Miracle-dz/UniFAR-FTUs huggingface dataset:
Seasonal dynamics and spatial patterns of soil moisture in a loess catchment

Abstract. The spatial and seasonal patterns in soil moisture and the processes controlling them in semi-arid landscapes are not well understood. Loess landscapes minimize any confounding effects of variation in soil characteristics and are thus ideal for studying topographic influences on soil moisture in drylands. In this study, volumetric soil moisture was monitored monthly for 5.5 years at 20 cm intervals between the surface and 5 m depth at 89 sites across a small (0.43 km2) catchment on the Chinese Loess Plateau. The median soil moisture was computed for each month and depth for each monitoring site as a measure of the typical soil moisture conditions. Seasonal changes in soil moisture were mainly concentrated in the shallow (0–100 cm) soil, with a clear seasonal separation between wet conditions in October–March and dry conditions in May–July, even though precipitation is highest in July–August. Soil moisture was higher on the northwest-facing slopes due to increased drying from solar radiation on the southeast-facing slopes. This effect of slope aspect was greater between October and March, when the zenith angle of the sun was lower and the aspect-dependent difference in solar radiation reaching the surface was larger. The wetter, northwest-facing slopes were also characterized by larger annual soil moisture storage changes. Soil texture was nearly uniform across both slopes, and soil moisture was not correlated with the topographic wetness index, suggesting that variations in evapotranspiration dominated the spatial pattern of soil moisture in shallow soils under both wet and dry conditions. Water balance calculations indicate that over 90 % of the annual precipitation was seasonally cycled in the soil between 0 and 300 cm, suggesting that only a minor fraction infiltrates to groundwater and becomes streamflow. Our findings may be broadly applicable to loess regions with monsoonal climates and may have practical implications for catchment-scale hydrologic modeling and the design of soil moisture monitoring networks.
Eavesdropping Mobile App Activity via Radio-Frequency Energy Harvesting

Radio-frequency (RF) energy harvesting is a promising technology for Internet-of-Things (IoT) devices to power sensors and prolong battery life. In this paper, we present a novel side-channel attack that leverages RF energy harvesting signals to eavesdrop mobile app activities. To demonstrate this novel attack, we propose AppListener , an automated attack framework that recognizes ﬁne-grained mobile app activities from harvested RF energy. The RF energy is harvested from a custom-built RF energy harvester which generates voltage signals from ambient Wi-Fi transmissions, and app activities are recognized from a three-tier classiﬁcation algorithm. We evaluate AppListener with four mobile devices running 40 common mobile apps (e.g., YouTube, Facebook, and What-sApp) belonging to ﬁve categories (i.e., video, music, social media, communication, and game); each category contains ﬁve application-speciﬁc activities. Experiment results show that AppListener achieves over 99% accuracy in differentiating four different mobile devices, over 98% accuracy in classifying 40 different apps, and 86 . 7% accuracy in recognizing ﬁve sets of application-speciﬁc activities. Moreover, a comprehensive study is conducted to show AppListener is robust to a number of impact factors, such as distance, environment, and non-target connected devices. Practices of integrating Ap-pListener into commercial IoT devices also demonstrate that it is easy to deploy. Finally, countermeasures are presented as the ﬁrst step to defend against this novel attack.
DEUCE v1.0: A neural network for probabilistic precipitation nowcasting with aleatoric and epistemic uncertainties

Precipitation nowcasting (forecasting locally for 0–6h) serves both public security and industries, facilitating the mitigation of losses incurred due to e.g. flash floods, and is usually done by predicting weather radar echoes, which provides better performance than NWP at that scale. Probabilistic nowcasts are especially useful as they provide a desirable framework for operational decision-making. Many extrapolation-based statistical nowcasting methods exist, but they all suffer from a limited 5 ability to capture the nonlinear growth and decay of precipitation, leading to a recent paradigm shift towards deep learning methods, more capable of representing these patterns. Despite of its potential advantages, the application of deep learning in probabilistic nowcasting has only recently started to be explored. Here we develop a novel probabilistic precipitation nowcasting method, based on Bayesian neural networks with variational inference and the U-Net architecture, named DEUCE. The method estimates the total predictive uncertainty 10 of precipitation by combining estimates of the epistemic (knowledge-related, reducible) and heteroscedastic aleatoric (data-dependent, irreducible) uncertainties, and produces an ensemble of development scenarios for the following 60 minutes. DEUCE is trained and verified using Finnish Meteorological Institute radar composites against established classical models. Our model is found to produce both skillful and reliable probabilistic nowcasts based on various evaluation criteria. It improves ROC Area Under the Curve scores 1–5% over STEPS and LINDA-P baselines, and comes close to the best-performer STEPS 15 on a CRPS metric. The reliability of DEUCE is demonstrated with, e.g., having the lowest Expected Calibration Error at 20 and 25 dBZ reflectivity thresholds, and coming second at 35 dBZ. On the other hand, deterministic performance of ensemble means is found to be worse than that of extrapolation
Automated Cookie Notice Analysis and Enforcement

Online websites use cookie notices to elicit consent from the users, as required by recent privacy regulations like the GDPR and the CCPA. Prior work has shown that these notices are designed in a way to manipulate users into making website-friendly choices which put users’ privacy at risk. In this work, we present CookieEnforcer , a new system for automatically discovering cookie notices and extracting a set of instructions that result in disabling all non-essential cookies. In order to achieve this, we ﬁrst build an automatic cookie notice detector that utilizes the rendering pattern of the HTML elements to identify the cookie notices. Next, we analyze the cookie notices and predict the set of actions required to disable all unnecessary cookies. This is done by modeling the problem as a sequence-to-sequence task, where the input is a machine-readable cookie notice and the output is the set of clicks to make. We demonstrate the efﬁcacy of CookieEnforcer via an end-to-end accuracy evaluation, showing that it can generate the required steps in 93.7% of the cases. Via a user study, we also show that CookieEnforcer can signiﬁcantly reduce the user effort. Finally, we characterize the behavior of CookieEnforcer on the top 100k websites from the Tranco list, showcasing its stability and scalability.
Evolution of the central safety factor during stabilized sawtooth instabilities at KSTAR

A motional Stark effect (MSE) diagnostic has recently been installed in the KSTAR tokamak. A difficulty faced at KSTAR and common to other MSE diagnostics is calibration of the system for absolute measurements. In this report we present our novel calibration routine and discuss first results, evaluating the evolution of the the central safety factor during sawtooth instabilities. The calibration scheme ensures that the bandpass filters typically used in MSE systems are aligned correctly and identifies and removes systematic offsets present in the measurement. This is verified by comparing the reconstructed safety factor profile against various discharges where the locations of rational q surfaces have been obtained from MHD markers. The calibration is applied to analyse the evolution of q0 in a shot where the sawteeth are stabilized by neutral beam injection. Within the analysed sawtooth periods q0 drops below unity during the quiescent phase and relaxes close to or slightly above unity at the sawtooth crash. This finding is in line with the classical Kadomtsev model of full magnetic reconnection and earlier findings at JET.
Cardiovascular health: A cross-national comparison between The Maine-Syracuse Longitudinal Study (Central New York, USA) and ORISCAV-LUX (Luxembourg)

: Comparative analyses of soft drink intakes in samples from the United States and Europe, and assessed intakes in relation to prevalence of metabolic syndrome (MetS) and its individual components are currently lacking. We used data collected on cardiovascular health and dietary intakes in participants from two cross-sectional studies: the Maine-Syracuse Longitudinal Study (MSLS), conducted in Central New York, USA in 2001–2006
What is the cause(s) of positive ozone trends in three megacity clusters in eastern China during 2015–2020?

Due to a robust emission control policy, significant reductions in major air pollutants, such as PM 2.5 , SO 2 , NO 2 , and CO, were observed in China between 2015 to 2020. On the other hand, during the same period, there was a notable increase in ozone (O 3 ) concentrations, making it a prominent air pollutant in eastern China. The annual mean concentration of maximum daily 8-hour average (MDA8) O 3 exhibited alarming linear trends of 2.4, 1.1, and 2.0 ppb yr –1 in three megacity clusters: Beijing-Tianjin-Hebei (BTH), Yangtze River Delta (YRD) and Pearl River Delta (PRD). Additionally, there was a significant 15 three-fold increase in the number of O 3 -exceeding days, defined as MDA8 O 3 >75 ppb during the same period. Our analysis indicated that the upward trends in the annual mean concentration of MDA8 were primarily driven by the rise in consecutive O 3 -exceeding days. Furthermore, from 2015 to 2017, there was a widespread expansion of high O 3 concentrations from urban centers to surrounding rural regions, resulting in a more uniform spatial distribution of O 3 after 2017. Lastly, we discovered a close association between O 3 episodes featuring four or more consecutive O 3 -exceeding days and the position and strength of 20 the West Pacific subtropical high (WPSH). The WPSH contributed to meteorological conditions characterized by clear skies, subsiding air motion, high vertical stability in the lower troposphere, increased solar radiation, and positive temperature anomaly at the surface. These favorable meteorological conditions greatly facilitated the formation of O 3 . Thus, we propose that the worsening O 3 trends observed in BTH, YRD and PRD from 2015 to 2020 can be attributed to enhanced photochemical O 3 production resulting from an increased occurrence of meteorological conditions with high solar radiation and positive 25 temperature anomalies under the influence of WPSH and tropical cyclones.
Stabilisation policy, rational expectations and price-level versus in(cid:135)ation targeting: a survey

We survey literature comparing in(cid:135)ation targeting (IT) and price-level targeting (PT) as macroeconomic stabilisation policies. Our focus is on New Keynesian models and areas that have seen signi(cid:133)cant developments since Ambler(cid:146)s (2009) survey: optimal monetary policy; the zero lower bound; (cid:133)nancial frictions; and transition costs of adopting a PT regime. Ambler(cid:146)s conclusion that PT improves social welfare in New Keynesian models is fairly robust, but we note an interesting split in the literature: PT consistently outperforms IT in models where policymakers commit to simple Taylor-type rules, but results in favour of PT when policy-makers minimise loss functions are overturned with small deviations from the baseline model. Since the bene(cid:133)cial e⁄ects of PT appear to hang on the joint assumption that agents are rational and the economy New Keynesian, we discuss survey and experimental evidence on rational expectations and the applied macro literature on the empirical performance of New Keynesian models. Overall, the evidence is not clear-cut, but we note that New Keynesian models can pass formal statistical tests against macro data and that models with rational expectations outperform those with behavioural expectations (i.e. heuristics) in direct statistical tests. We therefore argue that policymakers should continue to pay attention to PT.
Chronic Exposure to Low Molecular Weight Polycyclic Aromatic Hydrocarbons Promotes Metabolic Inflammation in Vertebrate and Invertebrate Systems

2-naphthol is a low molecular weight polycyclic aromatic hydrocarbon and ubiquitous air pollutant. Recent epidemiological reports suggest that exposure to individual low molecular weight polycyclic aromatic hydrocarbons is correlated with increased incidences of childhood obesity. Yet, most studies focus on the health effects of either high molecular weight molecules or polycyclic aromatic hydrocarbon mixtures. This research explores the potential obesogenic impact of 2-naphthol in cell and animal models. To assess the in vivo effects of 2-naphthol, we monitored life fitness traits and lipid accumulation in the invertebrate Daphnia magna across development. Results showed that D. magna directly exposed to 2-naphthol demonstrated delayed reproductive development, produced fewer offspring, and accumulated more lipids than D. magna that were not exposed to 2-naphthol. Analysis of lipid accumulation by Nile Red staining revealed a dosedependent increase in overall lipid quantity. To confirm these findings, we also examined the effect of 2-naphthol on lipid metabolism and inflammation in vitro. We employed 3T3-L1 and BAT1 mammalian cell lines to model white and brown adipocytes, respectively, alongside a murine macrophage cell line (RAW264.7). Cells were challenged with either acute or chronic 2-naphthol treatments, then differentiated adipocytes were assessed for changes in adipogenesis, lipid accumulation, and isoproterenol-stimulated thermogenesis. Additionally, LPS-induced inflammation was evaluated in both adipocytes and macrophages. 2-naphthol exposure increased the expression of key adipogenic and lipogenic genes, while lipolytic gene expression decreased in both chronically treated adipocyte lines. Further, 2-naphthol exposure suppressed isoproterenolstimulated thermogenic gene expression in BAT1 brown adipocytes. In consistence, adipocytes exposed to 2-naphthol and stained with BODIPY and Oil Red O showed an increase in lipid accumulation. Additionally, adipocytes and macrophages chronically exposed to 2-naphthol showed an up-regulation in mRNA expression of major inflammatory cytokines, such as Tnfα, Il1β, and Il-6. Overall, these findings support previous research that indicates 2-naphthol is a possible obesogen that may contribute to the development of obesity and associated metabolic disorders via modulating lipid metabolism and thermogenesis in adipocytes, and inflammation in both adipocytes and macrophages. INDEX WORDS: Polycyclic aromatic hydrocarbon, Daphnia, Adipocyte, Lipid metabolism, Inflammation, Differentiation, Adipogenesis Copyright by Asia S. Bright 2023 Chronic Exposure to Low Molecular Weight Polycyclic Aromatic Hydrocarbons Promotes Metabolic Inflammation in Vertebrate and Invertebrate Systems
Formation, dissociation, and diffusion of various hydrogen dimers in silicon

Available data on hydrogen in silicon samples quenched from a high T and in those exposed to a plasma at low T are analyzed and the existence of three different forms of diatomic hydrogen is concluded. One of them, H2A (a dimer of a moderate diffusivity) was fully characterized by the temperature dependence of its dissociation rate constant and its equilibrium constant and diffusivity. The two other forms, H2B (of a high diffusivity) and H2C (of a low diffusivity) are partially characterized. As a practical application of the properties of the dimers deduced here, the hydrogen content in solar cells was estimated from data on the permanent deactivation of boron–oxygen centers that degrade solar cell efficiency.
Towards a Reference-Based Genre Knowledge Model of Quechua Folk Tales

: This article presents a reference-based genre knowledge model for Quechua folk tales ( kwintus ) based on an in-depth analysis of referential categories with the potential of characterizing a genre. Embedded in a cognitive framework, the study combines a data-driven approach, analyzing both discrete and non-discrete units as well as constructions, with a top-down approach starting from (genre specific) conceptual structures such as the role types proposed by Propp. By examining six folk tales related in Cuzco Quechua, shared linguistic features are hypothesized as central features of the genre of kwintus , while features determined by individual or stylistic choices are less representative.
WORKING PAPER SERIES SHOULD ROBOTS BE TAXED ?

Using a quantitative model that features technical progress in automation and endogenous skill choice, we show that, given the current U.S. tax system, a sustained fall in automation costs can lead to a massive rise in income inequality. We characterize the optimal tax system in this model. We find that it is optimal to tax robots while the current generations of routine workers, who can no longer move to non-routine occupations, are active in the labor force. Once these workers retire, optimal robot taxes are zero. Joao Guerreiro Department of Economics Northwestern University Evanston, IL 60208 joao.guerreiro2022@u.northwestern.edu Sergio Rebelo Northwestern University Kellogg School of Management Department of Finance Leverone Hall Evanston, IL 60208-2001 and CEPR and also NBER s-rebelo@northwestern.edu Pedro Teles Banco de Portugal R. Francisco Ribeiro 2 1150 Lisboa Portugal and Univ Catolica Portuguesa and CEPR pteles@ucp.pt
Robustness of Consensus over Weighted Digraphs

This paper investigates the robustness of consensus protocols over weighted directed graphs using the Nyquist criterion and small gain theorem for agents with single and double integrator dynamics. For single integrators, the linear consensus protocol, described by the weighted Laplacian, is considered, while for double integrators a new consensus protocol is presented which also uses the weighted Laplacian. For both single and double integrators, the allowable bound on a single edge weight perturbation, while consensus among the agents can be achieved, is derived. Specific results are obtained for a directed acyclic graph and the directed cycle graph along with their graph theoretic interpretations. For double integrators, a dual problem is formulated and solved, whereby it is shown that, subject to certain conditions, perturbing a single edge weight may stabilize the consensus protocol. Simulations support the theoretical results.
A New Approach to Decoder-Side Depth Estimation in Immersive Video Transmission

This paper presents a novel approach to decoder-side depth estimation (DSDE) in immersive video transmission. The proposal improves the state-of-the-art coding approach by changing the encoding and decoding processes so that the decoder-side depth estimator can utilize partial geometry information about the scene from several available input depth maps. Proposed changes allow for significantly faster decoding and better quality of virtual viewports presented to the viewer. The paper proposes two approaches, namely input depth map assistance (IDMA) and extended IDMA (eIDMA), both compliant with the bitstream of MPEG immersive video (MIV) standard. IDMA involves sending full depth maps for a selected subset of input views, which are then used to refine and enhance the depth maps for the remaining views. In eIDMA, the decoder additionally reprojects decoded depth maps to the remaining views and enhances them with depth patches containing difficult-to-estimate areas. Proposed methods were tested under the ISO/IEC MPEG Video Coding common test conditions for MIV. The combination of two proposals, the adaptive IDMA, was shown to outperform the current state-of-the-art DSDE approach in the rendered video quality and the computational complexity of decoder. MPEG experts have appreciated the proposed approaches, which will comply with a new DSDE profile of the incoming second edition of the MIV standard.
Profitability: Third Party Funds and Credit Risk Study on Indonesian Digital Banking

With advances in technological development, people’s needs for fast and readily available services are on the rise. Therefore, many conventional banks started to develop their own brand of digital banking to accommodate these needs. This research is aimed to analyze the effect of third party funds (TPF) toward profitability and credit risk, effect of credit risk toward profitability, and indirect effect of third party funds toward profitability through credit risk by using path analysis method. This research shows that TPF do have a significantly negative effect toward credit risk and similar result of TPF toward profitability, a positive but insignificant effect of credit risk toward profitability, and lastly, TPF shows a direct effect toward profitability, which means that credit risk functioned as a partial mediation effect between TPF and profitability.
Digital Ageism: Emerging Challenges and Best Practices of Age-Friendly Digital Urban Governance

The ageing of urban populations poses serious challenges for policymakers and urban planners alike. While the number of people over 65 is increasing in urban areas, the digital transition in cities raises concerns about the persisting digital divide facing older citizens, as well as the digital inequality and ageism inherent to most digital domains. As ageing in place and place attachment play a significant role in the social engagement and well-being of older people, the purpose of this research is to shed light on the novel approaches taken by local governments to foster active participation among senior residents in the digital public sphere. Using semi-structured interviews with public officials from three age-friendly cities in Spain, we have explored innovative urban projects for digital inclusion, active ageing, and autonomy for older people. The findings of the study reveal the importance of coordinated multi-stakeholder initiatives in promoting digital literacy and overcoming barriers rooted in ageism in the digital world. True representation of older people in local governments, the promotion of co-creation initiatives led by seniors, and the standardisation of universal design and accessibility are some of the key contributions made by Spanish cities in their transition toward places that are digitally inclusive and age-friendly.
Cross-Layer Design for Energy-Efficient Reliable Multi-Path Transmission in Event-Driven Wireless Sensor Networks

In event-driven wireless sensor networks (WSNs), a reliable, efficient, and scalable routing solution is required for the reliable delivery of sensory data to the base station (BS). However, existing routing algorithms rarely address the issue of energy efficiency under multi-path conflicts for multi-event-driven scenarios. In order to maximize energy efficiency while maintaining a manageable conflict probability, this paper investigates a cross-layer design of routing and power control for multi-event-driven WSNs. We first develop a mathematical characterization of the conflict probability in multi-path routing, and we then formulate the energy efficiency maximization problem as a non-convex combinatorial fractional optimization problem subject to a maximum conflict probability constraint. By utilizing non-linear fractional programming and dual decomposition, an iterative search algorithm was used to obtain near-optimal power allocation and routing solutions. Extensive results demonstrate that our proposed algorithm achieved a gain of 9.09% to 35.05% in energy efficiency compared to other routing algorithms, thus indicating that our proposed algorithm can avoid unnecessary control overhead from multi-path conflicts with a lower conflict probability and can ensure maximum energy efficiency through routing and power control design.