bluuebunny/crossref_metadata_2025 huggingface dataset:
SAP: Self-Adaptive Proposal Model for Temporal Action Detection Based on Reinforcement Learning
Authors: Huang Jingjia , Li Nannan , Zhang Tao , Li Ge , Huang Tiejun , Gao Wen
Published: June 2022

Existing action detection algorithms usually generate action proposals through an extensive search over the video at multiple temporal scales, which brings about huge computational overhead and deviates from the human perception procedure. We argue that the process of detecting actions should be naturally one of observation and refinement: observe the current window and refine the span of attended window to cover true action regions. In this paper, we propose a Self-Adaptive Proposal (SAP) model that learns to find actions through continuously adjusting the temporal bounds in a self-adaptive way. The whole process can be deemed as an agent, which is firstly placed at the beginning of the video and traverse the whole video by adopting a sequence of transformations on the current attended region to discover actions according to a learned policy. We utilize reinforcement learning, especially the Deep Q-learning algorithm to learn the agent’s decision policy. In addition, we use temporal pooling operation to extract more effective feature representation for the long temporal window, and design a regression network to adjust the position offsets between predicted results and the ground truth. Experiment results on THUMOS’14 validate the effectiveness of SAP, which can achieve competitive performance with current action detection algorithms via much fewer proposals.
Action Recognition With Coarse-to-Fine Deep Feature Integration and Asynchronous Fusion
Authors: Lin Weiyao , Zhang Chongyang , Lu Ke , Sheng Bin , Wu Jianxin , Ni Bingbing , Liu Xin , Xiong Hongkai
Published: June 2022

Action recognition is an important yet challenging task in computer vision. In this paper, we propose a novel deep-based framework for action recognition, which improves the recognition accuracy by: 1) deriving more precise features for representing actions, and 2) reducing the asynchrony between different information streams. We first introduce a coarse-to-fine network which extracts shared deep features at different action class granularities and progressively integrates them to obtain a more accurate feature representation for input actions. We further introduce an asynchronous fusion network. It fuses information from different streams by asynchronously integrating stream-wise features at different time points, hence better leveraging the complementary information in different streams. Experimental results on action recognition benchmarks demonstrate that our approach achieves the state-of-the-art performance.
SEE: Towards Semi-Supervised End-to-End Scene Text Recognition
Authors: Bartz Christian , Yang Haojin , Meinel Christoph
Published: June 2022

Detecting and recognizing text in natural scene images is a challenging, yet not completely solved task. In recent years several new systems that try to solve at least one of the two sub-tasks (text detection and text recognition) have been proposed. In this paper we present SEE, a step towards semi-supervised neural networks for scene text detection and recognition, that can be optimized end-to-end. Most existing works consist of multiple deep neural networks and several pre-processing steps. In contrast to this, we propose to use a single deep neural network, that learns to detect and recognize text from natural images, in a semi-supervised way. SEE is a network that integrates and jointly learns a spatial transformer network, which can learn to detect text regions in an image, and a text recognition network that takes the identified text regions and recognizes their textual content. We introduce the idea behind our novel approach and show its feasibility, by performing a range of experiments on standard benchmark datasets, where we achieve competitive results.
Temporal-Enhanced Convolutional Network for Person Re-Identification
Authors: Wu Yang , Qiu Jie , Takamatsu Jun , Ogasawara Tsukasa
Published: June 2022

We propose a new neural network called Temporal-enhanced Convolutional Network (T-CN) for video-based person reidentification. For each video sequence of a person, a spatial convolutional subnet is first applied to each frame for representing appearance information, and then a temporal convolutional subnet links small ranges of continuous frames to extract local motion information. Such spatial and temporal convolutions together construct our T-CN based representation. Finally, a recurrent network is utilized to further explore global dynamics, followed by temporal pooling to generate an overall feature vector for the whole sequence. In the training stage, a Siamese network architecture is adopted to jointly optimize all the components with losses covering both identification and verification. In the testing stage, our network generates an overall discriminative feature representation for each input video sequence (whose length may vary a lot) in a feed-forward way, and even a simple Euclidean distance based matching can generate good re-identification results. Experiments on the most widely used benchmark datasets demonstrate the superiority of our proposal, in comparison with the state-of-the-art.
Deep Structured Learning for Visual Relationship Detection
Authors: Zhu Yaohui , Jiang Shuqiang
Published: June 2022

In the research area of computer vision and artificial intelligence, learning the relationships of objects is an important way to deeply understand images. Most of recent works detect visual relationship by learning objects and predicates respectively in feature level, but the dependencies between objects and predicates have not been fully considered. In this paper, we introduce deep structured learning for visual relationship detection. Specifically, we propose a deep structured model, which learns relationship by using feature-level prediction and label-level prediction to improve learning ability of only using feature-level predication. The feature-level prediction learns relationship by discriminative features, and the label-level prediction learns relationships by capturing dependencies between objects and predicates based on the learnt relationship of feature level. Additionally, we use structured SVM (SSVM) loss function as our optimization goal, and decompose this goal into the subject, predicate, and object optimizations which become more simple and more independent. Our experiments on the Visual Relationship Detection (VRD) dataset and the large-scale Visual Genome (VG) dataset validate the effectiveness of our method, which outperforms state-of-the-art methods.
Game of Sketches: Deep Recurrent Models of Pictionary-Style Word Guessing
Authors: Sarvadevabhatla Ravi Kiran , Surya Shiv , Mittal Trisha , Babu R. Venkatesh
Published: June 2022

The ability of machine-based agents to play games in human-like fashion is considered a benchmark of progress in AI. In this paper, we introduce the first computational model aimed at Pictionary, the popular word-guessing social game. We first introduce Sketch-QA, an elementary version of Visual Question Answering task. Styled after Pictionary, Sketch-QA uses incrementally accumulated sketch stroke sequences as visual data. Notably, Sketch-QA involves asking a fixed question ("What object is being drawn?") and gathering open-ended guess-words from human guessers. To mimic Pictionary-style guessing, we propose a deep neural model which generates guess-words in response to temporally evolving human-drawn sketches. Our model even makes human-like mistakes while guessing, thus amplifying the human mimicry factor. We evaluate our model on the large-scale guess-word dataset generated via Sketch-QA task and compare with various baselines. We also conduct a Visual Turing Test to obtain human impressions of the guess-words generated by humans and our model. Experimental results demonstrate the promise of our approach for Pictionary and similarly themed games.
Facial Landmarks Detection by Self-Iterative Regression Based Landmarks-Attention Network
Authors: Hu Tao , Qi Honggang , Xu Jizheng , Huang Qingming
Published: June 2022

Cascaded Regression (CR) based methods have been proposed to solve facial landmarks detection problem, which learn a series of descent directions by multiple cascaded regressors separately trained in coarse and fine stages. They outperform the traditional gradient descent based methods in both accuracy and running speed. However, cascaded regression is not robust enough because each regressor's training data comes from the output of previous regressor. Moreover, training multiple regressors requires lots of computing resources, especially for deep learning based methods. In this paper, we develop a Self-Iterative Regression (SIR) framework to improve the model efficiency. Only one self-iterative regressor is trained to learn the descent directions for samples from coarse stages to fine stages, and parameters are iteratively updated by the same regressor. Specifically, we proposed Landmarks-Attention Network (LAN) as our regressor, which concurrently learns features around each landmark and obtains the holistic location increment. By doing so, not only the rest of regressors are removed to simplify the training process, but the number of model parameters is significantly decreased. The experiments demonstrate that with only 3.72M model parameters, our proposed method achieves the state-of-the-art performance.
UnFlow: Unsupervised Learning of Optical Flow With a Bidirectional Census Loss
Authors: Meister Simon , Hur Junhwa , Roth Stefan
Published: June 2022

In the era of end-to-end deep learning, many advances in computer vision are driven by large amounts of labeled data. In the optical flow setting, however, obtaining dense per-pixel ground truth for real scenes is difficult and thus such data is rare. Therefore, recent end-to-end convolutional networks for optical flow rely on synthetic datasets for supervision, but the domain mismatch between training and test scenarios continues to be a challenge. Inspired by classical energy-based optical flow methods, we design an unsupervised loss based on occlusion-aware bidirectional flow estimation and the robust census transform to circumvent the need for ground truth flow. On the KITTI benchmarks, our unsupervised approach outperforms previous unsupervised deep networks by a large margin, and is even more accurate than similar supervised methods trained on synthetic datasets alone. By optionally fine-tuning on the KITTI training data, our method achieves competitive optical flow accuracy on the KITTI 2012 and 2015 benchmarks, thus in addition enabling generic pre-training of supervised networks for datasets with limited amounts of ground truth.
Adversarial Discriminative Heterogeneous Face Recognition
Authors: Song Lingxiao , Zhang Man , Wu Xiang , He Ran
Published: June 2022

The gap between sensing patterns of different face modalities remains a challenging problem in heterogeneous face recognition (HFR). This paper proposes an adversarial discriminative feature learning framework to close the sensing gap via adversarial learning on both raw-pixel space and compact feature space. This framework integrates cross-spectral face hallucination and discriminative feature learning into an end-to-end adversarial network. In the pixel space, we make use of generative adversarial networks to perform cross-spectral face hallucination. An elaborate two-path model is introduced to alleviate the lack of paired images, which gives consideration to both global structures and local textures. In the feature space, an adversarial loss and a high-order variance discrepancy loss are employed to measure the global and local discrepancy between two heterogeneous distributions respectively. These two losses enhance domain-invariant feature learning and modality independent noise removing. Experimental results on three NIR-VIS databases show that our proposed approach outperforms state-of-the-art HFR methods, without requiring of complex network or large-scale training dataset.
Multi-Rate Gated Recurrent Convolutional Networks for Video-Based Pedestrian Re-Identification
Authors: Li Zhihui , Yao Lina , Nie Feiping , Zhang Dingwen , Xu Min
Published: June 2022

Matching pedestrians across multiple camera views has attracted lots of recent research attention due to its apparent importance in surveillance and security applications.While most existing works address this problem in a still-image setting, we consider the more informative and challenging video-based person re-identification problem, where a video of a pedestrian as seen in one camera needs to be matched to a gallery of videos captured by other non-overlapping cameras. We employ a convolutional network to extract the appearance and motion features from raw video sequences, and then feed them into a multi-rate recurrent network to exploit the temporal correlations, and more importantly, to take into account the fact that pedestrians, sometimes even the same pedestrian, move in different speeds across different camera views. The combined network is trained in an end-to-end fashion, and we further propose an initialization strategy via context reconstruction to largely improve the performance. We conduct extensive experiments on the iLIDS-VID and PRID-2011 datasets, and our experimental results confirm the effectiveness and the generalization ability of our model.
Weakly Supervised Salient Object Detection Using Image Labels
Authors: Li Guanbin , Xie Yuan , Lin Liang
Published: June 2022

Deep learning based salient object detection has recently achieved great success with its performance greatly outperforms any other unsupervised methods. However, annotating per-pixel saliency masks is a tedious and inefficient procedure. In this paper, we note that superior salient object detection can be obtained by iteratively mining and correcting the labeling ambiguity on saliency maps from traditional unsupervised methods. We propose to use the combination of a coarse salient object activation map from the classification network and saliency maps generated from unsupervised methods as pixel-level annotation, and develop a simple yet very effective algorithm to train fully convolutional networks for salient object detection supervised by these noisy annotations. Our algorithm is based on alternately exploiting a graphical model and training a fully convolutional network for model updating. The graphical model corrects the internal labeling ambiguity through spatial consistency and structure preserving while the fully convolutional network helps to correct the cross-image semantic ambiguity and simultaneously update the coarse activation map for next iteration. Experimental results demonstrate that our proposed method greatly outperforms all state-of-the-art unsupervised saliency detection methods and can be comparable to the current best strongly-supervised methods training with thousands of pixel-level saliency map annotations on all public benchmarks.
Co-Saliency Detection Within a Single Image
Authors: Yu Hongkai , Zheng Kang , Fang Jianwu , Guo Hao , Feng Wei , Wang Song
Published: June 2022

Recently, saliency detection in a single image and co-saliency detection in multiple images have drawn extensive research interest in the vision community. In this paper, we investigate a new problem of co-saliency detection within a single image, i.e., detecting within-image co-saliency. By identifying common saliency within an image, e.g., highlighting multiple occurrences of an object class with similar appearance, this work can benefit many important applications, such as the detection of objects of interest, more robust object recognition, reduction of information redundancy, and animation synthesis. We propose a new bottom-up method to address this problem. Specifically, a large number of object proposals are first detected from the image. Then we develop an optimization algorithm to derive a set of proposal groups, each of which contains multiple proposals showing good common saliency in the original image. For each proposal group, we calculate a co-saliency map and then use a low-rank based algorithm to fuse the maps calculated from all the proposal groups for the final co-saliency map in the image. In the experiment, we collect a new dataset of 364 color images with within-image cosaliency. Experiment results show that the proposed method can better detect the within-image co-saliency than existing algorithms.
3D Box Proposals From a Single Monocular Image of an Indoor Scene
Authors: Zhuo Wei , Salzmann Mathieu , He Xuming , Liu Miaomiao
Published: June 2022

Modern object detection methods typically rely on bounding box proposals as input. While initially popularized in the 2D case, this idea has received increasing attention for 3D bounding boxes. Nevertheless, existing 3D box proposal techniques all assume having access to depth as input, which is unfortunately not always available in practice. In this paper, we therefore introduce an approach to generating 3D box proposals from a single monocular RGB image. To this end, we develop an integrated, fully differentiable framework that inherently predicts a depth map, extracts a 3D volumetric scene representation and generates 3D object proposals. At the core of our approach lies a novel residual, differentiable truncated signed distance function module, which, accounting for the relatively low accuracy of the predicted depth map, extracts a 3D volumetric representation of the scene. Our experiments on the standard NYUv2 dataset demonstrate that our framework lets us generate high-quality 3D box proposals and that it outperforms the two-stage technique consisting of successively performing state-of-the-art depth prediction and depth-based 3D proposal generation.
Towards Perceptual Image Dehazing by Physics-Based Disentanglement and Adversarial Training
Authors: Yang Xitong , Xu Zheng , Luo Jiebo
Published: June 2022

Single image dehazing is a challenging under-constrained problem because of the ambiguities of unknown scene radiance and transmission. Previous methods solve this problem using various hand-designed priors or by supervised training on synthetic hazy image pairs. In practice, however, the predefined priors are easily violated and the paired image data is unavailable for supervised training. In this work, we propose Disentangled Dehazing Network, an end-to-end model that generates realistic haze-free images using only unpaired supervision. Our approach alleviates the paired training constraint by introducing a physical-model based disentanglement and reconstruction mechanism. A multi-scale adversarial training is employed to generate perceptually haze-free images. Experimental results on synthetic datasets demonstrate our superior performance compared with the state-of-the-art methods in terms of PSNR, SSIM and CIEDE2000. Through training on purely natural haze-free and hazy images from our collected HazyCity dataset, our model can generate more perceptually appealing dehazing results.
Cross-Domain Human Parsing via Adversarial Feature and Label Adaptation
Authors: Liu Si , Sun Yao , Zhu Defa , Ren Guanghui , Chen Yu , Feng Jiashi , Han Jizhong
Published: June 2022

Human parsing has been extensively studied recently due to its wide applications in many important scenarios. Mainstream fashion parsing models (i.e., parsers) focus on parsing the high-resolution and clean images. However, directly applying the parsers trained on benchmarks of high-quality samples to a particular application scenario in the wild, e.g., a canteen, airport or workplace, often gives non-satisfactory performance due to domain shift. In this paper, we explore a new and challenging cross-domain human parsing problem: taking the benchmark dataset with extensive pixel-wise labeling as the source domain, how to obtain a satisfactory parser on a new target domain without requiring any additional manual labeling? To this end, we propose a novel and efficient cross-domain human parsing model to bridge the cross-domain differences in terms of visual appearance and environment conditions and fully exploit commonalities across domains. Our proposed model explicitly learns a feature compensation network, which is specialized for mitigating the cross-domain differences. A discriminative feature adversarial network is introduced to supervise the feature compensation to effectively reduces the discrepancy between feature distributions of two domains. Besides, our proposed model also introduces a structured label adversarial network to guide the parsing results of the target domain to follow the high-order relationships of the structured labels shared across domains. The proposed framework is end-to-end trainable, practical and scalable in real applications. Extensive experiments are conducted where LIP dataset is the source domain and 4 different datasets including surveillance videos, movies and runway shows without any annotations, are evaluated as target domains. The results consistently confirm data efficiency and performance advantages of the proposed method for the challenging cross-domain human parsing problem.
Co-Domain Embedding Using Deep Quadruplet Networks for Unseen Traffic Sign Recognition
Authors: Kim Junsik , Lee Seokju , Oh Tae-Hyun , Kweon In So
Published: June 2022

Recent advances in visual recognition show overarching success by virtue of large amounts of supervised data. However, the acquisition of a large supervised dataset is often challenging. This is also true for intelligent transportation applications, i.e., traffic sign recognition. For example, a model trained with data of one country may not be easily generalized to another country without much data. We propose a novel feature embedding scheme for unseen class classification when the representative class template is given. Traffic signs, unlike other objects, have official images. We perform co-domain embedding using a quadruple relationship from real and synthetic domains. Our quadruplet network fully utilizes the explicit pairwise similarity relationships among samples from different domains. We validate our method on three datasets with two experiments involving one-shot classification and feature generalization. The results show that the proposed method outperforms competing approaches on both seen and unseen classes.
Action Prediction From Videos via Memorizing Hard-to-Predict Samples
Authors: Kong Yu , Gao Shangqian , Sun Bin , Fu Yun
Published: June 2022

Action prediction based on video is an important problem in computer vision field with many applications, such as preventing accidents and criminal activities. It's challenging to predict actions at the early stage because of the large variations between early observed videos and complete ones. Besides, intra-class variations cause confusions to the predictors as well. In this paper, we propose a mem-LSTM model to predict actions in the early stage, in which a memory module is introduced to record several "hard-to-predict" samples and a variety of early observations. Our method uses Convolution Neural Network (CNN) and Long Short-Term Memory (LSTM) to model partial observed video input. We augment LSTM with a memory module to remember challenging video instances. With the memory module, our mem-LSTM model not only achieves impressive performance in the early stage but also makes predictions without the prior knowledge of observation ratio. Information in future frames is also utilized using a bi-directional layer of LSTM. Experiments on UCF-101 and Sports-1M datasets show that our method outperforms state-of-the-art methods.
A Deep Ranking Model for Spatio-Temporal Highlight Detection From a 360◦ Video
Authors: Yu Youngjae , Lee Sangho , Na Joonil , Kang Jaeyun , Kim Gunhee
Published: June 2022

We address the problem of highlight detection from a 360◦ video by summarizing it both spatially and temporally. Given a long 360◦ video, we spatially select pleasantly-looking normal field-of-view (NFOV) segments from unlimited field of views (FOV) of the 360◦ video, and temporally summarize it into a concise and informative highlight as a selected subset of subshots. We propose a novel deep ranking model named as Composition View Score (CVS) model, which produces a spherical score map of composition per video segment, and determines which view is suitable for highlight via a sliding window kernel at inference. To evaluate the proposed framework, we perform experiments on the Pano2Vid benchmark dataset (Su, Jayaraman, and Grauman 2016) and our newly collected 360◦ video highlight dataset from YouTube and Vimeo. Through evaluation using both quantitative summarization metrics and user studies via Amazon Mechanical Turk, we demonstrate that our approach outperforms several state-of-the-art highlight detection methods.We also show that our model is 16 times faster at inference than AutoCam (Su, Jayaraman, and Grauman 2016), which is one of the first summarization algorithms of 360◦ videos.
Flexible FOND Planning with Explicit Fairness Assumptions
Authors: Rodriguez Ivan D. , Bonet Blai , Sardina Sebastian , Geffner Hector
Published: June 2022

We consider the problem of reaching a propositional goal condition in fully-observable nondeterministic (FOND) planning under a general class of fairness assumptions that are given explicitly. The fairness assumptions are of the form A/B and say that state trajectories that contain infinite occurrences of an action a from A in a state s and finite occurrence of actions from B, must also contain infinite occurrences of action a in s followed by each one of its possible outcomes. The infinite trajectories that violate this condition are deemed as unfair, and the solutions are policies for which all the fair trajectories reach a goal state. We show that strong and strong-cyclic FOND planning, as well as QNP planning, a planning model introduced recently for generalized planning, are all special cases of FOND planning with fairness assumptions of this form which can also be combined. FOND+ planning, as this form of planning is called, combines the syntax of FOND planning with some of the versatility of LTL for expressing fairness constraints. A sound and complete FOND+ planner is implemented by reducing FOND+ planning to answer set programs, and its performance is evaluated in comparison with FOND and QNP planners, and LTL synthesis tools. Two other FOND+ planners are introduced as well which are more scalable but are not complete.
Exploring Rabbinic Approaches to the Psalms
Authors: Michal Gavin
Published: June 2022

Today, the Jewish world has adopted a popularist - if not theurgical -approach to the Book of Psalms, where the Psalms take on a mystical and almost magical function.1 However widespread, this is only one facet of a kaleidoscope of multifaceted and divergent methodologies that lie within the rubric of rabbinic Psalm interpretation. This article looks at some of the theology underpinning the essential structures of the Psalms as seen through the eyes of the classical rabbis. The analysis begins with the overall edifice of the Psalter, its division into books and their order, discusses the nomenclature and the aspect of musicology, and rabbinic views concerning their authorship and provenance. The article proceeds to investigate diverse and sometimes mutually-exclusive rabbinic opinions regarding the essential intent, usage and status of the Psalms. In the final analysis, readers are left bewildered as to whether the Psalms hold the key to the secrets of the universe or whether Jews are even allowed to pray by using the Psalms because of their exalted spiritual stature, or on the contrary, whether the Psalms are merely human expressions of prayer and grappling attempts at making sense of a difficult world, and therefore, of diminished and mundane status.
African somaesthetics: cultures, feminisms, politics
Authors: Lauwrens Jenni
Published: June 2022

The American pragmatist, Richard Shusterman, has given shape to a field of study known as somaesthetics. In his formulation of this field, Shusterman (1999:302) recommends that, in philosophical discourse and in relation to aesthetic experience, close attention should be paid to 'bodily states and experiences'. His concern is especially to place the thinking body and its capacity of knowing at the centre of academic attention and to develop awareness of how the living body is experienced, used, and cultivated in particular situations. African somaesthetics: cultures, feminisms, politics (2020) takes up Shusterman's proposal by applying the discourse of somaesthetics to issues of race and gender in the contemporary African context. The chapters in this volume, therefore, focus on interrogating the body in African cultures in the context of colonisation, decolonisation, and globalisation. In her introductory chapter, editor Catherine F. Botha briefly explains that the contributors take Shusterman's conception of somaesthetics as a provocation that arouses and stimulates thinking around the importance of attending to the lived body in understanding human existence. Thus, each chapter offers a unique and refreshing view on the significance of the bodily dimension of aesthetic expression and experience in general. An interesting array of topics are covered in the volume, including albinism, film, philosophy, cultural activism, and various forms of dance including ballet, contemporary dance, and break dancing.
[RETRACTED] ProDentim Powerful New Formula 2022 v1
Authors: not provided officialprodentim
Published: June 2022

[RETRACTED]

Prodentim is a new oral health product that keeps your teeth and gums as healthy as they should be and make them whiter. This quality is assured by means of the ingredients used in producing this product.
[RETRACTED] Vitality HQ Keto Gummies Its 100% Safe, With Natural Ingredients, Price, Offers, Benefits Updated 2022[Spam Or Legit] v1
Authors: HQ Keto Gummies Vitality
Published: June 2022

[RETRACTED]

https://vitalityhqketogummies.company.site/ https://vitality-hq-keto-acv-gummies.jimdosite.com/ https://vitalityhqket0acv.clubeo.com/page/vitality-hq-keto-gummies-its-100-safe-with-natural-ingredients-price-offers-benefits-updated-2022-spam-or-legit.html
A Mixed-Methods Catchment Area Modelling Approach Using Free DEMs to Inform Environmental Surveillance in Kolkata, India and Accra, Ghana v1
Authors: VanTassell Jamie
Published: June 2022

Environmental surveillance (ES) is an effective epidemiological tool that has successfully been used to monitor poliovirus circulation over the past three decades. ES has also emerged as a low-cost method to detect and estimate incidence of other diseases, such as typhoid fever and COVID-19. A limitation of ES in its current capacity stems from its reliance on sound working knowledge of the sewerageand sanitary system in the setting where it is being used. Without this knowledge, positive detection of a target pathogen in ES samples cannot be traced upstream to the likely source population. Additionally, selecting sites to collect samples for ES is speculative when this knowledge is absent, and samples may not capture the sub-population of interest and/or may capture overlapping sub-populations. Advances in digital elevation models (DEMs) captured through remote sensing, and their use in hydrologic modeling in ArcGIS, have expanded the potential for ES to be applied to areas where the sewage drainage systems are: 1) not well understood; 2) open to the environment; and/or 3) gravity-fed. Although a useful tool, historically this approach is less accurate when derived from free, open-access DEM datasets and applied to settings with hydraulic structures. These protocols, which were developed for a thesis project for a Master's in Public Health degree from Emory University, provide an innovative mixed-methods mapping approach that addresses the current limitations of ES and the use of catchment area delineation for ES using free, open-access data. DEM Reconditioning, a tool in ArcHydro, was applied to improve the accuracy of free DEMs. This methodology was applied to two case studies in unique settings where ES research is underway. The first case study is in Kolkata, India, where ES samples were collected and tested for Salmonella (S.) Typhi and S. Paratyphi Ato supplement clinical surveillance of typhoid and paratyphoid fever. A hybrid model was developed that integrates information about hydraulic structures and hydrologic processes that comprise the sewage drainage system in Kolkata. This model was used to estimate the geographic area and population size represented by sewage samples collected from thirteen pumping stations. In the second case study, a catchment area model was developed to identify optimal sampling locations for ES of COVID-19 in Accra, Ghana. This thesis confirmed that a mixed-methods approach can unify free, open-access information and be used to map sewage drainage systems in settings where ES has historically been limited. The Kolkata model demonstrated how this methodology can be used retroactively to inform interpretation of results from ES samples. The Accra model demonstrated that this process can be used proactively to identify strategic sampling points that capture sub-populations of interest, prevent catchment population overlap, and maximize coverage.
Assessment of cognition as a predictor of prognosis in inpatients with brain damage: A Scoping ReviewProtocol v1
Authors: Mitsuyasu Michihito
Published: June 2022

Objective: The purpose of this study is to determine what cognitive assessments are used to predict or determine outcomes in inpatients with brain damage. Introduction: The prognosis of hospitalized stroke and brain injury patients is related not only to physical function but also to cognition. Global mental functions such as the Mini-Mental State Exam (MMSE), Montreal Cognitive Assessment (MoCA), and Functional Independence Measure (FIM) are commonly used to assess cognition in stroke patients. It is not clear whether these assessments are used because they have better predictive discriminating ability than other assessments. Inclusion criteria: Patients with stroke, traumatic cerebral hemorrhage, or subarachnoid hemorrhage who are hospitalized are included in the study. Eligibility criteria will consist of studies that use cognitive assessments (global mental functions, higher brain function, memory, attention, neglect, apraxia, disorientation, executive function, multitasking, apathy) as exposure or covariates and examine the association between gait, falls, hospital discharge, activities of daily living, and quality of life. The study design will be observational, but case studies (case reports, case series), intervention studies, and systematic reviews will be excluded. In addition to peer-reviewed articles, conference abstracts will be included in the search. Countries and languages are not restricted. Methods: Databases (PubMed, Web of Science, Scopus, CINAHL, Igaku Chuo Zasshi) will be used for searching. NPO Japan Medical Abstracts Society operates Igaku Chuo Zasshi. Results will be tabulated by country, publication type, study design, type of analysis (univariate or multivariate), names of cognitive assessments and when to assess them, and type and timing of theoutcome. In addition, we will also add whether there are exclusion criteria due to cognitive impairment such as aphasia and whether studies are comparing the MMSE, MoCA, and FIM-Cog with other cognitive assessments.
[RETRACTED] Max Stallion Male Enhancement v1
Authors: Douglas Morris H
Published: June 2022

[RETRACTED]

Thus, assuming that they attempted to build the section of bloodlines down, they'll attempt to cause you to have a good time in bed again!
Cambio en los patrones territoriales y análisis diacrónico de inundabilidad y erodabilidad en cuencas de la provincia de Málaga (1956-2010)
Authors: Gallegos Reina Antonio
Published: June 2022

En el actual contexto de cambio global, el análisis de los riesgos socionaturales se han convertido en una temática prioritaria para las administraciones. Este trabajo cuantifica de manera diacrónica la evolución de la inundabilidad y erosión hídrica de suelos en cuatro áreas representativas del litoral mediterráneo español, situadas en la provincia de Málaga (España). Dicha evolución se ha puesto en relación con los cambios territoriales producidos en las áreas de estudio en los últimos 50 años. La inundabilidad se ha calculado mediante estudio hidrológico-hidráulico, usando el método racional modificado según Témez y HEC-RAS, y la erosión hídrica usando la formulación revisada de la Universal Soil Loss Equation (RUSLE). Los resultados muestran cambios urbanísticos insostenibles territorialmente y repercusiones directas en las inundaciones y pérdida de suelos, con incrementos de dichas peligrosidades que en algunos casos doblan, e incluso cuadruplican, los valores de área inundada y pérdida de suelo de 1957.
restauración del “Puente romano” de Cangas de Onís (Asturias, España) por Luis Menéndez-Pidal (1940-1942).
Authors: Vergara-Muñoz Jaime , Martínez-Monedero Miguel
Published: June 2022

El “puente romano” de Cangas de Onís es una de las imágenes más icónicas de la región asturiana. La obra que ha llegado a nuestros días es fruto de la importante restauración que el arquitecto Luis Menéndez-Pidal realizó entre los años 1940 y 42. El puente, desde su fundación medieval, había sido seriamente desfigurado en su transcurrir por el tiempo, variando la disposición de ojos, pilas, fábricas y aliviaderos. Su intervención consiguió recuperar el puente, conservando la funcionalidad de la infraestructura, recuperando una imagen homogénea y formalmente coherente, en conjunto, y finalmente respetando las modificaciones históricas que el arquitecto consideró valiosas y apropiadas para su conservación.
derecho humano al agua en la Franja de Gaza, en Palestina, desde una perspectiva de género, en el marco de los Objetivos de Desarrollo Sostenible
Authors: Rodríguez Martín José Antonio , Añaños Bedriñana Karen G. , Ruiz Carnero Miguel
Published: June 2022

La Franja de Gaza es una de las zonas más pobres del mundo en agua, a causa fundamentalmente de la contaminación y la sobreexplotación de los acuíferos. Adicionalmente, la región arrastra una crisis humanitaria como consecuencia de las numerosas escaladas bélicas, la falta de seguridad alimentaria o la carencia de agua y saneamiento. Una primera conclusión es que el reparto del agua, su acceso y gestión constituye un elemento central para avanzar hacia la estabilidad económica, social y política en la región. Gaza se encuentra en una insostenible situación de escasez hídrica permanente, afectando especialmente a las más vulnerables como las mujeres. Una segunda conclusión es que, para mejorar la situación actual del derecho al agua, se debe enlazar con los derechos de las mujeres, junto con el reforzamiento de su papel en las negociaciones y la gestión en aspectos básicos para la transformación económica y social en la Franja.
Legal Protection of Employee’s Personal Rights under International Labor Standards and Labor Legislation of the EAEU Countries
Authors: Pryazhennikov M. O.
Published: June 2022

Transformation of the labor sphere, transition to the service economy and information society raise the question of protecting not only “classical” labor rights, but also the personal non-property rights of employees, the formation of a safe and comfortable working environment. This is especially relevant for the branches of labor law of the countries of the Eurasian Economic Union (Armenia, Belarus, Kazakhstan, Kyrgyzstan, Russia), where effective legal mechanisms for protecting personal rights remained beyond the attention of the legislator. The paper analyzes labor, administrative and criminal legislation of the countries of the Eurasian Economic Union from the standpoint of their compliance with international labor standards reflected in the conventions and recommendations of the International Labor Organization in terms of protecting workers from discrimination, harassment and mobbing. The paper describes the content of the institution of labor protection and provides an overview of the existing legal mechanisms for protecting the personal rights of employees. It also contains recommendations on changing the labor legislation of the countries of the Eurasian Economic Union.
Universidad en contexto - Editorial
Authors: Sánchez-Cabrera Samuel Alberto
Published: June 2022

La situación actual demanda retos significativos para la Universidad, toda vez que, se muestra la importancia de las instituciones de educación, las cuales son fundamentales para la sociedad a partir de las respuestas a los problemas sentidos, tales como los originados por la pandemia, los de orden económico y la confianza social.  De tal manera que, la Universidad se manifiesta no solo como un espacio físico, sino en un concepto más amplio donde los miembros de la comunidad académica interactúan con los actores nucleares de la sociedad para dar respuesta adecuadas a problemas de orden regional y nacional. Se reclama oportunidades de empleo, mayor educación y de calidad y es aquí donde la articulación del estado, la universidad y la empresa es imprescindible.
LÍQUIDOS IÔNICOS COM APLICAÇÃO NA CAPTURA DE CARBONO
Authors: Martins Rafaela Nascimento , Ferrari Vanesa Claudia Gisela Mitchell , Martins Luís Filipe Guerreiro
Published: June 2022

Ionic liquids have been highlighted for several applications that contribute to promote green chemistry and sustainability concepts in processes of gas capture, especially greenhouse gases such as CO2. This work aims to characterize ionic liquids, such as 1-alkyl-3-methylimidazolium chloride, via computational simulation. Molecular dynamics methods were used to obtain relevant thermophysical properties in the temperature range from 298.15K to 363.15K, which was compared to experimental data obtained from the literature in order to validate the molecular models and the force field. The quantum-mechanical calculations based on density functional theory (DFT) provided relevant information about the most stable molecular geometries of these compounds in their isolated and dimer forms. The results obtained from different computational methodologies elucidated the preferred position of chloride ions surrounding the imidazolium ring, as well as the stabilization of ionic pairs that form a three-dimensional structure. This work will guide future works in which it is intended to study gel formation, such as of ionic liquids and solvent mixtures, particularly water and short alcohols.
Opodatkowanie osób duchownych zryczałtowanym podatkiem dochodowym
Authors: Święch- Kujawska Katarzyna
Published: June 2022

Zryczałtowane opodatkowanie osób duchownych stanowi jedną z tzw. uproszczonych form opodatkowania osób fizycznych, w których obowiązki instrumentalne podatników zostały w istotny sposób ograniczone. Z kolei uprawnionymi do objęcia tym podatkiem są jedynie osoby duchowne, spełniające wymogi określone ustawowo. Jest to typowy podatek ryczałtowy, w którym bez znaczenia pozostaje fakt uzyskiwania lub nie przychodów. W związku z tym podatnikom przyznano uprawnienie do zrzeczenia się tego sposobu opodatkowania. Zakresem opracowania objęto kwestie zarówno powyżej eksponowane, jak i problematykę pozostałych elementów konstrukcji tego świadczenia podatkowego. Dokonane analizy pozwoliły na wskazanie podstawowych zasad podatku ryczałtowego, jak i jego usytuowanie we współczesnym polskim systemie podatkowym.
Ubezwłasnowolnienie seniora. Ograniczenie czy pomoc?
Authors: Kołodziej Marcin
Published: June 2022

Instytucja ubezwłasnowolnienia, posiadająca ogromne znaczenie z punktu widzenia prawnego, społecznego i rodzinnego, wzbudza liczne kontrowersje. Niektórzy autorzy zastanawiają się, czy powinna mieć obecnie rację bytu, z racji ograniczenia zdolności do czynności prawnych osoby, której dotyczy. Z drugiej zaś strony – z perspektywy osób podejmujących się opieki nad seniorami – wydaje się być niezwykle pomocna. Chociaż pojawiają się nowe propozycje, które mogły zastąpić ubezwłasnowolnienie, pozostają one póki co na poziomie postulatów. Dlatego niniejsze opracowanie stanowi po pierwsze prezentację, a obok tego ocenę instytucji ubezwłasnowolnienia w polskim systemie prawnym. Aby mogła ona spełnić swoje przedmiotowe zadanie, a jednocześnie wykluczyć wszelkie nadużycia w jej stosowaniu, konieczne jest zachowanie stosownych przepisów prawa, które zapewnią podmiotowe traktowanie osoby i ochronę jej interesów.
Formacja intelektualna w seminarium duchownym
Authors: Potocki Wojciech
Published: June 2022

Formacja intelektualna kandydatów do stanu duchownego jest tylko jednym z elementów całościowego przygotowania przyszłego kapłana i jego późniejszej stałej pracy nad kształtowaniem swej osobowości i realizacją powierzonej mu w Kościele misji. Ten rodzaj formacji powinien nauczyć kleryka samodzielności w formułowaniu własnych ocen, jak również uważnego słuchania innych, sięgania po lekturę i korzystania z niej. Niniejsze opracowanie opisuje cel i przebieg formacji, uwzględniając podział na studia filozoficzne oraz teologiczne, jak również przedstawia sposób nauczania kleryków w seminarium duchownym.
Effects of long-term tea (Camellia sinensis) cultivation on the earthworm populations in northern Iran
Authors: KAHNEH Ehsan , SHİRİNFEKR Ahmad , RAMZİ Samar , SALİMİ Korosh Majd
Published: June 2022

The earthworms’ abundance is usually one of the main indicators of healthy and productive soils. However, agricultural management practices affect the earthworm population and activities. Although there is a lot of information that shows the relation between land use/land cover change and earthworms activity, very little is known about these effects under tea cultivation. Thus the current study was done to determine the effects of long-term tea cultivation on the earthworm’s population and abundance in the tea plantations of Iran to distinguish effects of these practices on soil properties concerning earthworms. Hence, 58 locations of tea cultivations were randomly selected in Guilan and Mazandaran province. Earthworm were sampled by manually excavating and sorting four 30×30 cm pits by 30 cm deep in each location. Earthworms were enumerated in the field and taken to the lab for identification. Once identified, the earthworms will dry in the oven at 60oC for 48h and the dry weight registers. Some physicochemical properties of the mineral soils were determined in the laboratory. The finding indicated that the earthworms were only observed in the two from 58 locations: Bazkiagorab (Lahijan) and Shekarposhteh (Tonekabon). Three species as Perelia kaznakovi, Aporrectodea trapezoides, and Dendrobaena veneta were recorded from Bazkiagorab but only P. kaznakovi was identified in Shekarposhteh. The total population of all identified earthworms was 22 and 3 m-2 in the Bazkiagorab and Shekarposhteh, respectively. Results of the physicochemical analysis showed that 35% and 51% of the soils had a pH less than4.5 and organic carbon less than2%, respectively. Available phosphorus and potassium in 80% and 65% of the soils were less than 25 and 150 mg/kg, respectively. It can be concluded that monoculture and long-term tea cultivations had a negative effect on the earthworm population, in addition, it has strongly acidified the soil. It is recommended that native nitrogen-fixing trees mixed planted with tea, and more attention should be paid to nutrient Best Management Practices in tea plantations.