Miracle-dz/UniFAR-FTUs huggingface dataset:
Oblique aerial images: a review focusing on georeferencing procedures

ABSTRACT The automatic processing of oblique aerial images is currently one of the most attractive topics in the fields of photogrammetry, remote sensing and computer vision. This kind of images has gained popularity throughout the last years, as it enables depiction of both vertical and horizontal structures of the urban environment, being ideal for various photogrammetric applications, apart from visualization and interpretation purposes. In most cases, the georeferencing of the images is indispensable for their metric exploitation, usually in terms of computation of their camera exterior orientation parameters. For other applications, georeferencing by means of image orthorectification is required, whereas a simple georeferencing in terms of mapping of the center point of the images or the outline of their footprint or image rectification via a 2D transformation also suffices in some cases. However, oblique airborne images pose several challenges in automated georeferencing procedures, due to complex image geometry and strongly varying appearance between images of the same scene taken from different viewing angles. In this paper, a thorough literature review is conducted in order to inform about the recent research in georeferencing of datasets including oblique aerial imagery. The review starts with a classification of oblique aerial imagery and a presentation of the most commonly used camera configurations for oblique imagery acquisition. A brief presentation of their history along with old applications is then made, followed by a categorization of the literature regarding state-of-the-art georeferencing procedures as well as other research trends and applications using oblique aerial images. Then, a more in-depth description of recent research on georeferencing of oblique image datasets is presented. Finally, the significant points, research results and conclusions derived from the literature are discussed, so that they can be utilized by future researchers in the development of georeferencing methods targeted to oblique airborne imagery.
Three human cell types respond to multi-walled carbon nanotubes and titanium dioxide nanobelts with cell-specific transcriptomic and proteomic expression patterns

Abstract The growing use of engineered nanoparticles (NPs) in commercial and medical applications raises the urgent need for tools that can predict NP toxicity. Global transcriptome and proteome analyses were conducted on three human cell types, exposed to two high aspect ratio NP types, to identify patterns of expression that might indicate high versus low NP toxicity. Three cell types representing the most common routes of human exposure to NPs, including macrophage-like (THP-1), small airway epithelial and intestinal (Caco-2/HT29-MTX) cells, were exposed to TiO2 nanobelts (TiO2-NB; high toxicity) and multi-walled carbon nanotubes (MWCNT; low toxicity) at low (10 µg/mL) and high (100 µg/mL) concentrations for 1 and 24 h. Unique patterns of gene and protein expressions were identified for each cell type, with no differentially expressed (p < 0.05, 1.5-fold change) genes or proteins overlapping across all three cell types. While unique to each cell type, the early response was primarily independent of NP type, showing similar expression patterns in response to both TiO2-NB and MWCNT. The early response might, therefore, indicate a general response to insult. In contrast, the 24 h response was unique to each NP type. The most significantly (p < 0.05) enriched biological processes in THP-1 cells indicated TiO2-NB regulation of pathways associated with inflammation, apoptosis, cell cycle arrest, DNA replication stress and genomic instability, while MWCNT-regulated pathways indicated increased cell proliferation, DNA repair and anti-apoptosis. These two distinct sets of biological pathways might, therefore, underlie cellular responses to high and low NP toxicity, respectively.
Socioeconomic inequality and urban-rural disparity of antenatal care visits in Bangladesh: A trend and decomposition analysis

Background Socioeconomic inequality in antenatal care visits is a great concern in developing countries including Bangladesh; however, there is a scarcity of investigation to assess the factors of inequality and these changes over time. In this study, we investigated the trend of socioeconomic inequalities (2004–2017) in 1+ANC and 4+ANC visits, and extracted determinants contributions to the observed inequalities and urban-rural disparities in Bangladesh over the period from 2011 to 2017. Methods The data from the Bangladesh Demographic and Health Surveys (BDHS) conducted in 2004, 2007, 2011 and 2017 were analyzed in this study. The analysis began with exploratory and bivariate analysis, followed by the application of logistic regression models. To measure the inequalities, the Erreygers concentration index was used, and regression-based decomposition analyses were utilized to unravel the determinant’s contribution to the observed inequalities. The Blinder-Oaxaca type decomposition is also used to decompose the urban-rural disparity into the factors. Results Our analysis results showed that the prevalence of 1+ANC and 4+ANC visits has increased across all the determinants, although the rate of 4+ANC visits remains notably low. The magnitudes of socioeconomic inequality in 4+ANC visits represented an irregular pattern at both the national and urban levels, whereas it increased gradually in rural Bangladesh. However, inequalities in 1+ANC visits declined substantially after 2011 across the national, rural and urban areas of Bangladesh. Decomposition analyses have suggested that wealth status, women’s education, place of residence (only for 4+ANC visits), caesarean delivery, husband education, and watching television (TV) are the main determinants to attribute and changes in the level of inequality and urban-rural disparity between the years 2011 and 2017. Conclusions According to the findings of our study, it is imperative for authorities to ensure antenatal care visits are more accessible for rural and underprivileged women. Additionally, should focus on delivering high-quality education, ensuring the completion of education, reducing income disparity as well as launching a program to enhance awareness about health facilities, and the impact of caesarean delivery.
A Bayesian Approach to Privacy Enforcement in Smartphones

Mobile apps often require access to private data, such as the device ID or location. At the same time, popular platforms like Android and iOS have limited support for user privacy. This frequently leads to unauthorized disclosure of private information by mobile apps, e.g. for advertising and analytics purposes. This paper addresses the problem of privacy enforcement in mobile systems, which we formulate as a classification problem: When arriving at a privacy sink (e.g., database update or outgoing web message), the runtime system must classify the sink's behavior as either legitimate or illegitimate. The traditional approach of information-flow (or taint) tracking applies "binary" classification, whereby information release is legitimate iff there is no data flow from a privacy source to sink arguments. While this is a useful heuristic, it also leads to false alarms. We propose to address privacy enforcement as a learning problem, relaxing binary judgments into a quantitative/ probabilistic mode of reasoning. Specifically, we propose a Bayesian notion of statistical classification, which conditions the judgment whether a release point is legitimate on the evidence arising at that point. In our concrete approach, implemented as the BAYESDROID system that is soon to be featured in a commercial product, the evidence refers to the similarity between the data values about to be released and the private data stored on the device. Compared to TaintDroid, a state-of-the-art taint-based tool for privacy enforcement, BAYESDROID is substantially more accurate. Applied to 54 top-popular Google Play apps, BAYESDROID is able to detect 27 privacy violations with only 1 false alarm.
A smartphone-centered wearable sensor network for fall risk assessment in the elderly

Fall prevention is an important aspect to keep high the quality of life in aging. In this work, a wearable sensor network to automatically assess movement and its indicator for fall risk is proposed. The method is based on a smartphone linked to wearable devices. The proposed approach starts estimating the physical activity level using activity recognition algorithms running on the phone. Such estimation can be conducted by simply using the smartphone on its own. Activity recognition can also identify walking bouts to drive gait assessment tools using different sensing sources. In this regard a strategy to estimate stride-based gait stability indexes during ambulation was implemented. Waist and shank sensing nodes were involved in the computation of stride time, stride time variability, vertical acceleration variability and harmonic ratios. The proposed method can correctly recognize activity with a 95% accuracy within four classes. Ambulation in particular is recognized correctly in 97.2% of cases and gait stability indexes are then estimated capturing differences between controls and patients at high risk of falling.
Multi-view stereo via volumetric graph-cuts

This paper presents a novel formulation for the multi-view scene reconstruction problem. While this formulation benefits from a volumetric scene representation, it is amenable to a computationally tractable global optimisation using Graph-cuts. The algorithm proposed uses the visual hull of the scene to infer occlusions and as a constraint on the topology of the scene. A photo consistency-based surface cost functional is defined and discretised with a weighted graph. The optimal surface under this discretised functional is obtained as the minimum cut solution of the weighted graph. Our method provides a viewpoint independent surface regularisation, approximate handling of occlusions and a tractable optimisation scheme. Promising experimental results on real scenes as well as a quantitative evaluation on a synthetic scene are presented.
Complex Dependencies in the Alliance Network

The multifaceted and strategic interactions inherent in the formation of international military pacts render the alliance decisions of states highly interdependent. Our aim here is to model the network of alliances in such a way as to capture the effects of covariates and account for the complex dependencies inherent in the network. Regression analysis, due to its foundational assumption of conditional independence, cannot be used to analyze alliance decisions specifically and interdependent decisions generally. We demonstrate how alliance decisions are interdependent and define the problems associated with the regression analysis of nonindependent dyads. We then show that alliances can naturally be conceived of as constituting a network, where alliance formation is an inherently interdependent process. We proceed by introducing the exponential random graph model for analyzing interdependence in the alliance network and estimating the effect of covariates on alliances.
Systemic intracellular analysis for balancing complex biosynthesis in a transcriptionally deregulated Escherichia coli l‐Methionine producer

Abstract l‐Methionine (l‐Met) has gained remarkable interest due to its multifaceted and versatile applications in the fields of nutrition, pharmaceuticals and clinical practice. In this study, the fluxes of the challenging l‐Met biosynthesis in the producer strain Escherichia coli (E. coli) DM2853 were fine‐tuned to enable improved l‐Met production. The potential bottlenecks identified in sulfur assimilation and l‐Met synthesis downstream of O‐succinyl‐l‐homoserine (OSHS) were addressed by overexpressing glutaredoxin 1 (grxA), thiosulfate sulfurtransferase (pspE) and O‐succinylhomoserine lyase (metB). Although deemed as a straightforward target for improving glucose‐to‐Met conversion, the yields remained at approximately 12%–13% (g/g). Instead, intracellular l‐Met pools increased by up to four‐fold with accelerated kinetics. Overexpression of the Met exporter ygaZH may serve as a proper valve for releasing the rising internal Met pressure. Interestingly, the export kinetics revealed maximum saturated export rates already at low growth rates. This scenario is particularly advantageous for large‐scale fermentation when product formation is ideally uncoupled from biomass formation to achieve maximum performance within the technical limits of large‐scale bioreactors.
Penalty function maximization for large margin HMM training

We perform large margin training of HMM acoustic parameters by maximizing a penalty function which combines two terms. The first term is a scale which gets multiplied with the Hamming distance between HMM state sequences to form a multi-label (or sequence) margin. The second term arises from constraints on the training data that the joint log-likelihoods of acoustic and correct word sequences exceed the joint log-likelihoods of acoustic and incorrect word sequences by at least the multi-label margin between the corresponding Viterbi state sequences. Using the softmax trick, we collapse these constraints into a boosted MMI-like term. The resulting objective function can be efficiently maximized using extended Baum-Welch updates. Experimental results on multiple LVCSR tasks show a good correlation between the objective function and the word error rate.
puffMarker: a multi-sensor approach for pinpointing the timing of first lapse in smoking cessation

Recent researches have demonstrated the feasibility of detecting smoking from wearable sensors, but their performance on real-life smoking lapse detection is unknown. In this paper, we propose a new model and evaluate its performance on 61 newly abstinent smokers for detecting a first lapse. We use two wearable sensors --- breathing pattern from respiration and arm movements from 6-axis inertial sensors worn on wrists. In 10-fold cross-validation on 40 hours of training data from 6 daily smokers, our model achieves a recall rate of 96.9%, for a false positive rate of 1.1%. When our model is applied to 3 days of post-quit data from 32 lapsers, it correctly pinpoints the timing of first lapse in 28 participants. Only 2 false episodes are detected on 20 abstinent days of these participants. When tested on 84 abstinent days from 28 abstainers, the false episode per day is limited to 1/6.
New Smith Predictor and Generalized Predictive Control for Wireless Networked Control Systems

(WNCS), as well as Smith predictor models might be imprecise, a novel approach is proposed that new Smith predictor combined with generalized predictive control (GPC) for the WNCS. Because new Smith dynamic predictor hides predictor model of the network delay into real transmission process of the network data, further the network delay no longer need to be measured, identified or estimated on-line. It is applicable to some occasions that wireless network delays are random, time-variant or uncertain, possibly large compared to one, even tens sampling periods, meanwhile, there are some data dropouts in closed loop. Based on IEEE 802.11b/g (WLAN), the results of simulation show validity of the control scheme, and indicate that system has better dynamic performance and robustness.
mRNA vaccine designs for optimal adjuvanticity and delivery

ABSTRACT Adjuvanticity and delivery are crucial facets of mRNA vaccine design. In modern mRNA vaccines, adjuvant functions are integrated into mRNA vaccine nanoparticles, allowing the co-delivery of antigen mRNA and adjuvants in a unified, all-in-one formulation. In this formulation, many mRNA vaccines utilize the immunostimulating properties of mRNA and vaccine carrier components, including lipids and polymers, as adjuvants. However, careful design is necessary, as excessive adjuvanticity and activation of improper innate immune signalling can conversely hinder vaccination efficacy and trigger adverse effects. mRNA vaccines also require delivery systems to achieve antigen expression in antigen-presenting cells (APCs) within lymphoid organs. Some vaccines directly target APCs in the lymphoid organs, while others rely on APCs migration to the draining lymph nodes after taking up mRNA vaccines. This review explores the current mechanistic understanding of these processes and the ongoing efforts to improve vaccine safety and efficacy based on this understanding.
The DLRUS Temporal Description Logic

This paper introduces a new logical formalism, intended for temporal conceptual modelling, as a natural combination of the well-kno w description logic DLR and point-based linear temporal logic with Sinceand Until. We define a query language (where queries are non-recursive Datal og programs and atoms are complex DLRUS expressions) and investigate the problem of checking query containment under the constraints defined by DLRUS conceptual schemas, as well as the problems of schema satisfiability and logical implication. Although it is shown that reasoning in full DLRUS is undecidable, we identify the decidable (in a sense, maximal) fragment DLR US by allowing applications of temporal operators to formulas and entities only (but not to relation expressions). We obtain the following hierarchy of complexity res ult : (a) reasoning in DLR US with atomic formulas is EXPTIME-complete, (b) satisfiabili ty and logical implication of arbitraryDLR US formulas is EXPSPACE-complete, and (c) the problem of checking query containment of non-recurs ive Datalog queries underDLR US constraints is decidable in 2EXPTIME.
Application of Object-Based Time-Domain Diagnostics for Tracking Precipitation Systems in Convection-Allowing Models

AbstractMeaningful verification and evaluation of convection-allowing models requires approaches that do not rely on point-to-point matches of forecast and observed fields. In this study, one such approach—a beta version of the Method for Object-Based Diagnostic Evaluation (MODE) that incorporates the time dimension [known as MODE time-domain (MODE-TD)]—was applied to 30-h precipitation forecasts from four 4-km grid-spacing members of the 2010 Storm-Scale Ensemble Forecast system with different microphysics parameterizations. Including time in MODE-TD provides information on rainfall system evolution like lifetime, timing of initiation and dissipation, and translation.The simulations depicted the spatial distribution of time-domain precipitation objects across the United States quite well. However, all simulations overpredicted the number of objects, with the Thompson microphysics scheme overpredicting the most and the Morrison method the least. For the smallest smoothing radius and rainfall threshold use.
High Level Quantum Structures in Linguistics and Multi Agent Systems

We present two applications to AI of recently introduced high level quantum structures. These structures are the categorical quantum logic of (Abramsky & Coecke 2004) and the quantale quantum logic of (Coecke, Moore, & Stubbe 2001). Firstly, we show how the diagrammatic toolkit of categorical quantum logic, when restricted to its pregroup fragment (Lambek 1999; 2001), simplifies analysis of sentence structure of different languages. Moreover, the quantitative values that arise in these diagrams signify different degrees of complexity of sentences, which turn out to vary for different languages. Secondly, we show how expanding the quantale quantum logic with epistemic modalities provides a powerful system to reason about information update in multi-agent systems. Finally, we indicate how the above two applications to non-quantum domains can themselves be ‘re-quantized’, providing applications to quantum informatics of distributed systems.
Influence of external magnetic field and its orientation on transport AC losses in Bi-2223 and Tl-1223 silver sheathed tapes

In the paper we report measurements of energy losses caused by an AC transport current in silver sheathed multifilamentary Bi-2223 and mono core Tl-1223 tapes, as a function of the magnitude and orientation of the applied external magnetic field. The external magnetic field was rotated in the range of 0 to /spl plusmn/90 degrees with respect to the plane of the tapes, and was In all cases perpendicular to the transport current flow. The transport current losses in a range of DC applied fields are consistent with the Norris equation provided the DC critical current at the same applied field is used in his expression for the loss.
A model study of protein nascent chain and cotranslational folding using hydrophobic‐polar residues

To study protein nascent chain folding during biosynthesis, we investigate the folding behavior of models of hydrophobic and polar (HP) chains at growing length using both two‐dimensional square lattice model and an optimized three‐dimensional 4‐state discrete off‐lattice model. After enumerating all possible sequences and conformations of HP heteropolymers up to length N = 18 and N = 15 in two and three‐dimensional space, respectively, we examine changes in adopted structure, stability, and tolerance to single point mutation as the nascent chain grows. In both models, we find that stable model proteins have fewer folded nascent chains during growth, and often will only fold after reaching full length. For the few occasions where partial chains of stable proteins fold, these partial conformations on average are very similar to the corresponding parts of the final conformations at full length. Conversely, we find that sequences with fewer stable nascent chains and sequences with native‐like folded nascent chains are more stable. In addition, these stable sequences in general can have many more point mutations and still fold into the same conformation as the wild type sequence. Our results suggest that stable proteins are less likely to be trapped in metastable conformations during biosynthesis, and are more resistant to point‐mutations. Our results also imply that less stable proteins will require the assistance of chaperone and other factors during nascent chain folding. Taken together with other reported studies, it seems that cotranslational folding may not be a general mechanism of in vivo protein folding for small proteins, and in vitro folding studies are still relevant for understanding how proteins fold biologically. Proteins 2008. © 2007 Wiley‐Liss, Inc.
Evaluation of Cancer Risk of Heavy Metals in the Air of a High Traffic Urban Region and Its Source Identification

Background: Sampling was conducted on particles smaller than ten microns (PM 10 ) in a high-traffic urban region once a week for two years in which fifteen heavy metals were measured. Methods: positive matrix factorization (EPA-PMF 5 ), was used for source apportionment and characterization of the collected PM10. Assessment of cancer risk resulting from metals including arsenic, cadmium, chromium, nickel and lead was conducted in three concentration ranges of maximum, average and minimum. Results: Results for children and adults living in the region indicated that cancer risk indexes at different concentration ranges of carcinogenic