Pruning encompasses a range of techniques aimed at increasing the sparsity of neural networks (NNs). These techniques can generally be framed as minimizing a loss function subject to an $L_0$-norm constraint. This paper introduces CoNNect, a novel differentiable regularizer for sparse NN training that ensures connectivity between input and output layers. CoNNect integrates with established pruning strategies and supports both structured and unstructured pruning. We proof that CoNNect approximates $L_0$-regularization, guaranteeing maximally connected network structures while avoiding issues like layer collapse. Numerical experiments demonstrate that CoNNect improves classical pruning strategies and enhances state-of-the-art one-shot pruners, such as DepGraph and LLM-pruner.

Enhancing Campus Mobility: Achievements and Challenges of Autonomous Shuttle "Snow Lion" In recent years, the rapid evolution of autonomous vehicles (AVs) has reshaped global transportation systems. Leveraging the accomplishments of our earlier endeavor, particularly "Hercules" [1], an autonomous logistics vehicle for transporting goods, we introduce "Snow Lion", an autonomous shuttle vehicle meticulously designed to transform on-campus transportation, providing a safe and efficient mobility solution for students, faculty, and visitors. The main aim of this research is to improve campus mobility through a dependable, efficient, and eco-friendly autonomous transportation solution tailored to meet the diverse requirements of a university setting. This initiative significantly differs from the experiences of "Hercules" [1], as the campus environment presents a notable contrast to the structured environments of highways and urban streets. Emphasizing both security and passenger comfort, the primary focus is Figure 1: This figure illustrates the operational scenario of our on passenger transportation. Achieving this goal involves a autonomous shuttle during its service period at The Hong detailed examination of complex system designs that integrate Kong University of Science and Technology (Guangzhou) trajectory planning adjustments, prioritizing pedestrian safety (referred to as HKUST (GZ)).

Read on to learn about the best AI-chatbot options for your business! We all expect a quick answer or result for almost everything today, especially if it's online. Unlike the pre-internet age, customers of the 21st century have many options. They also have an increasing desire for quick and efficient service round the clock, and brands are doing their best to adapt to their needs. AI Chatbots (Artificial Intelligence) have evolved over the same time to meet this demand.

１．Connect the Dots:差分プライバシーのより良いプライバシーコスト推定(1/2)まとめ・差分プライバシーはプライバシーを保証した上で分析や機械学習を可能にする・差分プライバシーでは個々のアルゴリズムを合成した際の特性が重要と

Do you know that, for the past 5 years, 'Data Scientist' has consistently ranked among the top 3 job professions in the US market? Having Technical skills and knowledge is one of the best ways to get a hike in your career path. Keeping this in mind, many working professionals and students have started upskilling themselves. To upskill yourself, and expand your Data Tech knowledge, mark your calendars and benefit from the FREE DataHour sessions. Here, at Analytics Vidhya, we aim to build a future Data-Tech Community.

Do you have trouble hearing what people are saying on TV? There are many ways you can improve the audio if you know where to look. More people have turned on closed captions in recent years, reading along as they watch a movie or show. Most say they have trouble hearing dialogue. But even clean audio can be harder to hear now that TVs have become so thin.

In this paper, we present ExtremeBERT, a toolkit for accelerating and customizing BERT pretraining. Our goal is to provide an easy-to-use BERT pretraining toolkit for the research community and industry. Thus, the pretraining of popular language models on customized datasets is affordable with limited resources. Experiments show that, to achieve the same or better GLUE scores, the time cost of our toolkit is over $6\times$ times less for BERT Base and $9\times$ times less for BERT Large when compared with the original BERT paper. The documentation and code are released at https://github.com/extreme-bert/extreme-bert under the Apache-2.0 license.

In this talk were addressed some of the main modern challenges of geospatial data science grouped around five main aspects: (i) training data issues -- these include mismatches in standards when recording measurements, legal and technical issues that limit data use, high costs of producing new observational data; (ii) modeling issues -- these include point clustering and extrapolation problems, model overfitting, artifacts in input data, lack of historical data, lack of consistent global monitoring stations (Kulmala, 2018), limited availability and/or quality of global covariate layers; (iii) data distribution issues -- these include unusable file formats, high data volumes, incomplete or inaccurate metadata; (iv) usability issues -- these include incompleteness of data, unawareness of user communities and/or data limitations, data being irrelevant for decision making; and (v) governance issues -- these include closed data policies from (non-)governmental organizations, datasets not used by organizations, lack of baseline estimates, absence of strategies for updates of data products. OpenGeoHub, together with 21 partners have launched an ambitious new European Commission-funded Horizon Europe project called "Open-Earth-Monitor" which aims at tackling some of the bottlenecks in the uptake and effective usage of environmental data, both ground observations and measurements and EO data. They plan to build upon existing Open Source and Open Data projects and build a number of tools, good practice guidelines and datasets that can help cope with some of the modern challenges.

In this conversation with Brad Nickle, CEO at The Strickland Group, he talks about digital transformation and AI. Hear about data management, virtual interactions, automating processes, resource integration, and data security and control.

This project is definitely for you! Linguistic Project for #Icelandic Speakers How would you tell your virtual assistant to do these in your language? On this project, you will be reviewing sentences and choosing the best options/translations for your language, and judging naturalness for completed sentences. This is an AI Specialist project. Your task is to produce high-quality translations of user-facing content for our client's systems.