Miracle-dz/UniFAR-FTUs huggingface dataset:
Pairing dome from an emergent Feshbach resonance in a strongly repulsive bilayer model

A key to understanding unconventional superconductivity lies in unraveling the pairing mechanism of mobile charge carriers in doped antiferromagnets, yielding an effective attraction between charges even in the presence of strong repulsive Coulomb interactions. Here, we study pairing in a minimal model of bilayer nickelates, featuring robust binding energies - despite dominant repulsive interactions - that are strongly enhanced in the finite doping regime. The mixed-dimensional (mixD) $t-J$ ladder we study features a crossover from tightly bound pairs of holes (closed channel) at small repulsion, to more spatially extended, correlated pairs of individual holes (open channel) at large repulsion. We derive an effective model for the latter, in which the attraction is mediated by the closed channel, in analogy to atomic Feshbach resonances. Using density matrix renormalization group (DMRG) simulations we reveal a dome of large binding energies at around $30\%$ doping and we observe the formation of a tetraparton density wave of plaquettes consisting of two spin-charge excitation pairs on neighboring rungs. Our work paves the way towards a microscopic theory of pairing in doped quantum magnets, in particular Ni-based superconductors, and our predictions can be tested in state-of-the-art quantum simulators.
Design and Verification of a Novel Triphibian Robot

Multi-modal robots expand their operations from one working medium to another, land to air for example. The majorities of multi-modal robots mainly refer to platforms that operate in two different media. However, for all-terrain tasks, there are seldom research to date in the literature. Generally, locomotions in different working media, i.e. land, water and air, require different propelling actuators, and thus the triphibian system becomes bulky. To overcome this challenge, we proposed a triphibian robot and provide the robot with driving forces to perform all-terrain operations in an efficient way. A morphable mechanism is designed to enable the transition between different motion modes, and specifically a cylindrical body is implemented as the rolling mechanism in land mode. Detailed design principles of different mechanisms and the transition between various locomotion modes are analyzed. Finally, a triphibian robot prototype is fabricated and tested in various working media with both mono-modal and multi-modal functionalities. Experiments have verified our platform, and the results show promising adaptions in future exploration tasks in various working scenarios.
Higher-order Graph Convolutional Network with Flower-Petals Laplacians on Simplicial Complexes

Despite the recent successes of vanilla Graph Neural Networks (GNNs) on various tasks, their foundation on pairwise networks inherently limits their capacity to discern latent higher-order interactions in complex systems. To bridge this capability gap, we propose a novel approach exploiting the rich mathematical theory of simplicial complexes (SCs) - a robust tool for modeling higher-order interactions. Current SC-based GNNs are burdened by high complexity and rigidity, and quantifying higher-order interaction strengths remains challenging. Innovatively, we present a higher-order Flower-Petals (FP) model, incorporating FP Laplacians into SCs. Further, we introduce a Higher-order Graph Convolutional Network (HiGCN) grounded in FP Laplacians, capable of discerning intrinsic features across varying topological scales. By employing learnable graph filters, a parameter group within each FP Laplacian domain, we can identify diverse patterns where the filters' weights serve as a quantifiable measure of higher-order interaction strengths. The theoretical underpinnings of HiGCN's advanced expressiveness are rigorously demonstrated. Additionally, our empirical investigations reveal that the proposed model accomplishes state-of-the-art performance on a range of graph tasks and provides a scalable and flexible solution to explore higher-order interactions in graphs. Codes and datasets are available at https://github.com/Yiminghh/HiGCN.
Multi-Modal Embeddings for Isolating Cross-Platform Coordinated Information Campaigns on Social Media

Coordinated multi-platform information operations are implemented in a variety of contexts on social media, including state-run disinformation campaigns, marketing strategies, and social activism. Characterized by the promotion of messages via multi-platform coordination, in which multiple user accounts, within a short time, post content advancing a shared informational agenda on multiple platforms, they contribute to an already confusing and manipulated information ecosystem. To make things worse, reliable datasets that contain ground truth information about such operations are virtually nonexistent. This paper presents a multi-modal approach that identifies the social media messages potentially engaged in a coordinated information campaign across multiple platforms. Our approach incorporates textual content, temporal information and the underlying network of user and messages posted to identify groups of messages with unusual coordination patterns across multiple social media platforms. We apply our approach to content posted on four platforms related to the Syrian Civil Defence organization known as the White Helmets: Twitter, Facebook, Reddit, and YouTube. Results show that our approach identifies social media posts that link to news YouTube channels with similar factuality score, which is often an indication of coordinated operations.
Affect Recognition in Conversations Using Large Language Models

Affect recognition, encompassing emotions, moods, and feelings, plays a pivotal role in human communication. In the realm of conversational artificial intelligence (AI), the ability to discern and respond to human affective cues is a critical factor for creating engaging and empathetic interactions. This study delves into the capacity of large language models (LLMs) to recognise human affect in conversations, with a focus on both open-domain chit-chat dialogues and task-oriented dialogues. Leveraging three diverse datasets, namely IEMOCAP, EmoWOZ, and DAIC-WOZ, covering a spectrum of dialogues from casual conversations to clinical interviews, we evaluated and compared LLMs' performance in affect recognition. Our investigation explores the zero-shot and few-shot capabilities of LLMs through in-context learning (ICL) as well as their model capacities through task-specific fine-tuning. Additionally, this study takes into account the potential impact of automatic speech recognition (ASR) errors on LLM predictions. With this work, we aim to shed light on the extent to which LLMs can replicate human-like affect recognition capabilities in conversations.
Adaptive Model Predictive Control for Engine-Driven Ducted Fan Lift Systems using an Associated Linear Parameter Varying Model

Ducted fan lift systems (DFLSs) powered by two-stroke aviation piston engines present a challenging control problem due to their complex multivariable dynamics. Current controllers for these systems typically rely on proportional-integral algorithms combined with data tables, which rely on accurate models and are not adaptive to handle time-varying dynamics or system uncertainties. This paper proposes a novel adaptive model predictive control (AMPC) strategy with an associated linear parameter varying (LPV) model for controlling the engine-driven DFLS. This LPV model is derived from a global network model, which is trained off-line with data obtained from a general mean value engine model for two-stroke aviation engines. Different network models, including multi-layer perceptron, Elman, and radial basis function (RBF), are evaluated and compared in this study. The results demonstrate that the RBF model exhibits higher prediction accuracy and robustness in the DFLS application. Based on the trained RBF model, the proposed AMPC approach constructs an associated network that directly outputs the LPV model parameters as an adaptive, robust, and efficient prediction model. The efficiency of the proposed approach is demonstrated through numerical simulations of a vertical take-off thrust preparation process for the DFLS. The simulation results indicate that the proposed AMPC method can effectively control the DFLS thrust with a relative error below 3.5%.
PI-RADS v2 Compliant Automated Segmentation of Prostate Zones Using co-training Motivated Multi-task Dual-Path CNN

The detailed images produced by Magnetic Resonance Imaging (MRI) provide life-critical information for the diagnosis and treatment of prostate cancer. To provide standardized acquisition, interpretation and usage of the complex MRI images, the PI-RADS v2 guideline was proposed. An automated segmentation following the guideline facilitates consistent and precise lesion detection, staging and treatment. The guideline recommends a division of the prostate into four zones, PZ (peripheral zone), TZ (transition zone), DPU (distal prostatic urethra) and AFS (anterior fibromuscular stroma). Not every zone shares a boundary with the others and is present in every slice. Further, the representations captured by a single model might not suffice for all zones. This motivated us to design a dual-branch convolutional neural network (CNN), where each branch captures the representations of the connected zones separately. Further, the representations from different branches act complementary to each other at the second stage of training, where they are fine-tuned through an unsupervised loss. The loss penalises the difference in predictions from the two branches for the same class. We also incorporate multi-task learning in our framework to further improve the segmentation accuracy. The proposed approach improves the segmentation accuracy of the baseline (mean absolute symmetric distance) by 7.56%, 11.00%, 58.43% and 19.67% for PZ, TZ, DPU and AFS zones respectively.
Investigation of flexural behaviour of composite rebars for concrete reinforcement with experimental, numerical and machine learning approaches

Three different types (with glass, basalt and hybrid fibres) of composite rebars manufactured using the pultrusion process were loaded in four-point bending tests. All tests were carried out with acoustic emission sensors to better understand the mechanisms of damage. The data obtained were investigated using standard parameter analysis and also using unsupervised machine learning techniques called K-means. It was found that the best number of clusters is four or five. The numerical model using the finite-element method was calibrated on the basis of the experimental data. Further research will focus on numerical modelling of flexural behaviour of concrete beams reinforced with the presented composite rebars. The presented paper focuses on the characterization of the mechanical properties of composite rebars using a micromechanical approach, as well as analysis of progression damage processes appearing under flexural loading, using different perspectives provided by techniques such as acoustic emission analysis with machine learning-based clustering and numerical simulations. The presented research confirms that the proposed experimental–numerical approach can be applied in order to describe the flexural behaviour of Fibre Reinforcement Polymer (FRP) rods, which is relevant for investigating more complex cases of FRP concrete structures. This article is part of the theme issue 'Physics-informed machine learning and its structural integrity applications (Part 1)'.
A machine learning study on the fatigue crack path of short crack on an α titanium alloy

In the present study, a physics-informed neural network model based on Bayesian hyperparameter optimization is proposed for the prediction of short crack growth paths. A large number of cyclic loadings at a lower amplitude were applied to an α titanium sample by an ultrasonic fatigue machine to ensure a sufficient amount of data for machine learning. The grain size, grain orientation and grain boundary direction on the path, as well as crack growth direction, were selected as feature data for training the prediction model. The optimizations of the size ratio and the angle operation were conducted to compare different data processing methods, respectively. After evaluation, eventually, a model for predicting crack growth path is obtained with a reliable performance of 10% tolerance on the path angle at each grain boundary. And the prediction effect of the proposed model is better than that of some classic machine learning models and slip trace analysis. This article is part of the theme issue 'Physics-informed machine learning and its structural integrity applications (Part 1)'.
The current status of red spruce in the eastern United States: distribution, population trends, and environmental drivers

Red spruce (Picea rubens Sarg.) was affected by an array of direct (logging, fire, and grazing) and indirect human activities (acid deposition) over the past centuries. To adequately assess past impacts on red spruce, thus helping frame its restoration potential, requires a clear understanding of its current status. To achieve this, Forest and Inventory Analysis data from the U.S. Department of Agriculture, Forest Service, were analyzed from 2,458 plots having one or more red spruce trees (≥5 in. diameter at breast height). Red spruce was widespread across the Northeast, associating with many tree species. Southward, along the Appalachian Chain, red spruce became increasingly restricted to high elevations and had fewer associates. Red spruce stands in the Southern Appalachians were distinctly different from those in other regions, having higher red spruce density, basal area, and overall importance. No problems were detected with red spruce regeneration and ecruitment under the current climate.
A conserved cell-pole determinant organizes proper polar flagellum formation

The coordination of cell cycle progression and flagellar synthesis is a complex process in motile bacteria. In γ-proteobacteria, the localization of the flagellum to the cell pole is mediated by the SRP-type GTPase FlhF. However, the mechanism of action of FlhF, and its relationship with the cell pole landmark protein HubP remain unclear. In this study, we discovered a novel protein called FipA that is required for normal FlhF activity and function in polar flagellar synthesis. We demonstrated that membrane-localized FipA interacts with FlhF and is required for normal flagellar synthesis in Vibrio parahaemolyticus, Pseudomonas putida, and Shewanella putrefaciens, and it does so independently of the polar localization mediated by HubP. FipA exhibits a dynamic localization pattern and is present at the designated pole before flagellar synthesis begins, suggesting its role in licensing flagellar formation. This discovery provides insight into a new pathway for regulating flagellum synthesis and coordinating cellular organization in bacteria that rely on polar flagellation and FlhF-dependent localization.
Micro-Scale Control of Oligodendrocyte Morphology and Myelination by the Intellectual Disability-Linked Protein Acyltransferase ZDHHC9

Mutations in the X-linked ZDHHC9 gene cause cognitive deficits in humans, with a subset of patients suffering from epilepsy. X-linked intellectual disability (XLID) is often ascribed to neuronal deficits, but here we report that expression of human and mouse ZDHHC9 orthologs is far higher in myelinating oligodendrocytes (OLs) than in other CNS cell types. ZDHHC9 codes for a protein acyltransferase (PAT), and we found that ZDHHC9 is the most highly expressed PAT in OLs. Wild type ZDHHC9 localizes to Golgi outposts in OL processes, but other PATs and XLID mutant forms of ZDHHC9 are restricted to OL cell bodies. Using genetic tools for OL progenitor fate tracing and sparse cell labeling, we show that mice lacking Zdhhc9 have grossly normal OL development but display extensive morphological and structural myelin abnormalities. Consistent with the hypothesis that these deficits are OL-autonomous, they are broadly phenocopied by acute Zdhhc9 knockdown in cultured conditions. Finally, we found that ZDHHC9 palmitoylates Myelin Basic Protein (MBP) in heterologous cells, and that palmitoylation of MBP is impaired in the Zdhhc9 knockout brain. Our findings provide critical insights into the mechanisms of ZDHHC9-associated XLID and shed new light on the palmitoylation-dependent control of myelination.
RNA G-quadruplex folding is a multi-pathway process driven by conformational entropy

The kinetics of folding is crucial for the function of many regulatory RNAs including RNA G-quadruplexes (rG4s). Here, we characterize the folding pathways of a G-quadruplex from the telomeric repeat-containing RNA by combining all-atom molecular dynamics and coarse-grained simulations with circular dichroism experiments. The quadruplex fold is stabilized by cations and thus, the ion atmosphere forming a double layer surrounding the highly charged quadruplex guides the folding process. To capture the ionic double layer in implicit solvent coarse-grained simulations correctly, we develop a matching procedure based on all-atom simulations in explicit water. The procedure yields quantitative agreement between simulations and experiments as judged by the populations of folded and unfolded states at different salt concentrations and temperatures. Subsequently, we show that coarse-grained simulations with a resolution of three interaction sites per nucleotide are well suited to resolve the folding pathways and their intermediate states. The results reveal that the folding progresses from unpaired chain via hairpin, triplex and double-hairpin constellations to the final folded structure. The two- and three-strand intermediates are stabilized by transient Hoogsteen interactions. Each pathway passes through two on-pathway intermediates. We hypothesize that conformational entropy is a hallmark of rG4 folding. Conformational entropy leads to the observed branched multi-pathway folding process for TERRA25. We corroborate this hypothesis by presenting the free energy landscapes and folding pathways of four rG4 systems with varying loop length.
Acute p38-Mediated Modulation of Tetrodotoxin-Resistant Sodium Channels in Mouse Sensory Neurons by Tumor Necrosis Factor-α

Tumor necrosis factor-α (TNFα) is a proinflammatory cytokine involved in the development and maintenance of inflammatory and neuropathic pain conditions. TNFα can have long-lasting effects by regulating the expression of a variety of inflammatory mediators, including other cytokines and TNFα itself. However, the speed with which TNFα induces tactile and thermal hypersensitivity suggests that transcriptional regulation cannot fully account for its sensitizing effects, and some recent findings suggest that TNFα may act directly on primary afferent neurons to induce pain hypersensitivity. In the present study, we show that peripheral administration of TNFα induces thermal hypersensitivity in wild-type mice but not in transient receptor potential vanilloid receptor TRPV1–/– mice. In contrast, TNFα produced equivalent mechanical hypersensitivity in TRPV1–/– mice and wild-type littermates, suggesting a role for TRPV1 in TNFα-induced thermal, but not mechanical, hypersensitivity. Because tetrodotoxin (TTX)-resistant Na+ channels are a critical site of modulation underlying mechanical hypersensitivity in inflammatory and neuropathic pain conditions, we tested the effects of TNFα on these channels in isolated mouse dorsal root ganglion (DRG) neurons. We report that acute application of TNFα rapidly enhances TTX-resistant Na+ currents in isolated DRG neurons. This potentiation of TTX-resistant currents by TNFα is dramatically reduced in DRG neurons from TNF receptor 1 (TNFR1) knock-out mice and is blocked by the p38 mitogen-activated protein kinase inhibitor SB202190 [4-(4-fluorophenyl)-2-(4-hydroxyphenyl)-5-(4-pyridyl)1H-imidazole]. Mechanical hypersensitivity induced by peripherally applied TNFα is also significantly reduced by SB202190. These results suggest that TNFα may induce acute peripheral mechanical sensitization by acting directly on TNFR1 in primary afferent neurons, resulting in p38-dependent modulation of TTX-resistant Na+ channels.
Schedulability analysis for the abort-and-restart model

In real-time systems, a schedulable task-set guarantees that all tasks complete before their deadlines. In functional programming, atomic execution provides the correctness of the program. Priority-based functional reactive programming (P-FRP) allows the usage of functional programming in the real-time system environment. The abort-and-restart (AR) is a scheme to implement P-FRP but an appropriate scheduling approach does not exist at the moment. Hence, efficient analysis is needed for the AR model. In this thesis, the schedulability analysis for the AR model is introduced and it shows that finding the critical instant for the AR model with periodic and sporadic tasks is intractable, and a new formulation is derived. Afterwards, a new priority assignment scheme is developed that has the performance close to the exhaustive search method, which is intractable for large systems. The technique of deferred preemption is employed and a new model, deferred abort (DA), provides better schedulability and dominates the nonpreemptive model. Lastly, a tighter analysis is introduced and the technique of the multi-set approach from the analysis of cache related preemption delay is employed to introduce a new approach, multi-bag. The multi-bag approach can apply to both the AR model and the DA model. In the experiments, the schedulability of the AR model is improved at each stage of the research in this thesis.
Command filtered path tracking control of saturated ASVs based on time‐varying disturbance observer

This paper investigates the path‐tracking control problem for an autonomous surface vessel (ASV) with unknown time‐varying disturbances and input saturation. A robust nonlinear control law is proposed based on a disturbance observer and an auxiliary system in the context of command filtered control. The disturbance observer is constructed to estimate the unknown time‐varying disturbances, the auxiliary dynamic system is employed to handle input saturation, and the compensator based command filtered control technique makes the designed path‐tracking control law simple and easy to implement in practice. It is proved that the nonlinear control law can track the desired vessel's position and heading, while guaranteeing the uniform ultimate boundedness of all signals in the path‐tracking control system. Simulation results further demonstrate the effectiveness of the method.