Unusual Objects Research Articles

Uncertain features exploration in temporal moment localization via language by utilizing customized temporal transformer

Temporal moment localization in videos using natural language (TMLVNL) is challenging problem in computer vision. TMLVNL aims to determine the correct moment in a lengthy, untrimmed video that corresponds to the input query. In addition to its inherent complexity, TMLVNL encounters several additional difficulties that can degrade performance, such as rare object positions, occlusions, camera focus issues, and motion blurriness. To address these issues, this study proposes a novel solution called the Temporal Ziggurat Transformer Network. First, we developed a novel method rather than relying on various combinational approaches. Second, we proposed complicated scenarios, such as unusual object postures, object occlusions, camera focus issues, and motion blurriness, by incorporating specialized blocks into our Customized Ziggurat Transformer (ZT) to thoroughly explore visual features. Third, to facilitate the understanding of visual features associated with query words, we proposed a query word-specific transformer (QWST) as a submodule of ZT. QWST integrates query word feature representations with extensively investigated visual features. Fourth, in our module named STDF, we managed query-sentence representations along with query word attributes to extract semantic context from video chunks. The moment was then localized, with its start and end borders identified using the moment localization module. Comprehensive experiments on the Charades-STA, TACos, and Activity-Netcaption datasets demonstrated that our strategy outperformed existing state-of-the-art methods.

Enhancing Video Anomaly Detection with Improved UNET and Cascade Sliding Window Technique

Computer vision video anomaly detection still needs to be improved, especially when identifying images with unusual motions or objects. Current approaches mainly concentrate on reconstruction and prediction methods, and unsupervised video anomaly detection faces difficulties because there are not enough tagged abnormalities, which reduces accuracy. This paper presents a novel framework called the Improved UNET (I-UNET), designed to counteract overfitting by addressing the need for complex models that can extract subtle information from video anomalies. Video frame noise can be eliminated by preprocessing the frames with a Weiner filter. Moreover, the system uses Convolution Long Short-Term Memory (ConvLSTM) layers to smoothly integrate temporal and spatial data into its encoder and decoder portions, improving the accuracy of anomaly identification. The Cascade Sliding Window Technique (CSWT) is used post-processing to identify anomalous frames and generate anomaly scores. Compared to baseline approaches, experimental results on the UCF, UCSDped1, and UCSDped2 datasets demonstrate notable performance gains, with 99% accuracy, 90.8% Area Under Curve (AUC), and 10.9% Equal Error Rate (EER). This study provides a robust and accurate framework for video anomaly detection with the highest accuracy rate.

The technology of masonry and processing of stone materials in the buildings of the 13th-century castle of King Danylo Romanovych in Kholm

The relevance of the study is conditioned by the great cultural significance of King Danylo’s Castle, one of the most unusual architectural objects of the 13th century Rus’. The purpose of the study was to reveal construction technologies, techniques for processing and carving architectural details made of stone, which are used in the construction of castle objects. The research methodology was based on a detailed analysis of the remains of buildings and individual construction or architectural details discovered due to architectural and archaeological research. The study analysed the characteristics of the used natural stones. Archaeological remains of the defensive wall, towers, foundations, and walls of several buildings, the purpose of which has not yet been clarified, present objects created by a highly professional construction workshop. Excavations have revealed a large number of carved decorative architectural details made of glauconitite and limestone, the use of which indicates the richness of architecture and presents specific architectural forms of the Romanesque style. At the first stage, the castle was built only of stone, and at the second stage, brick was also used. The oldest buildings were made of natural glauconite stone. In addition to local green glauconite sandstone, limestone, and fossilised chalk were also used. An interesting feature of the castle is the use of typical Romanesque masonry technology, stone block hewing techniques, and carving of decorative stone details. Based on the results of research, it can be stated that a standard set of hand tools was used. The remnants of carved stone details show the rich plastic structure of the castle’s facades and interiors. Similar products and technologies are not found in other castles of Rus’ at that time. Finds of masonry tools in the castle ruins suggest that most of the technological process of stone processing took place right there in the castle yard. The results of the study allow presenting for the first time a detailed description of the masonry artel of “Master Avdii” – a character in the chronicles about the construction of the castle in Kholm

Is a knife the same as a plunger? Comparing the attentional effects of weapons and non-threatening unusual objects in dynamic scenes.

Observers' memory for a person's appearance can be compromised by the presence of a weapon, a phenomenon known as the weapon-focus effect (WFE). According to the unusual-item hypothesis, attention shifts from the perpetrator to the weapon because a weapon is an unusual object in many contexts. To test this assumption, we monitored participants' eye movements while they watched a mock-crime video. The video was presented with sound and featured a female perpetrator holding either a weapon, a non-threatening unusual object, or a neutral object. Contrary to the predictions of current theories, there were no significant differences in total viewing times for the three objects. For the perpetrator, total viewing time was reduced when she held the non-threatening unusual object, but not when she held the weapon. However, weapon presence led to an attentional shift from the perpetrator's face toward her body. Detailed time-course analyses revealed that the effects of object type were more pronounced during early scene viewing. Thus, our results do not support the idea of extended attentional shifts from the perpetrator toward the unusual objects, but instead suggest more complex attentional effects. Contrary to previous research, memory for the perpetrator's appearance was not affected by object type. Thus, there was no WFE. An additional online experiment using the same videos and methodology produced a WFE, but this effect disappeared when the videos were presented without sound.

Green infrastructure as a factor of sustainable urban development: current status and prospects

Abstract The article reflects the results of the assessment of the current state and functioning of the green infrastructure of the urban environment. A thorough survey of the territory and a clear functional zoning were carried out. A study of the features of Landscaping and landscaping of urban forests was conducted and a whole range of issues related to the formation of a full-fledged environment for human recreation was identified. The research has established plants that can withstand high temperature conditions and low humidity, high levels of dust and gases in the air of the urban environment, soil compaction, etc. In the course of research, it was revealed that coniferous plants play a large role in the creation of decorative compositions. For a shaded lawn, special types of herbs are used that can do without direct sunlight. Local creators and artists can be involved in the creation of unusual forest art objects. This will create additional interest in visiting and contribute to the dissemination of information about the city and its attractions on social networks, creating an attractive tourist image. The improvement of the city’s forest area will create opportunities for organizing full-fledged recreation for urban residents, and will have a beneficial effect on their psychological and physical condition. Thus, a sufficient number of well-maintained public spaces have a positive effect on the physical and psycho-emotional health of a city dweller, on the effectiveness of his work.

Chemoinformatic regression methods and their applicability domain.

The growing interest in chemoinformatic model uncertainty calls for a summary of the most widely used regression techniques and how to estimate their reliability. Regression models learn a mapping from the space of explanatory variables to the space of continuous output values. Among other limitations, the predictive performance of the model is restricted by the training data used for model fitting. Identification of unusual objects by outlier detection methods can improve model performance. Additionally, proper model evaluation necessitates defining the limitations of the model, often called the applicability domain. Comparable to certain classifiers, some regression techniques come with built-in methods or augmentations to quantify their (un)certainty, while others rely on generic procedures. The theoretical background of their working principles and how to deduce specific and general definitions for their domain of applicability shall be explained.

Речевой жанр пожелания в молодёжном интернет-дискурсе: новации и лингвокреативность

This paper analyses the use of the wishing genre among Russian young people. One of the features of youth speech is a shift from stereotypes, searching for new creative ways of expressing various intentions, such as evaluation, compliment, congratulation and wishing. The purpose of the study is to analyze the texts of wishes from the point of manifesting the linguistic creativity of the addressers; to identify the means and ways of destroying stereotypes. The research material is youth wishes, predominantly students’ wishes (more than 700 text fragments). The analysis of the content of the wishing texts enabled the author to classify all wishes into stereotypical, individual and combined. Empirical material shows that in the communication of friends most wishes refer to individual ones (64%), whereas combined and stereotypical wishes make up 25% and 11%, respectively. The analysis of the texts of wishes identified the ways and means of demonstrating the linguistic creativity of the addressee. These ways and means include the tactics of compliment, appeal, advice, switching the addressee to a humorous tone and some other tactics that are not discussed in this paper. All of them allow to make wishes less stereotypical. The material showed that addressers in wishes aim to supplement the universal components of the wishes with individualized elements, including in the text the utilitarian details of the addressee’s life. Most often, wishes contain references to the addressee’s profession or hobbies. Linguistic creativity of the addressers is manifested at all linguistic levels. First of all, addressees actively use tropes, such as epithets, metaphors, and comparisons. Addressers look for unusual objects for comparison in the linguistic repertoires; they often use hyperbolization. At the level of word-formation, we observe the use of affixes with an augmentative meaning, diminutives (predominantly in wishes to female addressees); speech innovations and occasional word formation. In wishes of young people, a shift from stereotypes reflects in reduplicating letters and symbols, changing letter case, breaking words into syllables and placing a hyphen between syllables; using a wide range of emojis and stickers; playing with colorful highlighting. Precedent texts play an important role in moving away from stereotypes. The author concludes that all the considered methods and means of linguistic creativity that are regularly used by authors when creating wishes make it possible to enhance the expressiveness and impact degree of the texts. They function to avoid stereotypes, achieve a perlocutionary effect, and contribute to the self-presentation of the addresser.

Intelligent railroad inspection and monitoring

Railways are essential to the global transportation infrastructure, providing eco-friendly and economical solutions for the movement of freight and passengers. Inspecting and maintaining extensive rail networks timely poses significant challenges. My group and collaborators have focused on automated railroad inspection technologies, emphasizing the use of deep learning and computer vision to overcome the limitations of traditional manual inspections. Our research introduces groundbreaking real-time inspection methods, leveraging a specialized dataset of railroad components for enhanced instance segmentation models, achieving unprecedented accuracy and inference speeds. The developed computer vision systems efficiently detect track components and their changes over time, and also quantify rail surface defects. Additionally, our work extends to improving railroad crossing safety, utilizing deep learning frameworks for the detection of unusual pedestrian behaviors and object identification, aimed at reducing crossing incidents and improving emergency response times. Our future research directions aim to further refine the cost-effectiveness and autonomy of railroad inspection systems. Through these innovations, we hope to aid in the inspection and maintenance of railroads, offering practical solutions for railroad and other civil engineering applications.

Deep Reinforcement Learning-Empowered Cost-Effective Federated Video Surveillance Management Framework.

Video surveillance systems are integral to bolstering safety and security across multiple settings. With the advent of deep learning (DL), a specialization within machine learning (ML), these systems have been significantly augmented to facilitate DL-based video surveillance services with notable precision. Nevertheless, DL-based video surveillance services, which necessitate the tracking of object movement and motion tracking (e.g., to identify unusual object behaviors), can demand a significant portion of computational and memory resources. This includes utilizing GPU computing power for model inference and allocating GPU memory for model loading. To tackle the computational demands inherent in DL-based video surveillance, this study introduces a novel video surveillance management system designed to optimize operational efficiency. At its core, the system is built on a two-tiered edge computing architecture (i.e., client and server through socket transmission). In this architecture, the primary edge (i.e., client side) handles the initial processing tasks, such as object detection, and is connected via a Universal Serial Bus (USB) cable to the Closed-Circuit Television (CCTV) camera, directly at the source of the video feed. This immediate processing reduces the latency of data transfer by detecting objects in real time. Meanwhile, the secondary edge (i.e., server side) plays a vital role by hosting a dynamically controlling threshold module targeted at releasing DL-based models, reducing needless GPU usage. This module is a novel addition that dynamically adjusts the threshold time value required to release DL models. By dynamically optimizing this threshold, the system can effectively manage GPU usage, ensuring resources are allocated efficiently. Moreover, we utilize federated learning (FL) to streamline the training of a Long Short-Term Memory (LSTM) network for predicting imminent object appearances by amalgamating data from diverse camera sources while ensuring data privacy and optimized resource allocation. Furthermore, in contrast to the static threshold values or moving average techniques used in previous approaches for the controlling threshold module, we employ a Deep Q-Network (DQN) methodology to manage threshold values dynamically. This approach efficiently balances the trade-off between GPU memory conservation and the reloading latency of the DL model, which is enabled by incorporating LSTM-derived predictions as inputs to determine the optimal timing for releasing the DL model. The results highlight the potential of our approach to significantly improve the efficiency and effective usage of computational resources in video surveillance systems, opening the door to enhanced security in various domains.

The Active Asteroids Citizen Science Program: Overview and First Results

We present the Citizen Science program Active Asteroids and describe discoveries stemming from our ongoing project. Our NASA Partner program is hosted on the Zooniverse online platform and launched on 2021 August 31, with the goal of engaging the community in the search for active asteroids—asteroids with comet-like tails or comae. We also set out to identify other unusual active solar system objects, such as active Centaurs, active quasi-Hilda asteroids (QHAs), and Jupiter-family comets (JFCs). Active objects are rare in large part because they are difficult to identify, so we ask volunteers to assist us in searching for active bodies in our collection of millions of images of known minor planets. We produced these cutout images with our project pipeline that makes use of publicly available Dark Energy Camera data. Since the project launch, roughly 8300 volunteers have scrutinized some 430,000 images to great effect, which we describe in this work. In total, we have identified previously unknown activity on 15 asteroids, plus one Centaur, that were thought to be asteroidal (i.e., inactive). Of the asteroids, we classify four as active QHAs, seven as JFCs, and four as active asteroids, consisting of one main-belt comet (MBC) and three MBC candidates. We also include our findings concerning known active objects that our program facilitated, an unanticipated avenue of scientific discovery. These include discovering activity occurring during an orbital epoch for which objects were not known to be active, and the reclassification of objects based on our dynamical analyses.

As finanças municipais nas reformas urbanas (Rio de Janeiro, 1902-1910)

This article deals with the period of urban reforms in Rio de Janeiro, focusing on the finances of the Federal District - an unusual object at the center of this theme, so widely debated by historiography. The central problem proposed seeks to measure and indicate the limits and challenges imposed by municipal finances on the reforming drive of district governments at the dawn of the 20th century. The study's methodology is based on confronting sources that systematically express quantitative information with others that describe and discuss the constitutive processes that outlined municipal finances in the period. In a critical-descriptive appraisal, we analyze the financial resources in their flow into the municipal coffers; we scrutinize the collection, spending and investments; we point out the sources, forms and costs of financing obtained by the municipality of Rio de Janeiro. The text looks for indications of the existence and degree of dependence, not only political, but especially financial, of the capital's government on the federal government. The analytical exercise of statistical tables and yearbooks, legislation and official reports - both municipal and federal - was complexified in the critical perspectives offered by texts and cartoons in periodicals, studies, memoirs and non-governmental reports, thus offering a contribution from Economic History to this subject of such long-standing interest to historians.

Diogenes syndrome - causes and treatment of pathological hoarding and self neglect

Introduction
 Diogenes syndrome is a behavioral disorder typically described in physically neglected people living in extreme poverty, unsanitary conditions and self-imposed isolation, who often refuse outside help and tend to accumulate unusual objects. This syndrome is gaining increasing attention in the world of neurology and psychiatry.
 
 The aim of the study
 To summarize current knowledge on the causes of Diogenes syndrome, its characteristics and treatment.
 
 Material and methods
 The search of the Pubmed and Google Scholar database was carried out using keywords, in order to find the latest publications.
 
 Summary
 Diogenes syndrome is a complex disease that will affect an increasing number of elderly people as the population ages. There is a lack of extensive research to create clear diagnostic criteria, classification and treatment for Diogenes syndrome. The creation of a disease entity would enable earlier diagnosis and help patients.

Adversarial random graph neural network for anomaly detection

Anomaly detection is distinguishing unusual objects from normal patterns. It is a complex task due to unpredictable nature of anomalies, which can appear in many forms or they can be hidden by mimicking normal behaviors in a graph structure. Such diversity makes this Deep learning approaches can solve these problems by extracting complex patterns from networks. However, addressing different forms of anomalous instances is essential for successfully implementing these approaches, as different anomaly types require further analysis. Additionally, it is challenging to interpret anomalies beforehand without focusing on every aspect of anomalies. Our objective is to propose an architecture capable of handling all types of anomalous entities by tackling challenges across various domains. In this paper, we introduce ARNAD, a novel framework that integrates three deep models to identify anomalies in graphs: graph neural network, autoencoder, and adversarial autoencoder. ARNAD approaches graph anomaly detection by utilizing the features of the deep parts, and four key elements stand out: (1) the autoencoder learns the overall graph structure and identifies highly deviated ones, (2) the graph neural network exploits graph structure to detect anomalies among the communities, (3) a fixed-size randomized neighborhood that prevents overfitting while reducing complexity (4) the adversarial autoencoder improves the robustness of the framework and discriminates anomalies. To detect anomalies, four receptive components assign risk scores to objects in the attributed network. We evaluated the framework with three synthetic datasets that simulate different behaviors of anomalies and six widely used real attributed networks. Our experimental results show that ARNAD performs competitively with other state-of-the-art models in detecting anomalous entities while minimizing false positives, demonstrating ARNAD's effectiveness in detecting graph anomalies.

Virtue of Homoeopathy in Treating Pica

Pica is an abnormal disorder where there is constant desire to eat undigestible things. This is usually observed in pregnant females and children. Pica gets its name from a bird species, named the Eurasian magpie. In latin language bird is called as pica pica. This bird has a reputation for eating unusual objects. Eating unusual things leads to many types of problems, in which Homoeopathy plays major role in treating this condition. Keywords: Pica, Homoeopathy, Children

The unique Kuchuk lake salt deposit and legal aspects of its exploitation

Data on the processes of mineral redistribution in the Kuchuk lake salt deposit are presented, and the difficulties arising in this regard during the mineral reserve accounting and write-off are discussed. Lake salt deposits are quite unusual objects with individual specifics. A unique technology using a geotechnological method for the sodium sulfate extraction with minimal environment impact has been developed for the Kuchuk deposit. This method allows one to change the volume of extraction of the main mineral without a damage for the deposit and the environment.

A Unique Assemblage of Late Islamic Magical Artifacts from Netafim 2: A Campsite on the Darb al-Hajj, Southern Israel

Abstract Many remains of the Muslim pilgrimage road from Cairo to Mecca, the Darb al-Hajj al-Maṣri are preserved in the Eilat region of southern Israel. These include sections of the road, camps, and other associated structures. Most of these remains date to the Mamluk and Ottoman periods. In one camp (Netafim 2), an assemblage of unusual objects was found, including fragmentary clay rattles, votive clay incense burners, anthropomorphic and perhaps zoomorphic clay figurines, a cluster of colored quartz pebbles, and some seashells. The fabric of the clay objects indicates that they originated in Egypt. Based on the nature of the assemblage and a literary survey, this study suggests that the discussed artifacts were related to popular magic. The magical rituals, whose nature is yet unclear, were likely carried out at the site by professional sorcerers, who offered their services to pilgrims making the hajj.

Dynamical He Flashes in Double White Dwarf Binaries

The detonation of an overlying helium layer on a 0.8–1.1 M ⊙ carbon–oxygen (CO) white dwarf (WD) can detonate the CO WD and create a thermonuclear supernova (SN). Many authors have recently shown that when the mass of the He layer is low (≲0.03 M ⊙), the ashes from its detonation minimally impact the spectra and light curve from the CO detonation, allowing the explosion to appear remarkably similar to Type Ia SNe. These new insights motivate our investigation of dynamical He shell burning and our search for a binary scenario that stably accumulates thermally unstable He shells in the 0.01–0.08 M ⊙ range, thick enough to detonate, but also often thin enough for minimal impact on the observables. We first show that our improved nonadiabatic evolution of convective He shell burning in this range of shell mass leads to conditions ripe for a He detonation. We also find that a stable mass transfer scenario with a high-entropy He WD donor of mass 0.15–0.25 M ⊙ yields the He shell masses needed to achieve the double detonations. This scenario also predicts that the surviving He donor leaves with a spatial velocity consistent with the unusual runaway object, D6-2. We find that hot He WD donors originate in common-envelope events when a 1.3–2.0 M ⊙ star fills its Roche lobe at the base of the red giant branch at orbital periods of 1–10 days with the CO WD.

Face-like pareidolia images are more difficult to detect than real faces in children with autism spectrum disorder.

Research on the diagnosis, treatment and pathophysiology of neurodevelopmental disorders is multifaceted, requiring the use of genetics, imaging, psychology, and artificial intelligence (AI). Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by a limited ability to communicate and a limited interest in social environments. Facial recognition is really important in daily life. Seeing faces in unusual objects, e.g., a face in a cloud, is called face pareidolia. Although more evidence points to a greater role of genetic factors in ASD, neuropsychological tests have an important role in diagnosing ASD. The aim of the study was to investigate how face perception is processed in children with autism using a new digital test that consists of faces and pareidolia images. Twenty typically developing (TD) children (8 male, 12 female) between 6 and 16 years of age and 21 children with ASD (14 male, 7 female) between 6 and 14 years of age were included in the study. A new neuropsychological test called the digital pareidolia test was administered to the participants. The study consisted of 2 stages: a face condition and a pareidolia condition. Our results showed that children with autism (n = 21) were less successful in identifying both face and pareidolia images, and were slower to react in both conditions than children from the TD group. Both children with ASD and the TD group reacted faster to face images than pareidolia images. The findings in this study are in agreement with atypical and different face perceptions in autism which cause social difficulties. We demonstrated that the digital face and pareidolia test has considerable potential for use as a neuropsychological test that can specify the diagnosis and progression of autism in subclinical areas. Pareidolia faces and real faces are processed in a common way.

Driving and forgetting: Why what we remember isn’t what occured

It is a commonly held belief that our memories are good, that we can recall all the things that we saw and that happened to us. On our drive to or from work we can describe with confidence the route we took and what the traffic was like. But driving is very proceduralised, and our memory for what happened most recently is often based on what we usually see, a pastiche of events that may have happened, or not. This experiment set out to extend our understanding of memory reliability by testing people’s recollections about a recent drive. Over two drives participants traversed familiar roads in the University of Waikato driving simulator. Participants took three memory tests about what they saw during the drives. The first one was after a control drive, the second one after a drive in which the participant called out interesting, unusual, and hazardous objects (a detection task), and a third memory test was 48 h after completing the second drive. The results of the memory test showed a typicality effect, with drivers’ memories being almost twice as accurate for very atypical events they encountered. There was also a delayed recognition effect, with recall 48 h after experiencing lower rates of recognition than either immediate condition. The rates of false alarms were over 50% for the delayed recognition of very typical and medium items. Immediate memories of very typical items by the control group were also high (nearly 40%). The act of looking for something while driving (the detection task) had the effect of reducing the false alarm rate for all items. The rate of false alarms was very low for very atypical items under all tasks. The results are interpreted as being of importance for those interested in memory and attention, as a means of determining what drivers attend to, and what they do not.

Антропоморфные изображения периода Яз-I

Two groups of unusual objects from contexts of the Early Iron Age Yaz-I period at the site of Maydatepa, southern Uzbekistan, are presented. The first group is interpreted as abstract anthropomorphic figurines. They are small rod-shaped clay objects (fired and unfired), with plastic noses and impressed eyes, sometimes with sparse additional ornaments. The second group is a collective find (and one separate individual object) from a closed pit context from the same site. The collective find consists of five, partly fragmentary, stele-like roughly hewn limestone objects, some of which have a marked off head. In view of the abstract clay objects, these small stelae can also be interpreted as anthropomorphic representations. The individual stone object is a stalagmite, which was brought intentionally to the site. Because of the natural grooved erosion at the top, which may represent hair, this object is included here. The entire collection contradicts the presumed lack of figurative art in the Early Iron Age Yaz-I communities, but it is also radically different from the preceding art of the Bronze Age.