Over the past two decades, researchers have made significant steps in simulating agent-based human crowds, yet most efforts remain focused on low-level tasks such as collision avoidance, path following, and flocking. Realistic simulations, however, require modeling high-level behaviors that emerge from agents interacting with each other and with their environment over time. We introduce Generative Crowds (Gen-C), a generative framework that produces crowd scenarios capturing agent-agent and agent-environment interactions, shaping coherent high-level crowd plans. To avoid the labor-intensive process of collecting and annotating real crowd video data, we leverage large language models (LLMs) to bootstrap synthetic datasets of crowd scenarios. We propose a time-expanded graph representation, encoding actions, interactions, and spatial context. Gen-C employs a dual Variational Graph Autoencoder (VGAE) architecture that jointly learns connectivity patterns and node features conditioned on textual and structural signals, overcoming the limitations of direct LLM generation to enable scalable, environment-aware multi-agent crowd simulations. We demonstrate the effectiveness of Gen-C on scenarios with diverse behaviors such as a University Campus and a Train Station, showing that it generates heterogeneous crowds, coherent interactions, and high-level decision-making patterns consistent with real-world crowd dynamics.

One key failure case was answers in the form of "the answer must be something that is ." Q: What might someone get from learning about science? Answer Choices: (a) headache (b) see things differently (c) increased knowledge (d) accidents (e) appreciation of nature A: The answer must be something that someone would get from learning about science. Learning about science would increase knowledge. Therefore, the answer is increased knowledge (c). These rationales, while perhaps useful to the model, read to us as opaque and unexplanatory. Sometimes the model will imply the answer that it has chosen in its question. Q: The christian rock band was on a large stage, the singer's foot got caught on a wire and he had a small what? Answer Choices: (a) commit sin (b) fail (c) disobey (d) stumble (e) boo boo A: The answer must be something that would cause the singer to stumble. The singer's foot got caught on a wire, so he would stumble.

A social media user filmed what appeared to be drones flying over the Fairfield train station. Several drones were allegedly spotted in the skies above a Connecticut suburb on Thursday night, adding to recent drone sightings that have perplexed locals and raised questions about possible national security and public safety concerns. A social media user on X posted videos of possible drones in Fairfield, 55 miles northeast of New York City. Drones hovering over New Jersey and near Staten Island, New York in recent weeks have raised concerns amid a lack of clarity over their origin. A social media user said she filmed several drones hovering over Fairfield, Connecticut on Thursday night.

Multimodal Large Language Models (MLLMs) are commonly evaluated using costly annotated multimodal benchmarks. However, these benchmarks often struggle to keep pace with the rapidly advancing requirements of MLLM evaluation. We propose GenCeption, a novel and annotation-free MLLM evaluation framework that merely requires unimodal data to assess inter-modality semantic coherence and inversely reflects the models' inclination to hallucinate. Analogous to the popular DrawCeption game, GenCeption initiates with a non-textual sample and undergoes a series of iterative description and generation steps. Semantic drift across iterations is quantified using the GC@T metric. Our empirical findings validate GenCeption's efficacy, showing strong correlations with popular MLLM benchmarking results. GenCeption may be extended to mitigate training data contamination by utilizing ubiquitous, previously unseen unimodal data.

Successful Machine Learning based Named Entity Recognition models could fail on texts from some special domains, for instance, Chinese addresses and e-commerce titles, where requires adequate background knowledge. Such texts are also difficult for human annotators. In fact, we can obtain some potentially helpful information from correlated texts, which have some common entities, to help the text understanding. Then, one can easily reason out the correct answer by referencing correlated samples. In this paper, we suggest enhancing NER models with correlated samples. We draw correlated samples by the sparse BM25 retriever from large-scale in-domain unlabeled data. To explicitly simulate the human reasoning process, we perform a training-free entity type calibrating by majority voting. To capture correlation features in the training stage, we suggest to model correlated samples by the transformer-based multi-instance cross-encoder. Empirical results on datasets of the above two domains show the efficacy of our methods.

An autonomous shuttle bus is currently being tested at Flinders University and has now entered the second stage of its trial. Dubbed the "Flinders University Express Shuttle" (FLEX), the bus can carry 11 seated passengers. It operates on a 2.8km route and is described as a "test bed" for the future of autonomous vehicles in South Australia. In what continues to be one of Australia's only public autonomous vehicle testing programs, the Flinders University autonomous shuttle bus travels around the Tonsely innovation district, between the train station, the residential village, the university and the TAFE. It's a walking distance route, but keep in mind that it's only a trial at the moment.

Today's urban life is out of imagination without the presence of elevators and the elevator controller algorithm has been well studied by different techniques including reinforcement learning [1]. A glance over the references gave the impression that the majority of studies has focused on elevators installed in high-rise buildings while those in train stations are barely discussed. Elevators in train stations, however, deserve their own attention because of their obvious difference from systems in buildings. A good example is the Gare de Lyon in Paris, a station with 2 underground floors on which you find 2 different train lines' platforms respectively. From my personal experience, it usually takes quite a while to get to floor -2 from floor -1 for a train change with my baby stroller by elevator.

Of course, this is not the first global epidemic and it will not be the last. Humans have experienced four global epidemics throughout history, if they are Black Death (14th Century), Spanish influenza (1918), The global epidemics of HIV / AIDS (20th century), and SARS (2002–2003) have affected many parts of the world. Covid-19 stands out because of such high international tours. It spread rapidly around the world at an unexpected time and as a result, many countries have completely shut it down. At the time of writing(07/06/2021), more than 173 million Covid-19 patients have been reported worldwide and more than 3.7 million have died. Covid-19 is also different from previous epidemics.

The power of AI to solve large-scale problems perhaps met no greater test than COVID-19. Around the world, medical experts have leveraged AI to drastically reduce the time scale of finding and developing a new vaccine to treat the pandemic. "One of the time-consuming pieces is really around the analysis of billions of different molecules and how those might be used to do chemical binding to the target protein that we're looking at," Dan Drapeau, an artificial intelligence (AI) expert and head of technology at Blue Fountain Media, told TechRepublic in August 2020. "Humans can't possibly do that. By November, two vaccines in the U.S. with 95% or greater efficacy were making their way through emergency approval processes. If the approval moves forward, the finding and development of a vaccine for COVID-19 will beat average vaccine development timelines by years. "The development of vaccines can take years," explains the Mayo Clinic website. "This is especially true when the vaccines ...