Fox News White House correspondent Jacqui Heinrich has the latest on the pressure on Biden to respond to the attack that killed three U.S. service members, on Special Report. President Joe Biden and First Lady Jill Biden will on Friday take part in the dignified transfer of the remains of three troops killed in the Iran-backed militia attack in Jordan last weekend. The Bidens will join the grieving families of the three American service members who died when a drone struck a base, known as Tower 22, near the demilitarized zone on the border between Jordan and Syria. The Iraqi border is only six miles away. The fallen troops were Sgt.

Joseph Votel discusses tensions in the Middle East and how the Biden administration could respond to a drone attack that killed three U.S. soldiers, on'The Story.' An Iran-backed militant group in Iraq has promised to continue attacks on U.S. troops after three American soldiers were killed by a drone strike in Jordan on Sunday. In a statement released Friday, Harakat al-Nujaba, one of the strongest Iraqi militias, announced that it plans to continue military operations against U.S. forces while allied factions have backed off their attacks after the Biden administration said there will be retaliation. Akram al-Kaabi, the group's leader, called for an end to the Israeli military operations in Gaza and withdrawal of the "American occupation of Iraq," in a statement posted on X. The announcement comes after Kataib Hezbollah, another powerful Iranian-backed Iraqi militia, which is closely monitored by the U.S. government, said on Tuesday that it would "suspend military and security operations against the occupying forces" to avoid embarrassing the Iraqi government.

The United States has approved plans for strikes in Iraq and Syria against multiple targets, including Iranian personnel and facilities, CBS News reported on Thursday, quoting US officials. President Joe Biden said on Tuesday that he had decided how to respond to a drone attack in northeastern Jordan near the Syrian border last Sunday, which killed three US service members and wounded more than 40. Sunday's attack was the first to result in the loss of American lives during the Israel-Hamas war. Here is what's known about the US plans: The US plans to retaliate against the drone attack on the US Tower 22 base near the border between Syria and Jordan by targeting Iranian personnel and facilities inside both countries. The Islamic Resistance in Iraq, believed to comprise multiple groups armed, funded and trained by Iran's Revolutionary Guards Corps, has claimed responsibility for Sunday's strike on the base. But on Wednesday, Kataib Hezbollah, the most powerful element in the force, announced the suspension of hostile operations against US troops.

About three hours later, U.S. forces shot down an armed Houthi drone flying over the Gulf of Aden, Central Command said. About five hours after that, the military destroyed an explosives-laden naval drone that Central Command said was launched toward commercial ships and Navy vessels in the Red Sea. There were no injuries or damage reported in either incident. Finally, around 12:45 p.m., two anti-ship ballistic missiles were launched from Houthi-controlled territory, probably toward a Liberian-flagged, Bermuda-owned cargo ship, M/V Koi, in the Red Sea, Central Command said. The missiles landed harmlessly in the water.

Instruction Tuning (IT), the process of training large language models (LLMs) using instruction-response pairs, has emerged as the predominant method for transforming base pre-trained LLMs into open-domain conversational agents. While IT has achieved notable success and widespread adoption, its limitations and shortcomings remain underexplored. In this paper, through rigorous experiments and an in-depth analysis of the changes LLMs undergo through IT, we reveal various limitations of IT. In particular, we show that (1) IT fails to enhance knowledge or skills in LLMs. LoRA fine-tuning is limited to learning response initiation and style tokens, and full-parameter fine-tuning leads to knowledge degradation. (2) Copying response patterns from IT datasets derived from knowledgeable sources leads to a decline in response quality. (3) Full-parameter fine-tuning increases hallucination by inaccurately borrowing tokens from conceptually similar instances in the IT dataset for generating responses. (4) Popular methods to improve IT do not lead to performance improvements over a simple LoRA fine-tuned model. Our findings reveal that responses generated solely from pre-trained knowledge consistently outperform responses by models that learn any form of new knowledge from IT on open-source datasets. We hope the insights and challenges revealed inspire future work.

Hypertension, defined as blood pressure (BP) that is above normal, holds paramount significance in the realm of public health, as it serves as a critical precursor to various cardiovascular diseases (CVDs) and significantly contributes to elevated mortality rates worldwide. However, many existing BP measurement technologies and standards might be biased because they do not consider clinical outcomes, comorbidities, or demographic factors, making them inconclusive for diagnostic purposes. There is limited data-driven research focused on studying the variance in BP measurements across these variables. In this work, we employed GPT-35-turbo, a large language model (LLM), to automatically extract the mean and standard deviation values of BP for both males and females from a dataset comprising 25 million abstracts sourced from PubMed. 993 article abstracts met our predefined inclusion criteria (i.e., presence of references to blood pressure, units of blood pressure such as mmHg, and mention of biological sex). Based on the automatically-extracted information from these articles, we conducted an analysis of the variations of BP values across biological sex. Our results showed the viability of utilizing LLMs to study the BP variations across different demographic factors.

This model finds applications in diverse areas of science and engineering, including astronomy, medical imaging, and communications (Jefferies and Christou, 1993; W ang and Poor, 1998; Campisi and Egiazarian, 2017). For instance, it forms an example of the blind deconvolution problem in statistical signal processing (see, e.g., Recht et al. (2010); Ahmed et al. (2013) and the references therein for several applications of this problem). W e are interested in the model-fitting problem, and the natural least squares population objectiveL: R d R d R (corresponding to the scaled negative log-likelihood of our observations under Gaussian noise) can be written as L ( µ, ν) = E nullnull y x, µ z, ν null 2null, (2) where the conditional distribution of y given x, z is as specified by the model (1). Note that L is a jointly nonconvex function in the parameters ( µ, ν) . With the goal of minimizing the population loss L, we consider online algorithms which operate on a mini-batch of size m with 1 m d for which we draw a fresh 1 set of observations { y i, x i, z i} m i = 1 at each iteration and form the averaged loss L m( µ, ν) = 1 m m i = 1 null y i x i, µ z i, ν null 2 .

Anomaly detection in SDN using data flow prediction is a difficult task. This problem is included in the category of time series and regression problems. Machine learning approaches are challenging in this field due to the manual selection of features. On the other hand, deep learning approaches have important features due to the automatic selection of features. Meanwhile, RNN-based approaches have been used the most. The LSTM and GRU approaches learn dependent entities well; on the other hand, the IndRNN approach learns non-dependent entities in time series. The proposed approach tried to use a combination of IndRNN and LSTM approaches to learn dependent and non-dependent features. Feature selection approaches also provide a suitable view of features for the models; for this purpose, four feature selection models, Filter, Wrapper, Embedded, and Autoencoder were used. The proposed IndRNNLSTM algorithm, in combination with Embedded, was able to achieve MAE=1.22 and RMSE=9.92 on NSL-KDD data.

In this paper, we introduce a multi-label lazy learning approach to deal with automatic semantic indexing in large document collections in the presence of complex and structured label vocabularies with high inter-label correlation. The proposed method is an evolution of the traditional k-Nearest Neighbors algorithm which uses a large autoencoder trained to map the large label space to a reduced size latent space and to regenerate the predicted labels from this latent space. We have evaluated our proposal in a large portion of the MEDLINE biomedical document collection which uses the Medical Subject Headings (MeSH) thesaurus as a controlled vocabulary. In our experiments we propose and evaluate several document representation approaches and different label autoencoder configurations.

Causal discovery, the task of inferring causal structure from data, promises to accelerate scientific research, inform policy making, and more. However, the per-dataset nature of existing causal discovery algorithms renders them slow, data hungry, and brittle. Inspired by foundation models, we propose a causal discovery framework where a deep learning model is pretrained to resolve predictions from classical discovery algorithms run over smaller subsets of variables. This method is enabled by the observations that the outputs from classical algorithms are fast to compute for small problems, informative of (marginal) data structure, and their structure outputs as objects remain comparable across datasets. Our method achieves state-of-the-art performance on synthetic and realistic datasets, generalizes to data generating mechanisms not seen during training, and offers inference speeds that are orders of magnitude faster than existing models.