Miracle-dz/UniFAR-FTUs huggingface dataset:
Genetic Insights into Biofilm Formation by a Pathogenic Strain of Vibrio harveyi

The Vibrio genus includes bacteria widely distributed in aquatic habitats and the infections caused by these bacteria can affect a wide range of hosts. They are able to adhere to numerous surfaces, which can result in biofilm formation that helps maintain them in the environment. The involvement of the biofilm lifestyle in the virulence of Vibrio pathogens of aquatic organisms remains to be investigated. Vibrio harveyi ORM4 is a pathogen responsible for an outbreak in European abalone Haliotis tuberculata populations. In the present study, we used a dynamic biofilm culture technique coupled with laser scanning microscopy to characterize the biofilm formed by V. harveyi ORM4. We furthermore used RNA-seq analysis to examine the global changes in gene expression in biofilm cells compared to planktonic bacteria, and to identify biofilm- and virulence-related genes showing altered expression. A total of 1565 genes were differentially expressed, including genes associated with motility, polysaccharide synthesis, and quorum sensing. The up-regulation of 18 genes associated with the synthesis of the type III secretion system suggests that this virulence factor is induced in V. harveyi ORM4 biofilms, providing indirect evidence of a relationship between biofilm and virulence.
Induced Pluripotent Stem Cell-Derived Cardiomyocytes Therapy for Ischemic Heart Disease in Animal Model: A Meta-Analysis

Ischemic heart disease (IHD) poses a significant challenge in cardiovascular health, with current treatments showing limited success. Induced pluripotent derived–cardiomyocyte (iPSC-CM) therapy within regenerative medicine offers potential for IHD patients, although its clinical impacts remain uncertain. This study utilizes meta-analysis to assess iPSC-CM outcomes in terms of efficacy and safety in IHD animal model studies. A meta-analysis encompassing PUBMED, ScienceDirect, Web of Science, and the Cochrane Library databases, from inception until October 2023, investigated iPSC therapy effects on cardiac function and safety outcomes. Among 51 eligible studies involving 1012 animals, despite substantial heterogeneity, the iPSC-CM transplantation improved left ventricular ejection fraction (LVEF) by 8.23% (95% CI, 7.15 to 9.32%; p < 0.001) compared to control groups. Additionally, cell-based treatment reduced the left ventricle fibrosis area and showed a tendency to reduce left ventricular end-systolic volume (LVESV) and end-diastolic volume (LVEDV). No significant differences emerged in mortality and arrhythmia risk between iPSC-CM treatment and control groups. In conclusion, this meta-analysis indicates iPSC-CM therapy’s promise as a safe and beneficial intervention for enhancing heart function in IHD. However, due to observed heterogeneity, the efficacy of this treatment must be further explored through large randomized controlled trials based on rigorous research design.
Paramedic Education and Training for the Management of Patients Presenting with Low-Acuity Clinical Conditions: A Scoping Review

Ambulance services around the world are increasingly attending to calls for non-emergency conditions. These lower-acuity conditions do not always require patients to be transported to the emergency department. Consequently, over the past two decades, ambulance services have implemented strategies to support paramedics in diverting non-urgent patients to alternative care pathways. However, assessing and managing low-acuity conditions can be challenging for paramedics, especially when education and training has traditionally focussed on emergency care. This scoping review explores the education and training provided to paramedics on low-acuity clinical conditions and the use of alternative care pathways. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews was applied. The databases searched included Scopus, CINAHL, Embase, Emcare, and MEDLINE (PubMed). The search identified one-hundred sixty-six records, with a total of nine articles reviewed after the removal of duplicates and the screening process. The articles were diverse, with education and training ranging from university degrees for extended care practitioners to short in-service-based training for a suite of protocols or assessment tools. However, the literature addressing education and training on low-acuity conditions and alternative care pathways is limited, with the type and length of education programs appearing to influence practice. There is a need for further research to establish a low acuity education model.
An explainable framework for cross-platform DNA methylation-based classification of cancer

DNA methylation-based classification of brain tumors has emerged as a powerful and indispensable diagnostic technique. Initial implementations have used methylation microarrays for data generation, but different sequencing approaches are increasingly used. Most current classifiers, however, rely on a fixed methylation feature space, rendering them incompatible with other platforms, especially different flavors of DNA sequencing. Here, we describe crossNN, a neural network-based machine learning framework which can accurately classify tumor entities using DNA methylation profiles obtained from different platforms and with different epigenome coverage and sequencing depth. It outperforms other deep- and shallow machine learning models with respect to precision as well as simplicity and computational requirements while still being fully explainable. Validation in a large cohort of >1,900 tumors profiled using different microarray and sequencing platforms, including low-pass nanopore and targeted bisulfite sequencing, demonstrates the robustness and scalability of the model.
DexTouch: Learning to Seek and Manipulate Objects with Tactile Dexterity

The sense of touch is an essential ability for skillfully performing a variety of tasks, providing the capacity to search and manipulate objects without relying on visual information. Extensive research has been conducted over time to apply these human tactile abilities to robots. In this paper, we introduce a multi-finger robot system designed to search for and manipulate objects using the sense of touch without relying on visual information. Randomly located target objects are searched using tactile sensors, and the objects are manipulated for tasks that mimic daily-life. The objective of the study is to endow robots with human-like tactile capabilities. To achieve this, binary tactile sensors are implemented on one side of the robot hand to minimize the Sim2Real gap. Training the policy through reinforcement learning in simulation and transferring the trained policy to the real environment, we demonstrate that object search and manipulation using tactile sensors is possible even in an environment without vision information. In addition, an ablation study was conducted to analyze the effect of tactile information on manipulative tasks. Our project page is available at https://lee-kangwon.github. io/dextouch/
Near-Optimal Algorithms for Constrained k-Center Clustering with Instance-level Background Knowledge

Center-based clustering has attracted significant research interest from both theory and practice. In many practical applications, input data often contain background knowledge that can be used to improve clustering results. In this work, we build on widely adopted $k$-center clustering and model its input background knowledge as must-link (ML) and cannot-link (CL) constraint sets. However, most clustering problems including $k$-center are inherently $\mathcal{NP}$-hard, while the more complex constrained variants are known to suffer severer approximation and computation barriers that significantly limit their applicability. By employing a suite of techniques including reverse dominating sets, linear programming (LP) integral polyhedron, and LP duality, we arrive at the first efficient approximation algorithm for constrained $k$-center with the best possible ratio of 2. We also construct competitive baseline algorithms and empirically evaluate our approximation algorithm against them on a variety of real datasets. The results validate our theoretical findings and demonstrate the great advantages of our algorithm in terms of clustering cost, clustering quality, and running time.
Understanding atom probe’s analytical performance for iron oxides using correlation histograms and ab initio calculations

Field evaporation from ionic or covalently bonded materials often leads to the emission of molecular ions. The metastability of these molecular ions, particularly under the influence of the intense electrostatic field (1010 Vm-1), makes them prone to dissociation with or without an exchange of energy amongst them. These processes can affect the analytical performance of atom probe tomography (APT). For instance, neutral species formed through dissociation may not be detected at all or with a time of flight no longer related to their mass, causing their loss from the analysis. Here, we evaluated the changes in the measured composition of FeO, Fe2O3 and Fe3O4 across a wide range of analysis conditions. Possible dissociation reactions are predicted by density-functional theory (DFT) calculations considering the spin states of the molecules. The energetically favoured reactions are traced on to the multi-hit ion correlation histograms, to confirm their existence within experiments, using an automated Python-based routine. The detected reactions are carefully analysed to reflect upon the influence of these neutrals from dissociation reactions on the performance of APT for analysing iron oxides.
Superconvergent postprocessing of $C^0$ interior penalty method

This paper focuses on the superconvergence analysis of the Hessian recovery technique for the $C^0$ Interior Penalty Method (C0IP) in solving the biharmonic equation. We establish interior error estimates for C0IP method that serve as the superconvergent analysis tool. Using the argument of superconvergence by difference quotient, we prove superconvergent results of the recovered Hessian matrix on translation-invariant meshes. The Hessian recovery technique enables us to construct an asymptotically exact ${\it a\, posteriori}$ error estimator for the C0IP method. Numerical experiments are provided to support our theoretical results.
BiTA: Bi-Directional Tuning for Lossless Acceleration in Large Language Models

Large language models (LLMs) commonly employ autoregressive generation during inference, leading to high memory bandwidth demand and consequently extended latency. To mitigate this inefficiency, we present Bi-directional Tuning for lossless Acceleration (BiTA), an innovative method expediting LLMs via streamlined semi-autoregressive generation and draft verification. Inspired by the concept of prompt tuning, we enhance LLMs with a parameter-efficient design called bi-directional tuning for the capability in semi-autoregressive generation. Employing efficient tree-based decoding, the models perform draft candidate generation and verification in parallel, ensuring outputs identical to their autoregressive counterparts under greedy sampling. BiTA serves as a lightweight plug-in module, seamlessly boosting the inference efficiency of existing LLMs without requiring additional assistance models or incurring significant extra memory costs. Applying the proposed BiTA, LLaMA-2-70B-Chat achieves a 2.7$\times$ speedup on the MT-Bench benchmark. Extensive experiments confirm our method surpasses state-of-the-art acceleration techniques.
Distributed Empirical Likelihood Inference With or Without Byzantine Failures

Empirical likelihood is a very important nonparametric approach which is of wide application. However, it is hard and even infeasible to calculate the empirical log-likelihood ratio statistic with massive data. The main challenge is the calculation of the Lagrange multiplier. This motivates us to develop a distributed empirical likelihood method by calculating the Lagrange multiplier in a multi-round distributed manner. It is shown that the distributed empirical log-likelihood ratio statistic is asymptotically standard chi-squared under some mild conditions. The proposed algorithm is communication-efficient and achieves the desired accuracy in a few rounds. Further, the distributed empirical likelihood method is extended to the case of Byzantine failures. A machine selection algorithm is developed to identify the worker machines without Byzantine failures such that the distributed empirical likelihood method can be applied. The proposed methods are evaluated by numerical simulations and illustrated with an analysis of airline on-time performance study and a surface climate analysis of Yangtze River Economic Belt.
Fast Adversarial Training against Textual Adversarial Attacks

Many adversarial defense methods have been proposed to enhance the adversarial robustness of natural language processing models. However, most of them introduce additional pre-set linguistic knowledge and assume that the synonym candidates used by attackers are accessible, which is an ideal assumption. We delve into adversarial training in the embedding space and propose a Fast Adversarial Training (FAT) method to improve the model robustness in the synonym-unaware scenario from the perspective of single-step perturbation generation and perturbation initialization. Based on the observation that the adversarial perturbations crafted by single-step and multi-step gradient ascent are similar, FAT uses single-step gradient ascent to craft adversarial examples in the embedding space to expedite the training process. Based on the observation that the perturbations generated on the identical training sample in successive epochs are similar, FAT fully utilizes historical information when initializing the perturbation. Extensive experiments demonstrate that FAT significantly boosts the robustness of BERT models in the synonym-unaware scenario, and outperforms the defense baselines under various attacks with character-level and word-level modifications.
Subgraph Extraction-based Feedback-guided Iterative Scheduling for HLS

This paper proposes ISDC, a novel feedback-guided iterative system of difference constraints (SDC) scheduling algorithm for high-level synthesis (HLS). ISDC leverages subgraph extraction-based low-level feedback from downstream tools like logic synthesizers to iteratively refine HLS scheduling. Technical innovations include: (1) An enhanced SDC formulation that effectively integrates low-level feedback into the linear-programming (LP) problem; (2) A fanout and window-based subgraph extraction mechanism driving the feedback cycle; (3) A no-human-in-loop ISDC flow compatible with a wide range of downstream tools and process design kits (PDKs). Evaluation shows that ISDC reduces register usage by 28.5% against an industrial-strength open-source HLS tool.
An embedding-based distance for temporal graphs

We define a distance between temporal graphs based on graph embeddings built using time-respecting random walks. We study both the case of matched graphs, when there exists a known relation between the nodes, and the unmatched case, when such a relation is unavailable and the graphs may be of different sizes. We illustrate the interest of our distance definition, using both real and synthetic temporal network data, by showing its ability to discriminate between graphs with different structural and temporal properties. Leveraging state-of-the-art machine learning techniques, we propose an efficient implementation of distance computation that is viable for large-scale temporal graphs.
Variations of geese under domestication

Man has been in interrelationship with wild water fowl for thousands of years. Yet, in that time, only two species of wild geese have been domesticated (13.3% of all true geese); the Greylag goose (Anser anser) in the fourth millennium B.C. and the swan goose (Anser cygnoides) in the second millennium B.C. From these two species, numerous breeds (181) have been developed. Compared to other domestic animals, the goose has varied less during domestication, aside from a few modifications in appearance, reproductive traits, internal organs and behaviour. It has increased in body weight, and hence became a non-migratory bird. Compare to the greylag goose (gander 2.8-4.1 kg, goose 2.5-3.8 kg body weight), the body weight of the Toulouse gander has increased by 331-421%, and the Toulouse goose by 286-364%. The African goose developed higher body weights (gander by 285-362%, goose by 292-311%) compared to the weight of the swan goose (gander 3.5 kg, goose 2.8-3.5 kg body weight). Conformation traits have changed, whereby the natural grey feather colour has changed to white in some breeds. Furthermore, sexual maturity has accelerated, and prolificacy increased. Egg production of a significant proportion of the European goose breeds has increased by 600-1000% compared to the greylag goose (5-6 eggs/goose). For example, egg production of the Chinese goose has increased by 875-1200% compare to the wild swan goose (5-8 eggs/goose), and the monogamous partnership typical of wild geese has turned into polygamous matings.
A Precise Characterization of SGD Stability Using Loss Surface Geometry

Stochastic Gradient Descent (SGD) stands as a cornerstone optimization algorithm with proven real-world empirical successes but relatively limited theoretical understanding. Recent research has illuminated a key factor contributing to its practical efficacy: the implicit regularization it instigates. Several studies have investigated the linear stability property of SGD in the vicinity of a stationary point as a predictive proxy for sharpness and generalization error in overparameterized neural networks (Wu et al., 2022; Jastrzebski et al., 2019; Cohen et al., 2021). In this paper, we delve deeper into the relationship between linear stability and sharpness. More specifically, we meticulously delineate the necessary and sufficient conditions for linear stability, contingent on hyperparameters of SGD and the sharpness at the optimum. Towards this end, we introduce a novel coherence measure of the loss Hessian that encapsulates pertinent geometric properties of the loss function that are relevant to the linear stability of SGD. It enables us to provide a simplified sufficient condition for identifying linear instability at an optimum. Notably, compared to previous works, our analysis relies on significantly milder assumptions and is applicable for a broader class of loss functions than known before, encompassing not only mean-squared error but also cross-entropy loss.
On Basic Feasible Functionals and the Interpretation Method

The class of basic feasible functionals $(\mathtt{BFF})$ is the analog of $\mathtt{FP}$ (polynomial time functions) for type-2 functionals, that is, functionals that can take (first-order) functions as arguments. $\mathtt{BFF}$ can be defined through Oracle Turing machines with running time bounded by second-order polynomials. On the other hand, higher-order term rewriting provides an elegant formalism for expressing higher-order computation. We address the problem of characterizing $\mathtt{BFF}$ by higher-order term rewriting. Various kinds of interpretations for first-order term rewriting have been introduced in the literature for proving termination and characterizing (first-order) complexity classes. In this paper, we consider a recently introduced notion of cost-size interpretations for higher-order term rewriting and see definitions as ways of computing functionals. We then prove that the class of functionals represented by higher-order terms admitting a certain kind of cost-size interpretation is exactly $\mathtt{BFF}$.
Moving null curves and integrability

We study the null curves and their motion in a $3$-dimensional flat space-time $M_{3}$. We show that when the motion of null curves forms two surfaces in $M_{3}$ the integrability conditions lead to the well-known AKNS hierarchy. In this case we obtain all the geometrical quantities of the surfaces arising from the whole hierarchy but we particulary focus on the surfaces of the MKdV and KdV equations. We obtain one- and two-soliton surfaces associated to the MKdV equation and show that the Gauss and mean curvatures of these surfaces develop singularities in finite time. We show that the tetrad vectors on the curves satisfy the spin vector equation in the ferromagnetism model of Heisenberg.
Sequential discontinuity and first-order problems

We explore the low levels of the structure of the continuous Weihrauch degrees of first-order problems. In particular, we show that there exists a minimal discontinuous first-order degree, namely that of $\accn$, without any determinacy assumptions. The same degree is also revealed as the least sequentially discontinuous one, i.e. the least degree with a representative whose restriction to some sequence converging to a limit point is still discontinuous. The study of games related to continuous Weihrauch reducibility constitutes an important ingredient in the proof of the main theorem. We present some initial additional results about the degrees of first-order problems that can be obtained using this approach.
A corrected single element Maxwell visco-elastic model

The original Maxwell visco-elastic constitutive model cannot predict the correct mechanical properties for most fluids. In this work, the model is generalized with respect to wave-vector and extended with a correction function. This new model has only two free parameters and avoids the attenuation-frequency locking present in the original model. Through molecular simulations it is shown that the model satisfactory predicts the transverse dynamics of the binary Lennard-Jones system at different temperatures, as well as water and toluene at ambient conditions. From the correction function it is shown that the viscous response is significantly reduced compared to the predictions from the original model when taking the wave-vector dependency into account. Morover, a temperature dependent characteristic length scale of maximum reduced viscous response is identified.
Orion-14B: Open-source Multilingual Large Language Models

In this study, we introduce Orion-14B, a collection of multilingual large language models with 14 billion parameters. We utilize a data scheduling approach to train a foundational model on a diverse corpus of 2.5 trillion tokens, sourced from texts in English, Chinese, Japanese, Korean, and other languages. Additionally, we fine-tuned a series of models tailored for conversational applications and other specific use cases. Our evaluation results demonstrate that Orion-14B achieves state-of-the-art performance across a broad spectrum of tasks. We make the Orion-14B model family and its associated code publicly accessible https://github.com/OrionStarAI/Orion, aiming to inspire future research and practical applications in the field.