Miracle-dz/UniFAR-FTUs huggingface dataset:
Enhanced Long Wavelength Mermin-Wagner Fluctuations in Two-Dimensional Active Crystals and Glasses

In the realm of two-dimensional (2D) systems, the renowned Mermin-Wagner effect plays a significant role, giving rise to striking dimensionality effects marked by far-reaching density fluctuations and the divergence of various dynamic properties. This effect also unequivocally negates the possibility of stable crystalline phases in 2D particulate systems characterized by continuous degrees of freedom. This effect has been recently discerned in glass-forming liquids, displaying characteristic signatures like the logarithmic divergence of mean squared displacement in the plateau regime. We explored these long-wavelength fluctuations in crystalline solids and in glass-forming liquids in the presence of non-equilibrium active forces. These systems are known in the literature as active crystals and glasses, and they can be thought of as a minimalistic model for understanding various non-equilibrium systems where the constituent particles dynamics are controlled by both temperature and other active forces, which can be external or internal. Such models often offer valuable insights into the dynamical behavior of biological systems, such as collections of cells, bacteria, ant colonies, or even synthetic self-propelled particles like Janus colloids. Our study reveals that fluctuations stemming from active forces get strongly coupled with long wavelength fluctuations arising from thermal effects, resulting in dramatic dynamical effects, particularly in 2D systems. We also shed light on how these fluctuations impact dynamical heterogeneity, a defining characteristic of glassy dynamics.
Pressure-stabilized fixed-stress iterative solutions of compositional poromechanics

We consider the numerical behavior of the fixed-stress splitting method for coupled poromechanics as undrained regimes are approached. We explain that pressure stability is related to the splitting error of the scheme, not the fact that the discrete saddle point matrix never appears in the fixed-stress approach. This observation reconciles previous results regarding the pressure stability of the splitting method. Using examples of compositional poromechanics with application to geological CO$_2$ sequestration, we see that solutions obtained using the fixed-stress scheme with a low order finite element-finite volume discretization which is not inherently inf-sup stable can exhibit the same pressure oscillations obtained with the corresponding fully implicit scheme. Moreover, pressure jump stabilization can effectively remove these spurious oscillations in the fixed-stress setting, while also improving the efficiency of the scheme in terms of the number of iterations required at every time step to reach convergence.
Generalizing Geometric Nonwindowed Scattering Transforms on Compact Riemannian Manifolds

Let $\mathcal{M}$ be a compact, smooth, $n$-dimensional Riemannian manifold without boundary. In this paper, we generalize nonwindowed geometric scattering transforms, which we formulate as $\mathbf{L}^q(\mathcal{M})$ norms of a cascade of geometric wavelet transforms and modulus operators. We then provide weighted measures for these operators, prove that these operators are well-defined under specific conditions on the manifold, invariant to the action of isometries, and stable to diffeomorphisms for $\lambda$-bandlimited functions.
Influence of urban land cover changes and climate change for the exposure of European cities to flooding during extreme precipitation

investigated by simulating the occurrence of 5 distinctive high-intensity precipitation events (RP10, RP20, RP50, RP100, RP1000) under both current and future climate (RCP4.5, RCP 8.5) and for historical (1984) and current (2014) urban land cover. The hydrodynamic flow module in the MIKE21 software (MIKE By DHI) is used to simulate flooding during the individual high-intensity rainfall events. The primary outputs of the flood model are maps showing the maximum flood depth and extent for each individual simulation. A cross comparison of the multiple flood maps enables a quantification of the relative importance of land cover changes as compared to climate change. Abstract The extent and location of impervious surfaces, such as roads, buildings, parking lots and other paved areas, within urban areas strongly affects the amount and velocity of run-off during high-intensity rainfall and consequently influences the exposure of cities flooding . Also, climate change is to exacerbate urban flood risk as heavy precipitation is projected to increase in intensity and frequency over many areas of the globe, including central, northwest and northern If present urban development trends towards increasing IS fractions this is expected to further aggravate the risk of pluvial flooding. the hand the introduction of more pervious surfaces could serve as adaptation to climate change. Hence, detailed knowledge of the importance of both urban land cover changes and climate change the risk of areas towards
AbsInstruct: Eliciting Abstraction Ability from LLMs through Explanation Tuning with Plausibility Estimation

Abstraction ability is crucial in human intelligence, which can also benefit various tasks in NLP study. Existing work shows that LLMs are deficient in abstract ability, and how to improve it remains unexplored. In this work, we design the framework AbsInstruct to enhance LLMs' abstraction ability through instruction tuning. The framework builds instructions with in-depth explanations to assist LLMs in capturing the underlying rationale of abstraction. Meanwhile, we introduce a plausibility estimator to select instructions that are more consistent with the abstraction knowledge of LLMs to be aligned. Then, our framework combines abstraction instructions with general-purpose ones to build a hybrid dataset. Extensive experiments and analyses demonstrate that our framework can considerably enhance LLMs' abstraction ability with strong generalization performance while maintaining their general instruction-following abilities.
Iterative embedding and reweighting of complex networks reveals community structure

Graph embeddings learn the structure of networks and represent it in low-dimensional vector spaces. Community structure is one of the features that are recognized and reproduced by embeddings. We show that an iterative procedure, in which a graph is repeatedly embedded and its links are reweighted based on the geometric proximity between the nodes, reinforces intra-community links and weakens inter-community links, making the clusters of the initial network more visible and more easily detectable. The geometric separation between the communities can become so strong that even a very simple parsing of the links may recover the communities as isolated components with surprisingly high precision. Furthermore, when used as a pre-processing step, our embedding and reweighting procedure can improve the performance of traditional community detection algorithms.
A model of non-Fermi liquid with power law resistivity: strange metal with a not-so-strange origin

We construct a model which exhibits resistivity going as a power law in temperature $T$, as $T^\alpha$ down to the lowest temperature. There is no residual resistivity because we assume the absence of disorder and momentum relaxation is due to umklapp scattering. Our model consists of a quantum spin liquid state with spinon Fermi surface and a hole Fermi surface made out of doped holes. The key ingredient is a set of singular $2k_F$ modes living on a ring in momentum space. Depending on parameters, $\alpha$ may be unity (strange metal) or even smaller. The model may be applicable to a doped organic compound, which has been found to exhibit linear T resistivity. We conclude that it is possible to obtain strange metal behavior starting with a model which is not so strange.
3. PROVENANCE OF MIOCENE–PLEISTOCENE TURBIDITE SANDS AND SANDSTONES, NANKAI TROUGH, OCEAN DRILLING PROGRAM LEG 190

During Ocean Drilling Program Leg 190 several turbidite successions in the Nankai Trough were drilled through including Pleistocene trench fill (Sites 1173 and 1174), Pleistocene–Pliocene slope basin deposits and underlying trench fill (Sites 1175 and 1176), Miocene Shikoku Basin deposits (Site 1177), and upper Miocene trench fill (Site 1178). Sands from the Pleistocene trench-fill succession of the Nankai Trough are of mixed derivation with significant monomineralic components (quartz and feldspar) and mafic to intermediate volcanic rock fragments, in addition to sedimentary and less abundant metamorphic detritus. They have a source in the Izu collision zone in central Honshu. Sands from the slope and accreted trench fill at Sites 1175 and 1176 are dominated by quartz with less abundant feldspar, sedimentary rock fragments, and only minor volcanic and metamorphic rock fragments. In contrast to the trench turbidites of Sites 1173 and 1174, these sands are very quartzose with characteristic radiolarian chert fragments. Volcanic rock fragments are mainly of silicic composition. Potential sources of these sands are uplifted subduction complexes of southwest Japan. Sands from the accreted trench turbidites at Site 1178 have clast types similar to those at Sites 1175 and 1176. In contrast, however, framework detrital modes are distinctive, with Site 1178 sands having substantially lower total quartz contents and more abundant fine-grained sedimentary rock fragments. These sands were also probably derived from the island of Shikoku, but their composition indicates that sedimentary rocks 1Fergusson, C.L. , 2003. Provenance of Miocene–Pleistocene turbidite sands and sandstones, Nankai Trough, Ocean Drilling Program Leg 190. In Mikada, H. , Moore, G.F. , Taira, A. , Becker, K. , Moore, J.C. , and Klaus, A. (Eds.), Proc. ODP, Sci. Results, 190/196, 1–28 [Online]. Available from World Wide Web: . [Cited YYYY-MM-DD] 2School of Geosciences, University of Wollongong, NSW 2522, Australia. cferguss@uow.edu. au Initial receipt: 6 November 2002 Acceptance: 5 August 2003 Web publication: 14 November 2003 Ms 190SR-205 C.L. FERGUSSON MIOCENE–PLEISTOCENE TURBIDITE SANDS 2 were abundant in the source area and these may have been Miocene forearc basin successions that were largely removed by erosion. Erosional remnants of Miocene forearc basin deposits are present on the Kii Peninsula east-northeast of Shikoku. Erosion followed a phase of exhumation of the Shimanto Belt indicated by apatite fission track ages at ~10 Ma. Sand in the lower–upper Miocene turbidites of the lower Shikoku Basin section at Site 1177 is more varied in composition, with the upper part of the unit similar to Site 1178 (i.e., rich in sedimentary rock fragments) and the lower part similar to those at Sites 1175 and 1176 (i.e., rich in quartz with some silicic volcanic rock fragments). Sands from the lower part of the Miocene turbidite unit were derived from a continental source with plutonic and volcanic rocks, possibly the inner zone of southwest Japan.
'Keep it Together': Enforcing Cohesion in Extractive Summaries by Simulating Human Memory

Extractive summaries are usually presented as lists of sentences with no expected cohesion between them. In this paper, we aim to enforce cohesion whilst controlling for informativeness and redundancy in summaries, in cases where the input exhibits high redundancy. The pipeline controls for redundancy in long inputs as it is consumed, and balances informativeness and cohesion during sentence selection. Our sentence selector simulates human memory to keep track of topics --modeled as lexical chains--, enforcing cohesive ties between noun phrases. Across a variety of domains, our experiments revealed that it is possible to extract highly cohesive summaries that nevertheless read as informative to humans as summaries extracted by only accounting for informativeness or redundancy. The extracted summaries exhibit smooth topic transitions between sentences as signaled by lexical chains, with chains spanning adjacent or near-adjacent sentences.
Learning Planning Action Models from State Traces

Previous STRIPS domain model acquisition approaches that learn from state traces start with the names and parameters of the actions to be learned. Therefore their only task is to deduce the preconditions and effects of the given actions. In this work, we explore learning in situations when the parameters of learned actions are not provided. We define two levels of trace quality based on which information is provided and present an algorithm for each. In one level (L1), the states in the traces are labeled with action names, so we can deduce the number and names of the actions, but we still need to work out the number and types of parameters. In the other level (L2), the states are additionally labeled with objects that constitute the parameters of the corresponding grounded actions. Here we still need to deduce the types of the parameters in the learned actions. We experimentally evaluate the proposed algorithms and compare them with the state-of-the-art learning tool FAMA on a large collection of IPC benchmarks. The evaluation shows that our new algorithms are faster, can handle larger inputs and provide better results in terms of learning action models more similar to reference models.
Compact and De-biased Negative Instance Embedding for Multi-Instance Learning on Whole-Slide Image Classification

Whole-slide image (WSI) classification is a challenging task because 1) patches from WSI lack annotation, and 2) WSI possesses unnecessary variability, e.g., stain protocol. Recently, Multiple-Instance Learning (MIL) has made significant progress, allowing for classification based on slide-level, rather than patch-level, annotations. However, existing MIL methods ignore that all patches from normal slides are normal. Using this free annotation, we introduce a semi-supervision signal to de-bias the inter-slide variability and to capture the common factors of variation within normal patches. Because our method is orthogonal to the MIL algorithm, we evaluate our method on top of the recently proposed MIL algorithms and also compare the performance with other semi-supervised approaches. We evaluate our method on two public WSI datasets including Camelyon-16 and TCGA lung cancer and demonstrate that our approach significantly improves the predictive performance of existing MIL algorithms and outperforms other semi-supervised algorithms. We release our code at https://github.com/AITRICS/pathology_mil.
Discrete Probabilistic Inference as Control in Multi-path Environments

We consider the problem of sampling from a discrete and structured distribution as a sequential decision problem, where the objective is to find a stochastic policy such that objects are sampled at the end of this sequential process proportionally to some predefined reward. While we could use maximum entropy Reinforcement Learning (MaxEnt RL) to solve this problem for some distributions, it has been shown that in general, the distribution over states induced by the optimal policy may be biased in cases where there are multiple ways to generate the same object. To address this issue, Generative Flow Networks (GFlowNets) learn a stochastic policy that samples objects proportionally to their reward by approximately enforcing a conservation of flows across the whole Markov Decision Process (MDP). In this paper, we extend recent methods correcting the reward in order to guarantee that the marginal distribution induced by the optimal MaxEnt RL policy is proportional to the original reward, regardless of the structure of the underlying MDP. We also prove that some flow-matching objectives found in the GFlowNet literature are in fact equivalent to well-established MaxEnt RL algorithms with a corrected reward. Finally, we study empirically the performance of multiple MaxEnt RL and GFlowNet algorithms on multiple problems involving sampling from discrete distributions.
Pushing the Limits of Zero-shot End-to-End Speech Translation

Data scarcity and the modality gap between the speech and text modalities are two major obstacles of end-to-end Speech Translation (ST) systems, thus hindering their performance. Prior work has attempted to mitigate these challenges by leveraging external MT data and optimizing distance metrics that bring closer the speech-text representations. However, achieving competitive results typically requires some ST data. For this reason, we introduce ZeroSwot, a method for zero-shot ST that bridges the modality gap without any paired ST data. Leveraging a novel CTC compression and Optimal Transport, we train a speech encoder using only ASR data, to align with the representation space of a massively multilingual MT model. The speech encoder seamlessly integrates with the MT model at inference, enabling direct translation from speech to text, across all languages supported by the MT model. Our experiments show that we can effectively close the modality gap without ST data, while our results on MuST-C and CoVoST demonstrate our method's superiority over not only previous zero-shot models, but also supervised ones, achieving state-of-the-art results.
RAGIC: Risk-Aware Generative Adversarial Model for Stock Interval Construction

Efforts to predict stock market outcomes have yielded limited success due to the inherently stochastic nature of the market, influenced by numerous unpredictable factors. Many existing prediction approaches focus on single-point predictions, lacking the depth needed for effective decision-making and often overlooking market risk. To bridge this gap, we propose a novel model, RAGIC, which introduces sequence generation for stock interval prediction to quantify uncertainty more effectively. Our approach leverages a Generative Adversarial Network (GAN) to produce future price sequences infused with randomness inherent in financial markets. RAGIC's generator includes a risk module, capturing the risk perception of informed investors, and a temporal module, accounting for historical price trends and seasonality. This multi-faceted generator informs the creation of risk-sensitive intervals through statistical inference, incorporating horizon-wise insights. The interval's width is carefully adjusted to reflect market volatility. Importantly, our approach relies solely on publicly available data and incurs only low computational overhead. RAGIC's evaluation across globally recognized broad-based indices demonstrates its balanced performance, offering both accuracy and informativeness. Achieving a consistent 95% coverage, RAGIC maintains a narrow interval width. This promising outcome suggests that our approach effectively addresses the challenges of stock market prediction while incorporating vital risk considerations.
A Comparative Analysis of Hybrid-Quantum Classical Neural Networks

Hybrid Quantum-Classical Machine Learning (ML) is an emerging field, amalgamating the strengths of both classical neural networks and quantum variational circuits on the current noisy intermediate-scale quantum devices. This paper performs an extensive comparative analysis between different hybrid quantum-classical machine learning algorithms, namely Quantum Convolution Neural Network, Quanvolutional Neural Network and Quantum ResNet, for image classification. The experiments designed in this paper focus on different Quantum ML (QML) algorithms to better understand the accuracy variation across the different quantum architectures by implementing interchangeable quantum circuit layers, varying the repetition of such layers and their efficient placement. Such variations enable us to compare the accuracy across different architectural permutations of a given hybrid QML algorithm. The performance comparison of the hybrid models, based on the accuracy, provides us with an understanding of hybrid quantum-classical convergence in correlation with the quantum layer count and the qubit count variations in the circuit.
Free-discontinuity problems generated by higher-order singular perturbations

We carry on an asymptotic analysis for a class of non-convex integral functionals with a singular perturbation by derivatives of order $k\ge 2$. In the case $k=2$ these functional have been already proved to converge to a free-discontinuity functional whose jump part energy density is a constant $m_2$ times the square root of the jump size by Alicandro et al.~and by Bouchitt\'e et al. We show that the $k$-th order perturbation gives a constant, $m_k$, times the $k$-th root of the jump size as the jump part energy density. The constant $m_k$ is characterized by an optimal-profile problem with zero derivatives up to order $k-1$ at the boundary. Contrary to the case $k=2$, the set of points where these boundary conditions hold are not naturally selected from the energy, and the main technical part of the paper is the analysis of the properties of such sets, carried on by a combined use of interpolation inequalities and energy estimates. The result is proven for truncated quadratic energy densities and in the one-dimensional case, from which the general higher-dimensional case can be obtained by slicing techniques and a wide class of non-convex energies can be studied as an envelope of the particular ones. Finally, we remark that an approximation of the Mumford-Shah functional can be obtained by letting $k\to+\infty$.
RAG-Driver: Generalisable Driving Explanations with Retrieval-Augmented In-Context Learning in Multi-Modal Large Language Model

Robots powered by 'blackbox' models need to provide human-understandable explanations which we can trust. Hence, explainability plays a critical role in trustworthy autonomous decision-making to foster transparency and acceptance among end users, especially in complex autonomous driving. Recent advancements in Multi-Modal Large Language models (MLLMs) have shown promising potential in enhancing the explainability as a driving agent by producing control predictions along with natural language explanations. However, severe data scarcity due to expensive annotation costs and significant domain gaps between different datasets makes the development of a robust and generalisable system an extremely challenging task. Moreover, the prohibitively expensive training requirements of MLLM and the unsolved problem of catastrophic forgetting further limit their generalisability post-deployment. To address these challenges, we present RAG-Driver, a novel retrieval-augmented multi-modal large language model that leverages in-context learning for high-performance, explainable, and generalisable autonomous driving. By grounding in retrieved expert demonstration, we empirically validate that RAG-Driver achieves state-of-the-art performance in producing driving action explanations, justifications, and control signal prediction. More importantly, it exhibits exceptional zero-shot generalisation capabilities to unseen environments without further training endeavours.