Homelessness in the United States has surged to levels unseen since the Great Depression. However, existing methods for monitoring it, such as point-in-time (PIT) counts, have limitations in terms of frequency, consistency, and spatial detail. This study proposes a new approach using publicly available, crowdsourced data, specifically 311 Service Calls and street-level imagery, to track and forecast homeless tent trends in San Francisco. Our predictive model captures fine-grained daily and neighborhood-level variations, uncovering patterns that traditional counts often overlook, such as rapid fluctuations during the COVID-19 pandemic and spatial shifts in tent locations over time. By providing more timely, localized, and cost-effective information, this approach serves as a valuable tool for guiding policy responses and evaluating interventions aimed at reducing unsheltered homelessness.

In computer vision tasks, the ability to focus on relevant regions within an image is crucial for improving model performance, particularly when key features are small, subtle, or spatially dispersed. Convolutional neural networks (CNNs) typically treat all regions of an image equally, which can lead to inefficient feature extraction. To address this challenge, I have introduced Vision Eagle Attention, a novel attention mechanism that enhances visual feature extraction using convolutional spatial attention. The model applies convolution to capture local spatial features and generates an attention map that selectively emphasizes the most informative regions of the image. This attention mechanism enables the model to focus on discriminative features while suppressing irrelevant background information. I have integrated Vision Eagle Attention into a lightweight ResNet-18 architecture, demonstrating that this combination results in an efficient and powerful model. I have evaluated the performance of the proposed model on three widely used benchmark datasets: FashionMNIST, Intel Image Classification, and OracleMNIST, with a primary focus on image classification. Experimental results show that the proposed approach improves classification accuracy. Additionally, this method has the potential to be extended to other vision tasks, such as object detection, segmentation, and visual tracking, offering a computationally efficient solution for a wide range of vision-based applications. Code is available at: https://github.com/MahmudulHasan11085/Vision-Eagle-Attention.git

In the development of Large Language Models (LLMs), considerable attention has been given to the quality of training datasets. However, the role of tokenizers in the LLM training pipeline, particularly for multilingual models, has received less focus. The quality of tokenization can significantly impact a model's ability to handle diverse languages effectively. We introduce Qtok, a tool designed to assess tokenizer quality with a specific emphasis on their performance in multilingual contexts. Our research proposes a set of metrics for evaluating tokenizer quality, including measures of language coverage, token completeness, and distribution across languages and linguistic categories. Qtok applies these metrics to evaluate 13 distinct tokenizers from 58 publicly available models, analyzing their output across different linguistic contexts. Our analysis revealed significant variations in token distribution across languages and categories, highlighting potential biases and areas for improvement in current tokenization strategies. This research contributes to the field of tokenizer evaluation within multilingual LLM development by providing a systematic approach to assessing tokenizer quality. Our findings highlight the critical role of tokenization in multilingual LLM capability. The Qtok tool and our analysis methodology offer practical means for researchers to evaluate and improve tokenization strategies for multilingual applications. We offer a method to compare tokenizer quality across these metrics, which may be useful when selecting or adjusting tokenizers for specific multilingual LLM applications.

Advanced artificial intelligence has identified thousands of possible "gravitational lenses" -- warps in space-time predicted by Albert Einstein -- promising to enhance our understanding of dark matter and the evolution of galaxies. Einstein realized that mass warps space, and massive galaxies and galaxy clusters can distort space around them to such a degree that they form a cosmic lens, bending and magnifying the path of light from more distant galaxies through that warped space. Gravitational lenses are important tools for cosmologists. They can magnify the light of distant galaxies that are too faint to be otherwise seen in detail, or reveal where invisible dark matter is warping space. However, astronomers had only about a hundred good gravitational lenses to use.

Amazon's Echo Frames are compatible with most prescription lenses, but they only come with clear lenses out of the box -- until now, anyway. The retail giant has introduced three new options to choose from if you're looking to buy a pair of the Alexa-powered eyewear. You can get it with polarized blue mirror sunglass lenses starting today, and starting on June 9th, you can get a pair with blue-light-filtering lenses and polarized classic sunglass lenses. The Echo Frames have open-ear audio near your temples, so you can listen to music, audiobooks and podcasts, as well as take calls without blocking the world around you. Since it also gives you hands-free access to Alexa, you can simply issue voice commands to control it, such as "Alexa, play my followed podcasts on Amazon Music" or "Alexa, resume my audiobook."

Smartphones with multiple cameras and blurry photos could become a thing of the past as engineers have created a flat lens that doesn't need focusing. The technology -- which uses a single lens covered in tiny patterns that is just one-thousandth of an inch thick -- was developed by experts from the University of Utah. Not only could the lens lead to better and more compact cameras, but it could also find applications in biomedical imaging and remote sensing. Smartphones with multiple cameras and blurry photos could become a thing of the past as engineers have created a flat lens that doesn't need focusing, pictured'Our flat lenses can drastically reduce the weight, complexity and cost of cameras and other imaging systems, while increasing their functionality,' said research team leader and engineer Rajesh Menon, of the University of Utah. 'Such optics could enable thinner smartphone cameras, improved and smaller cameras for biomedical imaging such as endoscopy, and more compact cameras for automobiles.'

We live in an age where payments to other individuals or companies can be made with a single push of a button on a smartphone, where purchases can be made in an instant, and where loans can be applied for without meeting a person face-to-face. In addition, cars drive themselves, robots mimick human movements, and privately funded rockets soar into space while sending their boosters back to earth to land themselves for reuse. While modern technologies, data, advanced analytics and automation are transforming every facet of daily life, most organizations and executives are not fully prepared for what these massive changes mean to the role of leaders and for the future of work. Employees at all levels of the organization fear what these lightning fast changes will mean for their occupations. Will these jobs simply be modified based on new applications of data and insights and new digital tools?

Economist John Kenneth Galbraith once said, "Faced with the choice between changing one's mind and proving that there is no need to do so, almost everyone gets busy on the proof." Another famous man (though not so much as an economist), George Carlin said, "I put a dollar in a change machine. In periods of disruptive change -- and we're smack at the beginning of one right now, driven by dramatic improvements in machine learning and artificial intelligence --one of the hardest tasks to accomplish is to move past the lip service to the disruption that's taking place, to actually change the way you look at the world. Within the content space, many of us are getting pretty good at talking about disruption. But at its core, our change in vocabulary from "enterprise content management" to "content services" is just that: giving lip service to change.

When Apple first announced the iPhone X, it also announced that special Snapchat Lenses will be made available for the flagship smartphone in the future. Today, Snapchat is finally releasing the Lenses, which are capable of sticking to users' faces more accurately. The three iPhone X Snapchat Lenses are the Mardi Gras mask, a Day of the Dead Skull and the gold-plated eye cover, according to The Verge. The Lenses give off a more realistic look when taking photos or videos in Snapchat. The Lenses were first shown off by Apple's SVP of software engineering, Craig Federighi, back in September.