Large language models (LLMs) exhibit remarkable in-context learning (ICL) capabilities. However, the underlying working mechanism of ICL remains poorly understood. Recent research presents two conflicting views on ICL: One attributes it to LLMs' inherent ability of task recognition, deeming label correctness and shot numbers of demonstrations as not crucial; the other emphasizes the impact of similar examples in the demonstrations, stressing the need for label correctness and more shots. In this work, we provide a Two-Dimensional Coordinate System that unifies both views into a systematic framework. The framework explains the behavior of ICL through two orthogonal variables: whether LLMs can recognize the task and whether similar examples are presented in the demonstrations. We propose the peak inverse rank metric to detect the task recognition ability of LLMs and study LLMs' reactions to different definitions of similarity. Based on these, we conduct extensive experiments to elucidate how ICL functions across each quadrant on multiple representative classification tasks. Finally, we extend our analyses to generation tasks, showing that our coordinate system can also be used to interpret ICL for generation tasks effectively.

Israel strikes Yemen Houthis Dek: Israel launched its first ever strikes against Houthi rebels in Yemen just days after Jerusalem vowed revenge from a drone strike on Tel Aviv. A secret meeting between the U.S., Israel and the United Arab Emirates has been held to discuss a potential strategy on how the Gaza Strip will be governed once there is an end to the months-long war, Fox News confirmed Tuesday. The meeting, held in Abu Dhabi on Thursday, suggests that Israeli Prime Minister Benjamin Netanyahu may be looking to establish a plan for Gaza once the war is over, following repeated calls for a cease-fire. But details on the Thursday meeting – first reported by Axios – remain scarce, and it is unclear if options for ending the war were also discussed. Smoke and flames rise in the wake of an Israeli airstrike in Gaza on Nov. 2, 2023.

Meta AI launched in September 2023 using the Llama 2 learning language model. Nearly a year later, Meta has announced a new round of features for its AI assistant and a fresh LLM to support it: Llama 3.1. These updates include an expansion of who can access Meta AI. Thanks to the addition of Argentina, Chile, Colombia, Ecuador, Mexico, Peru and Cameroon, the assistant is now available in 22 countries. However, some of the new features are location or language-specific for the time being.

The Chinese Communist Party is a geopolitical cancer that will metastasize unless America can contain it with a once-in-a-generation investment in our national defense. Already, the CCP is actively colluding with Russia, prolonging Putin's war against Ukraine by blunting the impact of Western sanctions; it reaffirmed its support for Iran even after the deadly Oct. 7 attacks against Israel; and it has an explicit defense treaty with Kim Jung Un's North Korean dictatorship. To make matters even more dire, Chinese President Xi Jinping has instructed his People's Liberation Army to be ready to invade Taiwan by 2027. Chinese President Xi Jinping has instructed his People's Liberation Army to be ready to invade Taiwan by 2027. As George Washington counseled Congress in the nation's first ever inaugural address, "to be prepared for war is the most effectual means of preserving the peace."

We introduce Lifelong ICL, a problem setting that challenges long-context language models (LMs) to learn from a sequence of language tasks through in-context learning (ICL). We further introduce Task Haystack, an evaluation suite dedicated to assessing and diagnosing how long-context LMs utilizes contexts in Lifelong ICL. When given a task instruction and test inputs, long-context LMs are expected to leverage the relevant demonstrations in the Lifelong ICL prompt, avoid distraction and interference from other tasks, and achieve test accuracies that are not significantly worse than the Single-task ICL baseline. Task Haystack draws inspiration from the widely-adopted "needle-in-a-haystack" (NIAH) evaluation, but presents new and unique challenges. It demands that models (1) utilize the contexts with deeper understanding, rather than resorting to simple copying and pasting; (2) navigate through long streams of evolving topics and tasks, which closely approximates the complexities of real-world usage of long-context LMs. Additionally, Task Haystack inherits the controllability aspect of NIAH, providing model developers with tools and visualizations to identify model vulnerabilities effectively. We benchmark 12 long-context LMs using Task Haystack. We find that state-of-the-art closed models such as GPT-4o still struggle in this setting, failing 15% of the cases on average, while all open-weight models we evaluate further lack behind by a large margin, failing up to 61% of the cases. In our controlled analysis, we identify factors such as distraction and recency bias as contributors to these failure cases. Further, we observe declines in performance when task instructions are paraphrased at test time or when ICL demonstrations are repeated excessively, raising concerns about the robustness, instruction understanding, and true context utilization of current long-context LMs.

The full-dimensional (metric, Euclidean, least squares) multidimensional scaling stress loss function is combined with a quadratic external penalty function term. The trajectory of minimizers of stress for increasing values of the penalty parameter is then used to find (tentative) global minima for low-dimensional multidimensional scaling. This is illustrated with several one-dimensional and two-dimensional examples.

Abstract--We present a local-to-global and measure-theoretical approach to understanding datasets. The core idea is to form ulate a logifold structure and to interpret network models with restricted domains as local charts of datasets. In particul ar, this provides a mathematical foundation for ensemble machi ne learning. Our experiments demonstrate that logifolds can b e implemented to identify fuzzy domains and improve accuracy compared to taking average of model outputs. Additionally, we provide a theoretical example of a logifold, highlighting t he importance of restricting to domains of classifiers in an ens emble.

Deep learning-based fingerprinting is one of the current promising technologies for outdoor localization in cellular networks. However, deploying such localization systems for heterogeneous phones affects their accuracy as the cellular received signal strength (RSS) readings vary for different types of phones. In this paper, we introduce a number of techniques for addressing the phones heterogeneity problem in the deep-learning based localization systems. The basic idea is either to approximate a function that maps the cellular RSS measurements between different devices or to transfer the knowledge across them. Evaluation of the proposed techniques using different Android phones on four independent testbeds shows that our techniques can improve the localization accuracy by more than 220% for the four testbeds as compared to the state-of-the-art systems. This highlights the promise of the proposed device heterogeneity handling techniques for enabling a wide deployment of deep learning-based localization systems over different devices.

Continual learning is the problem of integrating new information in a model while retaining the knowledge acquired in the past. Despite the tangible improvements achieved in recent years, the problem of continual learning is still an open one. A better understanding of the mechanisms behind the successes and failures of existing continual learning algorithms can unlock the development of new successful strategies. In this work, we view continual learning from the perspective of the multi-task loss approximation, and we compare two alternative strategies, namely local and global approximations. We classify existing continual learning algorithms based on the approximation used, and we assess the practical effects of this distinction in common continual learning settings.Additionally, we study optimal continual learning objectives in the case of local polynomial approximations and we provide examples of existing algorithms implementing the optimal objectives

Data-driven deep learning models have shown great capabilities to assist radiologists in breast ultrasound (US) diagnoses. However, their effectiveness is limited by the long-tail distribution of training data, which leads to inaccuracies in rare cases. In this study, we address a long-standing challenge of improving the diagnostic model performance on rare cases using long-tailed data. Specifically, we introduce a pipeline, TAILOR, that builds a knowledge-driven generative model to produce tailored synthetic data. The generative model, using 3,749 lesions as source data, can generate millions of breast-US images, especially for error-prone rare cases. The generated data can be further used to build a diagnostic model for accurate and interpretable diagnoses. In the prospective external evaluation, our diagnostic model outperforms the average performance of nine radiologists by 33.5% in specificity with the same sensitivity, improving their performance by providing predictions with an interpretable decision-making process. Moreover, on ductal carcinoma in situ (DCIS), our diagnostic model outperforms all radiologists by a large margin, with only 34 DCIS lesions in the source data. We believe that TAILOR can potentially be extended to various diseases and imaging modalities. 1 Main Breast cancer has become the most common cancer among women globally [1-3], and early detection These authors carried out this work as interns at Yizhun Medical AI Co., Ltd. The distribution of pathological subtypes is long-tailed in our training set which has 1,387 biopsy-confirmed lesions. In benign lesions, the two most frequent subtypes together account for 49.7% of the lesions, with the remaining 13 subtypes comprising 50.3%. In malignant lesions, the most frequent subtype accounts for 81.8% of the lesions, while the remaining 15 subtypes comprise only 18.2%. In breast cancer detection, ultrasound (US) is an essential imaging method widely adopted worldwide for its safety and low cost [5-7].