The theory describes four physical zones (or territories) defined by growing distances around each person, as can be seen in Figure 3 (top left). With those hidden unwritten rules for spaces around a person, only socially close people are welcome within the intimate zone, while generally close people can enter the personal zone, followed by generally familiar people who are allowed in the social space. Otherwise, general public are only permitted within the public space. The concept of group proxemics has been investigated in literature with most attention being paid to detailing the classical proxemics theory. For instance, the authors of [14] explored proxemics and their impact on shape of group formation, the authors of [2] explored proxemics dispersion as average distances people maintain between each other as they walk in group, and in [18], [19] focus was given to studying the effect of proxemics on crowd and its traffic flow dynamics. Within robot-human interactions, the authors of [20]-[22] studied appropriate (safety, comfort, acceptability, etc) distance robots are expected to maintain from people (as individuals). It could be noticed that proxemics are structured around interactions between individuals and details are specified in terms of social relationships between them. In what follows, we explore the situation when an individual is part of a bigger and more complex social entity such as a group. We study the nature of such interactions and and explore associated proxemics.

Germany's Munich airport has resumed operations after drone sightings led to the cancellation of 17 flights, the diversion of 15 others and the stranding of some 3,000 passengers. Flights had restarted by early Friday, with flight tracking websites showing planes departing the airport at about 5:50am (03:50 GMT). At least 19 Lufthansa flights were affected, either cancelled or re-routed, because of the airport suspension, the spokesperson added. Earlier, the airport said that drone sightings were first reported by German air traffic control at 10:18pm local time [20:18 GMT] on Thursday, leading initially to a restriction on flights, which was then upgraded to a full suspension. Germany's DPA news agency said police reported that several people had seen a drone near the airport, with later sightings of a drone over the airport grounds.

A.1 List of Neural T opic Modeling Works used in our Meta-Analysis Corpus statistics are in Table 7. Document processing - We do not process documents with fewer than 25 whitespace-separated tokens. Following processing (e.g., stopword removal), we remove documents with fewer than The vocabulary is created from the training data. Stop-words are retained if they are contained within detected noun entities (e.g., "The United States of America" united_states_of_america). - We filter out tokens with two or fewer characters. Standard rules-of-thumb for vocabulary pruning, like removing terms that appear in fewer than 0.5% of To keep vocabulary sizes roughly consistent across datasets, we set the minimum document-frequency for terms as a (power) function of the total corpus size. We use gensim ( ˇ Reh u ˇ rek and Sojka, 2010) as a Python wrapper for running Mallet.

Several problems such as network intrusion, community detection, and disease outbreak can be described by observations attributed to nodes or edges of a graph. In these applications presence of intrusion, community or disease outbreak is characterized by novel observations on some unknown connected subgraph. These problems can be formulated in terms of optimization of suitable objectives on connected subgraphs, a problem which is generally computationally difficult. We overcome the combinatorics of connectivity by embedding connected subgraphs into linear matrix inequalities (LMI). Computationally efficient tests are then realized by optimizing convex objective functions subject to these LMI constraints. We prove, by means of a novel Euclidean embedding argument, that our tests are minimax optimal for exponential family of distributions on 1-D and 2-D lattices. We show that internal conductance of the connected subgraph family plays a fundamental role in characterizing detectability.

A group of researchers covertly ran a months-long "unauthorized" experiment in one of Reddit's most popular communities using AI-generated comments to test the persuasiveness of large language models. The experiment, which was revealed over the weekend by moderators of r/changemyview, is described by Reddit mods as "psychological manipulation" of unsuspecting users. "The CMV Mod Team needs to inform the CMV community about an unauthorized experiment conducted by researchers from the University of Zurich on CMV users," the subreddit's moderators wrote in a lengthy post notifying Redditors about the research. "This experiment deployed AI-generated comments to study how AI could be used to change views." The researchers used LLMs to create comments in response to posts on r/changemyview, a subreddit where Reddit users post (often controversial or provocative) opinions and request debate from other users.

An application of software known as an Intrusion Detection System (IDS) employs machine algorithms to identify network intrusions. Selective logging, safeguarding privacy, reputation-based defense against numerous attacks, and dynamic response to threats are a few of the problems that intrusion identification is used to solve. The biological system known as IoT has seen a rapid increase in high dimensionality and information traffic. Self-protective mechanisms like intrusion detection systems (IDSs) are essential for defending against a variety of attacks. On the other hand, the functional and physical diversity of IoT IDS systems causes significant issues. These attributes make it troublesome and unrealistic to completely use all IoT elements and properties for IDS self-security. For peculiarity-based IDS, this study proposes and implements a novel component selection and extraction strategy (our strategy). A five-ML algorithm model-based IDS for machine learning-based networks with proper hyperparamater tuning is presented in this paper by examining how the most popular feature selection methods and classifiers are combined, such as K-Nearest Neighbors (KNN) Classifier, Decision Tree (DT) Classifier, Random Forest (RF) Classifier, Gradient Boosting Classifier, and Ada Boost Classifier. The Random Forest (RF) classifier had the highest accuracy of 99.39%. The K-Nearest Neighbor (KNN) classifier exhibited the lowest performance among the evaluated models, achieving an accuracy of 94.84%. This study's models have a significantly higher performance rate than those used in previous studies, indicating that they are more reliable.

Seawater intrusion into coastal aquifers poses a significant threat to groundwater resources, especially with rising sea levels due to climate change. Accurate modeling and uncertainty quantification of this process are crucial but are often hindered by the high computational costs of traditional numerical simulations. In this work, we develop GeoFUSE, a novel deep-learning-based surrogate framework that integrates the U-Net Fourier Neural Operator (U-FNO) with Principal Component Analysis (PCA) and Ensemble Smoother with Multiple Data Assimilation (ESMDA). GeoFUSE enables fast and efficient simulation of seawater intrusion while significantly reducing uncertainty in model predictions. We apply GeoFUSE to a 2D cross-section of the Beaver Creek tidal stream-floodplain system in Washington State. Using 1,500 geological realizations, we train the U-FNO surrogate model to approximate salinity distribution and accumulation. The U-FNO model successfully reduces the computational time from hours (using PFLOTRAN simulations) to seconds, achieving a speedup of approximately 360,000 times while maintaining high accuracy. By integrating measurement data from monitoring wells, the framework significantly reduces geological uncertainty and improves the predictive accuracy of the salinity distribution over a 20-year period. Our results demonstrate that GeoFUSE improves computational efficiency and provides a robust tool for real-time uncertainty quantification and decision making in groundwater management. Future work will extend GeoFUSE to 3D models and incorporate additional factors such as sea-level rise and extreme weather events, making it applicable to a broader range of coastal and subsurface flow systems.

Generative AI's rapid advancement sparks interest in its cognitive abilities, especially given its capacity for tasks like language understanding and code generation. This study explores how several recent GenAI models perform on the Clock Drawing Test (CDT), a neuropsychological assessment of visuospatial planning and organization. While models create clock-like drawings, they struggle with accurate time representation, showing deficits similar to mild-severe cognitive impairment (Wechsler, 2009). Errors include numerical sequencing issues, incorrect clock times, and irrelevant additions, despite accurate rendering of clock features. Only GPT 4 Turbo and Gemini Pro 1.5 produced the correct time, scoring like healthy individuals (4/4). A follow-up clock-reading test revealed only Sonnet 3.5 succeeded, suggesting drawing deficits stem from difficulty with numerical concepts. These findings may reflect weaknesses in visual-spatial understanding, working memory, or calculation, highlighting strengths in learned knowledge but weaknesses in reasoning. Comparing human and machine performance is crucial for understanding AI's cognitive capabilities and guiding development toward human-like cognitive functions.

Supermarket thefts could be a thing of the past thanks to an'ominous' security robot that looks straight out of Doctor Who. Tesco has confirmed it is using the Dalek-like machines, which detect the presence of thieves thanks to 360-degree cameras. Placed near the entrance of Tesco stores in the small hours, the bot shouts at any intruders in an'angry Northern Irish accent' and sends alerts the authorities. It's hoped the robot does a better job than human watchmen because it can't fall asleep on the job, as long as it's been sufficiently charged. However, at 100,000 per month to hire, the robot doesn't come cheap.