Miracle-dz/UniFAR-FTUs huggingface dataset:
A square peg in a round hole: fitting impact assessment under the Antarctic Environmental Protocol to Antarctic tourism

The 1991 Protocol on Environmental Protection to the Antarctic Treaty develops and codifies Antarctic environmental impact assessment (EIA) obligations. This paper outlines the history and present nature of Antarctic EIA obligations and reviews the emergence of Antarctic tourism. The Treaty explicitly recognises the primacy of Antarctica's scientific and environmental values, whereas tourism, while a legitimate activity, is not otherwise an embedded Antarctic activity. The difficulties of applying an EIA system which has evolved primarily to deal with national programme activities to the different activities of commercial tourism is not an argument for absolving tourist activities from these obligations. If tourism activities are not adequately addressed, the impacts may not be appropriately considered and could pose unacceptable risks to an environment supposedly legally safeguarded by international treaty.
Mother-Father Relationship and Depressive Symptoms Among Pregnant Black Women

Background: Black women report higher levels of depressive symptoms during pregnancy than white women. A supportive relationship with the father of the baby may be protective and decrease depressive symptoms. Objective: We sought to examine the association between mother-father relationship and depressive symptoms among pregnant black women. Methods: Using a cross-sectional design, we conducted a secondary data analysis from a subsample of 405 pregnant black women who participated in the Biosocial Impact on Black Births study, a prospective cohort study. Participants completed questionnaires at 19- to 29-week gestation, including 6 measures of their relationship with the father of the baby: (1) contact, (2) involvement, (3) overall relationship, (4) change in relationship from prior to pregnancy to during pregnancy, (5) support, and (6) conflict. Latent class analysis was used to identify and classify the relationship construct. The Center for Epidemiologic Studies—Depression (CES-D) scale was used, with scores ≥23 considered high levels of depressive symptoms. Data were analyzed with logistic regression. Results: Following adjustment for maternal sociodemographic characteristics, comorbid conditions, and health behaviors, women in a conflictual relationship had higher odds of having depressive symptom scores ≥23 (adjusted odds ratio: 3.50, 95% confidence interval: 2.00, 6.12) than those having no relationship (adjusted odds ratio: 2.81, 95% confidence interval: 1.43, 5.52), when compared with those with a good relationship. Conclusions: These findings suggest that having either a conflictual or no relationship with the father of the baby during pregnancy increases the odds for higher maternal depressive symptoms (CES-D scores ≥ 23) among pregnant black women.
Bias correction in random effects models with sparse binary responses

Sparse correlated binary data are frequently encountered in many applications involving either rare event cases or small sample sizes. In this study, we consider correlated binary data and a logit random effects model framework. We discuss h-likelihood estimates and how the computational procedure is affected by sparseness. We propose an adjustment to the fitting process that involves the adaption of the regression calibration method to the estimation of random effects. Using this adjustment, we correct for the bias in the random effects estimates resulting in better properties for the fixed effects estimates of the model. This is supported by the results of the simulation study that was conducted under different sparseness levels. The proposed adjusted h-likelihood estimation approach is also used for the analysis of two real meta-analyses data sets.
Understanding the effects of atmospheric circulation in the relationships between water vapor and temperature through theoretical analyses

Theoretical analyses are provided to understand the effects of the atmospheric circulation in the relationships between water vapor and temperature. Relative humidity (r) analysis indicates that when temperature variation range ΔT ≡ T2 − T1 is larger than Dr ≡ 13 ln(r1/r2), a measure of the decrease of r over the temperature range, strong positive correlations can be expected between water vapor and temperature. Dynamic process analysis reveals, with expressing the change of water vapor as ΔW = fΔT + B (f > 0 and B represents atmospheric dynamic effect), the dual effects of the atmospheric circulation in the water vapor‐temperature relationships. When ΔT is large so that fΔT > ∣B∣, the thermodynamic effect of the earth‐atmosphere is more important; the thermally induced atmospheric circulation and the evaporation tend to establish strong positive correlations between water vapor and temperature. When ΔT is small so that fΔT < ∣B∣, the dynamic effect of the atmosphere is more important; the dynamic water vapor convergence and divergence make water vapor and temperature have weak correlations.
Response of the eastern North Atlantic subpolar gyre to the North Atlantic Oscillation

The salinity changes in the subpolar gyre (SPG) in response to the North Atlantic Oscillation (NAO) are studied in idealized numerical experiments. In the eastern SPG, anomalies of similar amplitude as those observed during the 1995–1996 shift of the NAO are mainly driven by the local response to the wind stress, through the set‐up of an anticyclonic “intergyre” anomaly. In positive NAO, this anomalous circulation advects altogether (i) fresh, cold water from the western to the eastern SPG, contributing there to the formation of negative salinity anomalies, and (ii) warm, saline subtropical water to the south of Newfoundland, forming positive anomalies there. The latter are subsequently transported with the North Atlantic Current to the eastern SPG where they could act to weaken the low‐salinity signal. The occurrence of this signal is concomitant with the acceleration of the gyre but, in contrast to earlier findings, is not subject to it.
Forest Mixing: investigating the impact of multiple search trees and a shared refinements pool on ontology learning

We aim at development white-box machine learning algorithms. We focus here on algorithms for learning axioms in description logic. We extend the Class Expression Learning for Ontology Engineering (CELOE) algorithm contained in the DL-Learner tool. The approach uses multiple search trees and a shared pool of refinements in order to split the search space in smaller subspaces. We introduce the conjunction operation of best class expressions from each tree, keeping the results which give the most information. The aim is to foster exploration from a diverse set of starting classes and to streamline the process of finding class expressions in ontologies. The current implementation and settings indicated that the Forest Mixing approach did not outperform the traditional CELOE. Despite these results, the conceptual proposal brought forward by this approach may stimulate future improvements in class expression finding in ontologies.
Tectono-sedimentary evolution of southwest Sardinia in the Paleogene: Pyrenaic or Apenninic Dynamic?

The Narcao and Cixerri basins in Southwestern Sardinia are east-west trending basins of Oligocene age. Recent geological mapping, combined with structural and stratigraphical analyses, support the proposed hypothesis that these basins were very open growth synclines confined within a structural high, delimited by northwest trending dextral strike slip faults. Previously the basins have been interpreted as fault-bounded grabens. The newer revised interpretation is consistent with the existence of NNW trending dextral strike-slip dynamic, related to a north-south shortening which has generated reverse faulting and tight folds in the underlying, pre-synclinal evolution, Eocene succession. This deformation, along with an interfering sub-orthogonal thrust and fold system which affects the Mesozoic sequence, was traditionally linked to the Pyrenean Orogenesis. The Oligocene–Aquitanian shortening, which resulted in the growth synclines and strike-slip faulting, is consistent with the structural development recognized in north-central Sardinia; there structures related to the collision between continental margins that resulted in the Northern Apennines are well documented. Therefore, the Oligocene tectonics of Southwestern Sardinia also must be related to the collision event between the Southern Europe margin (i.e. a crustal sector corresponding to the future Corsica-Sardinia block) and the Adria Plate, which generated the Northern Apennines. Conversely, the previous E-W shortening- related structures must be related to Pyrenean tectonics.
Broken Symmetry and Fractionalized Flux Strings in a Staggered U(1) Pure Gauge Theory

Inspired by self-adjoint extensions of the electric field operator in the Hamiltonian formalism, we extend the Wilsonian framework of Abelian lattice gauge theory by introducing a modified action parameterized by an angle $\alpha$, where the ordinary Wilson theory corresponds to $\alpha=0$. Choosing instead $\alpha=\pi$ (the"staggered"case) gives the only other theory in the family which preserves all symmetries of the original model at the microscopic level. We study the case of $3D$ $\mathrm{U}(1)$ pure gauge theory, simulating the staggered case of this model numerically in its dual formulation. We find evidence of a continuum limit with a spontaneously broken $\mathbb{Z}_2$ single-site translational symmetry, in contrast to the ordinary theory. Moreover, the confining string fractionalizes into multiple strands which separate spatial regions in distinct ground states of the broken symmetry.
A local intermittency based Reynolds-averaged transition model for turbulent mixing induced by interfacial instabilities

Accurate prediction of mixing transition induced by interfacial instabilities is vital for engineering applications, but has remained a great challenge for decades. For engineering practices, Reynolds-averaged Navier-Stokes simulation (RANS) is the most viable method. However, existing RANS models for mixing problems are mostly designed for fully developed turbulence, failing to depict the locally spatio-temporal-dependent characteristic of transition. In the present study, the idea of the intermittent factor (denoted as $\gamma$), which has been widely used in boundary layer transition in aerospace engineering, is extended to the mixing problems. Specifically, a transport equation for $\gamma$ is built based on local flow variables, which is used to describe the locally spatio-temporal-dependent characteristic of transition. Furthermore, $\gamma$ is coupled into the widely used K-L turbulent mixing model to constrain the two key product sources terms that dominate the evolution of mixing, i.e. the Reynolds stress and the buoyancy effect. Subsequently, the simulations of two reshocked Richtmyer-Meshkov mixing cases with remarkable transition effects confirm that the proposed model has a good performance for predicting mixing transition. To the best of our knowledge, it is the first study that an extra transport equation for intermittent factor has been proposed for a RANS mixing transition model. More importantly, the present modeling framework is flexible and has the potential to be applied to other RANS models. It provides a promising strategy for more advanced modeling for mixing transition.
Toward Universal Speech Enhancement For Diverse Input Conditions

The past decade has witnessed substantial growth of data-driven speech enhancement (SE) techniques thanks to deep learning. While existing approaches have shown impressive performance in some common datasets, most of them are designed only for a single condition (e.g., single-channel, multi-channel, or a fixed sampling frequency) or only consider a single task (e.g., denoising or dereverberation). Currently, there is no universal SE approach that can effectively handle diverse input conditions with a single model. In this paper, we make the first attempt to investigate this line of research. First, we devise a single SE model that is independent of microphone channels, signal lengths, and sampling frequencies. Second, we design a universal SE benchmark by combining existing public corpora with multiple conditions. Our experiments on a wide range of datasets show that the proposed single model can successfully handle diverse conditions with strong performance.
Maximal Volume Matrix Cross Approximation for Image Compression and Least Squares Solution

We study the classic cross approximation of matrices based on the maximal volume submatrices. Our main results consist of an improvement of a classic estimate for matrix cross approximation and a greedy approach for finding the maximal volume submatrices. Indeed, we present a new proof of a classic estimate of the inequality with an improved constant. Also, we present a family of greedy maximal volume algorithms which improve the error bound of cross approximation of a matrix in the Chebyshev norm and also improve the computational efficiency of classic maximal volume algorithm. The proposed algorithms are shown to have theoretical guarantees of convergence. Finally, we present two applications: one is image compression and the other is least squares approximation of continuous functions. Our numerical results in the end of the paper demonstrate the effective performances of our approach.
Resilient Model-Free Asymmetric Bipartite Consensus for Nonlinear Multi-Agent Systems against DoS Attacks

In this letter, we study an unified resilient asymmetric bipartite consensus (URABC) problem for nonlinear multi-agent systems with both cooperative and antagonistic interactions under denial-of-service (DoS) attacks. We first prove that the URABC problem is solved by stabilizing the neighborhood asymmetric bipartite consensus error. Then, we develop a distributed compact form dynamic linearization method to linearize the neighborhood asymmetric bipartite consensus error. By using an extended discrete state observer to enhance the robustness against unknown dynamics and an attack compensation mechanism to eliminate the adverse effects of DoS attacks, we finally propose a distributed resilient model-free adaptive control algorithm to solve the URABC problem. A numerical example validates the proposed results.
Robust 3D Object Detection from LiDAR-Radar Point Clouds via Cross-Modal Feature Augmentation

This paper presents a novel framework for robust 3D object detection from point clouds via cross-modal hallucination. Our proposed approach is agnostic to either hallucination direction between LiDAR and 4D radar. We introduce multiple alignments on both spatial and feature levels to achieve simultaneous backbone refinement and hallucination generation. Specifically, spatial alignment is proposed to deal with the geometry discrepancy for better instance matching between LiDAR and radar. The feature alignment step further bridges the intrinsic attribute gap between the sensing modalities and stabilizes the training. The trained object detection models can deal with difficult detection cases better, even though only single-modal data is used as the input during the inference stage. Extensive experiments on the View-of-Delft (VoD) dataset show that our proposed method outperforms the state-of-the-art (SOTA) methods for both radar and LiDAR object detection while maintaining competitive efficiency in runtime. Code is available at https://github.com/DJNing/See_beyond_seeing.
Efficient Interpretable Nonlinear Modeling for Multiple Time Series

Predictive linear and nonlinear models based on kernel machines or deep neural networks have been used to discover dependencies among time series. This paper proposes an efficient nonlinear modeling approach for multiple time series, with a complexity comparable to linear vector autoregressive (VAR) models while still incorporating nonlinear interactions among different time-series variables. The modeling assumption is that the set of time series is generated in two steps: first, a linear VAR process in a latent space, and second, a set of invertible and Lipschitz continuous nonlinear mappings that are applied per sensor, that is, a component-wise mapping from each latent variable to a variable in the measurement space. The VAR coefficient identification provides a topology representation of the dependencies among the aforementioned variables. The proposed approach models each component-wise nonlinearity using an invertible neural network and imposes sparsity on the VAR coefficients to reflect the parsimonious dependencies usually found in real applications. To efficiently solve the formulated optimization problems, a custom algorithm is devised combining proximal gradient descent, stochastic primal-dual updates, and projection to enforce the corresponding constraints. Experimental results on both synthetic and real data sets show that the proposed algorithm improves the identification of the support of the VAR coefficients in a parsimonious manner while also improving the time-series prediction, as compared to the current state-of-the-art methods.
Singular value decomposition quantum algorithm for quantum biology

There has been a recent interest in quantum algorithms for the modelling and prediction of non-unitary quantum dynamics using current quantum computers. The field of quantum biology is one area where these algorithms could prove to be useful, as biological systems are generally intractable to treat in their complete form, but amenable to an open quantum systems approach. Here we present the application of a recently developed singular value decomposition algorithm to two well-studied benchmark systems in quantum biology: excitonic energy transport through the Fenna-Matthews-Olson complex and the radical pair mechanism for avian navigation. We demonstrate that the singular value decomposition algorithm is capable of capturing accurate short- and long-time dynamics for these systems through implementation on a quantum simulator, and conclude that this algorithm has the potential to be an effective tool for the future study of systems relevant to quantum biology.
A Variational Spike-and-Slab Approach for Group Variable Selection

We introduce a class of generic spike-and-slab priors for high-dimensional linear regression with grouped variables and present a Coordinate-ascent Variational Inference (CAVI) algorithm for obtaining an optimal variational Bayes approximation. Using parameter expansion for a specific, yet comprehensive, family of slab distributions, we obtain a further gain in computational efficiency. The method can be easily extended to fitting additive models. Theoretically, we present general conditions on the generic spike-and-slab priors that enable us to derive the contraction rates for both the true posterior and the VB posterior for linear regression and additive models, of which some previous theoretical results can be viewed as special cases. Our simulation studies and real data application demonstrate that the proposed method is superior to existing methods in both variable selection and parameter estimation. Our algorithm is implemented in the R package GVSSB.
Multi-Bellman operator for convergence of Q-learning with linear function approximation

We study the convergence of $Q$-learning with linear function approximation. Our key contribution is the introduction of a novel multi-Bellman operator that extends the traditional Bellman operator. By exploring the properties of this operator, we identify conditions under which the projected multi-Bellman operator becomes contractive, providing improved fixed-point guarantees compared to the Bellman operator. To leverage these insights, we propose the multi $Q$-learning algorithm with linear function approximation. We demonstrate that this algorithm converges to the fixed-point of the projected multi-Bellman operator, yielding solutions of arbitrary accuracy. Finally, we validate our approach by applying it to well-known environments, showcasing the effectiveness and applicability of our findings.
Distributed Resilient Control of DC Microgrids Under Generally Unbounded FDI Attacks

Due to the nature of distributed secondary control paradigm, DC microgrids are prone to malicious cyber-physical attacks, which could be unbounded to maximize their damage. Existing resilient secondary control methods addressing unbounded attacks require that the first time derivatives of cyber-physical attack signals be bounded. The secondary defense strategy presented in this letter relax such a strict constraint by addressing more generally unbounded attack signals and hence, enhance the resilience of DC microgrids in adversarial environments. Rigorous proofs, based on Lyapunov techniques, show that the proposed method guarantees the uniformly ultimately bounded convergence for both voltage regulation and proportional load sharing under generally unbounded attacks. Comparative case studies further validate the enhanced resilience of the proposed attack-resilient control strategy.
Adversarial Imitation Learning from Visual Observations using Latent Information

We focus on the problem of imitation learning from visual observations, where the learning agent has access to videos of experts as its sole learning source. The challenges of this framework include the absence of expert actions and the partial observability of the environment, as the ground-truth states can only be inferred from pixels. To tackle this problem, we first conduct a theoretical analysis of imitation learning in partially observable environments. We establish upper bounds on the suboptimality of the learning agent with respect to the divergence between the expert and the agent latent state-transition distributions. Motivated by this analysis, we introduce an algorithm called Latent Adversarial Imitation from Observations, which combines off-policy adversarial imitation techniques with a learned latent representation of the agent's state from sequences of observations. In experiments on high-dimensional continuous robotic tasks, we show that our algorithm matches state-of-the-art performance while providing significant computational advantages. Additionally, we show how our method can be used to improve the efficiency of reinforcement learning from pixels by leveraging expert videos. To ensure reproducibility, we provide free access to our code.
Knowledge Graphs for the Life Sciences: Recent Developments, Challenges and Opportunities

The term life sciences refers to the disciplines that study living organisms and life processes, and include chemistry, biology, medicine, and a range of other related disciplines. Research efforts in life sciences are heavily data-driven, as they produce and consume vast amounts of scientific data, much of which is intrinsically relational and graph-structured. The volume of data and the complexity of scientific concepts and relations referred to therein promote the application of advanced knowledge-driven technologies for managing and interpreting data, with the ultimate aim to advance scientific discovery. In this survey and position paper, we discuss recent developments and advances in the use of graph-based technologies in life sciences and set out a vision for how these technologies will impact these fields into the future. We focus on three broad topics: the construction and management of Knowledge Graphs (KGs), the use of KGs and associated technologies in the discovery of new knowledge, and the use of KGs in artificial intelligence applications to support explanations (explainable AI). We select a few exemplary use cases for each topic, discuss the challenges and open research questions within these topics, and conclude with a perspective and outlook that summarizes the overarching challenges and their potential solutions as a guide for future research.