Ambiguity Issues Research Articles

A Depth Awareness and Learnable Feature Fusion Network for Enhanced Geometric Perception in Semantic Correspondence.

Deep learning is becoming the most widely used technology for multi-sensor data fusion. Semantic correspondence has recently emerged as a foundational task, enabling a range of downstream applications, such as style or appearance transfer, robot manipulation, and pose estimation, through its ability to provide robust correspondence in RGB images with semantic information. However, current representations generated by self-supervised learning and generative models are often limited in their ability to capture and understand the geometric structure of objects, which is significant for matching the correct details in applications of semantic correspondence. Furthermore, efficiently fusing these two types of features presents an interesting challenge. Achieving harmonious integration of these features is crucial for improving the expressive power of models in various tasks. To tackle these issues, our key idea is to integrate depth information from depth estimation or depth sensors into feature maps and leverage learnable weights for feature fusion. First, depth information is used to model pixel-wise depth distributions, assigning relative depth weights to feature maps for perceiving an object's structural information. Then, based on a contrastive learning optimization objective, a series of weights are optimized to leverage feature maps from self-supervised learning and generative models. Depth features are naturally embedded into feature maps, guiding the network to learn geometric structure information about objects and alleviating depth ambiguity issues. Experiments on the SPair-71K and AP-10K datasets show that the proposed method achieves scores of 81.8 and 83.3 on the percentage of correct keypoints (PCK) at the 0.1 level, respectively. Our approach not only demonstrates significant advantages in experimental results but also introduces the depth awareness module and a learnable feature fusion module, which enhances the understanding of object structures through depth information and fully utilizes features from various pre-trained models, offering new possibilities for the application of deep learning in RGB and depth data fusion technologies. We will also continue to focus on accelerating model inference and optimizing model lightweighting, enabling our model to operate at a faster speed.

Party Leadership of the Partisan Movement during the Occupation of Smolensk Region by Nazi Troops (1941–1942)

The article is devoted to the analysis of one of the most difficult, ambiguous and contradictory issues of the Great Patriotic War – the party leadership of the partisan movement in the Smolensk region in 1941–1942. Unfortunately, Soviet historiography used to evaluate the party leadership of the deployment of the guerrilla struggle exclusively in rosy tones, avoiding any critical assessments, which distorted the real picture of events. Party leadership and influence was a complex phenomenon. If at the level of ordinary party members heroism in the fight against the enemy, the personal qualities of real communists manifested themselves massively and everywhere, then confusion often reigned in regional party organizations, there was a lack of discipline. This was the result of serious failures in the leadership of the party members on the ground by the Smolensk Regional Committee of the CPSU (b). At the same time, after the occupation of the region, the regional committee had no systematic connection with district organizations and did not lead the partisan movement for five months. The situation began to change only after the defeat of the Nazis near Moscow. The turning point was the IX Plenum of the Smolensk Regional Committee of the CPSU (b) in April 1942, after which the systematic and active leadership of the underground district committees and the partisan movement in the Smolensk region began. But even then, in the Central Committee of the CPSU (b), in the NKVD, the partisans (many of whom were surrounded) were looked at suspiciously, with distrust. This can explain the enormous losses of the partisans in the summer and autumn of 1942. Thus, the thesis about the outstanding leadership role of the CPSU (b) in the deployment of the partisan movement is significantly embellished and does not correspond to reality.

Becoming a True Partner: How HR Leaders Can Earn a Seat at the Leadership Table

As the role of the chief human resources officer continues to evolve, gaining full acceptance as a peer and strategic partner to other C-suite executives remains a challenge for many HR leaders. This article outlines proven strategies for HR to earn a respected seat at the leadership table. Through analyzing academic research and real-world company examples, key recommendations are provided. HR must demonstrate strategic alignment by directly linking people initiatives to overall business goals. Equally important is providing insightful counsel on ambiguous issues through empathetic listening and probing questions. Effective communication and influence skills are also essential to bring others along on strategic changes. Supplementing internal expertise, cultivating external thought leadership through industry events and publications enhances broader credibility. When HR executives successfully operationalize these strategies, they authentically position their function as a driving force behind organizational success while also proving their worth among senior peers. The brief aims to equip aspiring CHROs with actionable guidance for positioning HR as a trusted advisor.

Taxation in insolvency (bankruptcy) procedures of legal entities

Subject. The article analyzes a set of norms related to the legal regulation of personal income tax, corporate income tax, VAT, and other taxes, as well as the order of their collection (payment) from a bankrupt, depending on its organizational and legal form. The choice of the research object is due to the presence of problems related to ambiguous issues in taxation in bankruptcy proceedings.The purpose of the study. The article attempts to comprehensively analyze the legal support of the current measures regulating the taxation of bankrupt organizations and individuals, current law enforcement practice, and the legal positions of the country's highest courts.Methodology. The leading research method is a formal legal one.Conclusions. The article reveals and concretizes a number of legal problems. The authors argue their own point of view on the taxation of bankrupt organizations and individuals, taking into account the practice of the highest judicial authorities, which, according to the authors.It is concluded that there are grounds to talk about the formation of such a new legal institution of financial law as "public bankruptcy law".

What can I do as a farmer to reduce losses? Willingness to use meteorological information as an exit strategy to deal with meteorological hazards

Meteorological information and forecasts are of great importance to reduce agro-meteorological hazards. However, the gap between production and application of these forecasts is one of the most ambiguous issues of crop management at the farmers’ level. In this regard, investigating the factors influencing Iranian farmers willingness to use meteorological information and predictions was selected as the main aim of the present study. To this end, an extended version of Theory of Planned Behavior (TPB) was employed and modeled. The results revealed that attitude towards the use of meteorological information and predictions, subjective norms in the use of meteorological information and predictions, perceived behavioral control and self-identity in the use of meteorological information and prediction, and moral norm variables regarding the use of meteorological information and predictions positively and significantly affected willingness to use meteorological information and predictions. These variables could account for 46% of willingness to use meteorological information and predictions variance. According to the results, it was suggested that by creating multimedia programs, the agricultural community become aware of the benefits and consequences of using meteorological information in their activities. Furthermore, it was recommended that social cooperation and research groups be formed on the use of meteorological information in agricultural activities. This can contribute to examine the various dimensions (strengths, weaknesses, threats, and opportunities) of using meteorological information from a collective perspective. Making a social decision in this regard can have a profound effect on a person’s subjective norms and dramatically increase the speed of using meteorological information. The results of this study can help policymakers and decision-makers in the field of agriculture to design suitable intervention programs for the effective use of meteorological information by farmers. Also, the results of this study help farmers to effectively reduce the impacts of meteorological hazards.

EvHandPose: Event-Based 3D Hand Pose Estimation With Sparse Supervision.

Event camera shows great potential in 3D hand pose estimation, especially addressing the challenges of fast motion and high dynamic range in a low-power way. However, due to the asynchronous differential imaging mechanism, it is challenging to design event representation to encode hand motion information especially when the hands are not moving (causing motion ambiguity), and it is infeasible to fully annotate the temporally dense event stream. In this paper, we propose EvHandPose with novel hand flow representations in Event-to-Pose module for accurate hand pose estimation and alleviating the motion ambiguity issue. To solve the problem under sparse annotation, we design contrast maximization and hand-edge constraints in Pose-to-IWE (Image with Warped Events) module and formulate EvHandPose in a weakly-supervision framework. We further build EvRealHands, the first large-scale real-world event-based hand pose dataset on several challenging scenes to bridge the real-synthetic domain gap. Experiments on EvRealHands demonstrate that EvHandPose outperforms previous event-based methods under all evaluation scenes, achieves accurate and stable hand pose estimation with high temporal resolution in fast motion and strong light scenes compared with RGB-based methods, generalizes well to outdoor scenes and another type of event camera, and shows the potential for the hand gesture recognition task.

Maritime Disputes from a Global Perspective and Solutions under International Law

This study explores the utilization and challenges of international laws in resolving global maritime disputes through a detailed analysis of representative cases from South America, Africa, and Asia. It highlights the crucial role of international law, particularly the United Nations Convention on the Law of the Sea (UNCLOS), in providing a legal framework and dispute resolution mechanisms. However, the study also identifies significant challenges, including political interference, legal ambiguity, and enforcement issues. By comparing regional dispute settlement mechanisms, the study demonstrates the effectiveness of multilateral negotiations in South America, regional legal frameworks in Africa, and a combination of multilateral and unilateral actions in Asia. Based on these insights, the study proposes several recommendations to enhance the international legal framework, promote regional cooperation, and innovate dispute resolution mechanisms. These measures aim to ensure fair and effective resolution of maritime disputes, maintain maritime peace and stability, and promote the healthy development of global ocean governance.

Investigation of source ambiguity of floor noises in a box-frame concrete building from the perspective of junction attenuations: A real case field experiment

This study investigated impact source ambiguity in box-frame multifamily buildings, prevalent in densely populated areas, by examining the transmission of heavy-impact noise and its relationship with junction attenuations across five floors. Contrary to the decline in noise levels with increased distance from the impact source, our findings uncover an intriguing deviation in the behaviour of vibration response levels. Surprisingly, vibrations are often intensified, rather than diminished, with distance. Notably, upon examining each room, instances where non-structural or thin concrete walls exhibited the highest vibration responses were frequently observed. Such instances were particularly prevalent in rooms not directly adjacent to the impact source. This trend markedly impedes residents’ ability to pinpoint the origin location of impact noises. This study reveals an unexpected weakening and reversal of distance attenuation in vibration, linked to the increasing number of valid flanking paths as the junction distance grows. The findings suggest that increasing slab thickness for noise insulation may unintentionally complicate source identification due to diminished junction attenuation in thicker horizontal components compared to vertical ones. To enhance acoustic comfort in such housing, this study recommends minimizing valid flanking paths through improved junction design, addressing an often-overlooked issue of source ambiguity of floor noises.

What are the methodological characteristics of evidence and gap maps? A systematic review and evidence and gap map

AbstractIntroductionClarity on the characteristics of methods used to produce evidence and gap maps (EGMs) will highlight areas where method development is needed to ensure these increasingly produced tools are made following best practice to assure their quality and utility. This paper aims to describe the range, nature and variability of key methodological characteristics of studies publishing EGMs.MethodsWe followed a protocol, written a‐prior and informed by PRISMA and MECCIR guidelines for undertaking systematic reviews. We searched nine data bases, from 2010, for studies across any discipline that included details of their methods used to produce an EGM. Search results were screened by two reviewers independently and the subsequent data was extracted and managed according to predefined criteria. We mapped these together with the year of publication and the area of research as the two primary dimensions. We followed established methods for mapping the evidence, including the process of developing the map framework and the filters for our interactive map. We sought input and involvement from stakeholders during this process.ResultsWe found 145 studies from nine distinct research areas, with health research accounting for 67%. There were 11 map designs found, of these bubble plots were the most common design, before 2019, since then it has been a matrix map design. Stakeholders were involved in 47.7% of studies, 48.35% of studies stated finding gaps was an aim of their work, 42% reported publishing or registering a protocol and only 9.39% of studies mentioned a plan to update their evidence maps/EGMs.Discussion/ConclusionKey areas of methodological development relate to: the involvement of stakeholders, the conceptualization of gaps and the practices for updating maps. The issues of ambiguity in terminology, the flexibility of visualizations of the data and the lack of reporting detail were other aspects that needs further consideration in studies producing an EGM.

Bayesian Neural Network Variational Autoencoder Inverse Mapper (BNN-VAIM) and its application in Compton Form Factors extraction

We extend the Variational Autoencoder Inverse Mapper (VAIM) framework for the inverse problem of extracting Compton Form Factors (CFFs) from deeply virtual exclusive reactions, such as the unpolarized Deeply virtual exclusive scattering (DVCS) cross section. VAIM is an end-to-end deep learning framework to address the solution ambiguity issue in ill-posed inverse problems, which comprises of a forward mapper and a backward mapper to simulate the forward and inverse processes, respectively. In particular, we incorporate Bayesian Neural Network (BNN) into the VAIM architecture (BNN-VAIM) for uncertainty quantification. By sampling the weights and biases distributions of the BNN in the backward mapper of the VAIM, BNN-VAIM is able to estimate prediction uncertainty associated with each individual solution obtained for an ill-posed inverse problem. We first demonstrate the uncertainty quantification capability of BNN-VAIM in a toy inverse problem. Then, we apply BNN-VAIM to the inverse problem of extracting 8 CFFs from the unpolarized DVCS cross section.

Shading-Guided Manga Screening From Reference.

Manga screening is a critical process in manga production, which still requires intensive labor and cost. Existing manga screening methods either generate simple dotted screentones only or rely on color information and manual hints during screentone selection. Due to the large domain gap between line drawings and screened manga, and the difficulties in generating high-quality, properly selected and shaded screentones, even state-of-the-art deep learning methods cannot convert line drawings to screened manga well. Besides, ambiguity exists in the screening process since different artists may screen differently for the same line drawing. In this article, we propose to introduce shaded line drawing as the intermediate counterpart of the screened manga so that the manga screening task can be decomposed into two sub-tasks, generating shading from a line drawing and replacing shading with proper screentones. The reference image is adopted to resolve the ambiguity issue and provides options and controls on the generated screened manga. We proposed a reference-based shading generation network and a reference-based screentone generation module to achieve the two sub-tasks individually. We conduct extensive visual and quantitative experiments to verify the effectiveness of our system. Results and statistics show that our method outperforms existing methods on the manga screening task.

ATC-SD Net: Radiotelephone Communications Speaker Diarization Network

This study addresses the challenges that high-noise environments and complex multi-speaker scenarios present in civil aviation radio communications. A novel radiotelephone communications speaker diffraction network is developed specifically for these circumstances. To improve the precision of the speaker diarization network, three core modules are designed: voice activity detection (VAD), end-to-end speaker separation for air–ground communication (EESS), and probabilistic knowledge-based text clustering (PKTC). First, the VAD module uses attention mechanisms to separate silence from irrelevant noise, resulting in pure dialogue commands. Subsequently, the EESS module distinguishes between controllers and pilots by levying voice print differences, resulting in effective speaker segmentation. Finally, the PKTC module addresses the issue of pilot voice print ambiguity using text clustering, introducing a novel flight prior knowledge-based text-related clustering model. To achieve robust speaker diarization in multi-pilot scenarios, this model uses prior knowledge-based graph construction, radar data-based graph correction, and probabilistic optimization. This study also includes the development of the specialized ATCSPEECH dataset, which demonstrates significant performance improvements over both the AMI and ATCO2 PROJECT datasets.

CharacterGen: Efficient 3D Character Generation from Single Images with Multi-View Pose Canonicalization

BNRist, Department of Computer Science and Technology, Tsinghua University, China In the field of digital content creation, generating high-quality 3D characters from single images is challenging, especially given the complexities of various body poses and the issues of self-occlusion and pose ambiguity. In this paper, we present CharacterGen, a framework developed to efficiently generate 3D characters. CharacterGen introduces a streamlined generation pipeline along with an image-conditioned multi-view diffusion model. This model effectively calibrates input poses to a canonical form while retaining key attributes of the input image, thereby addressing the challenges posed by diverse poses. A transformer-based, generalizable sparse-view reconstruction model is the other core component of our approach, facilitating the creation of detailed 3D models from multi-view images. We also adopt a texture-back-projection strategy to produce high-quality texture maps. Additionally, we have curated a dataset of anime characters, rendered in multiple poses and views, to train and evaluate our model. Our approach has been thoroughly evaluated through quantitative and qualitative experiments, showing its proficiency in generating 3D characters with high-quality shapes and textures, ready for downstream applications such as rigging and animation.

Ethical risks of artificial intelligence and prospects for joint decision-making in medicine

The article deals with ethical problems in the process of joint decision-making arising in connection with the active involvement of artificial intelligence systems in medical practice. Special attention is paid to the influence of artificial intelligence systems on the principle of respect for the patient’s autonomy, an ethical assessment of the main criteria of the patient’s autonomous action — voluntariness, awareness and competence is given. It is noted that the declared ethical values in the implementation of artificial intelligence in the field of medicine at this stage of its development cannot fully provide the standard of joint informed decision-making. Along with the extensive capabilities of artificial intelligence systems to increase the patient’s competence and responsibility for their health, ethically ambiguous issues remain related to the awareness and voluntary nature of the patient. Technological features of artificial intelligence create obstacles to the formation of trust between the doctor and the patient: they complicate the process of informing the patient, prevent the patient from voluntarily choosing the preferred treatment algorithm, which may make it difficult to comply with the principle of respect for patient autonomy. The study of trigger points in the mechanism of ethical management of artificial intelligence in the healthcare system is a promising task for the creation of reliable medical artificial intelligence.

Enhancing Word Sense Disambiguation for Amharic homophone words using Bidirectional Long Short-Term Memory network

Given the Amharic language has a lot of perplexing terminology since it features duplicate homophone letters, fidel's ሀ, ሐ, and ኀ (three of which are pronounced as HA), ሠ and ሰ (both pronounced as SE), አ and ዐ (both pronounced as AE), and ጸ and ፀ (both pronounced as TSE). The WSD (Word Sense Disambiguation) model, which tackles the issue of lexical ambiguity in the context of the Amharic language, is developed using a deep learning technique. Due to the unavailability of the Amharic wordnet, a total of 1756 examples of paired Amharic ambiguous homophonic words were collected. These words were ድህነት(dhnet) and ድኅነት(dhnet), ምሁር(m'hur) and ምሑር(m'hur), በአል(be'al) and በዢል(be'al), አቢይ (abiy) and ዐቢይ(abiy), with a total of 1756 examples. Following word preprocessing, word2vec, fasttext, Term Frequency-Inverse Document Frequency (TFIDF), and bag of words (BoW) were used to vectorize the text. The vectorized text was divided into train and test data. The train data was then analysed using Naive Bayes (NB), K-nearest neighbour (KNN), logistic regression (LG), decision trees (DT), random forests (RF), and random oversampling technique. Bidirectional Gate Recurrent Unit (BiGRU) and Bidirectional Long Short-Term Memory (BiLSTM) improved to 99.99 % accuracy even with limited datasets.

Meaning in life after cancer: Validation of the sources of meaning card method among participants in cancer rehabilitation.

Cancer survivors may be struggling to re-create meaning in life. Addressing their personal sources of meaning can support them in this process. The sources of meaning card method (SoMeCaM) aims to map and explore personal sources of meaning in a 1-h session. It includes 26 cards, each with a statement on a source of meaning. The purpose of this study was to validate the statements on the sources of meaning cards for use among participants in cancer rehabilitation by examining whether participants attribute the same meaning to the statements as intended. The three step test interview method was used to assess response processes to the sources of meaning cards among 12 participants in a 5-day cancer rehabilitation program in Denmark. The interviews were transcribed verbatim and analyzed using framework analysis. Nineteen of the 26 statements were interpreted congruently, that is, in line with the underlying theory, by all participants. Issues of incongruency, ambiguity and confusion were observed in participants' interpretations of the statements on religiosity (n = 6), spirituality (n = 10), and reason (n = 6). Minor issues were observed for the statements on practicality, achievement, knowledge, and attentiveness. In most statements, cancer survivors' interpretation aligned with the underlying theory. Problems were apparent regarding the sources of meaning religiosity, spirituality and reason, and a reconsideration of the wording of the statements is recommended. These problems may be due to cultural and linguistic interpretations rather than to being a cancer survivor. Future studies could focus on these issues in other target populations. Despite these minor issues, the SoMeCaM has proven useful in addressing the important topic of meaning in life in the cancer rehabilitation setting. Clinicians should pay attention to nuances in participants' understanding of the cards.

Boosting integral-based human pose estimation through implicit heatmap learning

Human pose estimation typically encompasses three categories: heatmap-, regression-, and integral-based methods. While integral-based methods possess advantages such as end-to-end learning, full-convolution learning, and being free from quantization errors, they have garnered comparatively less attention due to inferior performance. In this paper, we revisit integral-based approaches for human pose estimation and propose a novel implicit heatmap learning framework. The framework learns the true distribution of keypoints from the perspective of maximum likelihood estimation, aiming to mitigate inherent ambiguity in shape and variance associated with implicit heatmaps. Specifically, Simple Implicit Heatmap Normalization (SIHN) is first introduced to calculate implicit heatmaps as an efficient and effective representation for keypoint localization, which replaces the vanilla softmax normalization method. As implicit heatmaps may introduce potential challenges related to variance and shape ambiguity arising from the inherent nature of implicit heatmaps, we thus propose a Differentiable Spatial-to-Distributive Transform (DSDT) method to aptly map those implicit heatmaps onto the transformation coefficients of a deformed distribution. The deformed distribution is predicted by a likelihood-based generative model to unravel the shape ambiguity quandary effectively, and the transformation coefficients are learned by a regression model to resolve the variance ambiguity issue. Additionally, to expedite the acquisition of precise shape representations throughout the training process, we introduce a Wasserstein Distance-based Constraint (WDC) to ensure stable and reasonable supervision during the initial generation of implicit heatmaps. Experimental results on both the MSCOCO and MPII datasets demonstrate the effectiveness of our proposed method, achieving competitive performance against heatmap-based approaches while maintaining the advantages of integral-based approaches. Our source codes and pre-trained models are available at https://github.com/ducongju/IHL.

Germany’s Strategic Activity in the Sahel and the Problem of Sovereignty for Regional States: The Case of Mali

Using Mali as an example of a geostrategically important African state, the article explores the extent to which Germany’s political and military steps contributed or did not contribute to the sovereignty of the state that was trying to overcome the instability. Taking into account the ambiguous issue of the completeness of the sovereignty of Germany itself, the author analyzes the FRG’s fundamental ability to transfer its experience in this field to other states. In the second half of the 2010s, the scope and effectiveness of German efforts to stabilize the situation in Mali were insufficient. Germany, especially in the first stages of the settlement (from 2013 to 2015), did not make a noticeable contribution to peacemaking and peacekeeping activities. The use of the Bundeswehr in the field became noticeable only from 2016–2018; however, it did not lead to the necessary results. By the mid-2010s, Germany had already begun to take an active part in the security sector reform in Mali. However, the qualitative and quantitative parameters of the newly trained personnel of the Malian army were insufficient to ensure peace and security in the country. Having accepted the functions of one of the main external participants in the settlement, Germany did not accompany this with the necessary efforts. In addition, Germany was not ready to ensure efficiently the nationalization of the settlement, that is, to transfer control over the process to those internal forces that were capable of leading the country to peace. The incomplete resolution of the armed conflict and its degradation since the end of the 2010s have manifested the regress of Mali’s gaining sovereignty. Therefore, the Malian militaries, since 2020‒2021, have been trying to realize another scenario without the built-in participation of Western democracies. The paper explores the perception by the new Malian powers of the German military presence in the country.

Image of Women in Islam: Maqāsid Approach for Correct Interpretation

The current study attempted to outline the necessity of using the purposes method (al-manhaj al-maqāṣidi) to understand the correct meaning of Islamic religious texts, particularly those related to the image of women, by analysing a set of Prophetic Hadiths in the canonical works of al-Bukhari and Muslim. When the purposes method is not utilized, these texts are typically understood as hostile towards women, leading many to question their validity. The current study employed the analytical and critical methods derived from holistically viewing the religious texts. The study argued that the purposes method should be used by gathering all the texts relevant to a particular topic, deriving specific purposes from general ones, linking branch issues to their origins, and assessing other contextual and situational evidences to create a correct understanding of religious texts that avoids explanations and violates the general principles of Shariah. Moreover, the study also argued that the purposes methodology stands as a pivotal element in grasping the essence of an Islamic text, rooted in the overarching principles of the law, and is utilized to interpret and guide fragmented texts. It also serves as a subset of the principle of elucidating ambiguous issues and reverting the secondary matters to their original bases. Once validated, the objectives of the law serve as a benchmark for adjudicating any unresolved matter not explicitly addressed in the examination.

Bryson of Heraclea and Polyxenus, Megarian Philosophers

Abstract Bryson of Heraclea and Polyxenus have received little attention from scholars. Sources on these philosophers are few and difficult to interpret. However, they present interesting dialectical arguments that concern some of Plato’s and Aristotle’s most important theoretical elaborations: Bryson’s arguments on the issue of semantic ambiguity were explicitly discussed by Aristotle, and Polyxenus is credited with a particular version of the Third Man argument. My purpose in this paper is to reconstruct the historical background of these two philosophers and to analyze the philosophical implications of the arguments that the ancient tradition ascribes to them.