We provide the detailed of the PQ retrieval performance trends of Glint360K trained using the CosFace loss in Tab. 1.

Due to its promising performance, deep hashing has become a prevalent method for approximate nearest neighbors search (ANNs). However, most of current deep hashing methods are validated on relatively small-scale datasets, leaving potential threats when are applied to large-scale real-world scenarios. Specifically, they can be constrained either by the computational cost due to the large number of training categories and samples, or unsatisfactory accuracy. To tackle those issues, we propose a novel deep hashing framework based on product quantization (PQ). It uses a softmax-based differentiable PQ branch to learn a set of predefined PQ codes of the classes. Our method is easy to implement, does not involve large-scale matrix operations, and learns highly discriminate compact codes.

Resource limitations often constrain the parameter counts of Large Language Models (LLMs), hindering their performance. While existing methods employ parameter sharing to reuse the same parameter set under fixed budgets, such approaches typically force each layer to assume multiple roles with a predetermined number of iterations, restricting efficiency and adaptability. In this work, we propose the Zero Token Transformer (ZTT), which features a head-tail decoupled parameter cycling method. We disentangle the first (head) and last (tail) layers from parameter cycling and iteratively refine only the intermediate layers. Furthermore, we introduce a Zero-Token Mechanism, an internal architectural component rather than an input token, to guide layer-specific computation. At each cycle, the model retrieves a zero token (with trainable key values) from a Zero-Token Pool, integrating it alongside regular tokens in the attention mechanism. The corresponding attention scores not only reflect each layer's computational importance but also enable dynamic early exits without sacrificing overall model accuracy. Our approach achieves superior performance under tight parameter budgets, effectively reduces computational overhead via early exits, and can be readily applied to fine-tune existing pre-trained models for enhanced efficiency and adaptability.

Due to its promising performance, deep hashing has become a prevalent method for approximate nearest neighbors search (ANNs). However, most of current deep hashing methods are validated on relatively small-scale datasets, leaving potential threats when are applied to large-scale real-world scenarios. Specifically, they can be constrained either by the computational cost due to the large number of training categories and samples, or unsatisfactory accuracy. To tackle those issues, we propose a novel deep hashing framework based on product quantization (PQ). It uses a softmax-based differentiable PQ branch to learn a set of predefined PQ codes of the classes.

We all have expectations from our laptops. We want them to work snappily and let us get through whatever it is we're trying to get through, be it a big work projector or the next level of our favorite video game. Sadly, there's plenty of occasion for our laptops to let us down, running slowly and bringing all the smooth productivity to a grinding halt. Wrestling that performance back doesn't have to be hard, and there are quite a few things you can attempt to boost your speeds for free. If you're trying to get your laptop to run smoother and feel a little bit more like the day you first bought it (or you just bought one and don't think it's running as fast as it could), here are some free things to try in order to give it a boost.

People in Texas sounded off on AI job displacement, with half of people who spoke to Fox News convinced that the tech will rob them of work. The growing use of artificial intelligence will likely lead to biased and discriminatory outcomes for minorities and disabled people, several groups warned the federal government this week. The National Artificial intelligence Advisory Committee, an interagency group led by the Commerce Department, held a public hearing online Tuesday aimed at informing policymakers about how the government can best manage the use of AI. Panelists were told by most of the witnesses that bias and discrimination are the biggest fears for the people they represent. Patrice Willoughby, vice president of policy and legislative affairs at the NAACP, told panelists that technology has already been used as a means to disenfranchise and mislead voters, and said her group worries about AI for the same reason.

Active Learning (AL) is a human-in-the-loop framework to interactively and adaptively label data instances, thereby enabling significant gains in model performance compared to random sampling. AL approaches function by selecting the hardest instances to label, often relying on notions of diversity and uncertainty. However, we believe that these current paradigms of AL do not leverage the full potential of human interaction granted by automated label suggestions. Indeed, we show that for many classification tasks and datasets, most people verifying if an automatically suggested label is correct take $3\times$ to $4\times$ less time than they do changing an incorrect suggestion to the correct label (or labeling from scratch without any suggestion). Utilizing this result, we propose CLARIFIER (aCtive LeARnIng From tIEred haRdness), an Interactive Learning framework that admits more effective use of human interaction by leveraging the reduced cost of verification. By targeting the hard (uncertain) instances with existing AL methods, the intermediate instances with a novel label suggestion scheme using submodular mutual information functions on a per-class basis, and the easy (confident) instances with highest-confidence auto-labeling, CLARIFIER can improve over the performance of existing AL approaches on multiple datasets -- particularly on those that have a large number of classes -- by almost 1.5$\times$ to 2$\times$ in terms of relative labeling cost.

Large Language Models (LLMs) have emerged as a cornerstone of artificial intelligence research and development, revolutionizing how machines understand and process natural language. These models, based on advanced deep learning architectures, have become increasingly sophisticated, capable of generating human-like text, answering questions, summarizing content, and performing a plethora of other tasks. The remarkable growth in the capabilities of LLMs can be attributed to advancements in computational power, the availability of large-scale datasets, and the continuous refinement of algorithmic techniques. A key element in the success of LLMs is their use of transformer-based architectures, which employ self-attention mechanisms to capture contextual information across long text sequences. Transformers have demonstrated a remarkable ability to scale, enabling the development of larger models with billions of parameters.

In the words of J.R.R. Tolkien, "shortcuts make long delays." I get it, we live in an age of instant gratification, with Doordash and Grubhub meals on-demand, fast-paced social media and same-day Amazon Prime deliveries. But I've learned that in some cases, shortcuts are just not possible. Such is the case with comprehensive AI implementations; you cannot shortcut success. Operationalizing AI at scale mandates that your full suite of data–structured, unstructured and semi-structured get organized and architected in a way that makes it useable, readily accessible and secure.

The healthcare industry has undergone a significant digital transformation in recent years, which has given rise to digital healthcare ecosystems that have the potential to revolutionise patient care and provider services. However, to realise the full benefits of these ecosystems, several critical factors must be addressed to ensure their integration and effectiveness. At the micro-level, digital technologies such as data analytics, machine learning, and artificial intelligence can offer valuable insights to digital healthcare ecosystems. To achieve successful integration, the ecosystems must identify their data needs, have access to relevant data sources, invest in the right technology tools, and establish clear governance structures that align with strategic objectives. At the meso-level, supply chain collaboration is essential to streamline operations, optimise efficiency, and improve cost-effectiveness.