Miracle-dz/UniFAR-FTUs huggingface dataset:
Spatiotemporal Variations in Earthquake Triggering Mechanisms During Multistage Hydraulic Fracturing in Western Canada

Dense arrays deployed near the hydraulic fracturing (HF) wells greatly enhance the understanding of injection‐induced seismicity. In this study, we revisit the continuous recordings that are acquired by 69 three‐component nodes at an HF site in Alberta, Canada, taking advantage of a machine learning‐based seismic detection and location workflow. The obtained new earthquake catalog contains 21,619 events with relative location errors of <1 m, which exceeds ∼20% of the number of earthquakes (18,040) reported previously (Igonin et al., 2021). This high‐resolution catalog reveals the distribution of earthquake sequences at much improved spatiotemporal resolution and illustrates several previously unmapped faults/fractures. Earthquake frequency‐magnitude distribution reveals that the average b value increases with depth from ∼1.1 above 3.5 km to ∼2.5 at greater depths. Further spatial analysis of seismic clusters indicates that the b value varies laterally (∼1–1.7) at shallow depths and is inversely related to the proximity to injection wells in conjunction with the change in structural types (i.e., reactivated fault and pre‐existing fracture). The seismic sequence on the north‐south oriented faults also shows a distinctive occurrence pattern and temporal affinity to the fracturing network reactivation between the two stages of HF operations. The change in faulting behavior could reflect a shift of dominating triggering mechanisms and physical processes from (a) the rapid diffusion of pore fluid pressure along pre‐existing fracture corridors to (b) the cascade migration of earthquake sequences in response to the cumulative Coulomb stress perturbation on the fluid‐lubricated, critically stressed faults.
Privacy Risk Assessment of Training Data in Machine Learning

With the wide application of machine learning technology, more and more sensitive data were used to develop the machine learning model. Unfortunately, machine learning systems have been shown to be vulnerable to various privacy leakage attacks, such as membership inference attacks and model inversion attacks. These privacy threats break down the confidentiality of the training data; what’s more, the leakage of sensitive privacy will reduce the enthusiasm of data owners for sharing data with the machine learning system. Without enough training data, more seriously, it will hinder the application of machine learning. Therefore, it requires analyzing strategies for privacy risks to help data owners pre-assess the candidate dataset and assist data owners in implementing reasonable privacy control. However, the systematic privacy risk assessment is still absent from the data owner’s perspective.This paper investigates and analyzes machine learning privacy risks to understand the relationship between training data properties and privacy leakage. Based on this recognition, we introduce a privacy risk assessment scheme based on the clustering distance of training data. Our clustering distance-based method can reflect the privacy risk level of a different individual data record. And then, we combine both existing privacy analysis based on data features and our clustering distance-based method to investigate privacy risks systematically. Our experiments showed that our clustering distance method and other set properties are tightly related to privacy leakage. And data owners can pre-assess the privacy risks of candidate datasets before uploading or sharing them to the machine learning model. If needed, decide to re-choose the dataset to reduce privacy risks to an acceptable level.
An investigation of domestic water consumption in Sirte, Libya

ABSTRACT The paper investigates per capita water consumption in Sirte city using a questionnaire survey data. The data captured included frequency, duration, flow rate, time of water use for each water end-use and household characteristics (demographic and socio-economic). The results show that the average daily per capita water consumption is 255 l/p/d. The per capita water consumption decreases with the increase in family size, while household income does not affect water consumption. Handwash basin tap accounts for the highest water consumption. The diurnal water consumption pattern is different from developed countries patterns and has multiple peaks. To predict the city’s future water demand, three statistical models based on multiple linear regression technique (STEPWISE) were developed. The comparison of the developed models suggests that the models based on demographic variables can produce acceptable results and any further inclusion of additional variables (physical characteristics of households) do not necessarily improve the prediction accuracy.
SLFN11 Negatively Regulates Noncanonical NFκB Signaling to Promote Glioblastoma Progression

Glioblastoma (GBM) is an aggressive and incurable brain tumor in nearly all instances, whose disease progression is driven in part by the glioma stem cell (GSC) subpopulation. Here, we explored the effects of Schlafen family member 11 (SLFN11) in the molecular, cellular, and tumor biology of GBM. CRISPR/Cas9-mediated knockout of SLFN11 inhibited GBM cell proliferation and neurosphere growth and was associated with reduced expression of progenitor/stem cell marker genes, such as NES, SOX2, and CD44. Loss of SLFN11 stimulated expression of NFκB target genes, consistent with a negative regulatory role for SLFN11 on the NFκB pathway. Furthermore, our studies identify p21 as a direct transcriptional target of NFκB2 in GBM whose expression was stimulated by loss of SLFN11. Genetic disruption of SLFN11 blocked GBM growth and significantly extended survival in an orthotopic patient-derived xenograft model. Together, our results identify SLFN11 as a novel component of signaling pathways that contribute to GBM and GSC with implications for future diagnostic and therapeutic strategies. Significance: We identify a negative regulatory role for SLFN11 in noncanonical NFκB signaling that results in suppression of the cell-cycle inhibitor p21. We provide evidence that SLFN11 contributes to regulation of stem cell markers in GBM, promoting the malignant phenotype. In addition, SLFN11 targeting triggers p21 expression and antitumor responses. Our studies define a highly novel function for SLFN11 and identify it as a potential therapeutic target for GBM.
Pharmacological management, and potentially inappropriate prescriptions in patients with osteoporosis

ABSTRACT Background The treatment of osteoporosis involves medications that reduce the risk of fractures, but some medications can decrease bone density. The aim was to identify the treatments, comorbidities, and prescriptions related to reducing bone mineral density in patients with osteoporosis. Research design and methods A cross-sectional study that identified patterns of anti-osteoporotic drug prescriptions for outpatient use and potentially inappropriate prescriptions for patients with osteoporosis based on the drug-dispensing database of 8.5 million people in Colombia. All patients ≥65 years with a diagnosis of osteoporosis were included. Results A total of 16,362 patients with osteoporosis were identified. They had a median age of 74.4 years, and 47.9% received anti-osteoporotic therapy, especially bisphosphonates (41.6%), and 86.5% received calcium and/or vitamin D supplement. 41.6% of those who had a history of bone fractures were prescribed anti-osteoporotic drugs. Potentially inappropriate prescriptions, especially corticosteroids at doses greater than 5mg prednisolone equivalent (4.4%), were found in 41.4% of cases and were more common in older patients and those with a history of fractures or with cardiovascular, digestive, neurological, psychiatric or neoplastic comorbidities. Conclusions A significant proportion of patients had potentially inappropriate prescriptions, especially older patients, those with certain comorbidities, and those receiving comedication with antirheumatic drugs.
Optimal Rate and Distance Based Bandwidth Slicing in UAV Assisted 5G Networks

Unmanned Aerial Vehicles (UAVs) are expected to be an important component for supporting high data rate transmissions in network slice enabled 5G networks. UAVs provide the opportunity to realize the concept of aerial base stations which can help in boosting coverage, spectral efficiency, and overall quality of experience of users. Network slicing logically divides the network resources including bandwidth, and provides diversified service requirements for different application scenarios and enhances network performance. However, slicing and allocation of available bandwidth to various users act as a challenge in achieving desired network performance. Additionally, energy constraints leads to limited hover time at UAVs which introduces further challenges in serving users' requests. In this work, we propose optimal rate and distance based algorithms which allocate bandwidth slices to users with the objectives of minimizing the hover time and maximizing the number of users which are served, while considering limitations pertaining to available bandwidth, hover time of UAVs, and quality of service to the users. Simulation results show that our proposed algorithms result in significant improvement over state-of-the art bandwidth allocation techniques.
Turbulent 3D MHD dynamo model in spherical shells: regular oscillations of the dipolar field

We report the results of three-dimensional numerical simulations of convection-driven dynamos in relatively thin rotating spherical shells that show a transition from an strong non-oscillatory dipolar magnetic field to a weaker regularly oscillating dipolar field. The transition is induced primarily by the effects a stress-free boundary condition. The variation of the inner to outer radius ratio is found to have a less important effect.
Development of Structural and Pragmatic Language Skills in Children With Attention-Deficit/Hyperactivity Disorder

The few reports on the language skills of children with attention-deficit/hyperactivity disorder (ADHD) offer conflicting evidence on whether they face significant challenges, and if so, whether these challenges are present in all aspects of language. Here, we investigated a sample of Greek-speaking children with ADHD (n = 29) using a structural language (vocabulary, grammar) and a pragmatic language assessment. To ascertain the extent of strengths and weaknesses, we compared the performance of children with ADHD to typically developing (TD) peers (n = 29) and also to children with developmental language disorder (DLD; n = 25), who face challenges particularly in structural language. As regards structural language, ADHD children performed significantly lower than their TD peers but significantly higher than the DLD group. In pragmatics, ADHD children performed numerically lower than any other group, but differences did not reach statistical significance. Children with ADHD face difficulties with language skills and especially with structural language. Sophisticated linguistic assessment is crucial, as it facilitates the identification of children with different challenges by measuring performances on distinct components. Language difficulties in ADHD should not be overlooked but must be evaluated thoroughly for more effective intervention planning.
Bayesian analysis of lidar signals using reversible jump Markov chain Monte Carlo algorithms

Standard 3D ranging and imaging systems process only one return from an assumed single opaque surface. However, there are many situations when the laser return consists of multiple peaks, due for example to the footprint of the beam impinging on a target with surfaces distributed in depth, or on a target with semi-transparent surfaces. In this thesis, we present a unified theory of pixel processing based on a Bayesian framework, applicable to pulsed laser ranging and depth imaging systems, which allows for a careful and thorough treatment of all types of uncertainties associated with the data. We use reversible jump Markov chain Monte Carlo (RJMCMC) techniques to evaluate the posterior distribution of the parameters and to explore spaces with different dimensionality. Further, we use a delayed rejection step to allow the generated Markov chain to mix better through the use of different proposal distributions. Finally, for full 3D image data, we incorporate spatial constraints on the number of peaks through a particular Markov random field, the Potts prior model. This allows uncertainty about the underlying spatial process. To palliate some inherent deficiencies of this prior model, we also introduce two proposal distributions, one based on spatial mode jumping, the other on a spatial birth/death process. The methods we employ are demonstrated on simulated and real data, showing that the laser return parameters can be estimated to a high degree of accuracy. We show practical examples from both near and far range depth imaging. Two techniques of particular relevance for this work are Time-Correlated Single Photon Counting (TCSPC) and Burst Illumination Laser (BIL) imaging. In each case, the goal is a complete characterisation of the 3D surfaces viewed by the particular laser imaging system. For TCSPC data, we determine the number, positions and amplitudes of returns from a histogram, or histograms, of photon counts. For BIL data, we determine the same parameters from integrated intensities as a range gate is moved through an image sequence. In either case, a more informative multi-layered 3D image can be created through the spatial analysis.
Combined C-V/I-V and RTN CMOS Variability Characterization Using An On-Chip Measurement System

Combined C-V/I-V and RTN CMOS Variability Characterization Using An On-Chip Measurement System Simeon Dimitrov Realov With the number of transistors integrated into a single integrated circuit (IC) crossing the one-billion mark and complementary metal-oxide-semiconductor (CMOS) technology scaling pushing device dimensions ever-so-close to atomic scales, variability in transistor performance is becoming the dominant constraint in modern-day CMOS IC design. Developing novel approaches for device characterization, which allow a detailed study of electrical transistor characteristics across large statistical sample sets, is crucial for the proper identification, characterization, and modeling of different physical sources of device variability. On-chip characterization methodologies have the potential to address all of these issues by enabling the characterization of large statistical device sample sets, while also allowing for high measurement quality and throughput. In this work, a fully-integrated system for on-chip combined capacitance-voltage (C-V) and current-voltage (I-V) characterization of a large integrated test transistor array implemented in a 45-nm bulk CMOS process is presented. On-chip I-V characterization is implemented using a four-point Kelvin measurement technique with 12-bit sub-10 nA current measurement resolution, 10-bit sub-1 mV voltage measurement resolution, and sampling speeds on the order of 100 kHz. C-V characterization is performed using a novel leakageand parasitics-insensitive charge-based capacitance measurement (CBCM) technique with atto-Farad resolution. The on-chip system is employed in developing a comprehensive CMOS transistor variability characterization methodology, studying both random and systematic sources of quasi-static device variability. For the first time, combined C-V/I-V characterization of circuit-representative devices is demonstrated and used to extract variations in the underlying physical parameters of the device. Additionally, the fast current sampling capabilities of the system are used for the characterization of random telegraph noise (RTN) in small area devices. An automated methodology for the extraction of RTN parameters is developed, and the statistics of RTN are studied across device type, bias, and geometry.
Federated Learning for WiFi Fingerprinting

Channel State Information (CSI) based fingerprinting is surfacing as an accurate and robust method of indoor localization. However, the high-dimensional nature of CSI data impedes its adoption in multi access point (AP) systems. To reap the rewards of cooperative localization with privacy and limited system complexity in mind, we propose a federated learning (FL) architecture. Each AP has an individual model and a shared model, where the individual model parameters are unique to each AP and the shared model parameters are communicated to a central server for aggregation. The server averages the models and sends them back to each AP, which use this joint model as a regularization term. To capture the spatio-temporal characteristics of CSI, we propose a convolutional neural network (CNN) as each AP’s individual model and a multi layer perceptron (MLP) as the shared model. Extensive experimental studies verify the superiority of the proposed edge computing approach compared to the exiting methods in the literature. We use commercial off-the-shelf APs collecting CSI data in multiple indoor environments and compare the proposed system to a state-of-the-art deep learning model. Our approach shows significant improvement in the localization accuracy for both individual APs and aggregate predictions.
The Aurora and Magnetic Field of Uranus

[1] Resolution of the details of a planetary magnetic field from magnetometer measurements made during a single flyby can be severely limited because of the incomplete geometrical sampling of the planetary neighborhood by the flyby trajectory. This problem was especially severe for the only spacecraft encounter with Uranus, that of Voyager 2 in 1986. Fortunately, auroras at the magnetic field line footprints serve as additional constraints that may be used to determine the higher multipole moments of planetary fields (Connerney et al.'s (1998) VIP-4 model of for Jupiter). In the present work, this approach is applied to improving the resolution of the magnetic field of Uranus. The auroral emission distribution at Uranus is determined from scans by the Voyager 2 Ultraviolet Spectrometer (UVS), enhancing an earlier analysis by Herbert and Sandel (1994) by incorporating more observations and by using more powerful analysis techniques. The resulting new determination of the auroral ovals is well correlated with the field lines associated with the strongest plasma wave and radio emissions but differs from model ovals computed by Connerney et al. (1987) from the Q3 magnetic field model for Uranus. Consequently, a search has been initiated for model coefficients of the planetary magnetic field that agree both with the magnetic field observations and also with the reasonable assumption that the newly determined auroral emissions lie at the magnetic footprints of an equidistant circum-Uranian region of the magnetosphere. The dipole and quadrupole terms of the new field model, termed AH5, are similar to those of the dipole + quadrupole Q3 model, but the AH5 higher multipole terms diverge from the dipole + quadrupole + octupole I3E1 model of Connerney et al. (1987), from which the Q3 model was derived. Inasmuch as the I3E1 octupole terms were not resolved, the AH5 model derived here comprises a first estimate of the higher multipole moments of Uranus's magnetic field.
CSS090530:144011+494734: a new SU UMa-type dwarf nova in Bootes

We report photometry and analysis of a previously unknown SU UMa-type dwarf nova in Bootes detected in outburst by the Catalina Real-time Transient Survey on 2009 May 30 with the discovery identifier CSS090530:144011+494734. This apparently stellar object had previously been catalogued by the Sloan Digital Sky survey as SDSS J144011.01+494733.4. We measured its mean superhump period over the first 3 days following detection as 0.06500(4) d at which point this changed to 0.06438(3) d. We detected a possible orbital period of 0.06322(8) d implying a mean superhump period excess of 0.020(2). After detection, the dwarf nova maintained steady brightness for 5 days before starting to fade. The outburst amplitude was 5.5 magnitudes above its quiescent level.
Efficient global optimisation of microwave antennas based on a parallel surrogate model‐assisted evolutionary algorithm

Computational efficiency is a major challenge for evolutionary algorithm (EA)-based antenna optimisation methods due to the computationally expensive electromagnetic simulations. Surrogate model-assisted EAs considerably improve the optimisation efficiency, but most of them are sequential methods, which cannot benefit from parallel simulation of multiple candidate designs for further speed improvement. To address this problem, a new method, called parallel surrogate model-assisted hybrid differential evolution for antenna optimisation (PSADEA), is proposed. The performance of PSADEA is demonstrated by a dielectric resonator antenna, a Yagi-Uda antenna, and three mathematical benchmark problems. Experimental results show high operational performance in a few hours using a normal desktop 4-core workstation. Comparisons show that PSADEA possesses significant advantages in efficiency compared to a state-of-the-art surrogate model-assisted EA for antenna optimisation, the standard parallel differential evolution algorithm, and parallel particle swarm optimisation. In addition, PSADEA also shows stronger optimisation ability compared to the above reference methods for challenging design cases.
Statistical Applications in Genetics and Molecular Biology Sparse Canonical Covariance Analysis for High-throughput Data

Canonical covariance analysis (CCA) has gained popularity as a method for the analysis of two sets of high-dimensional genomic data. However, it is often difficult to interpret the results because canonical vectors are linear combinations of all variables, and the coefficients are typically nonzero. Several sparse CCA methods have recently been proposed for reducing the number of nonzero coefficients, but these existing methods are not satisfactory because they still give too many nonzero coefficients. In this paper, we propose a new random-effect model approach for sparse CCA; the proposed algorithm can adapt arbitrary penalty functions to CCA without much computational demands. Through simulation studies, we compare various penalty functions in terms of the performance of correct model identification. We also develop an extension of sparse CCA to address more than two sets of variables on the same set of observations. We illustrate the method with an analysis of the NCI cancer dataset.