Miracle-dz/UniFAR-FTUs huggingface dataset:
Determining the Hubble constant without the sound horizon: A 
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Many theoretical resolutions to the so-called"Hubble tension"rely on modifying the sound horizon at recombination, $r_s$, and thus the acoustic scale used as a standard ruler in the cosmic microwave background (CMB) and large scale structure (LSS) datasets. As shown in a number of recent works, these observables can also be used to compute $r_s$-independent constraints on $H_0$ by making use of the horizon scale at matter-radiation equality, $k_{\rm eq}$, which has different sensitivity to high redshift physics than $r_s$. As such, $r_s$- and $k_{\rm eq}$-based measurements of $H_0$ (within a $\Lambda$CDM framework) may differ if there is new physics present pre-recombination. In this work, we present the tightest constraints on the latter from current data, finding $H_0=64.8^{+2.2}_{-2.5}$ at 68% CL (in $\mathrm{km}\,\mathrm{s}^{-1}\mathrm{Mpc}^{-1}$ units) from a combination of BOSS galaxy power spectra, Planck CMB lensing, and the newly released Pantheon+ supernova constraints, as well as physical priors on the baryon density, neutrino mass, and spectral index. The BOSS and Planck measurements have different degeneracy directions, leading to the improved combined constraints, with a bound of $H_0 = 67.1^{+2.5}_{-2.9}$ ($63.6^{+2.9}_{-3.6}$) from BOSS (Planck) alone. The results show some dependence on the neutrino mass bounds, with the constraint broadening to $H_0 = 68.0^{+2.9}_{-3.2}$ if we instead impose a weak prior on $\sum m_\nu$ from terrestrial experiments, or shifting to $H_0 = 64.6\pm2.4$ if the neutrino mass is fixed to its minimal value. Even without dependence on the sound horizon, our results are in $\approx 3\sigma$ tension with those obtained from the Cepheid-calibrated distance ladder, which begins to cause problems for new physics models that vary $H_0$ by changing acoustic physics or the expansion history immediately prior to recombination.
A review of exact results for fluctuation formulas in random matrix theory

Covariances and variances of linear statistics of a point process can be written as integrals over the truncated two-point correlation function. When the point process consists of the eigenvalues of a random matrix ensemble, there are often large $N$ universal forms for this correlation after smoothing, which results in particularly simple limiting formulas for the fluctuation of the linear statistics. We review these limiting formulas, derived in the simplest cases as corollaries of explicit knowledge of the truncated two-point correlation. One of the large $N$ limits is to scale the eigenvalues so that limiting support is compact, and the linear statistics vary on the scale of the support. This is a global scaling. The other, where a thermodynamic limit is first taken so that the spacing between eigenvalues is of order unity, and then a scale imposed on the test functions so they are slowly varying, is the bulk scaling. The latter was already identified as a probe of random matrix characteristics for quantum spectra in the pioneering work of Dyson and Mehta.
SurroundDepth: Entangling Surrounding Views for Self-Supervised Multi-Camera Depth Estimation

Depth estimation from images serves as the fundamental step of 3D perception for autonomous driving and is an economical alternative to expensive depth sensors like LiDAR. The temporal photometric constraints enables self-supervised depth estimation without labels, further facilitating its application. However, most existing methods predict the depth solely based on each monocular image and ignore the correlations among multiple surrounding cameras, which are typically available for modern self-driving vehicles. In this paper, we propose a SurroundDepth method to incorporate the information from multiple surrounding views to predict depth maps across cameras. Specifically, we employ a joint network to process all the surrounding views and propose a cross-view transformer to effectively fuse the information from multiple views. We apply cross-view self-attention to efficiently enable the global interactions between multi-camera feature maps. Different from self-supervised monocular depth estimation, we are able to predict real-world scales given multi-camera extrinsic matrices. To achieve this goal, we adopt the two-frame structure-from-motion to extract scale-aware pseudo depths to pretrain the models. Further, instead of predicting the ego-motion of each individual camera, we estimate a universal ego-motion of the vehicle and transfer it to each view to achieve multi-view ego-motion consistency. In experiments, our method achieves the state-of-the-art performance on the challenging multi-camera depth estimation datasets DDAD and nuScenes.
A Two Species Thomas-Fermi Model for Stellar Ground States

We present an energy functional for a Thomas-Fermi type two-ﬂuid model of a self-gravitating non-rotating charged body, with a non-relativistic kinetic energy. We prove that, under certain conditions on the total number of positively charged and negatively charged particles, a minimizer exists and both ﬂuids have compact support. We prove the same result for special relativistic kinetic energy, assuming further conditions on the total number of particles. In the non-relativistic kinetic energy case, we further prove the uniqueness of the minimizer, as well as present results relating the general shape of the minimizer to the total number of particles.
Hierarchical Annotation for Building A Suite of Clinical Natural Language Processing Tasks: Progress Note Understanding

Applying methods in natural language processing on electronic health records (EHR) data has attracted rising interests. Existing corpus and annotation focus on modeling textual features and relation prediction. However, there are a paucity of annotated corpus built to model clinical diagnostic thinking, a processing involving text understanding, domain knowledge abstraction and reasoning. In this work, we introduce a hierarchical annotation schema with three stages to address clinical text understanding, clinical reasoning and summarization. We create an annotated corpus based on a large collection of publicly available daily progress notes, a type of EHR that is time-sensitive, problem-oriented, and well-documented by the format of Subjective, Objective, Assessment and Plan (SOAP). We also define a new suite of tasks, Progress Note Understanding, with three tasks utilizing the three annotation stages. This new suite aims at training and evaluating future NLP models for clinical text understanding, clinical knowledge representation, inference and summarization.
Finding Counterfactual Explanations through Constraint Relaxations

Interactive constraint systems often suffer from infeasibil- ity (no solution) due to conﬂicting user constraints. A com-mon approach to recover infeasibility is to eliminate the con- straints that cause the conﬂicts in the system. This approach allows the system to provide an explanation as: “if the user is willing to drop out some of their constraints, there exists a so-lution”. However, one can criticise this form of explanation as not being very informative. A counterfactual explanation is a type of explanation that can provide a basis for the user to re- cover feasibility by helping them understand which changes can be applied to their existing constraints rather than remov- ing them. This approach has been extensively studied in the machine learning ﬁeld, but requires a more thorough investi- gation in the context of constraint satisfaction. We propose an iterative method based on conﬂict detection and maximal re- laxations in over-constrained constraint satisfaction problems to help compute a counterfactual explanation.
Enabling Deep Learning for All-in EDGE paradigm

Deep Learning-based models have been widely investigated, and they have demonstrated significant performance on non-trivial tasks such as speech recognition, image processing, and natural language understanding. However, this is at the cost of substantial data requirements. Considering the widespread proliferation of edge devices ( e.g. , Internet of Things devices) over the last decade, Deep Learning in the edge paradigm, such as device-cloud integrated platforms, is required to leverage its superior performance. Moreover, it is suitable from the data requirements perspective in the edge paradigm because the proliferation of edge devices has resulted in an explosion in the volume of generated and collected data. However, there are difficulties due to other requirements such as high computation, high latency, and high bandwidth caused by Deep Learning applications in real-world scenarios. In this regard, this survey paper investigates Deep Learning at the edge, its architecture, enabling technologies, and model adaption techniques, where edge servers and edge devices participate in deep learning training and inference. For simplicity, we call this paradigm the All-in EDGE paradigm. Besides, this paper presents the key performance metrics for Deep Learning at the All-in EDGE paradigm to evaluate various deep learning techniques and choose a suitable design. Moreover, various open challenges arising from the deployment of Deep Learning at the All-in EDGE paradigm are identified and discussed.
Learning and scoring Gaussian latent variable causal models with unknown additive interventions

With observational data alone, causal structure learning is a challenging problem. The task becomes easier when having access to data collected from perturbations of the underlying system, even when the nature of these is unknown. Existing methods either do not allow for the presence of latent variables or assume that these remain unperturbed. However, these assumptions are hard to justify if the nature of the perturbations is unknown. We provide results that enable scoring causal structures in the setting with additive, but unknown interventions. Specifically, we propose a maximum-likelihood estimator in a structural equation model that exploits system-wide invariances to output an equivalence class of causal structures from perturbation data. Furthermore, under certain structural assumptions on the population model, we provide a simple graphical characterization of all the DAGs in the interventional equivalence class. We illustrate the utility of our framework on synthetic data as well as real data involving California reservoirs and protein expressions. The software implementation is available as the Python package \emph{utlvce}.
A novel approach based on X-ray fluorescence and photon attenuation to the analysis of soils for forensic investigation

: Use of soil in forensic science is related to its several chemical, physical, and biological properties associated with its capacity to adhere to different materials and surfaces. Forensic soil experts and police investigators have been using soil samples as an aid in criminal investigations. Soils can act as fingerprints because they present contrasting physical, chemical, biological, and mineralogical properties. However, depending on the analytical tools utilized to characterize the soil, differentiating the samples when they have similar properties might be impossible. Thus, soil utilization as a fingerprint material requires increasing the number of variables measured as well as the accuracy and precision of the measurements. This study presents the feasibility of methods based on X-ray fluorescence (XRF) and radiation interaction parameters to discriminate soils for forensic investigations. Analyses of soil particle size, elemental composition (XRF), mass attenuation coefficient (μ), atomic (σ a ) and electronic (σ e ) cross-sections, effective atomic number (Z eff ), and electron density (N el ) were carried out to evaluate the potential of nuclear parameters to differentiate soils. Ten different soil types collected at 0.00-0.05 m layer were studied. The radiation interaction parameters were obtained through the XCOM computer code, while the experimental measurements were carried out by the traditional gamma-ray attenuation method utilizing the radioactive sources of 241 Am and 137 Cs. The results showed that the soils presented broad differences in terms of clay, silt, and sand contents as well as in the major oxides. These differences influenced the radiation attenuation properties as verified through the multivariate analysis. For the lowest photon energy studied (10 and 30 keV), σ e was the most interesting parameter to discriminate the soils. For energies above 59.5 keV, Z eff and N el were the most important parameters. Good agreement was found between the calculated and measured parameters. The findings of this study indicate that radiation interaction parameters have great potential for crime scene investigation providing new parameters for better discrimination of soils. The main advantage of the method presented here is that it is fast, easy to implement, does not require powerful computers, and the XCOM code can be run online at the NIST (National Institute of Standards and Technology, USA) website.
Amber Gemstones Sorting By Colour

The objective of this study is to create computer vision algorithms for autonomous multiclass identification of amber nuggets by their colour. By applying the proposed methods an automated production sorting system has been developed. This system can be used, for example in combination with conveyor systems, and in any other case that requires distinguishing objects of many classes in a high-rate flow of objects. In order to achieve this, the proposed system operates with colour features selection, algorithm for classifier training, grouping, and voting with reject option have been developed. The developed system has been used in an automated amber sorting line to increase the quantities of sorted amber nuggets. The applied algorithms gave 88.21 % as the highest accuracy for the amber nugget expert database consisting of 30 classes. DOI: http://dx.doi. org/10.5755/j01.eie. 23.2. 17993
Promoting the Higher Education Excellence in Jordan: Factors Influencing Learner Attitude toward E-Learning Environment Based on the Integrated Platform

Electronic learning (e-learning) is considered in a beginning stage in the developing nations such as Arab world especially in the field of higher education system. Jordan as one of the developing nation though appreciates the importance of universities and their role in achieving an economic prosperity through the growth of human resources; unfortunately, the adoption of e-learning is quite low among the students in the Jordanian universities. This study therefore, is concerned with the improvement of educational process through the adoption of e-learning tools among the students in the Jordanian higher education institutions. The main purpose of this study is to examine the potential prominent factors related to the adoption and usage of Information and Communication Technology (ICT) in the Jordanian public universities among the students. The main challenge of the study is to provide such an understanding on the e-learning usage by applying the Technology Acceptance Model (TAM), Diffusion of Innovation (DOI) theory, and Decomposed Theory of Planned Behaviour (DTPB). A self-administrated survey was conducted on 350 of students selected from public universities in Jordan. A total of 253 participants (72%) have responded, and series of data analyses of variables measurement for reliability and validity test of predictors were performed. The results of the analysis, however, contribute a new model which is considered as a novel model in such studies. The findings show that Attitude towards Technology (ATT), have positively affected the Behavioural Intention (BI) to use technologies in the higher educational system among students. Moreover, there is a significant relationship between students’ perception of technology characteristics and their ATT using the e-learning technologies in the higher education system.
Re-attention vs. Bi-attention: Investigating two Alignment Architectures for Neural Question Answering

In this project, I investigated how re-attention and bi-attention mechanisms focus on relevant information in Neural Machine Comprehension Question Answering models. I implemented a Mnemonic Reader which uses re-attention alignment architecture and compared multiple aspects with a BiDAF baseline network that uses bi-attention alignment. I incrementally added features to context and question embeddings to investigate their effects on the performance of the model
An ENS-Oriented Voltage Protection Scheme for Inverter-Based Generators in Active Distribution Networks

This paper unveils that maintaining a proper voltage protection for inverter-based distributed generations (IBDGs) using overcurrent relays and during the power grids’ faults with considerable resistance stands as a complicated issue. To overcome this challenge, this paper presents a new protection scheme through developing a voltage index (VI) and launching a new characteristic that concentrates on minimizing energy-not-supplied (ENS). If the developed voltage index is in place, the near area faults with considerable resistance could be effectively discriminated from those at a far distance. Moreover, the adopted characteristic represents two operation modes known by fast and slow modes. For the faults in the vicinity of the IBDG, independent from resistances, the fast part of the proposed characteristic and VI are jointly corresponded to clear the faults swiftly. Moreover, to satisfy coordination time interval (CTI) among relays pairs, the slow part acts in the conventional manner which is corresponded during the backup process. To evaluate the performance of the proposed method, numerical studies are carried out on IEEE 33-bus test system. Results show that the ENS index is enhanced while meeting IBDGs’ voltage protection requirements and satisfying selectivity tasks.
Alteration of Mitochondrial Integrity as Upstream Event in the Pathophysiology of SOD1-ALS

Little is known about the early pathogenic events by which mutant superoxide dismutase 1 (SOD1) causes amyotrophic lateral sclerosis (ALS). This lack of mechanistic understanding is a major barrier to the development and evaluation of efficient therapies. Although protein aggregation is known to be involved, it is not understood how mutant SOD1 causes degeneration of motoneurons (MNs). Previous research has relied heavily on the overexpression of mutant SOD1, but the clinical relevance of SOD1 overexpression models remains questionable. We used a human induced pluripotent stem cell (iPSC) model of spinal MNs and three different endogenous ALS-associated SOD1 mutations (D90Ahom, R115Ghet or A4Vhet) to investigate early cellular disturbances in MNs. Although enhanced misfolding and aggregation of SOD1 was induced by proteasome inhibition, it was not affected by activation of the stress granule pathway. Interestingly, we identified loss of mitochondrial, but not lysosomal, integrity as the earliest common pathological phenotype, which preceded elevated levels of insoluble, aggregated SOD1. A super-elongated mitochondrial morphology with impaired inner mitochondrial membrane potential was a unifying feature in mutant SOD1 iPSC-derived MNs. Impaired mitochondrial integrity was most prominent in mutant D90Ahom MNs, whereas both soluble disordered and detergent-resistant misfolded SOD1 was more prominent in R115Ghet and A4Vhet mutant lines. Taking advantage of patient-specific models of SOD1-ALS in vitro, our data suggest that mitochondrial dysfunction is one of the first crucial steps in the pathogenic cascade that leads to SOD1-ALS and also highlights the need for individualized medical approaches for SOD1-ALS.
Urine-Derived Kidney Progenitor Cells in Cystinosis

Nephropathic cystinosis is an inherited lysosomal storage disorder caused by pathogenic variants in the cystinosin (CTNS) gene and is characterized by the excessive shedding of proximal tubular epithelial cells (PTECs) and podocytes into urine, development of the renal Fanconi syndrome and end-stage kidney disease (ESKD). We hypothesized that in compensation for epithelial cell losses, cystinosis kidneys undertake a regenerative effort, and searched for the presence of kidney progenitor cells (KPCs) in the urine of cystinosis patients. Urine was cultured in a specific progenitor medium to isolate undifferentiated cells. Of these, clones were characterized by qPCR, subjected to a differentiation protocol to PTECs and podocytes and assessed by qPCR, Western blot, immunostainings and functional assays. Cystinosis patients voided high numbers of undifferentiated cells in urine, of which various clonal cell lines showed a high capacity for self-renewal and expressed kidney progenitor markers, which therefore were assigned as cystinosis urine-derived KPCs (Cys-uKPCs). Cys-uKPC clones showed the capacity to differentiate between functional PTECs and/or podocytes. Gene addition with wild-type CTNS using lentiviral vector technology resulted in significant reductions in cystine levels. We conclude that KPCs present in the urine of cystinosis patients can be isolated, differentiated and complemented with CTNS in vitro, serving as a novel tool for disease modeling.
A Comprehensive Correlation Study of Structured Soils in Coastal Area of South China about Structural Characteristics

Granite residual soil is a common engineering material, and its mechanical properties are of great importance to engineering safety. This kind of soil presents obvious structural characteristics, and many researchers have emphasized the significance of its structural features. According to previous experiments, from a macroscopic perspective, many researchers have investigated the structural relationship between undisturbed and remolded soils, but few studies have considered it in the mesoscopic aspect. Adopting DEM (a mesomechanical simulation method), we can study how the structure affects the mechanical process between undisturbed and remolded soil. Therefore, this paper combines DEM with laboratory tests to study the structural characteristic correlation between undisturbed and remolded soil. The results indicate that a weak cohesion effect exists in undisturbed soil, and the damage of weak cohesion elements accompanies the failure process. Weak cohesion elements in undisturbed soil cause inhomogeneities in deformation, stress state, and damage accumulation, which ultimately causes differences in strength curves. This paper explains the mechanism of the structural effect on mechanical evolution from a mesomechanical perspective. The DEM simulation method proposed in this paper can be applied to structured soils and better guide engineering practice.
Global dissipativity of fuzzy genetic regulatory networks with mixed delays

The synthetic genetic regulatory networks have proven to be a powerful tool in studying gene regulation processes in living organisms. In this article, the global dissipativity and corresponding attractive set for the fuzzy genetic regulatory networks with mixed delays are investigated. By utilising the Lyapunov functional method and the linear matrix inequalities (LMIs) techniques, new sufficient conditions ensuring the global dissipativity and the global exponential dissipativity of the suggested system are given. Moreover, the global attractive set and global exponential attractive set are obtained. The derived criteria are of the form of LMI, and hence they can be verified easily by the numerical software. Lastly, two numerical examples with its simulations are given to illustrate the effectiveness of the obtained results.