Miracle-dz/UniFAR-FTUs huggingface dataset:
Enhancing the Urban Atlas Features with the Water Balance Estimates Using the Landsat 8 Imagery

Between climate change and anthropogenic forcing, pressure on water resources is continually increasing all over the world. The growing population is gathering more and more in cities where the artificialization of soils and urban developments of all kinds are modifying the local water cycle. However, knowledge about the hydrological cycle in urban areas is not well developed today. Urbanization in a watershed can produce significant changes to the natural water cycle, mainly because permeable soils have been covered by less permeable surfaces. The consequences of this urbanization can be significant and multiple. The assessment of urban water resources aims to provide a suitable circumstances for planning, conditions for the planning, design, construction, operation, and maintenance of works used for irrigation and sanitation, among other applications. The methodology of the research is based on the data collection, which includes climatic data from June 2021 to June 2022 and the use of Digital elevation model to create a hydrographic network. The second part is land use data processing to create a land cover map using maximum Likelihood Supervised Classification, by combining different band composites of Landsat 8 images and NDVI to differentiate between vegetative and non-vegetative areas. However, CORINE Land Cover database was utilized to distinguish between continuous and discontinuous urban area in this classification. The final step in the process is to determine the hydrological coefficients by calculating the water cycle parameters (Evapotranspiration Runoff and Infiltration) for each land-use class. Furthermore, establishing a link between land use and hydrological behaviour and conducting a precise calculation of water balance-related coefficients in urban areas using Urban Atlas 2018.
Nematode and Coccidia Infections in Singing Birds Kept in Bird Stalls

Singing birds are adored by so many people in the Special Region of Yogyakarta because of their beautiful voices. Even so, these birds have the possibility to carry endoparasitic infections, namely nematode and coccidia. This study was conducted to determine the incidence of nematode and coccidia infection in singing birds traded in bird stalls in the Special Region of Yogyakarta. The singing birds were categorized into five feeding guilds, consisting of 19 herbivores, 19 granivores, 31 omnivores, 20 insectivores, and five carnivores. Thirty-seven birds kept in colony cages (eg. 4-6 birds of the same species in one cage) and 57 birds kept in individual cages were used in this study. Fresh faeces was sampled and transported in ice box to the laboratory for parasitological examination. Whitlock method was used to assess the presence of endoparasites. The incidence of endoparasites infection was analyzed descriptively. The result showed that 5.32% of the birds were infected by nematode and 30% by coccidia. Based on the cage systems, the incidence of nematode infection in the birds kept in the colony cage was 10.8%, whereas those in the individual cage was at the lower level of 1.8%. The incidence of coccidia infection in birds kept in colony and in individual cages was 35% and 26%, respectively. Based on the feeding guild categories, nematode infection was found in herbivores (10.5%), granivores (10.5%), and omnivores (3.2%). The coccidia infection was observed in herbivorous (47.4%), omnivorous (41.9%), granivorous (15.7%), and insectivorous (15%) birds. Maintenance management needs to be considered to prevent and eradicate endoparasites infection in singing birds kept individually or in colonies.
Dense Molecular Gas Properties of the Central Kiloparsec of Nearby Ultraluminous Infrared Galaxies Constrained by ALMA Three Transition-line Observations

We report the results of Atacama Large Millimeter/submillimeter Array (ALMA) 1–2 kpc resolution, three rotational transition-line (J = 2–1, J = 3–2, and J = 4–3) observations of multiple dense molecular gas tracers (HCN, HCO+, and HNC) for 10 nearby (ultra)luminous infrared galaxies ((U)LIRGs). Following the matching of beam sizes to 1–2 kpc for each (U)LIRG, the high-J-to-low-J transition-line flux ratios of each molecule and the emission-line flux ratios of different molecules at each J transition are derived. We conduct RADEX non-LTE model calculations and find that, under a wide range of gas density and kinetic temperature, the observed HCN-to-HCO+ flux ratios in the overall (U)LIRGs are naturally reproduced with enhanced HCN abundance compared to HCO+. Thereafter, molecular gas properties are constrained primarily through the use of HCN and HCO+ data and the adoption of fiducial values for the HCO+ column density and HCN-to-HCO+ abundance ratio. We quantitatively confirm the following: (i) molecular gas at the (U)LIRGs’ nuclei is dense (≳103–4 cm−3) and warm (≳100 K), (ii) the molecular gas density and temperature in nine ULIRGs’ nuclei are significantly higher than those of one LIRG’s nucleus, (iii) molecular gas in starburst-dominated sources tends to be less dense and cooler than ULIRGs with luminous AGN signatures. For six selected sources, we also apply a Bayesian approach by freeing all parameters and support the above main results. Our ALMA 1–2 kpc resolution, multiple transition-line data of multiple molecules are a very powerful tool for scrutinizing the properties of molecular gas concentrated around luminous energy sources in nearby (U)LIRGs’ nuclei.
Using physical parameters for tourism potential mapping: Study case of beach tourism destination in Gajahrejo, Malang

Beach tourism potential in Desa Gajahrejo, Kecamatan Gedangan, Kabupaten Malang can optimize its domestic income if developed and managed properly. Therefore, it is essential to map and thus analyze its tourism potential. This research used four physical indicators to determine the classification of the beach tourism potential of Gajahrejo: 1) distance from the city center, 2) amenities, 3) accessibility, and 4) attractiveness. After data collection using a survey method, the data was scored based on its potential values. After that, it was classified into five tourism potential classes from “very low” to “very high”. Out of 14 beaches in Gajahrejo, two are considered to have “low” potential, five are considered to have “moderate” potential, and seven are considered to have “high” potential. Amenities and accessibility indicators had the most influence on the final tourism potential score of the area. As a recommendation, each beach needs to have a distinctive tourist attraction to meet diverse tourist segmentations. Moreover, it is vital to improve the accessibility of each beach tourism destination by connecting it to the province’s main road.
Numerical analysis of discontinuous berm-supported shallow excavations for building construction

The use of discontinuous berms remains a common strategy in medium-rise urban excavations when a uniform slope is not safe enough and certain conditions are met. However, despite its frequent use, there is no broad and contrasted reference that allows its design and verification in a similar way to how it is done in the usual homogeneous slopes, either by abacus such as those of Taylor or Hoek and Bray or the two-dimensional numerical calculation with bishop’s method, finite elements, or finite differences, among others. The difficulty associated with the calculation of slope stability with discontinuous berms lies in the fact that it is basically a three-dimensional problem, not admitting any of the simplifications that allow it to be reduced to a two-dimensional case and use one of the methods mentioned above. The present research is oriented to model an excavation with discontinuous berms, considering its three-dimensional configuration and including as main variables the height of the excavation, the width between berms, and the properties of the terrain, represented with a Mohr-Coulomb failure criterion. From a large number of calculations, the necessary regressions will be obtained to allow a pre-design and verification of the main cases that arise in practice. The article presents the results obtained and details the advantages and disadvantages of the associated methodology.
Standardization of Offshore Surface Wind Speeds

AbstractWind measurement offers an essential data source for a wide range of practices in the fields of meteorology and wind engineering. However, records of surface winds are usually influenced by terrain/topographic effects, and direct usage of raw data may bring in nonignorable errors for follow-up applications. A data-driven standardization scheme was recently proposed by the authors to convert the surface wind measurements over rugged terrain into their potential values corresponding to reference conditions, that is, for neutral winds at a height of 10 m above open flat terrain (z0 = 0.03 m). As a complementary part of the preceding work, this study focuses on the standardization of surface wind speeds with marine exposures. The effect of wind strength on the roughness of the sea surface is further taken into account, with emphasis on the difference between deep-ocean and shallow-water cases. As an application example, wind measurements at a buoy site near the coastal line (water depth is 14 m) are a.
EcoFed: Efficient Communication for DNN Partitioning-Based Federated Learning

Efficiently running federated learning (FL) on resource-constrained devices is challenging since they are required to train computationally intensive deep neural networks (DNN) independently. DNN partitioning-based FL (DPFL) has been proposed as one mechanism to accelerate training where the layers of a DNN (or computation) are offloaded from the device to the server. However, this creates significant communication overheads since the intermediate activation and gradient need to be transferred between the device and the server during training. While current research reduces the communication introduced by DNN partitioning using local loss-based methods, we demonstrate that these methods are ineffective in improving the overall efficiency (communication overhead and training speed) of a DPFL system. This is because they suffer from accuracy degradation and ignore the communication costs incurred when transferring the activation from the device to the server. This article proposes EcoFed – a communication efficient framework for DPFL systems. EcoFed eliminates the transmission of the gradient by developing pre-trained initialization of the DNN model on the device for the first time. This reduces the accuracy degradation seen in local loss-based methods. In addition, EcoFed proposes a novel replay buffer mechanism and implements a quantization-based compression technique to reduce the transmission of the activation. It is experimentally demonstrated that EcoFed can reduce the communication cost by up to 133× and accelerate training by up to 21× when compared to classic FL. Compared to vanilla DPFL, EcoFed achieves a 16× communication reduction and 2.86× training time speed-up.
Density-Softmax: Scalable and Calibrated Uncertainty Estimation under Distribution Shifts

Prevalent deterministic deep-learning models suffer from significant over-confidence under distribution shifts. Probabilistic approaches can reduce this problem but struggle with computational efficiency. In this paper, we propose Density-Softmax, a fast and lightweight deterministic method to improve calibrated uncertainty estimation via a combination of density function with the softmax layer. By using the latent representation's likelihood value, our approach produces more uncertain predictions when test samples are distant from the training samples. Theoretically, we show that Density-Softmax can produce high-quality uncertainty estimation with neural networks, as it is the solution of minimax uncertainty risk and is distance-aware, thus reducing the over-confidence of the standard softmax. Empirically, our method enjoys similar computational efficiency as a single forward pass deterministic with standard softmax on the shifted toy, vision, and language datasets across modern deep-learning architectures. Notably, Density-Softmax uses 4 times fewer parameters than Deep Ensembles and 6 times lower latency than Rank-1 Bayesian Neural Network, while obtaining competitive predictive performance and lower calibration errors under distribution shifts.
Modelling and well-posedness of evolutionary differential variational-hemivariational inequalities

In this paper, we study the well-posedness of a class of evolutionary variational-hemivariational inequalities coupled with a nonlinear ordinary differential equation in Banach spaces. The proof is based on an iterative approximation scheme showing that the problem has a unique mild solution. In addition, we established continuity of the flow map with respect to the initial data. Under the general framework, we consider two new applications for modelling of frictional contact with viscoelastic materials, where the friction coefficient $\mu$ depends on an external state variable $\alpha$ and the slip rate $|\dot{u}_\tau|$. In the first application, we consider Coulomb friction with normal compliance, and in the second, normal damped response. In both cases, we present a new first-order approximation of the Dieterich rate-and-state friction law.
Democratizing LEO Satellite Network Measurement

Low Earth Orbit (LEO) satellite networks are quickly gaining traction with promises of impressively low latency, high bandwidth, and global reach. However, the research community knows relatively little about their operation and performance in practice. The obscurity is largely due to the high barrier of entry for measuring LEO networks, which requires deploying specialized hardware or recruiting large numbers of satellite Internet customers. In this paper, we introduce HitchHiking, a methodology that democratizes global visibility into LEO satellite networks. HitchHiking builds on the observation that Internet-exposed services that use LEO Internet can reveal satellite network architecture and performance, bypassing the need for specialized hardware. We evaluate HitchHiking against ground truth measurements and prior methods, showing that it provides more coverage and accuracy. With HitchHiking, we complete the largest study to date of Starlink network latency, measuring over 2,400 users across 27 countries. We uncover unexpected patterns in latency that surface how LEO routing is more complex than previously understood. Finally, we conclude with recommendations for future research on LEO networks.
Nested sequents for intermediate logics: the case of Gödel-Dummett logics

We present nested sequent systems for propositional Gödel-Dummett logic and its first-order extensions with non-constant and constant domains, built atop nested calculi for intuitionistic logics. To obtain nested systems for these Gödel-Dummett logics, we introduce a new structural rule, called the linearity rule, which (bottom-up) operates by linearising branching structure in a given nested sequent. In addition, an interesting feature of our calculi is the inclusion of reachability rules, which are special logical rules that operate by propagating data and/or checking if data exists along certain paths within a nested sequent. Such rules require us to generalise our nested sequents to include signatures (i.e. finite collections of variables) in the first-order cases, thus giving rise to a generalisation of the usual nested sequent formalism. Our calculi exhibit favourable properties, admitting the height-preserving invertibility of every logical rule and the (height-preserving) admissibility of a large collection of structural and reachability rules. We prove all of our systems sound and cut-free complete, and show that syntactic cut-elimination obtains for the intuitionistic systems. We conclude the paper by discussing possible extensions and modifications, putting forth an array of structural rules that could be used to provide a sizable class of intermediate logics with cut-free nested sequent systems.
G-invariant diffusion maps

The diffusion maps embedding of data lying on a manifold have shown success in tasks ranging from dimensionality reduction and clustering, to data visualization. In this work, we consider embedding data sets which were sampled from a manifold which is closed under the action of a continuous matrix group. An example of such a data set is images who's planar rotations are arbitrary. The G-invariant graph Laplacian, introduced in a previous work of the authors, admits eigenfunctions in the form of tensor products between the elements of the irreducible unitary representations of the group and eigenvectors of certain matrices. We employ these eigenfunctions to derive diffusion maps that intrinsically account for the group action on the data. In particular, we construct both equivariant and invariant embeddings which can be used naturally to cluster and align the data points. We demonstrate the effectiveness of our construction with simulated data.
The ALMA Interferometric Pipeline Heuristics

We describe the calibration and imaging heuristics developed and deployed in the Atacama Large Millimeter/submillimeter Array (ALMA) interferometric data processing pipeline, as of ALMA Cycle 9 operations. The pipeline software framework is written in Python, with each data reduction stage layered on top of tasks and toolkit functions provided by the Common Astronomy Software Applications package. This framework supports a variety of tasks for observatory operations, including science data quality assurance, observing mode commissioning, and user reprocessing. It supports ALMA and Very Large Array interferometric data along with ALMA and NRO 45 m single dish data, via different stages and heuristics. In addition to producing calibration tables, calibrated measurement sets, and cleaned images, the pipeline creates a WebLog which serves as the primary interface for verifying the quality assurance of the data by the observatory and for examining the contents of the data by the user. Following the adoption of the pipeline by ALMA Operations in 2014, the heuristics have been refined through annual prioritized development cycles, culminating in a new pipeline release aligned with the start of each ALMA Cycle of observations. Initial development focused on basic calibration and flagging heuristics (Cycles 2–3), followed by imaging heuristics (Cycles 4–5). Further refinement of the flagging and imaging heuristics, including the introduction of parallel processing, proceeded for Cycles 6–7. In the 2020 release, the algorithm to identify channels to use for continuum subtraction and imaging was substantially improved by the addition of a moment difference analysis. A spectral renormalization stage was added for the 2021 release (Cycle 8) to correct high spectral resolution visibility data acquired on targets exhibiting strong celestial line emission in their autocorrelation spectra. The calibration heuristics used in the low signal-to-noise regime were improved for the 2022 release (Cycle 9). In the two most recent Cycles, 97% of ALMA data sets were calibrated and imaged with the pipeline, ensuring long-term automated reproducibility of results. We conclude with a brief description of plans for future additions, including a self-calibration stage, support for multi-configuration imaging, and complete calibration and imaging of full polarization data.
Rethinking Incentives in Recommender Systems: Are Monotone Rewards Always Beneficial?

The past decade has witnessed the flourishing of a new profession as media content creators, who rely on revenue streams from online content recommendation platforms. The reward mechanism employed by these platforms creates a competitive environment among creators which affect their production choices and, consequently, content distribution and system welfare. It is thus crucial to design the platform's reward mechanism in order to steer the creators' competition towards a desirable welfare outcome in the long run. This work makes two major contributions in this regard: first, we uncover a fundamental limit about a class of widely adopted mechanisms, coined Merit-based Monotone Mechanisms, by showing that they inevitably lead to a constant fraction loss of the optimal welfare. To circumvent this limitation, we introduce Backward Rewarding Mechanisms (BRMs) and show that the competition game resultant from BRMs possesses a potential game structure. BRMs thus naturally induce strategic creators' collective behaviors towards optimizing the potential function, which can be designed to match any given welfare metric. In addition, the BRM class can be parameterized to allow the platform to directly optimize welfare within the feasible mechanism space even when the welfare metric is not explicitly defined.
Multiple-Step Quantized Triplet STDP Implemented with Memristive Synapse

As an extension of the pairwise spike-timing-dependent plasticity (STDP) learning rule, the triplet STDP is provided with greater capability in characterizing the synaptic changes in the biological neural cell. In this work, a novel mixed-signal circuit scheme, called multiple-step quantized triplet STDP, is designed to provide a precise and flexible implementation of coactivation triplet STDP learning rule in memristive synapse spiking neural network. The robustness of the circuit is greatly improved through the utilization of pulse-width encoded weight modulation signals. The circuit performance is studied through the simulations which are carried out in MATLAB Simulink&Simscape, and assessment is given by comparing the results of circuits with the algorithmic approaches.
Variational Positive-incentive Noise: How Noise Benefits Models

A large number of works aim to alleviate the impact of noise due to an underlying conventional assumption of the negative role of noise. However, some existing works show that the assumption does not always hold. In this paper, we investigate how to benefit the classical models by random noise under the framework of Positive-incentive Noise (Pi-Noise). Since the ideal objective of Pi-Noise is intractable, we propose to optimize its variational bound instead, namely variational Pi-Noise (VPN). With the variational inference, a VPN generator implemented by neural networks is designed for enhancing base models and simplifying the inference of base models, without changing the architecture of base models. Benefiting from the independent design of base models and VPN generators, the VPN generator can work with most existing models. From the experiments, it is shown that the proposed VPN generator can improve the base models. It is appealing that the trained variational VPN generator prefers to blur the irrelevant ingredients in complicated images, which meets our expectations.
Self-Supervised Hyperspectral Inpainting with the Optimisation inspired Deep Neural Network Prior

Hyperspectral Image (HSI)s cover hundreds or thousands of narrow spectral bands, conveying a wealth of spatial and spectral information. However, due to the instrumental errors and the atmospheric changes, the HSI obtained in practice are often contaminated by noise and dead pixels(lines), resulting in missing information that may severely compromise the subsequent applications. We introduce here a novel HSI missing pixel prediction algorithm, called Low Rank and Sparsity Constraint Plug-and-Play (LRS-PnP). It is shown that LRS-PnP is able to predict missing pixels and bands even when all spectral bands of the image are missing. The proposed LRS-PnP algorithm is further extended to a self-supervised model by combining the LRS-PnP with the Deep Image Prior (DIP), called LRS-PnP-DIP. In a series of experiments with real data, It is shown that the LRS-PnP-DIP either achieves state-of-the-art inpainting performance compared to other learning-based methods, or outperforms them.