Miracle-dz/UniFAR-FTUs huggingface dataset:
SRVIO: Super Robust Visual Inertial Odometry for Dynamic Environments and Challenging Loop-Closure Conditions

There has been extensive research on visual localization and odometry for autonomous robots and virtual reality during the past decades. Traditionally, this problem has been solved with the help of expensive sensors, such as light detection and ranging (LiDAR). Nowadays, the focus of the leading research in this field is on robust localization using more economic sensors, such as cameras and inertial measurement units. Consequently, geometric visual localization methods have become more accurate over time. However, these methods still suffer from significant loss and divergence in challenging environments, such as a room full of moving people. Scientists started using deep neural networks (DNNs) to mitigate this problem. The main idea behind using DNNs is to better understand challenging aspects of the data and overcome complex conditions such as the movement of a dynamic object in front of the camera that covers the full view of the camera, extreme lighting conditions, and the high speed of the camera. Prior end-to-end DNN methods did overcome some of these challenges. However, no general and robust framework is available to overcome all challenges together. In this article, we have combined geometric and DNN-based methods to have the generality and speed of geometric SLAM frameworks and overcome most of these challenging conditions with the help of DNNs and deliver the most robust framework so far. To do so, we have designed a framework based on VINS-Mono and shown that it can achieve state-of-the-art results on TUM-Dynamic, TUM-VI, ADVIO, and EuRoC datasets compared to geometric and end-to-end DNN-based simultaneous localization and mappings. Our proposed framework can also achieve outstanding results on extreme simulated cases resembling the aforementioned challenges.
A Geometrically Consistent Trace Finite Element Method For The Laplace-Beltrami Eigenvalue Problem

In this paper, we propose a new trace finite element method for the {Laplace-Beltrami} eigenvalue problem. The method is proposed directly on a smooth manifold which is implicitly given by a level-set function and require high order numerical quadrature on the surface. A comprehensive analysis for the method is provided. We show that the eigenvalues of the discrete Laplace-Beltrami operator coincide with only part of the eigenvalues of an embedded problem, which further corresponds to the finite eigenvalues for a singular generalized algebraic eigenvalue problem. The finite eigenvalues can be efficiently solved by a rank-completing perturbation algorithm in {\it Hochstenbach et al. SIAM J. Matrix Anal. Appl. , 2019} \cite{hochstenbach2019solving}. We prove the method has optimal convergence rate. Numerical experiments verify the theoretical analysis and show that the geometric consistency can improve the numerical accuracy significantly.
Arbitrage problems with reflected geometric Brownian motion

Contrary to the claims made by several authors, a financial market model in which the price of a risky security follows a reflected geometric Brownian motion is not arbitrage-free. In fact, such models violate even the weakest no-arbitrage condition considered in the literature. Consequently, they do not admit numéraire portfolios or equivalent risk-neutral probability measures, which makes them unsuitable for contingent claim valuation. Unsurprisingly, the published option pricing formulae for such models violate classical no-arbitrage bounds.
The Boundary Yamabe Problem, I: Minimal Boundary Case

We apply iteration schemes and perturbation methods to provide a complete solution of the boundary Yamabe problem with minimal boundary scenario, or equivalently, the existence of a real, positive, smooth solution of $ -\frac{4(n -1)}{n - 2} \Delta_{g} u + S_{g} u = \lambda u^{\frac{n+2}{n - 2}} $ in $ M $, $ \frac{\partial u}{\partial \nu} + \frac{n-2}{2} h_{g} u = 0 $ on $ \partial M $. Thus $ g $ is conformal to to the metric $ \tilde{g} = u^{\frac{4}{n -2}} g $ of constant scalar curvature $ \lambda $ with minimal boundary. In contrast to the classical method of calculus of variations with assumptions on Weyl tensors and classification of types of points on $ \partial M $, the boundary Yamabe problem is fully solved here in three cases classified by the sign of the first eigenvalue $ \eta_{1} $ of the conformal Laplacian with Robin condition. When $ \eta_{1}<0 $, a pair of global sub-solution and super-solution are constructed. When $ \eta_{1}>0 $, a perturbed boundary Yamabe equation $ -\frac{4(n -1)}{n - 2} \Delta_{g} u_{\beta} + \left( S_{g} + \beta \right) u_{\beta} = \lambda_{\beta} u_{\beta}^{\frac{n+2}{n - 2}} $ in $ M $, $ \frac{\partial u_{\beta}}{\partial \nu} + \frac{n-2}{2} h_{g} u_{\beta} = 0 $ on $ \partial M $ is solved with $ \beta<0 $. The boundary Yamabe equation is then solved by taking $ \beta \rightarrow 0^{-} $. The signs of scalar curvature $ S_{g} $ and mean curvature $ h_{g} $ play important roles in this existence result.
Lebanon Solar Rooftop Potential Assessment Using Buildings Segmentation From Aerial Images

Estimating solar rooftop potential at a national level is a fundamental building block for every country to utilize solar power efficiently. Solar rooftop potential assessment relies on several features such as building geometry, location, and surrounding facilities. Hence, national-level approximations that do not take these factors into deep consideration are often inaccurate. This article introduces Lebanon’s first comprehensive footprint and solar rooftop potential maps using deep learning-based instance segmentation to extract buildings’ footprints from satellite images. A photovoltaic panels placement algorithm that considers the morphology of each roof is proposed. We show that the average rooftop’s solar potential can fulfill the yearly electric needs of a single-family residence while using only 5% of the roof surface. The usage of 50% of a residential apartment rooftop area would achieve energy security for up to 8 households. We also compute the average and total solar rooftop potential per district to localize regions corresponding to the highest and lowest solar rooftop potential yield. Factors such as size, ground coverage ratio and $PV_{out}$ are carefully investigated for each district. Baalbeck district yielded the highest total solar rooftop potential despite its low built-up area. While Beirut capital city has the highest average solar rooftop potential due to its extremely populated urban nature. Reported results and analysis reveal solar rooftop potential urban patterns and provides policymakers and key stakeholders with tangible insights. Lebanon’s total solar rooftop potential is about 28.1 $TWh/year$, two times larger than the national energy consumption in 2019.
‘They need to ask me first’. Community engagement with low‐income citizens. A realist qualitative case‐study

Community engagement is seen as key to citizen‐centred and sustainable healthcare systems as involving citizens in the designing, implementation and improvement of services and policies is thought to tailor these more closely to communities’ own needs and experiences. Organizations have struggled to reach out to and involve disadvantaged citizens. This paper examines how if, why, and when low‐income citizens wish to be involved.
A Clinician’s Guide to Artiﬁcial Intelligence (AI): Why and How Primary Care Should Lead the Health Care AI Revolution

Artiﬁcial intelligence (AI) in health care is the future that is already here. Despite its potential as a transformational force for primary care, most primary care providers (PCPs) do not know what it is, how it will impact them and their patients, and what its key limitations and ethical pitfalls are. This article is a beginner’s guide to health care AI, written for the frontline PCP. Primary care—as the dominant force at the base of the health care pyramid, with its unrivaled interconnectedness to every part of the health system and its deep relationship with patients and communities—is the most uniquely suited specialty to lead the health care AI revolution. PCPs can advance health care AI by partnering with tech-nologists to ensure that AI use cases are relevant and human-centered, applying quality improvement methods to health care AI implementations, and advocating for inclusive and ethical AI that combats, rather than worsens, health inequities. ( J Am Board Fam Med 2022;35:175–184.)
Ten statements related to qualitative research in IS: true or false?

When Professor Bansal, the EIC of JITCAR, asked me to write a guest editorial for JITCAR, I was delighted. However, as my submission deadline approached, I wondered what my editorial would be about. After some thought, I decided to write this short piece to highlight 10 frequently held beliefs about qualitative research in our information systems (IS) community, some of which are not necessarily true. As an author, reviewer, and reader of JITCAR, you may find it interesting to see where you stand with respect to the statements listed below. I realize that many of you will find this list to be elementary. Nevertheless, I suggest you try to assess the following statements, by marking them as True or False, before consulting the discussion that follows.
Bacterial Indicators Are Ubiquitous Members of Pelagic Microbiome in Anthropogenically Impacted Coastal Ecosystem

Coastal zones are exposed to various anthropogenic impacts, such as different types of wastewater pollution, e.g., treated wastewater discharges, leakage from sewage systems, and agricultural and urban runoff. These various inputs can introduce allochthonous organic matter and microbes, including pathogens, into the coastal marine environment. The presence of fecal bacterial indicators in the coastal environment is usually monitored using traditional culture-based methods that, however, fail to detect their uncultured representatives. We have conducted a year-around in situ survey of the pelagic microbiome of the dynamic coastal ecosystem, subjected to different anthropogenic pressures to depict the seasonal and spatial dynamics of traditional and alternative fecal bacterial indicators. To provide an insight into the environmental conditions under which bacterial indicators thrive, a suite of environmental factors and bacterial community dynamics were analyzed concurrently. Analyses of 16S rRNA amplicon sequences revealed that the coastal microbiome was primarily structured by seasonal changes regardless of the distance from the wastewater pollution sources. On the other hand, fecal bacterial indicators were not affected by seasons and accounted for up to 34% of the sequence proportion for a given sample. Even more so, traditional fecal indicator bacteria (Enterobacteriaceae) and alternative wastewater-associated bacteria (Lachnospiraceae, Ruminococcaceae, Arcobacteraceae, Pseudomonadaceae and Vibrionaceae) were part of the core coastal microbiome, i.e., present at all sampling stations. Microbial source tracking and Lagrangian particle tracking, which we employed to assess the potential pollution source, revealed the importance of riverine water as a vector for transmission of allochthonous microbes into the marine system. Further phylogenetic analysis showed that the Arcobacteraceae in our data set was affiliated with the pathogenic Arcobacter cryaerophilus, suggesting that a potential exposure risk for bacterial pathogens in anthropogenically impacted coastal zones remains. We emphasize that molecular analyses combined with statistical and oceanographic models may provide new insights for environmental health assessment and reveal the potential source and presence of microbial indicators, which are otherwise overlooked by a cultivation approach.
Integrated Analysis of Basic Helix Loop Helix Transcription Factor Family and Targeted Terpenoids Reveals Candidate AarbHLH Genes Involved in Terpenoid Biosynthesis in Artemisia argyi

Artemisia argyi is a valuable traditional medicinal plant in Asia. The essential oil from its leaves is rich in terpenoids and has been used to enhance health and well-being. In China, the market scale of industries related to A. argyi has attained tens of billions of Chinese Yuan. The basic helix-loop-helix (bHLH) family is one of the largest transcription factors families in plants that plays crucial roles in diverse biological processes and is an essential regulatory component of terpenoid biosynthesis. However, the bHLH TFs and their regulatory roles in A. argyi remain unknown. Here, 53 AarbHLH genes were identified from the transcriptome of A. argyi and were classified into 15 subfamilies based on the classification of bHLH proteins in Arabidopsis thaliana. The MEME analysis showed that the conserved motif 1 and motif 2 constituted the most conserved bHLH domain and distributed in most AarbHLH proteins. Additionally, integrated analysis of the expression profiles of AarbHLH genes and the contents of targeted terpenoids in different tissues group and JA-treated group were performed. Eleven up-regulated AarbHLHs and one down-regulated AarbHLH were screened as candidate genes that may participate in the regulation of terpenoid biosynthesis (TPS-AarbHLHs). Correlation analysis between gene expression and terpenoid contents indicated that the gene expression of these 12 TPS-AarbHLHs was significantly correlated with the content changes of 1,8-cineole or β-caryophyllene. Protein–protein interaction networks further illustrated that these TPS-AarbHLHs might be involved in terpenoid biosynthesis in A. argyi. This finding provides a basis to further investigate the regulation mechanism of AarbHLH genes in terpenoid biosynthesis, and will be helpful to improve the quality of A. argyi.
Association Mapping for Yield Attributing Traits and Yellow Mosaic Disease Resistance in Mung Bean [Vigna radiata (L.) Wilczek]

Mung bean [Vigna radiata (L.) Wilczek] is an important short-duration grain legume widely known for its nutritional, soil ameliorative, and cropping system intensification properties. This study aims at evaluating genetic diversity among mung bean genotypes and detecting genomic regions associated with various yield attributing traits and yellow mosaic disease (YMD) resistance by association mapping. A panel of 80 cultivars and advanced breeding lines was evaluated for 10 yield-related and YMD resistance traits during kharif (monsoon) and summer seasons of 2018–2019 and 2019–2020. A total of 164 genome-wide simple sequence repeat (SSR) markers were initially screened, out of which 89 were found polymorphic which generated 317 polymorphic alleles with an average of 3.56 alleles per SSR locus. The number of alleles at each locus varied from 2 to 7. The population genetic structure analysis grouped different genotypes in three major clusters and three genetically distinct subpopulations (SPs) (i.e., SP-1, SP-2, and SP-3) with one admixture subpopulation (SP-4). Both cluster and population genetic structure analysis categorized the advanced mung bean genotypes in a single group/SP and the released varieties in other groups/SPs, suggesting that the studied genotypes may have common ancestral history at some level. The population genetic structure was also in agreement with the genetic diversity analysis. The estimate of the average degree of linkage disequilibrium (LD) present at the genome level in 80 mung bean genotypes unveiled significant LD blocks. Over the four seasons, 10 marker-trait associations were observed significant for YMD and four seed yield (SY)-related traits viz., days to flowering, days to maturity, plant height, and number of pods per plant using the mixed linear model (MLM) method. These associations may be useful for marker-assisted mung bean yield improvement programs and YMD resistance.
Oncogenic Roles of Small Nucleolar RNA Host Gene 7 (SNHG7) Long Noncoding RNA in Human Cancers and Potentials

Long noncoding RNAs (lncRNAs) are a class of noncoding transcripts characterized with more than 200 nucleotides of length. Unlike their names, some short open reading frames are recognized for them encoding small proteins. LncRNAs are found to play regulatory roles in essential cellular processes such as cell growth and apoptosis. Therefore, an increasing number of lncRNAs are identified with dysregulation in a wide variety of human cancers. SNHG7 is an lncRNA with upregulation in cancer cells and tissues. It is frequently reported with potency of promoting malignant cell behaviors in vitro and in vivo. Like oncogenic/tumor suppressor lncRNAs, SNHG7 is found to exert its tumorigenic functions through interaction with other biological substances. These include sponging target miRNAs (various numbers are identified), regulation of several signaling pathways, transcription factors, and effector proteins. Importantly, clinical studies demonstrate association between high SNHG7 expression and clinicopathological features in cancerous patients, worse prognosis, and enhanced chemoresistance. In this review, we summarize recent studies in three eras of cell, animal, and human experiments to bold the prognostic, diagnostic, and therapeutic potentials.
The Role of miR-4256/HOXC8 Signaling Axis in the Gastric Cancer Progression: Evidence From lncRNA-miRNA-mRNA Network Analysis

Gastric cancer is a deadly human malignancy and the molecular mechanisms underlying gastric cancer pathophysiology are very complicated. Thus, further investigations are warranted to decipher the underlying molecular mechanisms. With the development of high-throughput screening and bioinformatics, gene expression profiles with large scale have been performed in gastric cancer. In the present study, we mined The Cancer Genome Atlas (TCGA) database and analyzed the gene expression profiles between gastric cancer tissues and normal gastric tissues. A series of differentially expressed lncRNAs, miRNAs and mRNAs between gastric cancer tissues and normal gastric tissues were identified. Based on the differentially expressed genes, we constructed miRNA-mRNA network, lncRNA-mRNA network and transcriptional factors-mRNA-miRNA-lncRNA network. Furthermore, the Kaplan survival analysis showed that high expression levels of EVX1, GBX2, GCM1, HOXC8, HOXC9, HOXC10, HOXC11, HOXC12 and HOXC13 were all significantly correlated with shorter overall survival of the patients with gastric cancer. On the other hand, low expression level of HOXA13 was associated with shorter overall survival of patients with gastric cancer. Among these hub genes, we performed the in vitro functional studies of HOXC8 in the gastric cancer cells. Knockdown of HOXC8 and overexpression of miR-4256 both significantly repressed the gastric cancer cell proliferation and migration, and miR-4256 repressed the expression of HOXC8 via targeting its 3’ untranslated region in gastric cancer cells. Collectively, our results revealed that a complex interaction networks of differentially expressed genes in gastric cancer, and further functional studies indicated that miR-4256/HOXC8 may be an important axis in regulating gastric cancer progression.
Quantifying the Kinetics of Signaling and Arrestin Recruitment by Nervous System G-Protein Coupled Receptors

Neurons integrate inputs over different time and space scales. Fast excitatory synapses at boutons (ms and μm), and slow modulation over entire dendritic arbors (seconds and mm) are all ultimately combined to produce behavior. Understanding the timing of signaling events mediated by G-protein-coupled receptors is necessary to elucidate the mechanism of action of therapeutics targeting the nervous system. Measuring signaling kinetics in live cells has been transformed by the adoption of fluorescent biosensors and dyes that convert biological signals into optical signals that are conveniently recorded by microscopic imaging or by fluorescence plate readers. Quantifying the timing of signaling has now become routine with the application of equations in familiar curve fitting software to estimate the rates of signaling from the waveform. Here we describe examples of the application of these methods, including (1) Kinetic analysis of opioid signaling dynamics and partial agonism measured using cAMP and arrestin biosensors; (2) Quantifying the signaling activity of illicit synthetic cannabinoid receptor agonists measured using a fluorescent membrane potential dye; (3) Demonstration of multiplicity of arrestin functions from analysis of biosensor waveforms and quantification of the rates of these processes. These examples show how temporal analysis provides additional dimensions to enhance the understanding of GPCR signaling and therapeutic mechanisms in the nervous system.
Drivers and Seasonal Variability of Redox-Sensitive Metal Chemistry in a Shallow Subterranean Estuary

The subterranean estuary (STE) has been historically defined in terms of the mixing of saline and fresh water, in an analogy to surface estuaries. However, redox gradients are also a defining characteristic of the STE and influence its role as a source or sink for metals in the environment. Approaching the STE from a redox-focused biogeochemical perspective (e.g., considering the role of microbial respiration and availability of organic matter) provides the ability to quantify drivers of metal transport across spatial and temporal scales. This study measured the groundwater composition of a shallow STE over 2 years and used multiple linear regression to characterize the influence of salinity and redox chemistry on the behavior of redox-sensitive metals (RSMs) including Mo, U, V, and Cr. Molybdenum and uranium were both supplied to the STE by surface water, but differed in their removal mechanisms and seasonal behavior. Molybdenum showed non-conservative removal by reaction with sulfide in all seasons. Sulfide concentrations at this site were consistently higher than required for quantitative reaction with Mo (10 µM sulfide), evidently leading to quantitative removal at the same depth regardless of season. In contrast, U appeared to depend directly on microbial activity for removal, and showed more extensive removal at shallower depths in summer. Both V and Cr were elevated in meteoric groundwater (2.5–297 nM and 2.6–236 nM, respectively), with higher endmember concentrations in summer. Both V and Cr also showed non-conservative addition within the STE relative to conservative mixing among the observed endmembers. The mobility of V and Cr in the STE, and therefore their supply to the coastal ocean, was controlled by the availability of dissolved organic matter and Fe, suggesting V and Cr were potentially complexed in the colloidal fraction. Complexation by different organic matter pools led to seasonal variations in V but greater interannual variability of Cr. These results reveal distinct behaviors of RSMs in response to seasonal biogeochemical processes that drive microbial activity, organic matter composition, and complexation by inorganic species.