Miracle-dz/UniFAR-FTUs huggingface dataset:
Progressive Cleaning and Mining of Uncertain Smart Water Meter Data

Several municipalities have recently installed wireless 'smart' water meters that allow functionalities such as demand response, leak alerts, identification of characteristic demand patterns, and detailed consumption analysis. To achieve these benefits, the meter data needs to be error-free, which is not necessarily available in practice, due to 'dirtiness' or 'uncertainty' of data, which is mostly unavoidable. The focus of this paper is to investigate practical solutions to mine uncertain data for reliable results and to evaluate the impact of dirty data on filters. This evaluation would eventually lead to valuable information, which can be used for educated decision making on water planning strategies. We perform a systematic study of the errors existing in a large-scale smart water meter deployments, which is helpful to better understand the nature of errors. Identifying customers contributing to a load peak is used as the main filter. The filter outputs are then combined with the domain expert knowledge to evaluate their accuracy and validity and also to look for potential errors. After discovering each error, we analyze its trails in the data and track back its source, which would eventually lead to the removal of the error or dealing with it accordingly. This procedure is applied progressively to ensure that all detectable errors are discovered and characterized in the data model. We evaluate the performance of the proposed approach using the smart water meter consumption data obtained from the City of Abbotsford, British Columbia, Canada. We present the results of both unprocessed and cleaned data and analyze, in detail, the sensitivity of the selected filter to the errors.
Causally Correct Partial Models for Reinforcement Learning

In reinforcement learning, we can learn a model of future observations and rewards, and use it to plan the agent's next actions. However, jointly modeling future observations can be computationally expensive or even intractable if the observations are high-dimensional (e.g. images). For this reason, previous works have considered partial models, which model only part of the observation. In this paper, we show that partial models can be causally incorrect: they are confounded by the observations they don't model, and can therefore lead to incorrect planning. To address this, we introduce a general family of partial models that are provably causally correct, yet remain fast because they do not need to fully model future observations.
Statistical Outlier Identification in Multi-robot Visual SLAM using Expectation Maximization

This paper introduces a novel and distributed method for detecting inter-map loop closure outliers in simultaneous localization and mapping (SLAM). The proposed algorithm does not rely on a good initialization and can handle more than two maps at a time. In multi-robot SLAM applications, maps made by different agents have nonidentical spatial frames of reference which makes initialization very difficult in the presence of outliers. This paper presents a probabilistic approach for detecting incorrect orientation measurements prior to pose graph optimization by checking the geometric consistency of rotation measurements. Expectation-Maximization is used to fine-tune the model parameters. As ancillary contributions, a new approximate discrete inference procedure is presented which uses evidence on loops in a graph and is based on optimization (Alternate Direction Method of Multipliers). This method yields superior results compared to Belief Propagation and has convergence guarantees. Simulation and experimental results are presented that evaluate the performance of the outlier detection method and the inference algorithm on synthetic and real-world data.
A Review of the Exceptional Supersymmetric Standard Model

Local supersymmetry (SUSY) provides an attractive framework for the incorporation of gravity and unification of gauge interactions within Grand Unified Theories (GUTs). Its breakdown can lead to a variety of models with softly broken SUSY at low energies. In this review article, we focus on the SUSY extension of the Standard Model (SM) with an extra U ( 1 ) N gauge symmetry originating from a string-inspired E 6 GUTs. Only in this U ( 1 ) extension of the minimal supersymmetric standard model (MSSM) can the right-handed neutrinos be superheavy, providing a mechanism for the baryon asymmetry generation. The particle content of this exceptional supersymmetric standard model (E 6 SSM) includes three 27 representations of the E 6 group, to ensure anomaly cancellation. In addition it also contains a pair of S U ( 2 ) W doublets as required for the unification of gauge couplings. Thus, E 6 SSM involves exotic matter beyond the MSSM. We consider symmetries that permit suppressing flavor changing processes and rapid proton decay, as well as gauge coupling unification, the gauge symmetry breaking and the spectrum of Higgs bosons in this model. The possible Large Hadron Collider (LHC) signatures caused by the presence of exotic states are also discussed.
On the Theoretical Guarantees for Parameter Estimation of Gaussian Random Field Models: A Sparse Precision Matrix Approach

Iterative methods for fitting a Gaussian Random Field (GRF) model via maximum likelihood (ML) estimation requires solving a nonconvex optimization problem. The problem is aggravated for anisotropic GRFs where the number of covariance function parameters increases with the dimension. Even evaluation of the likelihood function requires $O(n^3)$ floating point operations, where $n$ denotes the number of data locations. In this paper, we propose a new two-stage procedure to estimate the parameters of second-order stationary GRFs. First, a convex likelihood problem regularized with a weighted $\ell_1$-norm, utilizing the available distance information between observation locations, is solved to fit a sparse precision (inverse covariance) matrix to the observed data. Second, the parameters of the covariance function are estimated by solving a least squares problem. Theoretical error bounds for the solutions of stage I and II problems are provided, and their tightness are investigated.
Anemonin Attenuates RANKL-Induced Osteoclastogenesis and Ameliorates LPS-Induced Inflammatory Bone Loss in Mice via Modulation of NFATc1

Osteoporosis is a metabolic bone disease characterized by insufficient osteoblastic function and/or excessive osteoclastic activity. One promising strategy for treating osteoporosis is inhibiting excessive osteoclast resorbing activity. Previous studies have revealed that anemonin (ANE), isolated from various types of Chinese natural herbs, has anti-inflammatory and anti-oxidative properties. However, whether ANE regulates osteoclastogenesis is unknown. This study aimed to investigate the potential effect of ANE on osteoclastogenesis and inflammatory bone loss in mice. In in vitro studies, ANE suppressed RANKL-stimulated osteoclast differentiation and function by downregulating the expression of osteoclast master transcriptor NFATc1, as well as its upstream transcriptor c-Fos, by decreasing NF-κB and ERK1/2 signaling. Interestingly, ANE did not change the phosphorylation and degradation of IκB-α and activation of JNK and p38 MAPKs. However, ANE repressed the phosphorylation of MSK-1 which is the downstream target of ERK1/2 and p38 MAPK and can phosphorylate NF-κB p65 subunit. These results implicated that ANE might suppress NF-κB activity via modulation of ERK1/2 mediated NF-κB phosphorylation. In addition, ANE directly suppressed NFATc1 transcription by inhibiting Blimp-1 expression, and the subsequent enhancement of the expression of NFATc1 negative regulators, Bcl-6 and IRF-8. Moreover, in vivo studies were conducted using an LPS-induced inflammatory bone loss mice model. Micro-CT and histology analysis showed that ANE treatment significantly improved trabecular bone parameters and bone destruction. These data indicate that ANE can attenuate RANKL-induced osteoclastogenesis and ameliorate LPS-induced inflammatory bone loss in mice through modulation of NFATc1 via ERK1/2-mediated NF-κB phosphorylation and Blimp1 signal pathways. ANE may provide new treatment options for osteoclast-related diseases.
Epigenome-wide analysis of common warts reveals aberrant promoter methylation

Epigenetic alteration of host DNA is a common occurrence in both low- and high-risk human papillomavirus (HPV) infection. Although changes in promoter methylation have been widely studied in HPV-associated cancers, they have not been the subject of much investigation in HPV-induced warts, which are a temporary manifestation of HPV infection. The present study sought to examine the differences in promoter methylation between warts and normal skin. To achieve this, DNA was extracted from 24 paired wart and normal skin samples and inputted into the Infinium MethylationEPIC BeadChip microarray. Differential methylation analysis revealed a clear pattern of hyper- and hypomethylation in warts compared to normal skin, and the most differentially methylated promoters were found within the EIF3EP2, CYSLTR1, C10orf99, KRT6B, LAMA4, and H3F3B genes as well as the C9orf30 pseudogene. Moreover, pathway analysis showed that the H3F3A, CDKN1A, and MAPK13 genes were the most common regulators among the most differentially methylated promoters. Since the tissue samples were excised from active warts, however, this differential methylation could either be a cellular response to HPV infection or an HPV-driven process to establish the wart and/or promote disease progression. Conclusively, it is apparent that HPV infection alters the methylation status of certain genes to possibly initiate the formation of a wart and maintain its presence.
The new application of mifepristone in the relief of adenomyosis-caused dysmenorrhea

Background: Adenomyosis is a quite common gynecological disorder and above 30% of patients have typical secondary and progressive dysmenorrhea. Current treatments still have many disadvantages and thereby the novel treatment aiming to relieve dysmenorrhea still needs to be further investigated. Mifepristone is a wonderful drug because it is effective, safe and cheap in many diseases including adenomyosis. In this study, we aim to investigate if mifepristone could be used in the treatment of adenomyosis-associated dysmenorrhea. Methods: Human primary endometrial epithelial and stromal cells from adenomyosis patients were isolated and treated with mifepristone. RNA-sequencing was then performed to detect the gene changes of pain-related inflammatory mediators. Meanwhile, the effect of mifepristone on the infiltration and degranulation of mast cells were investigated in adenomyosis lesions. Additionally, the role of mifepristone on the density of nerve fibers was also studied in the ectopic endometrium. At last, to evaluate the therapeutic efficacy of mifepristone on dysmenorrhea of adenomyosis, twenty participants were included and the visual analog scale (VAS) score was assessed and compared before and after treatment with mifepristone. Results: We demonstrated that mifepristone reduced the secretion of IL-6 and TNF-α from endometrial epithelial and stromal cells, restricted the infiltration and degranulation of mast cells in eutopic and ectopic endometrium and decreased the density of nerve fibers by inhibiting the migration capacity of nerve cells in adenomyosis. Meanwhile, we found that mifepristone could significantly relieve dysmenorrhea of adenomyosis. Conclusion: The findings demonstrated that mifepristone could be applied in the treatment of dysmenorrhea for the adenomyosis patients.
Deep expression scrutiny of juxtaposed wild and cultivated lentil furnishes new insight into aluminium tolerance mechanism

Background: Aluminium (Al) stress hinders crop productivity in acidic soils. Lentil contains rich source of protein and micronutrients and cultivated in different parts of world. To enhance knowledge about Al toxicity tolerance, present study emphasizes on mechanistic analysis of genes associated with Al stress through de novo transcriptomic analysis of tolerant (L-4602), wild (ILWL-15) and sensitive (BM-4) genotypes. Result: Illumina HiSeq 2500 platform evaluated contigs ranging from 15,305 to 18,861 for all the samples with N 50 values of 1795 bp. Four annotation softwares revealed differential regulation of several genes where 30,158 genes were specifically up-regulated for combinations under Al stress conditions alone. Top up-regulated Differentially Expressed Genes (DEGs) in tolerant cultivar when compared to the sensitive one were found to be involved in protein transport as well as degradation, defences, cell growth and development. Wild v/s cultivar comparison revealed upregulation of wild DEGs that are involved in regulation of transcription in differentiating cells, pre-mRNA splicing, catalysis and protein ubiquitination. Based on assembled Unigenes, 89,722 high-quality SNPs and 39,874 SSRs were detected. Twelve selected genes were validated using qRT-PCR. KEGG pathway analysis extracted 8,757 GO annotation terms within molecular, cellular and biological processes. Pathway analysis indicated that organic acid synthesis and their transportation along with detoxification of ROS, an alternate pathway involving metacaspase-1,4,9 for programmed cell death were also significantly induced due to Al stress. Conclusion: Present study unveils the characterization of differential transcripts generated under Al stress indicating Al tolerance as a multiplex phenomenon which will directly widen crop improvement programmes for Al toxicity utilizing molecular approaches.
Cost-aware Targeted Viral Marketing: Approximation with Less Samples.

Cost-aware Targeted Viral Marketing (CTVM), a generalization of Influence Maximization (IM), has received a lot of attentions recently due to its commercial values. Previous approximation algorithms for this problem required a large number of samples to ensure approximate guarantee. In this paper, we propose an efficient approximation algorithm which uses fewer samples but provides the same theoretical guarantees based on generating and using important samples in its operation. Experiments on real social networks show that our proposed method outperforms the state-of-the-art algorithm which provides the same approximation ratio in terms of the number of required samples and running time.
NPLDA: A Deep Neural PLDA Model for Speaker Verification

The state-of-art approach for speaker verification consists of a neural network based embedding extractor along with a backend generative model such as the Probabilistic Linear Discriminant Analysis (PLDA). In this work, we propose a neural network approach for backend modeling in speaker recognition. The likelihood ratio score of the generative PLDA model is posed as a discriminative similarity function and the learnable parameters of the score function are optimized using a verification cost. The proposed model, termed as neural PLDA (NPLDA), is initialized using the generative PLDA model parameters. The loss function for the NPLDA model is an approximation of the minimum detection cost function (DCF). The speaker recognition experiments using the NPLDA model are performed on the speaker verificiation task in the VOiCES datasets as well as the SITW challenge dataset. In these experiments, the NPLDA model optimized using the proposed loss function improves significantly over the state-of-art PLDA based speaker verification system.
Weak-lensing observables in relativistic N-body simulations

We present a numerical weak-lensing analysis that is fully relativistic and non-perturbative for the scalar part of the gravitational potential and first-order in the vector part, frame dragging. Integrating the photon geodesics backwards from the observer to the emitters, we solve the Sachs optical equations and study in detail the weak-lensing convergence, ellipticity and rotation. For the first time, we apply such an analysis to a high-resolution relativistic N-body simulation, which consistently includes the leading-order corrections due to general relativity on both large and small scales. These are related to the question of gauge choice and to post-Newtonian corrections, respectively. We present the angular power spectra and one-point probability distribution functions for the weak-lensing variables, which we find are broadly in agreement with comparable Newtonian simulations. Our geometric approach, however, is more robust and flexible, and can therefore be applied consistently to non-standard cosmologies and modified theories of gravity.
Bone suppression on chest radiographs with adversarial learning

Dual-energy (DE) chest radiography provides the capability of selectively imaging two clinically relevant materials, namely soft tissues, and osseous structures, to better characterize a wide variety of thoracic pathology and potentially improve diagnosis in posteroanterior (PA) chest radiographs. However, DE imaging requires specialized hardware and a higher radiation dose than conventional radiography, and motion artifacts some- times happen due to involuntary patient motion. In this work, we learn the mapping between conventional radiographs and bone suppressed radiographs. Specifically, we propose to utilize two variations of generative adversarial networks (GANs) for image-to-image translation between conventional and bone suppressed radio- graphs obtained by DE imaging technique. We compare the effectiveness of training with patient-wisely paired and unpaired radiographs. Experiments show both training strategies yield radio-realistic" radiographs with suppressed bony structures and few motion artifacts on a hold-out test set. While training with paired images yields slightly better performance than that of unpaired images when measuring with two objective image quality metrics, namely Structural Similarity Index (SSIM) and Peak Signal-to-Noise Ratio (PSNR), training with unpaired images demonstrates better generalization ability on unseen anteroposterior (AP) radiographs than paired training.
WibsonTree: Efficiently Preserving Seller's Privacy in a Decentralized Data Marketplace

We present a cryptographic primitive called WibsonTree designed to preserve users' privacy by allowing them to demonstrate predicates on their personal attributes, without revealing the values of those attributes. We suppose that there are three types of agents --buyers, sellers and notaries-- who interact in a decentralized privacy-preserving data marketplace (dPDM) such as the Wibson marketplace. We introduce the WibsonTree protocol as an efficient cryptographic primitive that enables the exchange of private information while preserving the seller's privacy. Using our primitive, a data seller can efficiently prove that he/she belongs to the target audience of a buyer's data request, without revealing any additional information.
Fast Detection of Maximum Common Subgraph via Deep Q-Learning

Detecting the Maximum Common Subgraph (MCS) between two input graphs is fundamental for applications in biomedical analysis, malware detection, cloud computing, etc. This is especially important in the task of drug design, where the successful extraction of common substructures in compounds can reduce the number of experiments needed to be conducted by humans. However, MCS computation is NP-hard, and state-of-the-art exact MCS solvers do not have worst-case time complexity guarantee and cannot handle large graphs in practice. Designing learning based models to find the MCS between two graphs in an approximate yet accurate way while utilizing as few labeled MCS instances as possible remains to be a challenging task. Here we propose RLMCS, a Graph Neural Network based model for MCS detection through reinforcement learning. Our model uses an exploration tree to extract subgraphs in two graphs one node pair at a time, and is trained to optimize subgraph extraction rewards via Deep Q-Networks. A novel graph embedding method is proposed to generate state representations for nodes and extracted subgraphs jointly at each step. Experiments on real graph datasets demonstrate that our model performs favorably to exact MCS solvers and supervised neural graph matching network models in terms of accuracy and efficiency.
Miller-Abrahams random resistor network, Mott random walk and 2-scale homogenization

The Miller-Abrahams (MA) random resistor network is given by a complete graph on a marked simple point process with edge conductivities depending on the marks and decaying exponentially in the edge length. As Mott random walk, it is an effective model to study Mott variable range hopping in amorphous solids as doped semiconductors. By using 2-scale homogenization we prove that a.s. the infinite volume conductivity of the MA resistor network is given by an effective homogenized matrix $D$. Moreover $D$ admits a variational characterization and equals the limiting diffusion matrix of Mott random walk. This result clarifies the relation between the two models and it also allows to extend to the MA resistor network the existing bounds on $D$ in agreement with the physical Mott law [12,14]. The latter concerns the low temperature stretched exponential decay of conductivity in amorphous solids. The techniques developed here can be applied to other models, as e.g. the random conductance model [11], without ellipticity assumptions.
Ensemble of Deep Convolutional Neural Networks for Automatic Pavement Crack Detection and Measurement

Automated pavement crack detection and measurement are important road issues. Agencies have to guarantee the improvement of road safety. Conventional crack detection and measurement algorithms can be extremely time-consuming and low efficiency. Therefore, recently, innovative algorithms have received increased attention from researchers. In this paper, we propose an ensemble of convolutional neural networks (without a pooling layer) based on probability fusion for automated pavement crack detection and measurement. Specifically, an ensemble of convolutional neural networks was employed to identify the structure of small cracks with raw images. Secondly, outputs of the individual convolutional neural network model for the ensemble were averaged to produce the final crack probability value of each pixel, which can obtain a predicted probability map. Finally, the predicted morphological features of the cracks were measured by using the skeleton extraction algorithm. To validate the proposed method, some experiments were performed on two public crack databases (CFD and AigleRN) and the results of the different state-of-the-art methods were compared. To evaluate the efficiency of crack detection methods, three parameters were considered: precision (Pr), recall (Re) and F1 score (F1). For the two public databases of pavement images, the proposed method obtained the highest values of the three evaluation parameters: for the CFD database, Pr = 0.9552, Re = 0.9521 and F1 = 0.9533 (which reach values up to 0.5175 higher than the values obtained on the same database with the other methods), for the AigleRN database, Pr = 0.9302, Re = 0.9166 and F1 = 0.9238 (which reach values up to 0.7313 higher than the values obtained on the same database with the other methods). The experimental results show that the proposed method outperforms the other methods. For crack measurement, the crack length and width can be measure based on different crack types (complex, common, thin, and intersecting cracks.). The results show that the proposed algorithm can be effectively applied for crack measurement.