Viral News

Anomaly detection for the identification of volcanic unrest in satellite imagery
30 May

Anomaly detection for the identification of volcanic unrest in satellite imagery

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[Submitted on 28 May 2024] View a PDF of the paper titled Anomaly detection for the identification of volcanic unrest in satellite imagery, by Robert Gabriel Popescu and 2 other authors View PDF HTML (experimental) Abstract:Satellite images have the potential to detect volcanic deformation prior to eruptions, but while a vast number of images are routinely acquired, only a small percentage contain volcanic deformation events. Manual inspection could miss these anomalies, and an automatic system modelled with supervised learning requires suitably labelled datasets. To tackle these issues, this paper explores the use of unsupervised deep learning on satellite data for…
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Learning diverse attacks on large language models for robust red-teaming and safety tuning
30 May

Learning diverse attacks on large language models for robust red-teaming and safety tuning

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arXiv:2405.18540v1 Announce Type: new Abstract: Red-teaming, or identifying prompts that elicit harmful responses, is a critical step in ensuring the safe and responsible deployment of large language models (LLMs). Developing effective protection against many modes of attack prompts requires discovering diverse attacks. Automated red-teaming typically uses reinforcement learning to fine-tune an attacker language model to generate prompts that elicit undesirable responses from a target LLM, as measured, for example, by an auxiliary toxicity classifier. We show that even with explicit regularization to favor novelty and diversity, existing approaches suffer from mode collapse or fail to generate effective attacks. As a…
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The Unified Balance Theory of Second-Moment Exponential Scaling Optimizers in Visual Tasks
30 May

The Unified Balance Theory of Second-Moment Exponential Scaling Optimizers in Visual Tasks

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arXiv:2405.18498v1 Announce Type: new Abstract: We have identified a potential method for unifying first-order optimizers through the use of variable Second-Moment Exponential Scaling(SMES). We begin with back propagation, addressing classic phenomena such as gradient vanishing and explosion, as well as issues related to dataset sparsity, and introduce the theory of balance in optimization. Through this theory, we suggest that SGD and adaptive optimizers can be unified under a broader inference, employing variable moving exponential scaling to achieve a balanced approach within a generalized formula for first-order optimizers. We conducted tests on some classic datasets and networks to confirm the impact…
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Towards Open Domain Text-Driven Synthesis of Multi-Person Motions
30 May

Towards Open Domain Text-Driven Synthesis of Multi-Person Motions

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arXiv:2405.18483v1 Announce Type: new Abstract: This work aims to generate natural and diverse group motions of multiple humans from textual descriptions. While single-person text-to-motion generation is extensively studied, it remains challenging to synthesize motions for more than one or two subjects from in-the-wild prompts, mainly due to the lack of available datasets. In this work, we curate human pose and motion datasets by estimating pose information from large-scale image and video datasets. Our models use a transformer-based diffusion framework that accommodates multiple datasets with any number of subjects or frames. Experiments explore both generation of multi-person static poses and generation…
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LLMs and Memorization: On Quality and Specificity of Copyright Compliance
30 May

LLMs and Memorization: On Quality and Specificity of Copyright Compliance

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arXiv:2405.18492v1 Announce Type: new Abstract: Memorization in large language models (LLMs) is a growing concern. LLMs have been shown to easily reproduce parts of their training data, including copyrighted work. This is an important problem to solve, as it may violate existing copyright laws as well as the European AI Act. In this work, we propose a systematic analysis to quantify the extent of potential copyright infringements in LLMs using European law as an example. Unlike previous work, we evaluate instruction-finetuned models in a realistic end-user scenario. Our analysis builds on a proposed threshold of 160 characters, which we borrow…
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A Comparison of Past, Present, and Future Payroll Systems
30 May

A Comparison of Past, Present, and Future Payroll Systems

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Payroll systems have made remarkable progress from their inception to the digital era. In this article, we explore the evolution and revolutionary impact of technology on payroll management, helping readers better understand this critical business operation's future. Over the years, payroll systems have evolved tremendously. Payroll management has evolved into a sophisticated digital system that automates what used to be a manual process. To understand how emerging trends will shape the future of payroll systems, we'll look at their past, current state in the digital era, and potential. Taking a look back Starting at the beginning, let's look back at…
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Asymmetrical estimator for training grey-box deep photonic neural networks
30 May

Asymmetrical estimator for training grey-box deep photonic neural networks

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arXiv:2405.18458v1 Announce Type: new Abstract: Physical neural networks (PNNs) are emerging paradigms for neural network acceleration due to their high-bandwidth, in-propagation analogue processing. Despite the advantages of PNN for inference, training remains a challenge. The imperfect information of the physical transformation means the failure of conventional gradient-based updates from backpropagation (BP). Here, we present the asymmetrical training (AT) method, which treats the PNN structure as a grey box. AT performs training while only knowing the last layer output and neuron topological connectivity of a deep neural network structure, not requiring information about the physical control-transformation mapping. We experimentally demonstrated the…
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GHOST: Grounded Human Motion Generation with Open Vocabulary Scene-and-Text Contexts
30 May

GHOST: Grounded Human Motion Generation with Open Vocabulary Scene-and-Text Contexts

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[Submitted on 8 Apr 2024] View a PDF of the paper titled GHOST: Grounded Human Motion Generation with Open Vocabulary Scene-and-Text Contexts, by Zolt'an 'A. Milacski and 4 other authors View PDF Abstract:The connection between our 3D surroundings and the descriptive language that characterizes them would be well-suited for localizing and generating human motion in context but for one problem. The complexity introduced by multiple modalities makes capturing this connection challenging with a fixed set of descriptors. Specifically, closed vocabulary scene encoders, which require learning text-scene associations from scratch, have been favored in the literature, often resulting in inaccurate motion…
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Multi-objective Representation for Numbers in Clinical Narratives Using CamemBERT-bio
30 May

Multi-objective Representation for Numbers in Clinical Narratives Using CamemBERT-bio

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arXiv:2405.18448v1 Announce Type: new Abstract: This research aims to classify numerical values extracted from medical documents across seven distinct physiological categories, employing CamemBERT-bio. Previous studies suggested that transformer-based models might not perform as well as traditional NLP models in such tasks. To enhance CamemBERT-bio's performances, we introduce two main innovations: integrating keyword embeddings into the model and adopting a number-agnostic strategy by excluding all numerical data from the text. The implementation of label embedding techniques refines the attention mechanisms, while the technique of using a `numerical-blind' dataset aims to bolster context-centric learning. Another key component of our research is determining…
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No-code ETL for integration: best practices, trends and top tools
30 May

No-code ETL for integration: best practices, trends and top tools

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High-quality data integration is the cornerstone of informed decision-making.  Quality data is the bedrock of informed decision-making. Without it, enterprises fall prey to erroneous information, ultimately impacting their bottom line. In fact, in a groundbreaking 2018 report, Gartner claimed that businesses could be clocking losses of 15 million USD every year only because of poor data integration infrastructure. Exactly why no-code ETL tools have become increasingly popular for their ease of ability to empower non-tech users without compromising on data quality. They enable businesses to reduce traditional ETL costs and ensure timely data feeds through user-friendly automation.  In this article, we…
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