Miracle-dz/UniFAR-FTUs huggingface dataset:
Magnetically and electrically controllable valley splittings in MXene monolayers

The modulation of the valley structure in two-dimensional valley materials is vital in the field of valleytronics. The multiferroicity provides possibility for multiple modulations of the valley, including the magnetic and electric means. Based on the first-principle calculations, we study the valley properties and associated manipulations of multiferroic Co$_2$CF$_2$ monolayers with different stacking patterns. Our calculations show that the Co$_2$CF$_2$ monolayer in the H$^{\prime}$ phase is a ferrovalley material, with sizable valley splittings. By rotating the magnetization direction, the valley splittings can be tuned for both the magnitude and sign. The electric field, driving the reversal of the electric polarization, can also change the magnitude of the valley splittings. Besides, a metastable T$^{\prime}$ phase exhibits valley splittings as well, of which the magnitude and sign can be simultaneously controlled by applied magnetic and electric fields. These findings offer a practical way for realizing highly tunable valleys by multiferroic couplings.
A GPU-accelerated simulator for the DEM analysis of granular systems composed of clump-shaped elements

We discuss the use of the Discrete Element Method (DEM) to simulate the dynamics of granular systems made up of elements with nontrivial geometries. The DEM simulator is GPU accelerated and can handle elements whose shape is defined as the union with overlap of diverse sets of spheres with user-specified radii. The simulator can also handle complex materials since each sphere in an element can have its own Young's modulus $E$, Poisson ratio $\nu$, friction coefficient $\mu$, and coefficient of restitution CoR. To demonstrate the simulator, we produce a"digital simulant"(DS), a replica of the GRC-1 lunar simulant. The DS follows an element size distribution similar but not identical to that of GRC-1. We validate the predictive attributes of the simulator via several numerical experiments: repose angle, cone penetration, drawbar pull, and rover incline-climbing tests. Subsequently, we carry out a sensitivity analysis to gauge how the slope vs. slip curves change when the element shape, element size, and friction coefficient change. The paper concludes with a VIPER rover simulation that confirms a recently proposed granular scaling law. The simulation involves more than 11 million elements composed of more than 34 million spheres of different radii. The simulator works in the Chrono framework and utilizes two GPUs concurrently. The GPU code for the simulator and all numerical experiments discussed are open-source and available on GitHub for reproducibility studies and unfettered use and distribution.
Towards Robust SDRTV-to-HDRTV via Dual Inverse Degradation Network

In this study, we address the emerging necessity of converting Standard Dynamic Range Television (SDRTV) content into High Dynamic Range Television (HDRTV) in light of the limited number of native HDRTV content. A principal technical challenge in this conversion is the exacerbation of coding artifacts inherent in SDRTV, which detrimentally impacts the quality of the resulting HDRTV. To address this issue, our method introduces a novel approach that conceptualizes the SDRTV-to-HDRTV conversion as a composite task involving dual degradation restoration. This encompasses inverse tone mapping in conjunction with video restoration. We propose Dual Inversion Downgraded SDRTV to HDRTV Network (DIDNet), which can accurately perform inverse tone mapping while preventing encoding artifacts from being amplified, thereby significantly improving visual quality. DIDNet integrates an intermediate auxiliary loss function to effectively separate the dual degradation restoration tasks and efficient learning of both artifact reduction and inverse tone mapping during end-to-end training. Additionally, DIDNet introduces a spatio-temporal feature alignment module for video frame fusion, which augments texture quality and reduces artifacts. The architecture further includes a dual-modulation convolution mechanism for optimized inverse tone mapping. Recognizing the richer texture and high-frequency information in HDRTV compared to SDRTV, we further introduce a wavelet attention module to enhance frequency features. Our approach demonstrates marked superiority over existing state-of-the-art techniques in terms of quantitative performance and visual quality.
The CHiME-7 UDASE task: Unsupervised domain adaptation for conversational speech enhancement

Supervised speech enhancement models are trained using artificially generated mixtures of clean speech and noise signals, which may not match real-world recording conditions at test time. This mismatch can lead to poor performance if the test domain significantly differs from the synthetic training domain. This paper introduces the unsupervised domain adaptation for conversational speech enhancement (UDASE) task of the 7th CHiME challenge. This task aims to leverage real-world noisy speech recordings from the target domain for unsupervised domain adaptation of speech enhancement models. The target domain corresponds to the multi-speaker reverberant conversational speech recordings of the CHiME-5 dataset, for which the ground-truth clean speech reference is unavailable. Given a CHiME-5 recording, the task is to estimate the clean, potentially multi-speaker, reverberant speech, removing the additive background noise. We discuss the motivation for the CHiME-7 UDASE task and describe the data, the task, and the baseline system.
Learning from Heterogeneity: A Dynamic Learning Framework for Hypergraphs

Graph neural network (GNN) has gained increasing popularity in recent years owing to its capability and flexibility in modeling complex graph structure data. Among all graph learning methods, hypergraph learning is a technique for exploring the implicit higher-order correlations when training the embedding space of the graph. In this paper, we propose a hypergraph learning framework named LFH that is capable of dynamic hyperedge construction and attentive embedding update utilizing the heterogeneity attributes of the graph. Specifically, in our framework, the high-quality features are first generated by the pairwise fusion strategy that utilizes explicit graph structure information when generating initial node embedding. Afterwards, a hypergraph is constructed through the dynamic grouping of implicit hyperedges, followed by the type-specific hypergraph learning process. To evaluate the effectiveness of our proposed framework, we conduct comprehensive experiments on several popular datasets with eleven state-of-the-art models on both node classification and link prediction tasks, which fall into categories of homogeneous pairwise graph learning, heterogeneous pairwise graph learning, and hypergraph learning. The experiment results demonstrate a significant performance gain (average 12.5% in node classification and 13.3% in link prediction) compared with recent state-of-the-art methods.
Viscous Modified Chaplygin Gas in Classical and Loop Quantum Cosmology

We investigate the cosmological model of viscous modified Chaplygin gas (VMCG) in classical and loop quantum cosmology (LQC). Firstly, we constrain its equation of state parameters in the framework of standard cosmology from Union 2.1 SNe Ia data. Then, we probe the dynamical stability of this model in a universe filled with VMCG and baryonic fluid in LQC background. It is found that the model is very suitable with ( χ 2 / d . o . f = 0.974 ) and gives a good prediction of the current values of the deceleration parameter q 0 = ∈ ( − 0.60 , − 0.57 ) and the effective state parameter ω eff ∈ ( − 0.76 , − 0.74 ) that is consistent with the recent observational data. The model can also predict the time crossing when ( ρ DE ≈ ρ matter ) at z = 0.75 and can solve the coincidence problem. In LQC background, the Big Bang singularity found in classical cosmology ceases to exist and is replaced by a bounce when the Hubble parameter vanishes at ρ tot ≈ ρ c .
Machine Learning to detect cyber-attacks and discriminating the types of power system disturbances

This research proposes a machine learning-based attack detection model for power systems, specifically targeting smart grids. By utilizing data and logs collected from Phasor Measuring Devices (PMUs), the model aims to learn system behaviors and effectively identify potential security boundaries. The proposed approach involves crucial stages including dataset preprocessing, feature selection, model creation, and evaluation. To validate our approach, we used a dataset used, consist of 15 separate datasets obtained from different PMUs, relay snort alarms and logs. Three machine learning models: Random Forest, Logistic Regression, and K-Nearest Neighbour were built and evaluated using various performance metrics. The findings indicate that the Random Forest model achieves the highest performance with an accuracy of 90.56% in detecting power system disturbances and has the potential in assisting operators in decision-making processes
DEFT: Exploiting Gradient Norm Difference between Model Layers for Scalable Gradient Sparsification

Gradient sparsification is a widely adopted solution for reducing the excessive communication traffic in distributed deep learning. However, most existing gradient sparsifiers have relatively poor scalability because of considerable computational cost of gradient selection and/or increased communication traffic owing to gradient build-up. To address these challenges, we propose a novel gradient sparsification scheme, DEFT, that partitions the gradient selection task into sub tasks and distributes them to workers. DEFT differs from existing sparsifiers, wherein every worker selects gradients among all gradients. Consequently, the computational cost can be reduced as the number of workers increases. Moreover, gradient build-up can be eliminated because DEFT allows workers to select gradients in partitions that are non-intersecting (between workers). Therefore, even if the number of workers increases, the communication traffic can be maintained as per user requirement. To avoid the loss of significance of gradient selection, DEFT selects more gradients in the layers that have a larger gradient norm than the other layers. Because every layer has a different computational load, DEFT allocates layers to workers using a bin-packing algorithm to maintain a balanced load of gradient selection between workers. In our empirical evaluation, DEFT shows a significant improvement in training performance in terms of speed in gradient selection over existing sparsifiers while achieving high convergence performance.
To pretrain or not to pretrain? A case study of domain-specific pretraining for semantic segmentation in histopathology

Annotating medical imaging datasets is costly, so fine-tuning (or transfer learning) is the most effective method for digital pathology vision applications such as disease classification and semantic segmentation. However, due to texture bias in models trained on real-world images, transfer learning for histopathology applications might result in underperforming models, which necessitates the need for using unlabeled histopathology data and self-supervised methods to discover domain-specific characteristics. Here, we tested the premise that histopathology-specific pretrained models provide better initializations for pathology vision tasks, i.e., gland and cell segmentation. In this study, we compare the performance of gland and cell segmentation tasks with histopathology domain-specific and non-domain-specific (real-world images) pretrained weights. Moreover, we investigate the dataset size at which domain-specific pretraining produces significant gains in performance. In addition, we investigated whether domain-specific initialization improves the effectiveness of out-of-distribution testing on distinct datasets but the same task. The results indicate that performance gain using domain-specific pretrained weights depends on both the task and the size of the training dataset. In instances with limited dataset sizes, a significant improvement in gland segmentation performance was also observed, whereas models trained on cell segmentation datasets exhibit no improvement.
SAR: Generalization of Physiological Agility and Dexterity via Synergistic Action Representation

Learning effective continuous control policies in high-dimensional systems, including musculoskeletal agents, remains a significant challenge. Over the course of biological evolution, organisms have developed robust mechanisms for overcoming this complexity to learn highly sophisticated strategies for motor control. What accounts for this robust behavioral flexibility? Modular control via muscle synergies, i.e. coordinated muscle co-contractions, is considered to be one putative mechanism that enables organisms to learn muscle control in a simplified and generalizable action space. Drawing inspiration from this evolved motor control strategy, we use physiologically accurate human hand and leg models as a testbed for determining the extent to which a Synergistic Action Representation (SAR) acquired from simpler tasks facilitates learning more complex tasks. We find in both cases that SAR-exploiting policies significantly outperform end-to-end reinforcement learning. Policies trained with SAR were able to achieve robust locomotion on a wide set of terrains with high sample efficiency, while baseline approaches failed to learn meaningful behaviors. Additionally, policies trained with SAR on a multiobject manipulation task significantly outperformed (>70% success) baseline approaches (<20% success). Both of these SAR-exploiting policies were also found to generalize zero-shot to out-of-domain environmental conditions, while policies that did not adopt SAR failed to generalize. Finally, we establish the generality of SAR on broader high-dimensional control problems using a robotic manipulation task set and a full-body humanoid locomotion task. To the best of our knowledge, this investigation is the first of its kind to present an end-to-end pipeline for discovering synergies and using this representation to learn high-dimensional continuous control across a wide diversity of tasks.
Optimal Solutions for a Class of Set-Valued Evolution Problems

The paper is concerned with a class of optimization problems for moving sets $t\mapsto\Omega(t)\subset\mathbb{R}^2$, motivated by the control of invasive biological populations. Assuming that the initial contaminated set $\Omega_0$ is convex, we prove that a strategy is optimal if an only if at each given time $t\in [0,T]$ the control is active along the portion of the boundary $\partial \Omega(t)$ where the curvature is maximal. In particular, this implies that $\Omega(t)$ is convex for all $t\geq 0$. The proof relies on the analysis of a one-step constrained optimization problem, obtained by a time discretization.
SpawnNet: Learning Generalizable Visuomotor Skills from Pre-trained Networks

The existing internet-scale image and video datasets cover a wide range of everyday objects and tasks, bringing the potential of learning policies that generalize in diverse scenarios. Prior works have explored visual pre-training with different self-supervised objectives. Still, the generalization capabilities of the learned policies and the advantages over well-tuned baselines remain unclear from prior studies. In this work, we present a focused study of the generalization capabilities of the pre-trained visual representations at the categorical level. We identify the key bottleneck in using a frozen pre-trained visual backbone for policy learning and then propose SpawnNet, a novel two-stream architecture that learns to fuse pre-trained multi-layer representations into a separate network to learn a robust policy. Through extensive simulated and real experiments, we show significantly better categorical generalization compared to prior approaches in imitation learning settings. Open-sourced code and videos can be found on our website: https://xingyu-lin.github. io/spawnnet.
ISAC-Enabled Beam Alignment for Terahertz Networks: Scheme Design and Coverage Analysis

As a key pillar technology for the future 6G networks, terahertz (THz) communication can provide high-capacity transmissions, but suffers from severe propagation loss and line-of-sight (LoS) blockage that limits the network coverage. Narrow beams are required to compensate for the loss, but they in turn bring in beam misalignment challenge that degrades the THz network performance. The high sensing accuracy of THz signals enables integrated sensing and communication (ISAC) technology to assist the LoS blockage and user mobility-induced beam misalignment, enhancing THz network coverage. In line with the 5G beam management, we propose a joint synchronization signal block (SSB) and reference signal (RS)-based sensing (JSRS) scheme to predict the need for beam switches, and thus prevent beam misalignment. We further design an optimal sensing signal pattern that minimizes beam misalignment with fixed sensing resources, which reveals design insights into the time-to-frequency allocation. We derive expressions for the coverage probability and spatial throughput, which provide instructions on the ISAC-THz network deployment and further enable evaluations for the sensing benefit in THz networks. Numerical results show that the JSRS scheme is effective and highly compatible with the 5G air interface. Averaged in tested urban use cases, JSRS achieves near-ideal performance and reduces around 80% of beam misalignment, and enhances the coverage probability by about 75%, compared to the network with 5G-required positioning ability.
Need for shared internal mound conditions by fungus-growing Macrotermes does not predict their species distributions, in current or future climates

The large, iconic nests constructed by social species are engineered to create internal conditions buffered from external climatic extremes, to allow reproduction and/or food production. Nest-inhabiting eusocial Macrotermitinae (Blattodea: Isoptera) are outstanding palaeo-tropical ecosystem engineers that evolved fungus-growing to break down plant matter ca 62 Mya; the termites feed on the fungus and plant matter. Fungus-growing ensures a constant food supply, but the fungi need temperature-buffered, high humidity conditions, created in architecturally complex, often tall, nest-structures (mounds). Given the need for constant and similar internal nest conditions by fungi farmed by different Macrotermes species, we assessed whether current distributions of six African Macrotermes correlate with similar variables, and whether this would reflect in expected species' distribution shifts with climate change. The primary variables explaining species’ distributions were not the same for the different species. Distributionally, three of the six species are predicted to see declines in highly suitable climate. For two species, range increases should be small (less than 9%), and for a single species, M. vitrialatus, ‘very suitable’ climate could increase by 64%. Mismatches in vegetation requirements and anthropogenic habitat transformation may preclude range expansion, however, presaging disruption to ecosystem patterns and processes that will cascade through systems at both landscape and continental scales. This article is part of the theme issue ‘The evolutionary ecology of nests: a cross-taxon approach’.
Evolution of nest architecture in tyrant flycatchers and allies

Innovations in nest design are thought to be one potential factor in the evolutionary success of passerine birds (order: Passeriformes), which colonized new ecological niches as they diversified in the Oligocene and Miocene. In particular, tyrant flycatchers and their allies (parvorder: Tyrannida) are an extremely diverse group of New World suboscine passerines occupying a wide range of habitats and exhibiting substantial extant variation in nest design. To explore the evolution of nest architecture in this clade, we first described nest traits across the Tyrannida phylogeny and estimated ancestral nest conditions. We then quantified macroevolutionary transition rates between nest types, examined a potential coevolutionary relationship between nest type and habitat, and used phylogenetic mixed models to determine possible ecological and environmental correlates of nest design. The Tyrannida ancestor probably built a cup nest in a closed habitat, and dome nests independently evolved at least 15 times within this group. Both cup- and dome-nesting species diversified into semi-open and open habitats, and we did not detect a coevolutionary relationship between nest type and habitat. Furthermore, nest type was not significantly correlated with several key ecological, life-history and environmental traits, suggesting that broad variation in Tyrannida nest architecture may not easily be explained by a single factor. This article is part of the theme issue ‘The evolutionary ecology of nests: a cross-taxon approach’.
Towards modelling emergence in plant systems

Abstract Abstract Plants are complex systems made up of many interacting components, ranging from architectural elements such as branches and roots, to entities comprising cellular processes such as metabolic pathways and gene regulatory networks. The collective behaviour of these components, along with the plant’s response to the environment, give rise to the plant as a whole. Properties that result from these interactions and cannot be attributed to individual parts alone are called emergent properties, occurring at different time and spatial scales. Deepening our understanding of plant growth and development requires computational tools capable of handling a large number of interactions and a multiscale approach connecting properties across scales. There currently exist few methods able to integrate models across scales, or models capable of predicting new emergent plant properties. This perspective explores current approaches to modelling emergent behaviour in plants, with a focus on how current and future tools can handle multiscale plant systems.
Step Free Energy - The equilibrium shape of a hexagonal lattice

This thesis derives an expression of the step free energy for the hexagonal lattice in the armchair and zigzag direction. This solid-on-solid model with nearest neighbor interaction recaptures the exact result of Wannier in case the armchair edge is considered. The derivation of an exact expression for the edge free energy in the zigzag edge was attempted, but we did not manage to find it. The angular dependence of the step free energy is derived and with the use of the Wulff construction the equilibrium shape at different temperatures is determined.