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Copyright of Acta Geoscientica Sinica is the property of Acta Geoscientica Sinica Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Revista Foco (Interdisciplinary Studies Journal) is the property of Revista Foco and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The problems associated with the quantitative, qualitative, environmental, economic and other aspects of water resources, in particular the quality of drinking water, are of paramount importance today. The amount of water used is many times greater than other extractive resources. Water is the most important substance on our planet, primarily because it is necessary to sustain life. Providing the population with a sufficient amount of good quality drinking water is a strategic task for the national government. Drinking water used for centralised water supply to the population is drawn from surface water sources and groundwater aquifers. Historically, surface water has been preferred as the main source of water supply. The Dnipro River and its numerous tributaries are the main source of water supply for 75% of the Ukrainian population. However, due to significant technogenic pressures on the water quality in the Dnipro River and its catchment area the surface water is characterised as polluted and highly polluted in terms of chemical and bacterial contamination. In order to improve the quality component of the domestic drinking water supply to the population and industrial enterprises (mainly food production), particular attention should be paid to the increased use of groundwater. The experience gained during the war suggests the need to establish a local water supply system based on pumping stations (i.e., buvettes) with reserve water sources and means of delivering water to the consumption network. The article analyses the existing shortcomings and problems in the organisation of domestic drinking water supply in Ukraine and outlines some steps to improve the situation. [ABSTRACT FROM AUTHOR]

The degrowth project proposes a fundamental reorganisation of contemporary society. The existing literature focuses on explaining why degrowth is needed to tackle the multiple socioecological crises of our time and what needs to change in contemporary society. Recently, there have been explicit calls to moving on to thinking about the question of how a degrowth transition could be achieved. In this task, we identify the 'end' of the vision, that is, the cornerstones of a degrowth society, and focus on the suggested changes leading there. Therefore, we conceptualise a degrowth society as a regime that can be studied with the help of institutional theory and the change leading to a degrowth regime as a degrowth transition. To understand the constituents of such a regime, we conducted a systematic mapping of the degrowth literature by focusing on specific change proposals from 2000 to 2020. We analysed these change proposals in the framework of institutional theory and identified three overarching themes forming the backbone of a degrowth society: reduction, reorganisation and localisation. These themes represent the cultural–cognitive dimension of institutionalisation processes and entail varying degrees of normative and regulative dimensions. According to the degrowth change proposals in the literature, reduction is to be achieved mainly through top-down regulation, while reorganisation and localisation require a bottom-up approach to mobilising collective agency and changes in the normative orientation of society. Our analysis regarding the founding pillars of the institutional order of a degrowth society unveils essential signposts that could be considered when formulating policies and narratives compatible with a degrowth transition. [ABSTRACT FROM AUTHOR]

The problems associated with the quantitative, qualitative, environmental, economic and other aspects of water resources, in particular the quality of drinking water, are of paramount importance today. The amount of water used is many times greater than other extractive resources. Water is the most important substance on our planet, primarily because it is necessary to sustain life. Providing the population with a sufficient amount of good quality drinking water is a strategic task for the national government. Drinking water used for centralised water supply to the population is drawn from surface water sources and groundwater aquifers. Historically, surface water has been preferred as the main source of water supply. The Dnipro River and its numerous tributaries are the main source of water supply for 75% of the Ukrainian population. However, due to significant technogenic pressures on the water quality in the Dnipro River and its catchment area the surface water is characterised as polluted and highly polluted in terms of chemical and bacterial contamination. In order to improve the quality component of the domestic drinking water supply to the population and industrial enterprises (mainly food production), particular attention should be paid to the increased use of groundwater. The experience gained during the war suggests the need to establish a local water supply system based on pumping stations (i.e., buvettes) with reserve water sources and means of delivering water to the consumption network. The article analyses the existing shortcomings and problems in the organisation of domestic drinking water supply in Ukraine and outlines some steps to improve the situation.

During scenic opera rehearsals, the participants create performance bodies – fictive behaviours that portray the characters in the libretto. They use depictions – interactional practices comprised of short scenes staged for the other participants – to propose and negotiate performance bodies that suit the developing aesthetics of the production. In this paper, we focus on non-serious proposal depictions: depictions that become treated as laughable and not suitable for the performance. Non-serious depictions can accomplish joint fictionalizations, especially with teasing (Cantarutti, 2022), and are used in contrast with an ideal performance (Keevallik, 2010). Building on this work, we analyze how non-serious depictions are used to decide what the wished performance will be. We discuss two types of non-serious depictions in the workplace setting of the opera rehearsal process and show how negotiations over the seriousness of depictions achieve aesthetic intersubjectivity among the colleagues. The ambiguity between serious and non-serious proposals is exploited as a resource when navigating the unknown territories of a piece of art under development. The material consists of 20 h of video-recorded opera rehearsals in Swedish and English, with an Italian libretto. • Opera rehearsal participants use depictions in proposals to create a performance. • Depictions can be designed as either serious or non-serious. • This paper focuses on how depictions are designed and treated as non-serious. • Non-serious depictions can be treated as tangential to the interactional agenda. • They can also be a tool to manage aesthetic intersubjectivity and affiliation. [ABSTRACT FROM AUTHOR]

Copyright of Secretary (16742354) is the property of Secretary Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study aimed to study the educational loss in basic education schools in the Sultanate of Oman: in light of the Corona pandemic (Covid-19), in terms of the roles of school principals, teachers and parents in redressing the losses, in addition to revealing the challenges and proposals that faced schools during the pandemic period. To achieve the objectives of this study, the researcher followed the qualitative research method based on the case study method. The tool consisted of a structured interview that included four open-ended questions for school principals (15) individuals, and a questionnaire that included five open-ended questions for (49) teacher male and female, who were deliberately chosen from basic education schools in Muscat Governorate. The results revealed that among the manifestations of the loss: students’ poor mastery of basic knowledge and skills in basic subjects, and a loss of some learning skills such as (teamwork, communication, critical thinking, and problem-solving). The most important measures taken to address the wastage (enrichment classes - effective teaching strategies - activities and tests - psychological and social support for students), and the challenges included: poor digital infrastructure for schools, challenges related to parents, students and curricula. The most important proposals for addressing wastage from the point of view of school principals and teachers: developing an annual plan to address wastage, strengthening the technical infrastructure of schools, building supportive teaching programs and mechanisms, professional development programs for teachers in the field of technology, strengthening community partnership, and finally the study reached a set of recommendations. [ABSTRACT FROM AUTHOR]

According to the Quran, depression in general is attributed to a lack of religious commitment among Muslims, as well as the accumulation of sadness, anxiety, fear, disorder, psychological pressure, and issues related to family, social, cultural, economic, and political relationships. In the light of the challenges faced by the Yemeni community in Malaysia due to forced displacement from Yemen and the resulting lack of security from this traumatic experience and the inability to return, this study aims to uncover the nature, causes, and effects of psychological depression. Additionally, it seeks to identify proposals for alleviating symptoms based on the perspectives of a sample of 45 individuals from the Yemeni community in Malaysia. The researchers employed descriptive and analytical methods, and the study's theoretical findings were consistent with field observations. The primary cause of depression was found to be fear for the well-being of loved ones, with significant impact stemming from anxiety and stress about the future. One of the key suggestions for alleviation was fostering certainty in future benefits. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to examine the forms and functions of answers to proposals for joint action, implemented through polar interrogatives, in Greek telephone calls. Our analysis indicates a distinct functional distribution of three types of accepting answers to such proposals. Particle-type answers do 'simple' acceptance of the proposal, i.e. they only display the respondent's willingness to take on the proposed action and nothing else, while repetition-type answers display the speaker's epistemic/deontic stance towards additional aspects of the proposal. With a third type of responses, speakers accept the proposal in a mitigated manner. Our findings align with Enfield et al.'s (2019) conclusion that particles serve as pragmatically unmarked polar answers. They do not, however, evince the prevalence of this type of answer to proposals to the same extent as to epistemically oriented polar interrogatives. [ABSTRACT FROM AUTHOR]

Multi-modal classifiers for emotion recognition have become prominent, as the emotional states of subjects can be more comprehensively inferred from Electroencephalogram (EEG) signals and eye movements. However, existing classifiers experience a decrease in performance due to the distribution shift when applied to new users. Unsupervised domain adaptation (UDA) emerges as a solution to address the distribution shift between subjects by learning a shared latent feature space. Nevertheless, most UDA approaches focus on a single modality, while existing multi-modal approaches do not consider that fine-grained structures should also be explicitly aligned and the learned feature space must be discriminative. In this paper, we propose Coarse and Fine-grained Distribution Alignment with Correlated and Separable Features (CFDA-CSF), which performs a coarse alignment over the global feature space, and a fine-grained alignment between modalities from each domain distribution. At the same time, the model learns intra-domain correlated features, while a separable feature space is encouraged on new subjects. We conduct an extensive experimental study across the available sessions on three public datasets for multi-modal emotion recognition: SEED, SEED-IV, and SEED-V. Our proposal effectively improves the recognition performance in every session, achieving an average accuracy of 93.05%, 85.87% and 91.20% for SEED; 85.72%, 89.60%, and 86.88% for SEED-IV; and 88.49%, 91.37% and 91.57% for SEED-V. [ABSTRACT FROM AUTHOR]

Copyright of Dirasat: Educational Sciences is the property of University of Jordan and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The study analyses the family-school relationship at the secondary school stage in the educational centres of the Balearic Islands, Spain. In all cases, there is agreement on the scarce participation of families in the educational community. The studies show that in reality, many families do not participate actively and are not involved in the essential aspects of their children's education. This study analyses the factors that intervene in the process of family participation, at the same time as proposing a series of didactic proposals to be made with families in the area of Geography and History.

Current times call for continuous communication across countries, negotiations on several levels, and the creation of international relationships based on dialogue and participation. Those ideals are often pursued in intercultural communication contexts and written about, as a desideratum, in the Intercultural Communication literature. However, how can this be achieved concretely? In this article, we analyze how decisions are taken by newly founded intercultural teams of higher-education students playing a so-called intercultural game online via Zoom. The game revolves around the creation of a development plan for a fictitious city. In our study, we conducted a conversation-analytic investigation of decision-making processes by players oriented towards the ideal of 'intercultural speakers' as the ones mediating between different points of view and giving voice to all parties in an inclusive way. We illustrate our analysis with examples that range from unilateral decision making to decisions achieved through highly collaborative processes. We point to how expectations of inclusion-oriented interactional moves in intercultural situations are sometimes at odds with how these interactions and the related decision-making processes actually unfold. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study is to provide a holistic view of the emergence of shareholder activism (SA) in India. However, specifically, this study aims at fulfilling the research gap by discussing the policy and legal advancement in the area of SA and investigating the chronological evolution of SA, manifestations of SA, motives of SA, outcome of SAs and impact of SA on the financial performance of the firm. Design/methodology/approach: This study used a mixed methodology (both qualitative and quantitative) to draw inferences, including content analysis, descriptive statistics, independent sample t-test and paired sample t-test. The data has been collected from the annual reports of the sample companies and the Prowess database. Return on assets and return on equity have been used as measures of financial performance while investigating the difference in financial performance between firms subjected to SA and firms not subjected to SA. Findings: The findings of this study suggest that there has been significant growth in the occurrence of SA incidents in India in the past decade, with shareholders prominently manifesting by opposing the proposals at annual general meetings/extraordinary general meetings, mostly involving governance-related demands. The findings from the independent sample t-tests revealed that there has been a significant difference in the financial performance of the sample subjected to SA and firms not subjected to SA. Furthermore, the results of the paired sample t-test provide strong evidence of significant improvement in the financial performance of firms' post-SA. Practical implications: The findings of this study have implications for various stakeholders. The findings of this study suggest that SA has been relatively more successful in the Indian context and may encourage minority shareholders to follow active participation through shareholder proposals and votes rather than a passive strategy to trade and exit. For firms, it can provide valuable inferences about the emergence of SA and how it has a positive impact on the financial performance of the firm, which can lead to a change in the perception of investors and promoters who perceive SA as a threat (Gillan and Starks 2000; Hartzell and Starks, 2003). For policymakers, it can act as a tool to investigate whether the regulatory changes have been able to bring the intended transparency, accountability and enhanced shareholder participation. This will encourage policymakers to be more agile, as their efforts are bearing fruit. This will also act as a guide to formulating future policies and regulations. Originality/value: This study is an effort to provide a holistic view of SA scenarios in a developing economy setting like India, where SA is a very recent phenomenon. Although there are studies in the area of SA, there is a dearth of studies that have investigated the various dimensions of SA in the Indian context in a very systematic and extensive manner, investigating all the different dimensions of SA. Furthermore, this study also intends to investigate the impact of SA, which is normally perceived as a threat to financial performance and provide valuable contrasting evidence. [ABSTRACT FROM AUTHOR]

Studies of the grant proposal tend to conflate academic research grant proposals with other kinds of nonprofit grant proposal genres, even though research and nonprofit grant proposals have different audiences and goals. To address this gap, this study draws on the Aristotelian concept of topoi (or typical arguments) and uses corpus analysis, interview, and coding methods to answer the question, what topoi distinguish the academic research and nonprofit grant proposal genres? Findings suggest key differences in the topoi that research and nonprofit proposals use to advocate for problems and outcomes, set goals, and establish credibility. [ABSTRACT FROM AUTHOR]

Apresento neste informe informações sobre o Projeto para a História do Português Brasileiro, relacionando suas motivações, agenda, equipes regionais e principais publicações. Finalizo, propondo que o PHPB, o Projeto 3 (História do Português Brasileiro), o Projeto 4 (România), e Projeto 18 (História do Espanhol da América) da ALFAL venham a desenvolver atividades em comum.

Micro- and macro-expression spotting in an untrimmed video is a challenging task, due to the mass generation of false positive samples. Most existing methods localize higher response areas by extracting hand-crafted features or cropping specific regions from all or some key raw images. However, these methods either neglect the continuous temporal information or model the inherent human motion paradigms (background) as foreground. Consequently, we propose a novel two-stream network, named Local suppression and Global enhancement Spotting Network (LGSNet), which takes segment-level features from optical flow and videos as input. LGSNet adopts anchors to encode expression intervals and selects the encoded deviations as the object of optimization. Furthermore, we introduce a Temporal Multi-Receptive Field Feature Fusion Module (TMRF $^{3}$ 3 M) and a Local Suppression and Global Enhancement Module (LSGEM), which help spot short intervals more precisely and suppress background information. To further highlight the differences between positive and negative samples, we set up a large number of random pseudo ground truth intervals (background clips) on some discarded sliding windows to accomplish background clips modeling to counteract the effect of non-expressive face and head movements. Experimental results show that our proposed network achieves state-of-the-art performance on the CAS(ME) $^{2}$ 2 , CAS(ME) $^{3}$ 3 and SAMM-LV datasets. [ABSTRACT FROM AUTHOR]

The detection of objects with larger aspect ratios (OLARs) is a challenging problem in a special application scenario, such as remote-sensing object recognition and scene text detection. However, current object detectors perform poorly in OLAR feature extraction because they are incapable of adaptively responding to object shapes, which leads to severe misalignment between impure feature representations and region proposals. In this letter, we aim to solve this problem by proposing our shape-sensitive convolution network (SSC-Net). SSC-Net is carefully embedded with a feature enhancement module (SSC module) specifically suitable for OLAR. This module can use fewer sampling points to achieve more intelligent feature sampling area transformation, thus achieving the goal of enhancing OLAR feature representation. Extensive experiments on benchmark datasets that are rich in OLARs have proved the superiority of our method. Besides, we further verified the plug-and-play performance of the SSC module, and the experimental results show that it can significantly improve the detection performance of the detector for OLAR. [ABSTRACT FROM AUTHOR]

Copyright of Linguistica (1132-0214) is the property of Asociacion de Linguistica y Filologia de la America Latina and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Although research has shown vulnerabilities and shortcomings of the controller area network bus (CAN bus) and proposed alternatives, the CAN bus protocol is still the industry standard and present in most vehicles. Due to its vulnerability to potential intruders that can hinder execution or even take control of the vehicles, much work has focused on detecting intrusions on the CAN bus. However, most literature does not provide mechanisms to reason about, or respond to the attacks so that the system can continue to execute safely despite the intruder. This letter proposes a low-overhead methodology to automatically classify intrusions into predefined types once detected. Our framework: 1) groups messages of the same attacks into blocks; 2) extracts relevant features from each block; and 3) predicts the type of attack using a lightweight classifier model. The initial models depicted in this letter show an accuracy of up to 99.16% within the first 50 ms of the attack, allowing the system to quickly react to the intrusion before the malicious actor can conclude their attack. We believe this letter lays the groundwork for vehicles to have specialized runtime reactions based on the attack type. [ABSTRACT FROM AUTHOR]

Even though one-stage detectors have advantages for resource-constrained and real-time applications, they face the disadvantage of mediocre performance. Thus, this article proposes a novel one-stage parking slot detection method that achieves comparable performance to two (or multi-)stage detectors. The proposed method extracts the components and properties of the parking slots from input images. As the extracted components and properties are unorganized, it is significantly important to combine them correctly. To this end, this article introduces the component linkages that provide sufficient information for connecting the extracted components and properties. By the guide of the component linkages, the components and properties of the parking slots are progressively assembled to produce precise detection results. In experiments, the proposed method was evaluated using two large-scale parking slot detection datasets and showed state-of-the-art performances. Specifically, in the Seoul National University (SNU) dataset, the proposed method achieved 96.73% recall and 96.75% precision while maintaining a fast processing speed of 134 frames per second. In addition, this article provides a new set of labels for the SNU dataset, which covers more than 60,000 parking slots with high-quality annotations. [ABSTRACT FROM AUTHOR]

Objective: The aim of this systematic review is to estimate: (i) the overall effect of blinding models on bias; (ii) the effect of each blinding model; and (iii) the effect of un-blinding on reviewer's accountability in biomedical research proposals. Methods: Systematic review of prospective or retrospective comparative studies that evaluated two or more peer review blinding models for biomedical research proposals/funding applications and reported outcomes related to peer review efficiency. Results: Three studies that met the inclusion criteria were included in this review and assessed using the QualSyst tool by two authors. Conclusion: Our systematic review is the first to assess peer review blinding models in the context of funding. While only three studies were included, this highlighted the dire need for further RCTs that generate validated evidence. We also discussed multiple aspects of peer review, such as peer review in manuscripts vs proposals and peer review in other fields. [ABSTRACT FROM AUTHOR]

Connected automated vehicles (CAVs) can use vehicle-to-everything (V2X) communications to exchange their driving intentions and coordinate their maneuvers. Message generation rules are necessary to decide when and how maneuver coordination messages (MCMs) should be generated. The design of these generation rules must consider the critical nature of maneuver coordination and the limited bandwidth available for V2X communications. This study proposes the first two sets of V2X message generation rules for maneuver coordination between CAVs. The Risk proposal increases the rate at which vehicles generate MCMs when vehicles detect a potential safety risk. With the Tracking Trajectories proposal, vehicles generate a new maneuver coordination message when they significantly modify their planned trajectory. For both proposals, the messages include the planned and possible desired trajectories of the ego vehicle. The evaluation shows that the proposed generation rules efficiently support maneuver coordination and offer a balance between more frequent updates of the driving intentions of CAVs and lower coordination time and better control of the V2X communications channel load. This study also reveals that congestion control protocols can significantly impact maneuver coordination. [ABSTRACT FROM AUTHOR]

Differential deep learning analysis (DDLA) is the first deep-learning-based nonprofiled side-channel attack (SCA) on embedded systems. However, DDLA requires many training processes to distinguish the correct key. In this letter, we introduce a nonprofiled SCA technique using multi-output classification to mitigate the aforementioned issue. Specifically, a multi-output multilayer perceptron and a multi-output convolutional neural network are introduced against various SCA protected schemes, such as masking, noise generation, and trace de-synchronization countermeasures. The experimental results on different power side channel datasets have clarified that our model performs the attack up to 9–30 times faster than DDLA in the case of masking and de-synchronization countermeasures, respectively. In addition, regarding combined masking and noise generation countermeasure, our proposed model achieves a higher success rate of at least 20% in the cases of the standard deviation equal to 1.0 and 1.5. [ABSTRACT FROM AUTHOR]

Within most western countries, gendered proposal, surname, and wedding traditions remain widely endorsed. A previous study indicated that endorsement of proposal and surname traditions is associated with higher levels of benevolent sexism (BS) in university students in the USA. Three studies (N = 367) extended research to adolescents (dating age) and 30-year-olds (typical first-time marriage age). For the first time, these studies examined gendered wedding traditions (e.g., father walking a bride down the aisle). Different combinations of ambivalent sexism predicted participants' opinions about surname change after marriage and the choice of children's surnames. In younger adolescents (11–18 years; 56 boys, 88 girls, 68.1% White), hostile sexism (HS) predicted endorsement of surname change, whereas benevolent sexism predicted endorsement in 16- to 18-year-olds (58 boys, 84 girls, 76.8% White) and 30-year-olds (37 men, 44 women, 74.1% White). In adolescent samples, both BS and HS predicted endorsement of patronymic traditions for children, whereas only BS did in the adult sample. The findings suggest that different types of sexism predict traditional beliefs in specific age groups. [ABSTRACT FROM AUTHOR]

The problem of pedestrian detection is considered. The design of complexity-aware cascaded pedestrian detectors, combining features of very different complexities, is investigated. A new cascade design procedure is introduced, by formulating cascade learning as the Lagrangian optimization of a risk that accounts for both accuracy and complexity. A boosting algorithm, denoted as complexity aware cascade training (CompACT), is then derived to solve this optimization. CompACT cascades are shown to seek an optimal trade-off between accuracy and complexity by pushing features of higher complexity to the later cascade stages, where only a few difficult candidate patches remain to be classified. This enables the use of features of vastly different complexities in a single detector. In result, the feature pool can be expanded to features previously impractical for cascade design, such as the responses of a deep convolutional neural network (CNN). This is demonstrated through the design of pedestrian detectors with a pool of features whose complexities span orders of magnitude. The resulting cascade generalizes the combination of a CNN with an object proposal mechanism: rather than a pre-processing stage, CompACT cascades seamlessly integrate CNNs in their stages. This enables accurate detection at fairly fast speeds.

An essential need for accurate visual object tracking is to capture better correlations between the tracking target and the search region. However, the dominant Siamese-based trackers are limited to producing dense similarity maps at once via a cross-correlations operation, ignoring to remedy the contamination caused by erroneous or ambiguous matches. In this paper, we propose a novel tracker, termed neighborhood consensus constraint-based siamese tracker (NCSiam), which takes the idea of neighborhood consensus constraint to refine the produced correlation maps. The intuition behind our approach is that we can support the nearby erroneous or ambiguous matches by analyzing a larger context of the scene that contains a unique match. Specifically, we devise a 4D convolution-based multi-level similarity refinement (MLSR) strategy. Taking the primary similarity maps obtained from a cross-correlation as input, MLSR acquires reliable matches by analyzing neighborhood consensus patterns in 4D space, thus enhancing the discriminability between the tracking target and the distractors. Besides, traditional Siamese-based trackers directly perform classification and regression on similarity response maps which discard appearance or semantic information. Therefore, an appearance affinity decoder (AAD) is developed to take full advantage of the semantic information of the search region. To further improve performance, we design a task-specific disentanglement (TSD) module to decouple the learned representations into classification-specific and regression-specific embeddings. Extensive experiments are conducted on six challenging benchmarks, including GOT-10k, TrackingNet, LaSOT, UAV123, OTB2015, and VOT2020. The results demonstrate the effectiveness of our method. The code will be available at https://github.com/laybebe/NCSiam. [ABSTRACT FROM AUTHOR]

It is challenging to generate temporal action proposals from untrimmed videos. In general, boundary-based temporal action proposal generators are based on detecting temporal action boundaries, where a classifier is usually applied to evaluate the probability of each temporal action location. However, most existing approaches treat boundaries and contents separately, which neglect that the context of actions and the temporal locations complement each other, resulting in incomplete modeling of boundaries and contents. In addition, temporal boundaries are often located by exploiting either local clues or global information, without mining local temporal information and temporal-to-temporal relations sufficiently at different levels. Facing these challenges, a novel approach named multi-level content-aware boundary detection (MCBD) is proposed to generate temporal action proposals from videos, which jointly models the boundaries and contents of actions and captures multi-level (i.e., frame level and proposal level) temporal and context information. Specifically, the proposed MCBD preliminarily mines rich frame-level features to generate one-dimensional probability sequences, and further exploits temporal-to-temporal proposal-level relations to produce two-dimensional probability maps. The final temporal action proposals are obtained by a fusion of the multi-level boundary and content probabilities, achieving precise boundaries and reliable confidence of proposals. The extensive experiments on the three benchmark datasets of THUMOS14, ActivityNet v1.3 and HACS demonstrate the effectiveness of the proposed MCBD compared to state-of-the-art methods. The source code of this work can be found in https://mic.tongji.edu.cn. [ABSTRACT FROM AUTHOR]

Scene-text image synthesis techniques that aim to naturally compose text instances on background scene images are very appealing for training deep neural networks due to their ability to provide accurate and comprehensive annotation information. Prior studies have explored generating synthetic text images on two-dimensional and three-dimensional surfaces using rules derived from real-world observations. Some of these studies have proposed generating scene-text images through learning; however, owing to the absence of a suitable training dataset, unsupervised frameworks have been explored to learn from existing real-world data, which might not yield reliable performance. To ease this dilemma and facilitate research on learning-based scene text synthesis, we introduce DecompST, a real-world dataset prepared from some public benchmarks, containing three types of annotations: quadrilateral-level BBoxes, stroke-level text masks, and text-erased images. Leveraging the DecompST dataset, we propose a Learning-Based Text Synthesis engine (LBTS) that includes a text location proposal network (TLPNet) and a text appearance adaptation network (TAANet). TLPNet first predicts the suitable regions for text embedding, after which TAANet adaptively adjusts the geometry and color of the text instance to match the background context. After training, those networks can be integrated and utilized to generate the synthetic dataset for scene text analysis tasks. Comprehensive experiments were conducted to validate the effectiveness of the proposed LBTS along with existing methods, and the experimental results indicate the proposed LBTS can generate better pretraining data for scene text detectors. Our dataset and code are made available at: https://github.com/iiclab/DecompST. [ABSTRACT FROM AUTHOR]

Cross-domain pedestrian detection aims to generalize pedestrian detectors from one label-rich domain to another label-scarce domain, which is crucial for various real-world applications. Most recent works focus on domain alignment to train domain-adaptive detectors either at the instance level or image level. From a practical point of view, one-stage detectors are faster. Therefore, we concentrate on designing a cross-domain algorithm for rapid one-stage detectors that lacks instance-level proposals and can only perform image-level feature alignment. However, pure image-level feature alignment causes the foreground-background misalignment issue to arise, i.e., the foreground features in the source domain image are falsely aligned with background features in the target domain image. To address this issue, we systematically analyze the importance of foreground and background in image-level cross-domain alignment, and learn that background plays a more critical role in image-level cross-domain alignment. Therefore, we focus on cross-domain background feature alignment while minimizing the influence of foreground features on the cross-domain alignment stage. This paper proposes a novel framework, namely, background-focused distribution alignment (BFDA), to train domain adaptive one-stage pedestrian detectors. Specifically, BFDA first decouples the background features from the whole image feature maps and then aligns them via a novel long-short-range discriminator. Extensive experiments demonstrate that compared to mainstream domain adaptation technologies, BFDA significantly enhances cross-domain pedestrian detection performance for either one-stage or two-stage detectors. Moreover, by employing the efficient one-stage detector (YOLOv5), BFDA can reach 217.4 FPS ($640\times 480$ pixels) on NVIDIA Tesla V100 (7~12 times the FPS of the existing frameworks), which is highly significant for practical applications. The code from this study will be made publicly available. [ABSTRACT FROM AUTHOR]

As a prerequisite step of scene text reading, scene text detection is known as a challenging task due to natural scene text diversity and variability. Most existing methods either adopt bottom-up sub-text component extraction or focus on top-down text contour regression. From a hybrid perspective, we explore hierarchical text instance-level and component-level representation for arbitrarily-shaped scene text detection. In this work, we propose a novel Hierarchical Graph Reasoning Network (HGR-Net), which consists of a Text Feature Extraction Network (TFEN) and a Text Relation Learner Network (TRLN). TFEN adaptively learns multi-grained text candidates based on shared convolutional feature maps, including instance-level text contours and component-level quadrangles. In TRLN, an inter-text graph is constructed to explore global contextual information with position-awareness between text instances, and an intra-text graph is designed to estimate geometric attributes for establishing component-level linkages. Next, we bridge the cross-feed interaction between instance-level and component-level, and it further achieves hierarchical relational reasoning by learning complementary graph embeddings across levels. Experiments conducted on three publicly available benchmarks SCUT-CTW1500, Total-Text, and ICDAR15 have demonstrated that HGR-Net achieves state-of-the-art performance on arbitrary orientation and arbitrary shape scene text detection. [ABSTRACT FROM AUTHOR]

Video object detection is a fundamental and important task in computer vision. One mainstay solution for this task is to aggregate features from different frames to enhance the detection on the current frame. Off-the-shelf feature aggregation paradigms for video object detection typically rely on inferring feature-to-feature (Fea2Fea) relations. However, most existing methods are unable to stably estimate Fea2Fea relations due to the appearance deterioration caused by object occlusion, motion blur or rare poses, resulting in limited detection performance. In this paper, we study Fea2Fea relations from a new perspective, and propose a novel dual-level graph relation network (DGRNet) for high-performance video object detection. Different from previous methods, our DGRNet innovatively leverages the residual graph convolutional network to simultaneously model Fea2Fea relations at two different levels including frame level and proposal level, which facilitates performing better feature aggregation in the temporal domain. To prune unreliable edge connections in the graph, we introduce a node topology affinity measure to adaptively evolve the graph structure by mining the local topological information of pairwise nodes. To the best of our knowledge, our DGRNet is the first video object detection method that leverages dual-level graph relations to guide feature aggregation. We conduct experiments on the ImageNet VID dataset and the results demonstrate the superiority of our DGRNet against state-of-the-art methods. Especially, our DGRNet achieves 85.0% mAP and 86.2% mAP with ResNet-101 and ResNeXt-101, respectively. [ABSTRACT FROM AUTHOR]

Video object detection is a widely studied topic and has made significant progress in the past decades. However, the feature extraction and calculations in existing video object detectors demand decent imaging quality and avoidance of severe motion blur. Under extremely dark scenarios, due to limited sensor sensitivity, we have to trade off signal-to-noise ratio for motion blur compensation or vice versa, and thus suffer from performance deterioration. To address this issue, we propose to temporally multiplex a frame sequence into one snapshot and extract the cues characterizing object motion for trajectory retrieval. For effective encoding, we build a prototype for encoded capture by mounting a highly compatible programmable shutter. Correspondingly, in terms of decoding, we design an end-to-end deep network called detection from coded snapshot (DECENT) to retrieve sequential bounding boxes from the coded blurry measurements of dynamic scenes. For effective network learning, we generate quasi-real data by incorporating physically-driven noise into the temporally coded imaging model, which circumvents the unavailability of training data and with high generalization ability on real dark videos. The approach offers multiple advantages, including low bandwidth, low cost, compact setup, and high accuracy. The effectiveness of the proposed approach is experimentally validated under low illumination vision and provide a feasible way for night surveillance. [ABSTRACT FROM AUTHOR]

Pedestrian detection is still a challenging task for computer vision, especially in crowded scenes where the overlaps between pedestrians tend to be large. The non-maximum suppression (NMS) plays an important role in removing the redundant false positive detection proposals while retaining the true positive detection proposals. However, the highly overlapped results may be suppressed if the threshold of NMS is lower. Meanwhile, a higher threshold of NMS will introduce a larger number of false positive results. To solve this problem, we propose an optimal threshold prediction (OTP) based NMS method that predicts a suitable threshold of NMS for each human instance. First, a visibility estimation module is designed to obtain the visibility ratio. Then, we propose a threshold prediction subnet to determine the optimal threshold of NMS automatically according to the visibility ratio and classification score. Finally, we re-formulate the objective function of the subnet and utilize the reward-guided gradient estimation algorithm to update the subnet. Comprehensive experiments on CrowdHuman and CityPersons show the superior performance of the proposed method in pedestrian detection, especially in crowded scenes. [ABSTRACT FROM AUTHOR]

Measuring the similarity of two images is of crucial importance in computer vision. Class agnostic common object detection is a nascent research topic about mining image similarity, which aims to detect common object pairs from two images without category information. This task is general and less restrictive which explores the similarity between objects and can further describe the commonality of image pairs at the object level. However, previous works suffer from features with low discrimination caused by the lack of category information. Moreover, most existing methods compare objects extracted from two images in a simple and direct way, ignoring the internal relationships between objects in the two images. To overcome these limitations, in this paper, we propose a new framework called TransWeaver, which learns intrinsic relationships between objects. Our TransWeaver takes image pairs as input and flexibly captures the inherent correlation between candidate objects from two images. It consists of two modules (i.e., the representation-encoder and the weave-decoder) and captures efficient context information by weaving image pairs to make them interact with each other. The representation-encoder is used for representation learning, which can obtain more discriminative representations for candidate proposals. Furthermore, the weave-decoder weaves the objects from two images and is able to explore the inter-image and intra-image context information at the same time, bringing a better object matching ability. We reorganize the PASCAL VOC, COCO, and Visual Genome datasets to obtain training and testing image pairs. Extensive experiments demonstrate the effectiveness of the proposed TransWeaver which achieves state-of-the-art performance on all datasets. [ABSTRACT FROM AUTHOR]

We present an efficient algorithm to approximate the Automatic Color Equalization (ACE) of an input color image, with an upper-bound on the introduced approximation error. The computation is based on Summed Area Tables and a carefully optimized partitioning of the plane into rectangular regions, resulting in a pseudo-linear asymptotic complexity with the number of pixels (against a quadratic straightforward computation of ACE). Our experimental evaluation confirms both the speedups and high accuracy, reaching lower approximation errors than existing approaches. We provide a publicly available reference implementation of our algorithm. [ABSTRACT FROM AUTHOR]

Non-maximum suppression (NMS) is a post-processing step in almost every visual object detector. NMS aims to prune the number of overlapping detected candidate regions-of-interest (RoIs) on an image, in order to assign a single and spatially accurate detection to each object. The default NMS algorithm (GreedyNMS) is fairly simple and suffers from severe drawbacks, due to its need for manual tuning. A typical case of failure with high application relevance is pedestrian/person detection in the presence of occlusions, where GreedyNMS doesn’t provide accurate results. This paper proposes an efficient deep neural architecture for NMS in the person detection scenario, by capturing relations of neighboring RoIs and aiming to ideally assign precisely one detection per person. The presented Seq2Seq-NMS architecture assumes a sequence-to-sequence formulation of the NMS problem, exploits the Multihead Scale-Dot Product Attention mechanism and jointly processes both geometric and visual properties of the input candidate RoIs. Thorough experimental evaluation on three public person detection datasets shows favourable results against competing methods, with acceptable inference runtime requirements. [ABSTRACT FROM AUTHOR]

Few-shot learning is proposed to tackle the problem of scarce training data in novel classes. However, prior works in instance-level few-shot learning have paid less attention to effectively utilizing the relationship between categories. In this paper, we exploit the hierarchical information to leverage discriminative and relevant features of base classes to effectively classify novel objects. These features are extracted from abundant data of base classes, which could be utilized to reasonably describe classes with scarce data. Specifically, we propose a novel superclass approach that automatically creates a hierarchy considering base and novel classes as fine-grained classes for few-shot instance segmentation (FSIS). Based on the hierarchical information, we design a novel framework called Soft Multiple Superclass (SMS) to extract relevant features or characteristics of classes in the same superclass. A new class assigned to the superclass is easier to classify by leveraging these relevant features. Besides, in order to effectively train the hierarchy-based-detector in FSIS, we apply the label refinement to further describe the associations between fine-grained classes. The extensive experiments demonstrate the effectiveness of our method on FSIS benchmarks. The source code is available here: https://github.com/nvakhoa/superclass-FSIS [ABSTRACT FROM AUTHOR]

The area of domain adaptation has been instrumental in addressing the domain shift problem encountered by many deep learning applications. This problem arises due to the difference between the distributions of source data used for training in comparison with target data used during realistic testing scenarios. In this paper, we introduce a novel MultiScale Domain Adaptive YOLO (MS-DAYOLO) framework that employs multiple domain adaptation paths and corresponding domain classifiers at different scales of the YOLOv4 object detector. Building on our baseline multiscale DAYOLO framework, we introduce three novel deep learning architectures for a Domain Adaptation Network (DAN) that generates domain-invariant features. In particular, we propose a Progressive Feature Reduction (PFR), a Unified Classifier (UC), and an Integrated architecture. We train and test our proposed DAN architectures in conjunction with YOLOv4 using popular datasets. Our experiments show significant improvements in object detection performance when training YOLOv4 using the proposed MS-DAYOLO architectures and when tested on target data for autonomous driving applications. Moreover, MS-DAYOLO framework achieves an order of magnitude real-time speed improvement relative to Faster R-CNN solutions while providing comparable object detection performance. [ABSTRACT FROM AUTHOR]

Nucleus segmentation is a challenging task due to the crowded distribution and blurry boundaries of nuclei. Recent approaches represent nuclei by means of polygons to differentiate between touching and overlapping nuclei and have accordingly achieved promising performance. Each polygon is represented by a set of centroid-to-boundary distances, which are in turn predicted by features of the centroid pixel for a single nucleus. However, using the centroid pixel alone does not provide sufficient contextual information for robust prediction and thus degrades the segmentation accuracy. To handle this problem, we propose a Context-aware Polygon Proposal Network (CPP-Net) for nucleus segmentation. First, we sample a point set rather than one single pixel within each cell for distance prediction. This strategy substantially enhances contextual information and thereby improves the robustness of the prediction. Second, we propose a Confidence-based Weighting Module, which adaptively fuses the predictions from the sampled point set. Third, we introduce a novel Shape-Aware Perceptual (SAP) loss that constrains the shape of the predicted polygons. Here, the SAP loss is based on an additional network that is pre-trained by means of mapping the centroid probability map and the pixel-to-boundary distance maps to a different nucleus representation. Extensive experiments justify the effectiveness of each component in the proposed CPP-Net. Finally, CPP-Net is found to achieve state-of-the-art performance on three publicly available databases, namely DSB2018, BBBC06, and PanNuke. Code of this paper is available at https://github.com/csccsccsccsc/cpp-net. [ABSTRACT FROM AUTHOR]

Weakly supervised object detection (WSOD) has received widespread attention since it requires only image-category annotations for detector training. Many advanced approaches solve this problem by a two-phase learning framework, that is, instance mining that classifies generated proposals via multiple instance learning, and instance refinement that iteratively refines bounding boxes using the supervision produced by the preceding stage. In this paper, we observe that the detection performance is usually limited by imprecise supervision, including part domination and untight boxes. To mitigate their adverse effects, we focus on selecting high-quality proposals as the supervision for WSOD. To be specific, for the issue of part domination, we propose bottom-up aggregated attention which incorporates low-level features from shallow layers to improve location representation of top-level features. In this manner, the proposals corresponding to entire objects can get high scores. Its advantage is that it can be flexibly plugged into the WSOD framework since there is no need to attach learnable parameters or learning branches. As regards the problem of untight boxes, we propose a phase-aware loss, which is the first work to measure supervision quality by the loss in the instance mining phase, to highlight correct boxes and suppress untight ones. In this work, we unify the proposed two modules into the framework of online instance classifier refinement. Extensive experiments on the PASCAL VOC and the MS COCO demonstrate that our method can significantly improve the performance of WSOD and achieve the state-of-the-art results. The code is available at https://github.com/Horatio9702/BUAA_PALoss. [ABSTRACT FROM AUTHOR]

This article presents diagnoses, analyses and proposals about affective-sexual relationships, primarily cis-heterosexual ones, in a selection of widely distributed, commercially successful cultural products in Argentina, which engage in dialogue with and challenge the meanings of feminist discourse and slogans. In five non-academic books published by Grupo Editorial Planeta between 2018 and 2021, and a podcast launched between 2020 and 2022 —available on Spotify—, we identify four key discussion points: the novelty of a revolution of desirous women, affective responsibility as a requirement for a democratic and less painful love, the importance of sexual pleasure for women’s empowerment and alternatives to the monogamous system. We place these products in a context of feminist popularization, the dissemination of a therapeutic culture that promotes work on oneself and subjectivation scripts, linked to the market and sexuality, that configure new models of sentimental education. [ABSTRACT FROM AUTHOR]