Miracle-dz/UniFAR-FTUs huggingface dataset:
MARINE ZONE MANAGEMENT AND THE EPBC ACT—HOW ENVIRONMENTAL MARINE GEOLOGICAL INFORMATION PROVIDES CERTAINTY FOR PETROLEUM EXPLORATION

To protect the diversity of marine life in Australia’s Exclusive Economic Zone (EEZ), the federal parliament has passed the Environmental Protection and Biodiversity Conservation (EPBC) Act 1999. The Act is being implemented through the design of a national representative system of marine protected areas (MPAs) that will place under protection a representative portion of Australia’s EEZ by 2012. There have already been 13 MPAs nominated for the southeast region in 2006. Limited biological data in Australia’s EEZ has resulted in biophysical information compiled by Geoscience Australia being used as a proxy for seabed biodiversity in support of marine conservation planning. Information we use to characterise the seabed includes bathymetry, geomorphology, acoustic properties, sediment properties, and slope and sediment mobilisation due to waves and tides. To better characterise habitats on the Australian continental shelf, Geoscience Australia is creating seascape maps (similar to geological facies maps) that integrate these multiple layers of spatial data, and which are useful for the prediction of the distribution of biodiversity in Australia’s EEZ. This information provides 100% spatial coverage based on objective, multivariate statistical methods and offers certainty for managers and stakeholders including the oil and gas industry, who are involved with designing Australia’s national MPA system. Certainty for industries operating in the EEZ is enhanced by a reproducible, science-based approach for identifying conservation priorities and the classification of sea floor types within multiple use areas.
Genetic and environmental factors in essential arterial hypertension in an urban population of Cuba

: The present trial has been conducted in order to determine familia aggregation and the relationship between high blood pressure, inheritance and environmental factors. We studied 108 subjects (36 high blood pressure patients, 36 controls and 36 first degree relatives) ranging in age from 21 to 55 years. This case-control study was undertaken to determine risk factors. Multivariance analysis with logistic regression was carried out. Our results showed hereditary factors in 73.8% of the cases. Being the main gene autosomal recessive. Thus the main factor for high pressure is hereditary predisposition. Others factor are, body mass index and the coefficient waist-hip. We found no correlation with serum lipids.
Possibilities for Increased Carbon Sequestration through Improved Protection of Russian Forests

Improved protection of the Russian forests through more efficient forest fire protection and control of outbreaks of insects and disease may result in additional carbon sequestration - about 0.15 Pg (150 million tons) C annually during the next 100 years. The basic uncertainties in the scenario discussed in this article are linked to the paucity of inventory data for some areas of the study, the incomplete knowledge about the reaction of forests on different disturbance regimes, and the separation of the influences of different factors on the dynamics of the forests.
Nutrient restriction during late gestation reduces milk yield and mammary blood flow in lactating primiparous beef females

Abstract Fall-calving primiparous beef females [body weight (BW): 451 ± 28 (SD) kg; body condition score (BCS): 5.4 ± 0.7] were individually-fed 100% (control; CON; n = 13) or 70% (nutrient restricted; NR; n = 13) of estimated metabolizable energy and metabolizable protein requirements from day 160 of gestation to calving. Post-calving, all dams were individually-fed tall fescue hay supplemented to meet estimated nutrient requirements for maintenance, growth, and lactation until day 149 of lactation. Four-hour milk yields were collected on days 21, 42, 63, 84, 105, and 147 of lactation, and milk nutrient composition was determined. Doppler ultrasonography of both pudendoepigastric arterial trunks was conducted every 21 d from days 24 to 108 of lactation. Total mammary blood flow was calculated, and hemodynamics from both sides were averaged. Data were analyzed as repeated measures with nutritional plane, day of lactation, their interaction, calving date, and calf sex (if P < 0.25) as fixed effects. We previously reported that post-calving, NR dams weighed 64 kg less and were 2.0 BCS lower than CON, but calf birth weight was not affected. Milk weight and volume were 15% less (P = 0.04) for NR dams than CON. Milk protein concentration was lower (P = 0.008) for NR dams than CON, but triglyceride and lactose concentrations were not affected (P ≥ 0.20) by nutritional plane. Milk urea N concentration of NR dams tended to be greater (P = 0.07) on day 42 but was lower (P = 0.01) on day 147 of lactation than CON. Total milk protein, triglyceride, and lactose yields were less (P ≤ 0.05) for NR dams than CON. Total milk urea N yield was less (P ≤ 0.03) for NR dams than CON on days 21, 63, and 147 of lactation. Maternal heart rate was greater (P = 0.008), but pudendoepigastric arterial trunk peak systolic velocity, resistance index, and cross-sectional area were less (P ≤ 0.04) and pulsatility index tended to be less (P = 0.06) for NR dams than CON. Mammary blood flow was 19% less (P = 0.004) for NR dams than CON, but mammary blood flow relative to milk weight or dam BW was not affected (P ≥ 0.14) by nutritional plane. Most milk yield, milk nutrient composition, and mammary blood flow variables were affected (P ≤ 0.04) by day of lactation. In summary, first-parity beef females that were nutrient restricted during late gestation and then fed to meet estimated nutrient requirements during lactation had decreased milk nutrient yield and a similar reduction in mammary blood flow.
Penalized Semiparametric Estimation for Causal Inference with Possibly Invalid Instruments

Inferring causal effects with unmeasured confounder is a main challenge in causal inference. Many researchers impose parametric assumptions on the distribution of unmeasured confounder. However, due to the unobservable nature of the unmeasured confounder, it is more reasonable to leave its distribution unrestricted. Another key challenge in causal inference is the involvement of invalid instrumental variables, which may lead to biased inference and possibly misleading scientific conclusions. To this end, we employ a flexible semiparametric model that allows for possibly invalid instruments without specifying the distribution of unmeasured confounder in this work. A penalized semiparametric estimator for causal effects is constructed and its oracle and asymptotic properties are well established for statistical inference. We evaluate the performance of the estimator through simulation studies, revealing that our proposed estimator exhibits asymptotic unbiasedness and robustness in estimating causal effects, along with consistent selection of invalid instruments. We also demonstrate its application using Atherosclerosis Risk in Communities Study data set, which further validates its robustness in the presence of invalid instruments. Additionally, we have implemented the proposed method in R, and the corresponding R code is available for free download.
MixNet: Towards Effective and Efficient UHD Low-Light Image Enhancement

With the continuous advancement of imaging devices, the prevalence of Ultra-High-Definition (UHD) images is rising. Although many image restoration methods have achieved promising results, they are not directly applicable to UHD images on devices with limited computational resources due to the inherently high computational complexity of UHD images. In this paper, we focus on the task of low-light image enhancement (LLIE) and propose a novel LLIE method called MixNet, which is designed explicitly for UHD images. To capture the long-range dependency of features without introducing excessive computational complexity, we present the Global Feature Modulation Layer (GFML). GFML associates features from different views by permuting the feature maps, enabling efficient modeling of long-range dependency. In addition, we also design the Local Feature Modulation Layer (LFML) and Feed-forward Layer (FFL) to capture local features and transform features into a compact representation. This way, our MixNet achieves effective LLIE with few model parameters and low computational complexity. We conducted extensive experiments on both synthetic and real-world datasets, and the comprehensive results demonstrate that our proposed method surpasses the performance of current state-of-the-art methods. The code will be available at \url{https://github.com/zzr-idam/MixNet}.
Symmetry breaking in geometric quantum machine learning in the presence of noise

Geometric quantum machine learning based on equivariant quantum neural networks (EQNN) recently appeared as a promising direction in quantum machine learning. Despite the encouraging progress, the studies are still limited to theory, and the role of hardware noise in EQNN training has never been explored. This work studies the behavior of EQNN models in the presence of noise. We show that certain EQNN models can preserve equivariance under Pauli channels, while this is not possible under the amplitude damping channel. We claim that the symmetry breaking grows linearly in the number of layers and noise strength. We support our claims with numerical data from simulations as well as hardware up to 64 qubits. Furthermore, we provide strategies to enhance the symmetry protection of EQNN models in the presence of noise.
Cooperative Multi-Agent Graph Bandits: UCB Algorithm and Regret Analysis

In this paper, we formulate the multi-agent graph bandit problem as a multi-agent extension of the graph bandit problem introduced by Zhang, Johansson, and Li [CISS 57, 1-6 (2023)]. In our formulation, $N$ cooperative agents travel on a connected graph $G$ with $K$ nodes. Upon arrival at each node, agents observe a random reward drawn from a node-dependent probability distribution. The reward of the system is modeled as a weighted sum of the rewards the agents observe, where the weights capture some transformation of the reward associated with multiple agents sampling the same node at the same time. We propose an Upper Confidence Bound (UCB)-based learning algorithm, Multi-G-UCB, and prove that its expected regret over $T$ steps is bounded by $O(\gamma N\log(T)[\sqrt{KT} + DK])$, where $D$ is the diameter of graph $G$ and $\gamma$ a boundedness parameter associated with the weight functions. Lastly, we numerically test our algorithm by comparing it to alternative methods.
Seventeen 2 Micron All Sky Survey (2MASS) Hypervelocity Stars (HVS) from Gaia DR3

As part of an ongoing search for hypervelocity stars I found seventeen two micron all sky survey sources with Gaia G magnitudes less than 16.0 and radial velocities less than −600 km s−1. All these stars are brighter in the K band when compared with their V and G magnitudes. Ten of these (including three carbon stars) are long period variable stars of Mira type. One is a relatively nearby high proper motion star and one is a very high galactic latitude chemically peculiar metal-poor star. It may be a galactic halo star. One star is a Kepler red giant, two stars may be cluster members and two are in the star-forming region (probably YSOs). It is not clear how these stars acquired such high radial velocities. Further study of these seventeen stars is needed.
Coupling Dynamics and Linear Polarization Phenomena in Codirectional Polariton Waveguide Couplers

In this work, we explore the potential of spin-based integrated devices using polariton waveguides that form codirectional couplers. For suitable coupler parameters, a transfer of condensates between the arms of the coupler, occurs leading to the observation of Josephson-like oscillations. The ability to tune the periodicity of these oscillations opens the way to the design of polaritonic circuits in which the directionality of the signal towards the output terminals can be controlled. We also investigate the response of the devices to linearly polarized excitation, delving into the dynamics of linear polarization at the output terminals of long couplers, providing valuable insights into the potential applications of spin-based polariton devices, including polariton switches and logic gates with efficient operation. Our results are supported by numerical simulations based on generalized Gross-Pitaevskii equation describing the dynamics of coherent polaritons in spatially non-uniform system. We show, how the coupling and the controllable spin degree of freedom in polariton couplers opens avenues for innovative optical architectures and functionalities.
Personality Trait Inference Via Mobile Phone Sensors: A Machine Learning Approach

This study provides evidence that personality can be reliably predicted from activity data collected through mobile phone sensors. Employing a set of well informed indicators calculable from accelerometer records and movement patterns, we were able to predict users' personality up to a 0.78 F1 score on a two class problem. Given the fast growing number of data collected from mobile phones, our novel personality indicators open the door to exciting avenues for future research in social sciences. Our results reveal distinct behavioral patterns that proved to be differentially predictive of big five personality traits. They potentially enable cost effective, questionnaire free investigation of personality related questions at an unprecedented scale. We show how a combination of rich behavioral data obtained with smartphone sensing and the use of machine learning techniques can help to advance personality research and can inform both practitioners and researchers about the different behavioral patterns of personality. These findings have practical implications for organizations harnessing mobile sensor data for personality assessment, guiding the refinement of more precise and efficient prediction models in the future.
Analyzing Brain Activity During Learning Tasks with EEG and Machine Learning

This study aimed to analyze brain activity during various STEM activities, exploring the feasibility of classifying between different tasks. EEG brain data from twenty subjects engaged in five cognitive tasks were collected and segmented into 4-second clips. Power spectral densities of brain frequency waves were then analyzed. Testing different k-intervals with XGBoost, Random Forest, and Bagging Classifier revealed that Random Forest performed best, achieving a testing accuracy of 91.07% at an interval size of two. When utilizing all four EEG channels, cognitive flexibility was most recognizable. Task-specific classification accuracy showed the right frontal lobe excelled in mathematical processing and planning, the left frontal lobe in cognitive flexibility and mental flexibility, and the left temporoparietal lobe in connections. Notably, numerous connections between frontal and temporoparietal lobes were observed during STEM activities. This study contributes to a deeper understanding of implementing machine learning in analyzing brain activity and sheds light on the brain's mechanisms.
Polytopic Autoencoders with Smooth Clustering for Reduced-order Modelling of Flows

With the advancement of neural networks, there has been a notable increase, both in terms of quantity and variety, in research publications concerning the application of autoencoders to reduced-order models. We propose a polytopic autoencoder architecture that includes a lightweight nonlinear encoder, a convex combination decoder, and a smooth clustering network. Supported by several proofs, the model architecture ensures that all reconstructed states lie within a polytope, accompanied by a metric indicating the quality of the constructed polytopes, referred to as polytope error. Additionally, it offers a minimal number of convex coordinates for polytopic linear-parameter varying systems while achieving acceptable reconstruction errors compared to proper orthogonal decomposition (POD). To validate our proposed model, we conduct simulations involving two flow scenarios with the incompressible Navier-Stokes equation. Numerical results demonstrate the guaranteed properties of the model, low reconstruction errors compared to POD, and the improvement in error using a clustering network.
Area Modeling using Stay Information for Large-Scale Users and Analysis for Influence of COVID-19

Understanding how people use area in a city can be a valuable information in a wide range of fields, from marketing to urban planning. Area usage is subject to change over time due to various events including seasonal shifts and pandemics. Before the spread of smartphones, this data had been collected through questionnaire survey. However, this is not a sustainable approach in terms of time to results and cost. There are many existing studies on area modeling, which characterize an area with some kind of information, using Point of Interest (POI) or inter-area movement data. However, since POI is data that is statically tied to space, and inter-area movement data ignores the behavior of people within an area, existing methods are not sufficient in terms of capturing area usage changes. In this paper, we propose a novel area modeling method named Area2Vec, inspired by Word2Vec, which models areas based on people's location data. This method is based on the discovery that it is possible to characterize an area based on its usage by using people's stay information in the area. And it is a novel method that can reflect the dynamically changing people's behavior in an area in the modeling results. We validated Area2vec by performing a functional classification of areas in a district of Japan. The results show that Area2Vec can be usable in general area analysis. We also investigated area usage changes due to COVID-19 in two districts in Japan. We could find that COVID-19 made people refrain from unnecessary going out, such as visiting entertainment areas.
Applications of flow models to the generation of correlated lattice QCD ensembles

Machine-learned normalizing flows can be used in the context of lattice quantum field theory to generate statistically correlated ensembles of lattice gauge fields at different action parameters. This work demonstrates how these correlations can be exploited for variance reduction in the computation of observables. Three different proof-of-concept applications are demonstrated using a novel residual flow architecture: continuum limits of gauge theories, the mass dependence of QCD observables, and hadronic matrix elements based on the Feynman-Hellmann approach. In all three cases, it is shown that statistical uncertainties are significantly reduced when machine-learned flows are incorporated as compared with the same calculations performed with uncorrelated ensembles or direct reweighting.
Exploring the role of structure in a time constrained decision task

The structure of the basal ganglia is remarkably similar across a number of species (often described in terms of direct, indirect and hyperdirect pathways) and is deeply involved in decision making and action selection. In this article, we are interested in exploring the role of structure when solving a decision task while avoiding to make any strong assumption regarding the actual structure. To do so, we exploit the echo state network paradigm that allows to solve complex task based on a random architecture. Considering a temporal decision task, the question is whether a specific structure allows for better performance and if so, whether this structure shares some similarity with the basal ganglia. Our results highlight the advantage of having a slow (direct) and a fast (hyperdirect) pathway that allows to deal with late information during a decision making task.
Key to Kindness: Reducing Toxicity In Online Discourse Through Proactive Content Moderation in a Mobile Keyboard

Growing evidence shows that proactive content moderation supported by AI can help improve online discourse. However, we know little about designing these systems, how design impacts efficacy and user experience, and how people perceive proactive moderation across public and private platforms. We developed a mobile keyboard with built-in proactive content moderation which we tested (N=575) within a semi-functional simulation of a public and private communication platform. Where toxic content was detected, we used different interventions that embedded three design factors: timing, friction, and the presentation of the AI model output. We found moderation to be effective, regardless of the design. However, friction was a source of annoyance while prompts with no friction that occurred during typing were more effective. Follow-up interviews highlight the differences in how these systems are perceived across public and private platforms, and how they can offer more than moderation by acting as educational and communication support tools.
Mathematical Algorithm Design for Deep Learning under Societal and Judicial Constraints: The Algorithmic Transparency Requirement

Deep learning still has drawbacks in terms of trustworthiness, which describes a comprehensible, fair, safe, and reliable method. To mitigate the potential risk of AI, clear obligations associated to trustworthiness have been proposed via regulatory guidelines, e.g., in the European AI Act. Therefore, a central question is to what extent trustworthy deep learning can be realized. Establishing the described properties constituting trustworthiness requires that the factors influencing an algorithmic computation can be retraced, i.e., the algorithmic implementation is transparent. Motivated by the observation that the current evolution of deep learning models necessitates a change in computing technology, we derive a mathematical framework which enables us to analyze whether a transparent implementation in a computing model is feasible. We exemplarily apply our trustworthiness framework to analyze deep learning approaches for inverse problems in digital and analog computing models represented by Turing and Blum-Shub-Smale Machines, respectively. Based on previous results, we find that Blum-Shub-Smale Machines have the potential to establish trustworthy solvers for inverse problems under fairly general conditions, whereas Turing machines cannot guarantee trustworthiness to the same degree.
FARe: Fault-Aware GNN Training on ReRAM-based PIM Accelerators

Resistive random-access memory (ReRAM)-based processing-in-memory (PIM) architecture is an attractive solution for training Graph Neural Networks (GNNs) on edge platforms. However, the immature fabrication process and limited write endurance of ReRAMs make them prone to hardware faults, thereby limiting their widespread adoption for GNN training. Further, the existing fault-tolerant solutions prove inadequate for effectively training GNNs in the presence of faults. In this paper, we propose a fault-aware framework referred to as FARe that mitigates the effect of faults during GNN training. FARe outperforms existing approaches in terms of both accuracy and timing overhead. Experimental results demonstrate that FARe framework can restore GNN test accuracy by 47.6% on faulty ReRAM hardware with a ~1% timing overhead compared to the fault-free counterpart.