Miracle-dz/UniFAR-FTUs huggingface dataset:
Likelihood Based Inference for ARMA Models

Autoregressive moving average (ARMA) models are frequently used to analyze time series data. Despite the popularity of these models, algorithms for fitting ARMA models have weaknesses that are not well known. We provide a summary of parameter estimation via maximum likelihood and discuss common pitfalls that may lead to sub-optimal parameter estimates. We propose a random restart algorithm for parameter estimation that frequently yields higher likelihoods than traditional maximum likelihood estimation procedures. We then investigate the parameter uncertainty of maximum likelihood estimates, and propose the use of profile confidence intervals as a superior alternative to intervals derived from the Fisher's information matrix. Through a series of simulation studies, we demonstrate the efficacy of our proposed algorithm and the improved nominal coverage of profile confidence intervals compared to the normal approximation based on Fisher's Information.
Monitoring and Prediction of Urban Growth Using GIS Techniques: A Case Study of Dohuk City Kurdistan Region of Iraq

GIS, Remote Sensing Techniques and Information Technology are today used extensively for managing , controlling and predicting the rapidly growing urbanization of large cities, towns and villages. Urban growth is the expansion of towns and cities with respect to the increase of the size of a built-up area. Urban growth mainly depends upon the city requirement, facilities available and industrialization. These causes migration of people from rural to the urban areas, putting immense pressure on infrastructure, natural resources and lead to formation of slums or uncontrolled urban expansions. Urban growth areas increasingly encroach on the surrounding rural areas causing enormous pressure and dense on the limited scale of infrastructure, very often leading to the unplanned and unsustainable development. Urban growth is a recent phenomenon in Kurdistan Region of Iraq, as result of regional development plans that started during the end of 90th of the 20th century. The urban growth made in the study area has been occurred as a result of the stability of the security situation and the growth of the economic situation in the region. The growth of urban cities have been take uncontrolled policies,which lead the engineers , planners and decision makers to use information technologies such as GIS and Remote Sensing to monitor this type of growth and to study the future expectation or prediction . This paper aims to observe and predict the urban growth of Dohuk City using the twin techniques of GIS and Satellite images. Data have been derived from high resolution satellite imagery and filed survey. The outcome of this work shows that the area of Dohuk has been expanded through the period from 2003-2012 with a rapid growth. The main purpose of this paper is to use high resolution imagery for detecting urban growth changes occurred in residential areas of Dohuk city during the last ten years. The database are designed and developed to support application modules namely built up area, green and recreational areas. Finally, change process of Dohuk city residential areas over the last ten years was determined using GIS overlay analysis and its relation with population growth was studied. In this period, urban growth of residential areas have been expanded rapidly because of many reasons such as; the demographic migration from the villages and rural areas adjacent to the city center as well as migration from areas of central and southern Iraq, because of the stable security situation and moderation climate in addition to providing business and investment opportunities in addition to the fact that the city is considered as trade free zone with neighboring countries.
Oxysulfide Perovskites: Reduction of the Electronic Band Gap of RbTaO3 by Sulfur Substitution to Enhance Prospective Solar Cell and Thermoelectric Performances

In this study, the effects of sulfur substitution on the structural, mechanical, electronic, optical, and thermodynamic properties of RbTaO3-xSx have been investigated using the WIEN2k code in the framework of density functional theory (DFT). The cubic phase of RbTaO3 transforms to tetragonal for RbTaO2S and RbTaOS2, the later transforms again to a cubic phase with added sulfur for RbTaS3. The results showed that substituting S for O anions in RbTaO3 effectively decreased the band gap from 2.717 eV to 1.438 eV, 0.286 eV, and 0.103 eV for the RbTaO3,RbTaO2S, RbTaOS2, and RbTaS3 compounds, respectively. The optical constants such as dielectric constants, refractive index, absorption coefficient, photoconductivity, reflectivity and loss function have been calculated and analyzed. The elastic constants and moduli, and their anisotropic nature were also investigated. Finally, the Debye temperature, thermal conductivity, melting temperature, specific capacities and thermal expansion coefficients were computed and analyzed using established formalisms. The reduced band gap (1.438 eV) and high absorption coefficient (~106 cm-1) of RbTaO2S makes it suitable for solar cell applications and for other visible light devices. Reduction of the band gap and phonon thermal conductivity owing to Ssubstitution is expected to enhance thermoelectric performances of the S-containing phases
Fourier PD and PDUNet: Complex-valued networks to speed-up MR Thermometry during Hypterthermia

Hyperthermia (HT) in combination with radio- and/or chemotherapy has become an accepted cancer treatment for distinct solid tumour entities. In HT, tumour tissue is exogenously heated to temperatures of 39 to 43 $\degree$C for 60 minutes. Temperature monitoring can be performed noninvasively using dynamic magnetic resonance imaging (MRI). However, the slow nature of MRI leads to motion artefacts in the images due to the movements of patients during image acquisition time. By discarding parts of the data, the speed of the acquisition can be increased - known as Undersampling. However, due to the invalidation of the Nyquist criterion, the acquired images have lower resolution and can also produce artefacts. The aim of this work was, therefore, to reconstruct highly undersampled MR thermometry acquisitions with better resolution and with less artefacts compared to conventional techniques like compressed sensing. The use of deep learning in the medical field has emerged in recent times, and various studies have shown that deep learning has the potential to solve inverse problems such as MR image reconstruction. However, most of the published work only focusses on the magnitude images, while the phase images are ignored, which are fundamental requirements for MR thermometry. This work, for the first time ever, presents deep learning based solutions for reconstructing undersampled MR thermometry data. Two different deep learning models have been employed here, the Fourier Primal-Dual network and Fourier Primal-Dual UNet, to reconstruct highly undersampled complex images of MR thermometry. It was observed that the method was able to reduce the temperature difference between the undersampled MRIs and the fully sampled MRIs from 1.5 $\degree$C to 0.5 $\degree$C.
Revisiting Mobility Modeling with Graph: A Graph Transformer Model for Next Point-of-Interest Recommendation

Next Point-of-Interest (POI) recommendation plays a crucial role in urban mobility applications. Recently, POI recommendation models based on Graph Neural Networks (GNN) have been extensively studied and achieved, however, the effective incorporation of both spatial and temporal information into such GNN-based models remains challenging. Temporal information is extracted from users' trajectories, while spatial information is obtained from POIs. Extracting distinct fine-grained features unique to each piece of information is difficult since temporal information often includes spatial information, as users tend to visit nearby POIs. To address the challenge, we propose Mobility Graph Transformer (MobGT) that enables us to fully leverage graphs to capture both the spatial and temporal features in users' mobility patterns. MobGT combines individual spatial and temporal graph encoders to capture unique features and global user-location relations. Additionally, it incorporates a mobility encoder based on Graph Transformer to extract higher-order information between POIs. To address the long-tailed problem in spatial-temporal data, MobGT introduces a novel loss function, Tail Loss. Experimental results demonstrate that MobGT outperforms state-of-the-art models on various datasets and metrics, achieving 24% improvement on average. Our codes are available at https://github.com/Yukayo/MobGT.
Predicting emergence of crystals from amorphous matter with deep learning

Crystallization of the amorphous phases into metastable crystals plays a fundamental role in the formation of new matter, from geological to biological processes in nature to synthesis and development of new materials in the laboratory. Predicting the outcome of such phase transitions reliably would enable new research directions in these areas, but has remained beyond reach with molecular modeling or ab-initio methods. Here, we show that crystallization products of amorphous phases can be predicted in any inorganic chemistry by sampling the crystallization pathways of their local structural motifs at the atomistic level using universal deep learning potentials. We show that this approach identifies the crystal structures of polymorphs that initially nucleate from amorphous precursors with high accuracy across a diverse set of material systems, including polymorphic oxides, nitrides, carbides, fluorides, chlorides, chalcogenides, and metal alloys. Our results demonstrate that Ostwald's rule of stages can be exploited mechanistically at the molecular level to predictably access new metastable crystals from the amorphous phase in material synthesis.
BAAF: A Benchmark Attention Adaptive Framework for Medical Ultrasound Image Segmentation Tasks

The AI-based assisted diagnosis programs have been widely investigated on medical ultrasound images. Complex scenario of ultrasound image, in which the coupled interference of internal and external factors is severe, brings a unique challenge for localize the object region automatically and precisely in ultrasound images. In this study, we seek to propose a more general and robust Benchmark Attention Adaptive Framework (BAAF) to assist doctors segment or diagnose lesions and tissues in ultrasound images more quickly and accurately. Different from existing attention schemes, the BAAF consists of a parallel hybrid attention module (PHAM) and an adaptive calibration mechanism (ACM). Specifically, BAAF first coarsely calibrates the input features from the channel and spatial dimensions, and then adaptively selects more robust lesion or tissue characterizations from the coarse-calibrated feature maps. The design of BAAF further optimizes the"what"and"where"focus and selection problems in CNNs and seeks to improve the segmentation accuracy of lesions or tissues in medical ultrasound images. The method is evaluated on four medical ultrasound segmentation tasks, and the adequate experimental results demonstrate the remarkable performance improvement over existing state-of-the-art methods. In addition, the comparison with existing attention mechanisms also demonstrates the superiority of BAAF. This work provides the possibility for automated medical ultrasound assisted diagnosis and reduces reliance on human accuracy and precision.
Semidiscrete optimal transport with unknown costs

Semidiscrete optimal transport is a challenging generalization of the classical transportation problem in linear programming. The goal is to design a joint distribution for two random variables (one continuous, one discrete) with fixed marginals, in a way that minimizes expected cost. We formulate a novel variant of this problem in which the cost functions are unknown, but can be learned through noisy observations; however, only one function can be sampled at a time. We develop a semi-myopic algorithm that couples online learning with stochastic approximation, and prove that it achieves optimal convergence rates, despite the non-smoothness of the stochastic gradient and the lack of strong concavity in the objective function.
Effects of Fabric Anisotropy of Sand on Cyclic Undrained Triaxial and Torsional Strengths

ABSTRACT In order to examine the effects of fabric anisotropy on the relationships between the cyclic triaxial and torsional shear strengths, a series of cyclic undrained triaxial and torsional shear tests was performed on three kinds of sands made by three different sample preparation methods. The test results showed that the cyclic undrained strengths varied with sample preparation methods. In particular, the effects of sample preparation methods were clearly observed in the triaxial test. The relationships between the cyclic triaxial and torsional shear strengths also varied with sample preparation methods. In addition, it was found that the effects of fabric anisotropy due to the difference in the sample preparation methods appear in the axial deformation and the developed pore water pressure characteristics during the cyclic triaxial test. In order to evaluate quantitatively the effects of fabric anisotropy on the relationships between the cyclic triaxial and torsional strengths, new index parameters were introduced, which express the degree of the anisotropy of specimens. Consequently, it was indicated that there exists a unique rule between these parameters and the ratio of torsional strengths to triaxial strengths.
A Bayesian Bootstrap for Mixture Models

This paper proposes a new nonparametric Bayesian bootstrap for a mixture model, by developing the traditional Bayesian bootstrap. We first reinterpret the Bayesian bootstrap, which uses the P\'olya-urn scheme, as a gradient ascent algorithm which associated one-step solver. The key then is to use the same basic mechanism as the Bayesian bootstrap with the switch from a point mass kernel to a continuous kernel. Just as the Bayesian bootstrap works solely from the empirical distribution function, so the new Bayesian bootstrap for mixture models works off the nonparametric maximum likelihood estimator for the mixing distribution. From a theoretical perspective, we prove the convergence and exchangeability of the sample sequences from the algorithm and also illustrate our results with different models and settings and some real data.
NEUCORE: Neural Concept Reasoning for Composed Image Retrieval

Composed image retrieval which combines a reference image and a text modifier to identify the desired target image is a challenging task, and requires the model to comprehend both vision and language modalities and their interactions. Existing approaches focus on holistic multi-modal interaction modeling, and ignore the composed and complimentary property between the reference image and text modifier. In order to better utilize the complementarity of multi-modal inputs for effective information fusion and retrieval, we move the multi-modal understanding to fine-granularity at concept-level, and learn the multi-modal concept alignment to identify the visual location in reference or target images corresponding to text modifier. Toward the end, we propose a NEUral COncept REasoning (NEUCORE) model which incorporates multi-modal concept alignment and progressive multimodal fusion over aligned concepts. Specifically, considering that text modifier may refer to semantic concepts not existing in the reference image and requiring to be added into the target image, we learn the multi-modal concept alignment between the text modifier and the concatenation of reference and target images, under multiple-instance learning framework with image and sentence level weak supervision. Furthermore, based on aligned concepts, to form discriminative fusion features of the input modalities for accurate target image retrieval, we propose a progressive fusion strategy with unified execution architecture instantiated by the attended language semantic concepts. Our proposed approach is evaluated on three datasets and achieves state-of-the-art results.
Online Sensitivity Optimization in Differentially Private Learning

Training differentially private machine learning models requires constraining an individual's contribution to the optimization process. This is achieved by clipping the 2-norm of their gradient at a predetermined threshold prior to averaging and batch sanitization. This selection adversely influences optimization in two opposing ways: it either exacerbates the bias due to excessive clipping at lower values, or augments sanitization noise at higher values. The choice significantly hinges on factors such as the dataset, model architecture, and even varies within the same optimization, demanding meticulous tuning usually accomplished through a grid search. In order to circumvent the privacy expenses incurred in hyperparameter tuning, we present a novel approach to dynamically optimize the clipping threshold. We treat this threshold as an additional learnable parameter, establishing a clean relationship between the threshold and the cost function. This allows us to optimize the former with gradient descent, with minimal repercussions on the overall privacy analysis. Our method is thoroughly assessed against alternative fixed and adaptive strategies across diverse datasets, tasks, model dimensions, and privacy levels. Our results indicate that it performs comparably or better in the evaluated scenarios, given the same privacy requirements.
An estimate of the upwelling rate in the equatorial Atlantic based on the distribution of bomb radiocarbon

As shown by the Geosecs results the equatorial Atlantic Ocean has both a lower water column inventory and a lower surface water concentration of bomb-produced radiocarbon than do the temperate waters to the north and south. This anomaly can only be explained by equatorial upwelling. For the equatorial zone as a whole this rate must be about 17 sverdrups.
On Grid Graph Reachability and Puzzle Games

Many puzzle video games, like Sokoban, involve moving some agent in a maze. The reachable locations are usually apparent for a human player, and the difficulty of the game is mainly related to performing actions on objects, such as pushing (reachable) boxes. For this reason, the difficulty of a particular level is often measured as the number of actions on objects, other than agent walking, needed to find a solution. In this paper we study CP and SAT approaches for solving these kind of problems. We review some reachability encodings and propose a new one. We empirically show that the new encoding is well-suited for solving puzzle problems in the planning as SAT paradigm, especially when considering the execution of several actions in parallel.
Sparse Backpropagation for MoE Training

One defining characteristic of Mixture-of-Expert (MoE) models is their capacity for conducting sparse computation via expert routing, leading to remarkable scalability. However, backpropagation, the cornerstone of deep learning, requires dense computation, thereby posting challenges in MoE gradient computations. Here, we introduce SparseMixer, a scalable gradient estimator that bridges the gap between backpropagation and sparse expert routing. Unlike typical MoE training which strategically neglects certain gradient terms for the sake of sparse computation and scalability, SparseMixer provides scalable gradient approximations for these terms, enabling reliable gradient estimation in MoE training. Grounded in a numerical ODE framework, SparseMixer harnesses the mid-point method, a second-order ODE solver, to deliver precise gradient approximations with negligible computational overhead. Applying SparseMixer to Switch Transformer on both pre-training and machine translation tasks, SparseMixer showcases considerable performance gain, accelerating training convergence up to 2 times.
Accuracy validation and comparison of global digital elevation models over Croatia

Accuracy assessment of digital elevation models (DEMs) plays an important role in facing their use in geoscience applications. This study investigates the vertical accuracy of most recently published versions of global DEMs over Croatia: 1 arc-minute global relief model (ETOPO1), Global 30 Arc-Second Elevation (GTOPO30), SRTM30+, Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010), Altimeter Corrected Elevations 2 DEM (ACE2 DEM), Shuttle Radar Topography Mission GDEM (SRTM GDEM), and Advanced Spaceborne Thermal Emission and Reflection GDEM (ASTER GDEM). Geodetic ground control points (benchmarks) allowed the comparison in terms of vertical accuracy. Additionally, the differences between models in the identical points were determined and analysed after adjusting the models to the same resolution. The results have yielded information about the overall accuracy of models; the accuracy depending on height, land cover, and slope; presence of large and systematic errors; and mutual agreement between the models. Overall vertical accuracies according to the root mean square error over Croatia are: ETOPO1 27.6 m, GTOPO30 21.6 m, SRTM30+ 21.3 m, GMTED2010 7.4 m, ACE2 DEM 4.5 m, SRTM GDEM 3.8 m, and ASTER GDEM 7.1 m. The highest accuracy was shown by SRTM GDEM version 3, which is far better than the previously released versions. All models have proved to be worse in the rough and forest areas, whereas in flat and barelands, new-generation global DEMs SRTM GDEM, and ASTER GDEM are highly accurate.
Topological rigidity of quoric manifolds

Quoric manifolds are the quaternionic analogue of toric manifolds. They admit a locally nice action of $(S^3)^n$ and the quotient is a manifold with corners. We show that they satisfy equivariant rigidity. More precisely, any locally linear $(S^3)^n$-manifold that it is equivariantly homotopic equivalent to a quoric manifold is equivariantly homeomorphic to it. The proof is given by generalising the methods of used in Coxeter and toric manifolds.
Sequence of COVID-19 Vaccination and COVID-19 Infection and Their Association With the Development of Active Tuberculosis: A Case-Control Study

Introduction Information regarding the cross-risk of coronavirus disease 2019 (COVID-19) and tuberculosis (TB) is still sparse. This study aimed to identify the patterns of sequence of COVID-19 vaccination and COVID-19 infection and to explore the association between COVID-19 vaccination, COVID-19 infection, and the development of active TB. Methods It was a case-control study conducted in RSUD Dr. Iskak Hospital, Tulungagung, between October 2022 and April 2023. Active cases of TB patients were compared with non-TB controls in the same hospital, with the same age and sex. Their pattern of sequence of COVID-19 vaccination and infection was investigated. Logistic regression was used to assess the association between these key variables. Results Of 296 case-control sets, 64.2% were female. The mean ± standard deviation of age was 46 ± 15.6 years. 5.7% of the cases and 6.4% of the controls had a history of COVID-19 infection, whereas 58.8% and 68.4% had been vaccinated (mostly after infection). The adjusted odds ratio (95% confidence interval) of COVID-19 infection on risk to the development of active TB was 1.45 (0.58, 3.65). Those of COVID-19 vaccination of one to four doses were 0.42 (0.17, 1), 0.98 (0.58, 1.66), 0.48 (0.25, 0.93), and 0.09 (0.01, 0.81), respectively. Conclusion It was found that there were five patterns of sequence of COVID-19 infection and COVID-19 vaccination, with the most frequent being having COVID-19 infection before COVID-19 vaccination. Our data did not support the association between COVID-19 infection and the subsequent development of active TB. On the other hand, COVID-19 vaccination has been demonstrated to increase some protection against the development of active TB.