Miracle-dz/UniFAR-FTUs huggingface dataset:
Multidecadal variability of rainfall and streamflow: Eastern Australia

This study investigates the influence of the El Niño–Southern Oscillation (ENSO) and the Interdecadal Pacific Oscillation (IPO) on rainfall and streamflow regimes of eastern Australia. An analysis of historical rainfall and streamflow data for Queensland (QLD), New South Wales (NSW), and Victoria (VIC) reveals strong relationships between these indices and seasonal rainfall and streamflow totals. Rainfall and streamflow in NSW and QLD are shown to be significantly enhanced during the La Niña phase of ENSO, with La Niña impacts diminishing as one moves south into VIC. In addition, the study shows that on a multidecadal timescale the negative phase of the IPO is associated with “wetter” conditions than the positive phase. Importantly, the already enhanced La Niña rainfall and streamflow is demonstrated to be even further magnified during La Niña events that occur in the IPO negative phase. This result is of particular importance as the influence of ENSO in VIC appears to be weak; however, the results indicate that some useful predictability of ENSO impacts can be achieved during the negative phase of the IPO for VIC.
Statistical properties and privacy guarantees of an original distance-based fully synthetic data generation method

Introduction: The amount of data generated by original research is growing exponentially. Publicly releasing them is recommended to comply with the Open Science principles. However, data collected from human participants cannot be released as-is without raising privacy concerns. Fully synthetic data represent a promising answer to this challenge. This approach is explored by the French Centre de Recherche en {\'E}pid{\'e}miologie et Sant{\'e} des Populations in the form of a synthetic data generation framework based on Classification and Regression Trees and an original distance-based filtering. The goal of this work was to develop a refined version of this framework and to assess its risk-utility profile with empirical and formal tools, including novel ones developed for the purpose of this evaluation.Materials and Methods: Our synthesis framework consists of four successive steps, each of which is designed to prevent specific risks of disclosure. We assessed its performance by applying two or more of these steps to a rich epidemiological dataset. Privacy and utility metrics were computed for each of the resulting synthetic datasets, which were further assessed using machine learning approaches.Results: Computed metrics showed a satisfactory level of protection against attribute disclosure attacks for each synthetic dataset, especially when the full framework was used. Membership disclosure attacks were formally prevented without significantly altering the data. Machine learning approaches showed a low risk of success for simulated singling out and linkability attacks. Distributional and inferential similarity with the original data were high with all datasets.Discussion: This work showed the technical feasibility of generating publicly releasable synthetic data using a multi-step framework. Formal and empirical tools specifically developed for this demonstration are a valuable contribution to this field. Further research should focus on the extension and validation of these tools, in an effort to specify the intrinsic qualities of alternative data synthesis methods.Conclusion: By successfully assessing the quality of data produced using a novel multi-step synthetic data generation framework, we showed the technical and conceptual soundness of the Open-CESP initiative, which seems ripe for full-scale implementation.
Layerwise Linear Mode Connectivity

Averaging neural network parameters is an intuitive method for fusing the knowledge of two independent models. It is most prominently used in federated learning. If models are averaged at the end of training, this can only lead to a good performing model if the loss surface of interest is very particular, i.e., the loss in the midpoint between the two models needs to be sufficiently low. This is impossible to guarantee for the non-convex losses of state-of-the-art networks. For averaging models trained on vastly different datasets, it was proposed to average only the parameters of particular layers or combinations of layers, resulting in better performing models. To get a better understanding of the effect of layer-wise averaging, we analyse the performance of the models that result from averaging single layers, or groups of layers. Based on our empirical and theoretical investigation, we introduce a novel notion of the layer-wise linear connectivity, and show that deep networks do not have layer-wise barriers between them.
The extremal process of branching Brownian motion with absorption

In this paper, we study branching Brownian motion with absorption, in which particles undergo Brownian motions with drift and are killed upon reaching the origin. We prove that the extremal process of this branching Brownian motion with absorption converges to a random shifted decorated Poisson point process. Furthermore, we show that the law of the right-most particle converges to the law of a random shifted Gumbel random variable.
A dimension-reduced variational approach for solving physics-based inverse problems using generative adversarial network priors and normalizing flows

We propose a novel modular inference approach combining two different generative models -- generative adversarial networks (GAN) and normalizing flows -- to approximate the posterior distribution of physics-based Bayesian inverse problems framed in high-dimensional ambient spaces. We dub the proposed framework GAN-Flow. The proposed method leverages the intrinsic dimension reduction and superior sample generation capabilities of GANs to define a low-dimensional data-driven prior distribution. Once a trained GAN-prior is available, the inverse problem is solved entirely in the latent space of the GAN using variational Bayesian inference with normalizing flow-based variational distribution, which approximates low-dimensional posterior distribution by transforming realizations from the low-dimensional latent prior (Gaussian) to corresponding realizations of a low-dimensional variational posterior distribution. The trained GAN generator then maps realizations from this approximate posterior distribution in the latent space back to the high-dimensional ambient space. We also propose a two-stage training strategy for GAN-Flow wherein we train the two generative models sequentially. Thereafter, GAN-Flow can estimate the statistics of posterior-predictive quantities of interest at virtually no additional computational cost. The synergy between the two types of generative models allows us to overcome many challenges associated with the application of Bayesian inference to large-scale inverse problems, chief among which are describing an informative prior and sampling from the high-dimensional posterior. We demonstrate the efficacy and flexibility of GAN-Flow on various physics-based inverse problems of varying ambient dimensionality and prior knowledge using different types of GANs and normalizing flows.
Persistent organic pollutants in native grassland soils along a climosequence in North America.

Among the controls on the fate of harzardous persistent organic pollutants (POPs) in the environment, soil organic matter (SOM) and climate play an outstanding role. Thus, it may be possible to predict POP concentrations at background sites from SOM properties and climatic elements. We therefore related polycyclic aromatic hydrocarbon (PAH) and polychlorinated biphenyl (PCB) concentrations in 18 mollic epipedons under native grassland to SOM properties (lignin-derived phenols, polycarboxylic benzoic acids [PCBAs], aromaticity, and polarity of alkali-extractable SOM) and climatic elements. The sum of 20 PAH (Σ20PAHs) concentrations ranged from 63 to 321 μg kg -1 , and that of 14 PCB (ΣI4PCBs) concentrations ranged from 7.9 to 93 μg kg -1 , except at one contaminated site (3136 μg kg -1 ). On average, phenanthrene (PHEN, 38% of the Σ20PAHs concentrations) and naphthalene (NAPH, 28%) were the most abundant PAHs, congeners 28 (22% of the ΣI4PCB concentrations) and 101 (17%) were the most abundant PCBs. Soil organic C (SOC) concentrations correlated with the Σ20PAHs concentrations; the C concentration in the sum of eight PCBAs, a marker for black C, correlated with the concentrations of higher molecular weight PAHs, except in soils with cyric temperature regime. The ΣI4PCBs concentrations was independent of any soil property. The contribution of NAPH to the Σ20PAHs concentrations and that of the up to tetrachlorinated PCBs to the Σ14PCBs concentrations decreased with increasing mean annual temperature (MAT). The percentages of PCB 101 increased with increasing MAT. However, the temperature effect was not strong. Mean annual precipitation (MAP) neither effected PAH nor PCB patterns. Our results indicate that the easily measured SOC concentrations may be used to predict PAH concentrations in native grassland soils of the prairie. Including MAT improves the prediction of NAPH concentrations. The influence of MAT on PCB concentrations is obvious, but the correlation is too weak to be used for reliable predictions.
Human-Like Intuitive Behavior and Reasoning Biases Emerged in Language Models - and Disappeared in GPT-4

– Large language models (LLMs) are currently at the forefront of intertwining AI systems with human communication and everyday life. Therefore, it is of great importance to evaluate their emerging abilities. In this study, we show that LLMs, most notably GPT-3, exhibit behavior that strikingly resembles human-like intuition — and the cognitive errors that come with it. However, LLMs with higher cognitive capabilities, in particular ChatGPT and GPT-4, learned to avoid succumbing to these errors and perform in a hyperrational manner. For our experiments, we probe LLMs with the Cognitive Reflection Test (CRT) as well as semantic illusions that were originally designed to investigate intuitive decision-making in humans. Moreover, we probe how sturdy the inclination for intuitive-like decision-making is. Our study demonstrates that investigating LLMs with methods from psychology has the potential to reveal otherwise unknown emergent traits.
Indoor Positioning based on Active Radar Sensing and Passive Reflectors: Concepts & Initial Results

To navigate reliably in indoor environments, an industrial autonomous vehicle must know its position. However, current indoor vehicle positioning technologies either lack accuracy, usability or are too expensive. Thus, we propose a novel concept called local reference point assisted active radar positioning, which is able to overcome these drawbacks. It is based on distributing passive retroreflectors in the indoor environment such that each position of the vehicle can be identified by a unique reflection characteristic regarding the reflectors. To observe these characteristics, the autonomous vehicle is equipped with an active radar system. On one hand, this paper presents the basic idea and concept of our new approach towards indoor vehicle positioning and especially focuses on the crucial placement of the reflectors. On the other hand, it also provides a proof of concept by conducting a full system simulation including the placement of the local reference points, the radar-based distance estimation and the comparison of two different positioning methods. It successfully demonstrates the feasibility of our proposed approach.
Recurrent Neural Language Models as Probabilistic Finite-state Automata

Studying language models (LMs) in terms of well-understood formalisms allows us to precisely characterize their abilities and limitations. Previous work has investigated the representational capacity of recurrent neural network (RNN) LMs in terms of their capacity to recognize unweighted formal languages. However, LMs do not describe unweighted formal languages -- rather, they define \emph{probability distributions} over strings. In this work, we study what classes of such probability distributions RNN LMs can represent, which allows us to make more direct statements about their capabilities. We show that simple RNNs are equivalent to a subclass of probabilistic finite-state automata, and can thus model a strict subset of probability distributions expressible by finite-state models. Furthermore, we study the space complexity of representing finite-state LMs with RNNs. We show that, to represent an arbitrary deterministic finite-state LM with $N$ states over an alphabet $\alphabet$, an RNN requires $\Omega\left(N |\Sigma|\right)$ neurons. These results present a first step towards characterizing the classes of distributions RNN LMs can represent and thus help us understand their capabilities and limitations.
The Changing Nature of Mantle Hotspots through Time: Implications for the Chemical Evolution of the Mantle

The melting products of mantle plumes can place constraints on the geochemical structure of the mantle and show how it has varied through time. The highest-temperature melts (komatiites and picrites) derived from each plume are assumed to represent the best available sample of the thermal boundary layer from which the plume originates. A survey of the geochemistry of picrites and komatiites shows that they underwent a fundamental change at about the Archean-Proterozoic boundary. Archean komatiites have depleted or neutral geochemistry, whereas most Protero-zoic and younger picrites have originated from enriched (OIB-type) mantle, although rare depleted plumes persist to the Tertiary (e.g., Gorgona). If plumes originate from a thermal boundary layer near the core-mantle interface, the composition of this layer must have changed from a mixture of depleted and neutral mantle during the Archean to mainly enriched mantle in the Proterozoic. This change coincides with a drop in the maximum MgO content of the magmas from >30% in the Archean to <22% in younger magmas, indicating a decrease in plume source temperature. We suggest that the change in the temperature and composition of the hotspot sources reflects a fundamental change in the dominant form of the component of the earth's convection, driven by surface heat loss: from cold plumes or drips breaking away from beneath a stable lithosphere during the pre-Archean to lithospheric subduction in the Archean and post-Archean mantles. The absence of enriched or OIB-type plumes in the Archean is interpreted to reflect the length of time for oceanic crust to become unstable, sink to the core-mantle boundary, heat up, and return to the upper mantle in plumes. The fall in the maximum MgO content of plume-related magmas between the Archean and Proterozoic may be due to the accumulation of a compositional boundary layer above the core that lowers the temperature of the plume source region by partially insulating it from the core, or to the material in OIB-type plumes being slightly compositionally lighter than the rest of the mantle. Continental flood basalts and oceanic plateau basalts have less extreme geochemical characteristics than those of the associated komatiites or picrites. If these magmas form by melting in the head of a starting plume that is a mixture of mantle from the plume source and entrained lower mantle, the modern lower mantle must have geochemical and isotopie characteristics closer to chondritic than the depleted or enriched mantle reservoirs.
A Multi-facet Paradigm to Bridge Large Language Model and Recommendation

Large Language Models (LLMs) have garnered considerable attention in recommender systems. To achieve LLM-based recommendation, item indexing and generation grounding are two essential steps, bridging between recommendation items and natural language. Item indexing assigns a unique identifier to represent each item in natural language, and generation grounding grounds the generated token sequences to in-corpus items. However, previous works suffer from inherent limitations in the two steps. For item indexing, existing ID-based identifiers (e.g., numeric IDs) and description-based identifiers (e.g., titles) often compromise semantic richness or uniqueness. Moreover, generation grounding might inadvertently produce out-of-corpus identifiers. Worse still, autoregressive generation heavily relies on the initial token's quality. To combat these issues, we propose a novel multi-facet paradigm, namely TransRec, to bridge the LLMs to recommendation. Specifically, TransRec employs multi-facet identifiers that incorporate ID, title, and attribute, achieving both distinctiveness and semantics. Additionally, we introduce a specialized data structure for TransRec to guarantee the in-corpus identifier generation and adopt substring indexing to encourage LLMs to generate from any position. We implement TransRec on two backbone LLMs, i.e., BART-large and LLaMA-7B. Empirical results on three real-world datasets under diverse settings (e.g., full training and few-shot training with warm- and cold-start testings) attest to the superiority of TransRec.
Chlorophyll‐a bloom along the southern coasts of Java and Sumatra during 2006

Nine years of chlorophyll‐a concentration data provided by the Sea‐viewing Wide‐Field of view Sensor (SeaWiFS) revealed an unusual bloom along the southern coastal area of Java and Sumatra during 2006. The bloom was generated by anomalous strong southeasterly winds along the coasts of Java and Sumatra associated with the Indian Ocean Dipole (IOD) event. The bloom evolution started in July 2006 and intensified during August 2006. Peak positive anomalies exceeding 4.0 mg m−3 were evident in September–November coinciding with the peak phase of the IOD. The blooms, thereafter, diminished rapidly in December 2006. In addition, there was an offshore intensification of chlorophyll‐a distribution off eastern Java during November initiated by upwelling‐favourable winds along the coast. Concurrent altimeter data show that the offshore intensification was co‐located with the cyclonic eddies that further enhanced the intensification by increasing the concentration of nutrients in the euphotic zone. On leave from the Department of Physics, University of Sriwijaya, South Sumatra, Indonesia. Currently at the Indian Institute of Tropical Meteorology Pashan, Pune‐411 008, India.
Time-Varying Soft-Maximum Control Barrier Functions for Safety in an A Priori Unknown Environment

This paper presents a time-varying soft-maximum composite control barrier function (CBF) that can be used to ensure safety in an a priori unknown environment, where local perception information regarding the safe set is periodically obtained. We consider the scenario where the periodically obtained perception feedback can be used to construct a local CBF that models a local subset of the unknown safe set. Then, we use a novel smooth time-varying soft-maximum function to compose the N most recently obtained local CBFs into a single CBF. This composite CBF models an approximate union of the N most recently obtained local subsets of the safe set. Notably, this composite CBF can have arbitrary relative degree r. Next, this composite CBF is used as a rth-order CBF constraint in a real-time optimization to determine a control that minimizes a quadratic cost while guaranteeing that the state stays in a time-varying subset of the unknown safe set. We also present an application of the time-varying soft-maximum composite CBF method to a nonholonomic ground robot with nonnegligible inertia. In this application, we present a simple approach to generate the local CBFs from the periodically obtained perception data.
Non-local boundary energy forms for quasidiscs: Codimension gap and approximation

We consider non-local energy forms of fractional Laplace type on quasicircles and prove that they can be approximated by similar energy forms on polygonal curves. The approximation is in terms of generalized Mosco convergence along a sequence of varying Hilbert spaces. The domains of the energy forms are the natural trace spaces, and we focus on the case of quasicircles of Hausdorff dimension greater than one. The jump in Hausdorff dimension results in a mismatch of fractional orders, which we compensate by a suitable choice of kernels. We provide approximations of quasidiscs by polygonal $(\varepsilon,\infty)$-domains with common parameter $\varepsilon>0$ and show convergence results for superpositions of Dirichlet integrals and non-local boundary energy forms.
The Gulf of Interpretation: From Chart to Message and Back Again

Charts are used to communicate data visually, but designing an effective chart that a broad set of people can understand is challenging. Usually, we do not know whether a chart's intended message aligns with the message readers perceive. In this mixed-methods study, we investigate how data journalists encode data and how a broad audience engages with, experiences, and understands these data visualizations. We conducted a series of workshops and interviews with school students, university students, job seekers, designers, and senior citizens to collect perceived messages and subjective feedback on a sample of eight real-world charts. We analyzed these messages and compared them to the intended message of the chart producer. Four of the collected messages from consumers were then provided to data journalists (including the ones that created the original charts) as a starting point to re-design the charts accordingly. The results from our work underline the difficulty of complex charts such as stacked bar charts and Sankey diagrams. Consumers are often overwhelmed with the amount of data provided and are easily confused with terms (as text) not well known. Chart producers tend to be faithful with data but are willing to abstract further when asked to transport particular messages visually. There are strong conventions on how to visually encode particular information that might not be to the benefit of many consumers.
ROSTAM: A Passwordless Web Single Sign-on Solution Mitigating Server Breaches and Integrating Credential Manager and Federated Identity Systems

The challenge of achieving passwordless user authentication is real given the prevalence of web applications that keep asking passwords. Complicating this issue further, in an enterprise environment, a single sign-on (SSO) service is often maintained but not all applications can be integrated with it. We envision a passwordless future which provides a frictionless and trustworthy online experience for users by integrating credential management and federated identity systems. In this regard, our implementation ROSTAM offers a dashboard that presents all applications the user can access with a single click after a passwordless SSO. The security of web passwords on the credential manager is ensured with a Master Key, rather than a Master Password, so that encrypted passwords can remain secure even if stolen from the server. We propose and implement novel techniques for synchronization (pairing) and recovery of this Master Key. We compare our solution to previous work using different evaluation frameworks, demonstrating that our hybrid solution combines the benefits of credential management and federated identity systems.