarios like the presence of spelling mistakes and typos for multiple languages. The task poses significant challenges due to the scarcity of contextual information, the high granularity of the entities(up to 33 classes), and the interference of noisy data. To address these issues, our team PAI proposes a universal Named Entity Recognition (NER) system that integrates external entity information to improve performance. Specifically, our system retrieves entities with properties from the knowledge base (i.e. Wikipedia) for a given text, then concatenates entity information with the input sentence and feeds it into Transformer-based models. Finally, our system wins 2 first places, 4 second places, and 1 third place out of 13 tracks. The code is publicly available at https://github.com/diqiuzhuanzhuan/semeval-2023.
Divided prismatic Frobenius crystals of small height and the category $\mathcal{M}\mathcal{F}$

Let $\mathcal{X}$ be a smooth $p$-adic formal scheme over a mixed characteristic complete discrete valuation ring $\mathcal{O}_{K}$ with perfect residue field. We introduce a general category $\mathcal{M}\mathcal{F}_{[0, p-2]}^{tor-free}(\mathcal{X})$ of $p$-torsion free crystalline coefficient objects and show that this category is equivalent to the category of completed prismatic Frobenius crystals of height $p-2$, recently introduced by Du-Liu-Moon-Shimizu. In particular this shows that the category $\mathcal{M}\mathcal{F}^{tor-free}_{[0, p-2]}(\mathcal{X})$ is equivalent to the category of crystalline $\mathbb{Z}_p$-local systems on $\mathcal{X}$ with Hodge-Tate weights in $\{0,\ldots , p-2\}$, which generalizes the crystalline part of a theorem of Breuil-Liu to higher dimensions.
Pearson-Matthews correlation coefficients for binary and multinary classification and hypothesis testing

The Pearson-Matthews correlation coefficient (usually abbreviated MCC) is considered to be one of the most useful metrics for the performance of a binary classification or hypothesis testing method (for the sake of conciseness we will use the classification terminology throughout, but the concepts and methods discussed in the paper apply verbatim to hypothesis testing as well). For multinary classification tasks (with more than two classes) the existing extension of MCC, commonly called the $\text{R}_{\text{K}}$ metric, has also been successfully used in many applications. The present paper begins with an introductory discussion on certain aspects of MCC. Then we go on to discuss the topic of multinary classification that is the main focus of this paper and which, despite its practical and theoretical importance, appears to be less developed than the topic of binary classification. Our discussion of the $\text{R}_{\text{K}}$ is followed by the introduction of two other metrics for multinary classification derived from the multivariate Pearson correlation (MPC) coefficients. We show that both $\text{R}_{\text{K}}$ and the MPC metrics suffer from the problem of not decisively indicating poor classification results when they should, and introduce three new enhanced metrics that do not suffer from this problem. We also present an additional new metric for multinary classification which can be viewed as a direct extension of MCC.
SoGAR: Self-supervised Spatiotemporal Attention-based Social Group Activity Recognition

This paper introduces a novel approach to Social Group Activity Recognition (SoGAR) using Self-supervised Transformers network that can effectively utilize unlabeled video data. To extract spatio-temporal information, we created local and global views with varying frame rates. Our self-supervised objective ensures that features extracted from contrasting views of the same video were consistent across spatio-temporal domains. Our proposed approach is efficient in using transformer-based encoders to alleviate the weakly supervised setting of group activity recognition. By leveraging the benefits of transformer models, our approach can model long-term relationships along spatio-temporal dimensions. Our proposed SoGAR method achieved state-of-the-art results on three group activity recognition benchmarks, namely JRDB-PAR, NBA, and Volleyball datasets, surpassing the current numbers in terms of F1-score, MCA, and MPCA metrics.
An adaptable JSON Diff Framework

In this paper, we present an implementation of JSON-diff framework JYCM, extending the existing framework by introducing the concept of"unordered"comparisons and allowing users to customize their comparison scenarios flexibly. Furthermore, we provide a diff-result renderer to visualize better and understand the differences between JSON objects. Our work enables more adaptable and comprehensive comparisons to accommodate a wider range of use cases and requirements.
A Comprehensive Picture of Factors Affecting User Willingness to Use Mobile Health Applications

Mobile health (mHealth) applications have become increasingly valuable in preventive healthcare and in reducing the burden on healthcare organizations. The aim of this article is to investigate the factors that influence user acceptance of mHealth apps and identify the underlying structure that shapes users’ behavioral intention. An online study that employed factorial survey design with vignettes was conducted, and a total of 1,669 participants from eight countries across four continents were included in the study. Structural equation modeling was employed to quantitatively assess how various factors collectively contribute to users’ willingness to use mHealth apps. The results indicate that users’ digital literacy has the strongest impact on their willingness to use them, followed by their online habit of sharing personal information. Users’ concerns about personal privacy only had a weak impact. Furthermore, users’ demographic background, such as their country of residence, age, ethnicity, and education, has a significant moderating effect. Our findings have implications for app designers, healthcare practitioners, and policymakers. Efforts are needed to regulate data collection and sharing and promote digital literacy among the general population to facilitate the widespread adoption of mHealth apps.
Searching Mobile App Screens via Text + Doodle

Locating a specific mobile application screen from existing repositories is restricted to basic keyword searches, such as Google Image Search, or necessitates a complete query screen image, as in the case of Swire. However, interactive partial sketch-based solutions like PSDoodle have limitations, including inaccuracy and an inability to consider text appearing on the screen. A potentially effective solution involves implementing a system that provides interactive partial sketching functionality for efficiently structuring user interface elements. Additionally, the system should incorporate text queries to enhance its capabilities further. Our approach, TpD, represents the pioneering effort to enable an iterative search of screens by combining interactive sketching and keyword search techniques. TpD is built on a combination of the Rico repository of approximately 58k Android app screens and the PSDoodle. Our evaluation with third-party software developers showed that PSDoodle provided higher top-10 screen retrieval accuracy than state-of-the-art Swire and required less time to complete a query than other interactive solutions.
Multilevel Sentence Embeddings for Personality Prediction

Representing text into a multidimensional space can be done with sentence embedding models such as Sentence-BERT (SBERT). However, training these models when the data has a complex multilevel structure requires individually trained class-specific models, which increases time and computing costs. We propose a two step approach which enables us to map sentences according to their hierarchical memberships and polarity. At first we teach the upper level sentence space through an AdaCos loss function and then finetune with a novel loss function mainly based on the cosine similarity of intra-level pairs. We apply this method to three different datasets: two weakly supervised Big Five personality dataset obtained from English and Japanese Twitter data and the benchmark MNLI dataset. We show that our single model approach performs better than multiple class-specific classification models.
Experimental mitigation of fast magnetic reconnection in multiple interacting laser-produced plasmas

The meeting of astrophysical plasmas and their magnetic fields creates many reconnection sites. We experimentally compare the reconnection rate of laser-driven magnetic reconnection when it takes place at a single site and multiple sites. For a single site, where the ram pressure dominates the magnetic pressure, the measured reconnection rate exceeds the well-established rate of 0.1. However, in the case of multiple close-by sites, we observed a reduction of the reconnection rate. Hybrid-PIC simulations support this observation and suggest that the distortion of the Hall field as well as the concomitant obstruction of one of the outflows are detrimental to the magnetic reconnection rate.
Robust multi-agent coordination via evolutionary generation of auxiliary adversarial attackers

Cooperative Multi-agent Reinforcement Learning (CMARL) has shown to be promising for many real-world applications. Previous works mainly focus on improving coordination ability via solving MARL-specific challenges (e.g., non-stationarity, credit assignment, scalability), but ignore the policy perturbation issue when testing in a different environment. This issue hasn't been considered in problem formulation or efficient algorithm design. To address this issue, we firstly model the problem as a Limited Policy Adversary Dec-POMDP (LPA-Dec-POMDP), where some coordinators from a team might accidentally and unpredictably encounter a limited number of malicious action attacks, but the regular coordinators still strive for the intended goal. Then, we propose Robust Multi-Agent Coordination via Evolutionary Generation of Auxiliary Adversarial Attackers (ROMANCE), which enables the trained policy to encounter diversified and strong auxiliary adversarial attacks during training, thus achieving high robustness under various policy perturbations. Concretely, to avoid the ego-system overfitting to a specific attacker, we maintain a set of attackers, which is optimized to guarantee the attackers high attacking quality and behavior diversity. The goal of quality is to minimize the ego-system coordination effect, and a novel diversity regularizer based on sparse action is applied to diversify the behaviors among attackers. The ego-system is then paired with a population of attackers selected from the maintained attacker set, and alternately trained against the constantly evolving attackers. Extensive experiments on multiple scenarios from SMAC indicate our ROMANCE provides comparable or better robustness and generalization ability than other baselines.
SimLL: Similarity-Based Logic Locking Against Machine Learning Attacks

Logic locking is a promising technique for protecting integrated circuit designs while outsourcing their fabrication. Recently, graph neural network (GNN)-based link prediction attacks have been developed which can successfully break all the multiplexer-based locking techniques that were expected to be learning-resilient. We present SimLL, a novel similarity-based locking technique which locks a design using multiplexers and shows robustness against the existing structure-exploiting oracle-less learning-based attacks. Aiming to confuse the machine learning (ML) models, SimLL introduces key-controlled multiplexers between logic gates or wires that exhibit high levels of topological and functional similarity. Empirical results show that SimLL can degrade the accuracy of existing ML-based attacks to approximately 50%, resulting in a negligible advantage over random guessing.
A robust explanation of CMB anomalies with a new formulation of inflationary quantum fluctuations

The presence of CMB Hemispherical Asymmetry (HPA) challenges the current understanding of inflationary cosmology which does not generically predict the parity violation in the primordial correlations. In this paper, we shall review the recently proposed resolution to this based on a new formulation of quantizing inflationary fluctuations by focusing on the discrete spacetime transformations in a gravitational context. The predictive power of this formulation is that one can generate a scale dependent HPA in the context of single field inflation for all the primordial modes including scalar and tensor fluctuations without introducing any additional parameters. This result can be seen as an indication of spontaneous breaking of $\mathcal{C}\mathcal{P}\mathcal{T}$ symmetry in an expanding Universe, if confirmed by future observations it would be a great leap in the subject of quantum field theory in curved spacetime.