Fox News contributor Kellyanne Conway joined'Fox & Friends' to discuss her expectations for the CBS News Vice Presidential Debate between Tim JD Vance and Tim Walz and her Fox Nation interview with former President Trump. FIRST ON FOX – Rep. Brian Mast, R-Fla., is demanding that the Department of Veterans Affairs (VA) immediately fire the at least a dozen employees who reportedly improperly accessed the medical records of vice presidential candidates Sen. JD Vance of Ohio, and Minnesota Gov. Tim Walz this summer. Mast, chairman of Veterans For Trump, penned a letter urging VA Secretary Denis McDonough for a "swift response and action to prevent such egregious violations of privacy within the VA from occurring again." The Florida Republican is also calling for the FBI to get involved to investigate the possibility of foreign election interference. "I urge you to buck the employee union of the Department of Veterans Affairs (VA) and immediately fire the employees who were caught snooping into the private medical records of Ohio Sen. J.D. Vance and Minnesota Gov. Tim Walz – who are both running to be vice president. VA employees know this is not permitted," Mast wrote in a letter first obtained by Fox News Digital.

Alphabet's Google plans to invest 1 billion to build data centers in Thailand, joining global tech companies in adding cloud and artificial intelligence infrastructure in Southeast Asia. The company will add facilities in Bangkok and Chonburi, a province southeast of the capital. The outlay could help add 4 billion to Thailand's economy by 2029 and support 14,000 jobs annually over the next five years, Google said Monday, citing a Deloitte study. The investment was unveiled by Google and Paetongtarn Shinawatra, Thailand's recently appointed prime minister, underscoring the push by Southeast Asia's governments to attract foreign tech firms. Long seen as a tech hinterland, the region of about 675 million people is fast emerging as a growth opportunity for Apple, Microsoft, Nvidia and Amazon, which are spending billions of dollars to ramp up AI data centers from Thailand and Malaysia to Singapore and Indonesia.

The Pentagon has said the mass production and deployment of systems to detect, track and neutralize small drones will be the next focus of its Replicator initiative as it seeks to better protect the "most critical" U.S. installations and forces around the globe. "My expectation is that Replicator 2 will deliver meaningfully improved C-sUAS (counter-small unmanned aerial system) protection to critical assets within 24 months of Congress approving funding," Defense Secretary Lloyd Austin wrote in a memorandum published Monday in which he also charged his deputy, Kathleen Hicks, with spearheading the new effort. "I am confident the Replicator Initiative will complement and advance the significant C-sUAS work already underway in the DoD (Department of Defense)," Austin said in the memo.

Logical reasoning systems are essential for rigorous automatic reasoning. The focus of the 2024 Logic and Practice of Programming workshop is integrating reasoning systems for trustworthy AI, especially including integrating diverse models of programming with rules and constraints. Trustworthy AI requires programming with rules and constraints for expressing and solving knowledge-intensive inference and combinatorial problems. A wide range of programming models have been proposed, including but not limited to the following, and essentially all of them require or support imperative programming for use in practical applications.

The rise of foundation models (FMs), coupled with regulatory efforts addressing their risks and impacts, has sparked significant interest in open-source models. However, existing speech FMs (SFMs) fall short of full compliance with the open-source principles, even if claimed otherwise, as no existing SFM has model weights, code, and training data publicly available under open-source terms. In this work, we take the first step toward filling this gap by focusing on the 24 official languages of the European Union (EU). We collect suitable training data by surveying automatic speech recognition datasets and unlabeled speech corpora under open-source compliant licenses, for a total of 950k hours. Additionally, we release automatic transcripts for 441k hours of unlabeled data under the permissive CC-BY license, thereby facilitating the creation of open-source SFMs for the EU languages.

Monkeypox (MPox) has emerged as a significant global concern, with cases steadily increasing daily. Conventional detection methods, including polymerase chain reaction (PCR) and manual examination, exhibit challenges of low sensitivity, high cost, and substantial workload. Therefore, deep learning offers an automated solution; however, the datasets include data scarcity, texture, contrast, inter-intra class variability, and similarities with other skin infectious diseases. In this regard, a novel hybrid approach is proposed that integrates the learning capacity of Residual Learning and Spatial Exploitation Convolutional Neural Network (CNN) with a customized Swin Transformer (RS-FME-SwinT) to capture multi-scale global and local correlated features for MPox diagnosis. The proposed RS-FME-SwinT technique employs a transfer learning-based feature map enhancement (FME) technique, integrating the customized SwinT for global information capture, residual blocks for texture extraction, and spatial blocks for local contrast variations. Moreover, incorporating new inverse residual blocks within the proposed SwinT effectively captures local patterns and mitigates vanishing gradients. The proposed RS-FME-SwinT has strong learning potential of diverse features that systematically reduce intra-class MPox variation and enable precise discrimination from other skin diseases. Finally, the proposed RS-FME-SwinT is a holdout cross-validated on a diverse MPox dataset and achieved outperformance on state-of-the-art CNNs and ViTs. The proposed RS-FME-SwinT demonstrates commendable results of an accuracy of 97.80%, sensitivity of 96.82%, precision of 98.06%, and an F-score of 97.44% in MPox detection. The RS-FME-SwinT could be a valuable tool for healthcare practitioners, enabling prompt and accurate MPox diagnosis and contributing significantly to mitigation efforts.

We present FastLexRank\footnote{https://github.com/LiMaoUM/FastLexRank}, an efficient and scalable implementation of the LexRank algorithm for text ranking. Designed to address the computational and memory complexities of the original LexRank method, FastLexRank significantly reduces time and memory requirements from $\mathcal{O}(n^2)$ to $\mathcal{O}(n)$ without compromising the quality or accuracy of the results. By employing an optimized approach to calculating the stationary distribution of sentence graphs, FastLexRank maintains an identical results with the original LexRank scores while enhancing computational efficiency. This paper details the algorithmic improvements that enable the processing of large datasets, such as social media corpora, in real-time. Empirical results demonstrate its effectiveness, and we propose its use in identifying central tweets, which can be further analyzed using advanced NLP techniques. FastLexRank offers a scalable solution for text centrality calculation, addressing the growing need for efficient processing of digital content.

While large language models (LLMs) have seen unprecedented advancements in capabilities and applications across a variety of use-cases, safety alignment of these models is still an area of active research. The fragile nature of LLMs, even models that have undergone extensive alignment and safety training regimes, warrants additional safety steering steps via training-free, inference-time methods. While recent work in the area of mechanistic interpretability has investigated how activations in latent representation spaces may encode concepts, and thereafter performed representation engineering to induce such concepts in LLM outputs, the applicability of such for safety is relatively under-explored. Unlike recent inferencetime safety steering works, in this paper we explore safety steering of LLM outputs using: (i) category-specific steering vectors, thereby enabling fine-grained control over the steering, and (ii) sophisticated methods for extracting informative steering vectors for more effective safety steering while retaining quality of the generated text. We demonstrate our exploration on multiple LLMs and datasets, and showcase the effectiveness of the proposed steering method, along with a discussion on the implications and best practices. Content Warning: This paper contains examples of harmful language.

Large language models excel at creative generation but continue to struggle with the issues of hallucination and bias. While retrieval-augmented generation (RAG) provides a framework for grounding LLMs' responses in accurate and up-to-date information, it still raises the question of bias: which sources should be selected for inclusion in the context? And how should their importance be weighted? In this paper, we study the challenge of cross-lingual RAG and present a dataset to investigate the robustness of existing systems at answering queries about geopolitical disputes, which exist at the intersection of linguistic, cultural, and political boundaries. Our dataset is sourced from Wikipedia pages containing information relevant to the given queries and we investigate the impact of including additional context, as well as the composition of this context in terms of language and source, on an LLM's response. Our results show that existing RAG systems continue to be challenged by cross-lingual use cases and suffer from a lack of consistency when they are provided with competing information in multiple languages. We present case studies to illustrate these issues and outline steps for future research to address these challenges. We make our dataset and code publicly available at https://github.com/manestay/bordIRlines.

Acquiring new customers is a vital process for growing businesses. Prospecting is the process of identifying and marketing to potential customers using methods ranging from online digital advertising, linear television, out of home, and direct mail. Despite the rapid growth in digital advertising (particularly social and search), research shows that direct mail remains one of the most effective ways to acquire new customers. However, there is a notable gap in the application of modern machine learning techniques within the direct mail space, which could significantly enhance targeting and personalization strategies. Methodologies deployed through direct mail are the focus of this paper. In this paper, we propose a supervised learning approach for identifying new customers, i.e., prospecting, which comprises how we define labels for our data and rank potential customers. The casting of prospecting to a supervised learning problem leads to imbalanced tabular data. The current state-of-the-art approach for tabular data is an ensemble of tree-based methods like random forest and XGBoost. We propose a deep learning framework for tabular imbalanced data. This framework is designed to tackle large imbalanced datasets with vast number of numerical and categorical features. Our framework comprises two components: an autoencoder and a feed-forward neural network. We demonstrate the effectiveness of our framework through a transparent real-world case study of prospecting in direct mail advertising. Our results show that our proposed deep learning framework outperforms the state of the art tree-based random forest approach when applied in the real-world.