Furthermore, while the neuromorphic community has argued in favor of their higher We seek to enable classic processing of continuous energy efficiency for decades, recent research and breakthroughs ultra-sparse spatiotemporal data generated by in edge AI accelerators suggest that this might not event-based sensors with dense machine learning be the case (Dampfhoffer et al., 2023; Garrett et al., 2023; models. We propose a novel hybrid pipeline composed Moosmann et al., 2023; Caccavella et al., 2023). of asynchronous sensing and synchronous processing that combines several ideas: (1) an embedding Nevertheless, considering the inherent advantages of eventbased based on PointNet models - the ALERT vision sensors, namely high dynamic range (HDR) module - that can continuously integrate new and and high temporal resolution - simultaneously, without any dismiss old events thanks to a leakage mechanism, tradeoffs between the two -, we aim to find a way to leverage (2) a flexible readout of the embedded data this sparse and low-latency data for real-world situations.

Transformer-based models have greatly pushed the boundaries of time series forecasting recently. Existing methods typically encode time series data into $\textit{patches}$ using one or a fixed set of patch lengths. This, however, could result in a lack of ability to capture the variety of intricate temporal dependencies present in real-world multi-periodic time series. In this paper, we propose MultiResFormer, which dynamically models temporal variations by adaptively choosing optimal patch lengths. Concretely, at the beginning of each layer, time series data is encoded into several parallel branches, each using a detected periodicity, before going through the transformer encoder block. We conduct extensive evaluations on long- and short-term forecasting datasets comparing MultiResFormer with state-of-the-art baselines. MultiResFormer outperforms patch-based Transformer baselines on long-term forecasting tasks and also consistently outperforms CNN baselines by a large margin, while using much fewer parameters than these baselines.

Houthi militants in Yemen are using Iranian-supplied missiles and drones to attack civilian and military targets across the Middle East, analysis from the Defense Intelligence Agency (DIA) shows. The report, "Iran: Enabling Houthi Attacks Across the Middle East," aims to provide more insight into the relationship between Iran and the Houthis. The militant group, stationed in Yemen, has for months been striking commercial vessels traveling through the Red Sea in protest of Palestinian civilians killed during Israel's ongoing offensive against Hamas members in Gaza. Houthi fighters stage a rally in support of the Palestinians in the Gaza Strip and against the U.S.-led airstrikes on Yemen, in Sanaa, Yemen, Monday, Jan. 29, 2024. Most recently, Houthi rebels fired ballistic missiles at two ships traveling through Middle East waters.

Ancient rock carvings have been uncovered near the Amazon River amid drought conditions in Brazil. A set of ancient texts burned by the volcanic eruption on Mount Vesuvius in 79 A.D. have been deciphered thanks to a team of researchers using AI. The nearly 2,000-year-old texts were unreadable after being charred in a villa in Herculaneum, a Roman town near Pompeii. The texts were discovered in an ancient villa in the town of Herculaneum. Believed to have been owned by the father-in-law of Julius Caesar, the texts were carbonized by the heat of the volcanic debris.

Large Language Models (LLMs) have not only revolutionized natural language processing but also extended their prowess to various domains, marking a significant stride towards artificial general intelligence. The interplay between LLMs and Evolutionary Algorithms (EAs), despite differing in objectives and methodologies, share a common pursuit of applicability in complex problems. Meanwhile, EA can provide an optimization framework for LLM's further enhancement under black-box settings, empowering LLM with flexible global search capacities. On the other hand, the abundant domain knowledge inherent in LLMs could enable EA to conduct more intelligent searches. Furthermore, the text processing and generative capabilities of LLMs would aid in deploying EAs across a wide range of tasks. Based on these complementary advantages, this paper provides a thorough review and a forward-looking roadmap, categorizing the reciprocal inspiration into two main avenues: LLM-enhanced EA and EA-enhanced LLM. Some integrated synergy methods are further introduced to exemplify the amalgamation of LLMs and EAs in diverse scenarios, including neural architecture search, code generation, software engineering, and various generation tasks. As the first comprehensive review focused on the EA research in the era of LLMs, this paper provides a foundational stepping stone for understanding the collaborative potential of LLMs and EAs. By meticulous categorization and critical analysis, we contribute to the ongoing discourse on the cross-disciplinary study of these two powerful paradigms. The identified challenges and future directions offer guidance for researchers and practitioners aiming to unlock the full potential of this innovative collaboration in propelling advancements in optimization and artificial intelligence.

In this paper, we propose a new visual navigation method based on a single RGB perspective camera. Using the Visual Teach & Repeat (VT&R) methodology [8], the robot acquires a visual trajectory consisting of multiple subgoal images in the teaching step. In the repeat step, we propose two network architectures, namely ViewNet and VelocityNet. The combination of the two networks allows the robot to follow the visual trajectory. ViewNet is trained to generate a future image based on the current view and the velocity command. The generated future image is combined with the subgoal image for training VelocityNet. We develop an offline Model Predictive Control (MPC) policy within VelocityNet with the dual goals of (1) reducing the difference between current and subgoal images and (2) ensuring smooth trajectories by mitigating velocity discontinuities. Offline training conserves computational resources, making it a more suitable option for scenarios with limited computational capabilities, such as embedded systems. We validate our experiments in a simulation environment, demonstrating that our model can effectively minimize the metric error between real and played trajectories.

Along with factors such as cancer, blood pressure, street accidents and stroke, suicide has been one of Iran main causes of death. One of the main reasons for suicide is psychological stressors. Identifying psychological stressors in an at risk population can help in the early prevention of suicidal and suicidal behaviours. In recent years, the widespread popularity and flow of real time information sharing of social media have allowed for potential early intervention in large scale and even small scale populations. However, some automated approaches to extract psychiatric stressors from Twitter have been presented, but most of this research has been for non Persian languages. This study aims to investigate the techniques of detecting psychological stress related to suicide from Persian tweets using learning based methods. The proposed capsule based approach achieved a binary classification accuracy of 0.83.

Smart cities stand as pivotal components in the ongoing pursuit of elevating urban living standards, facilitating the rapid expansion of urban areas while efficiently managing resources through sustainable and scalable innovations. In this regard, as emerging technologies like Artificial Intelligence (AI), the Internet of Things (IoT), big data analytics, and fog and edge computing have become increasingly prevalent, smart city applications grapple with various challenges, including the potential for unauthorized disclosure of confidential and sensitive data. The seamless integration of emerging technologies has played a vital role in sustaining the dynamic pace of their development. This paper explores the substantial potential and applications of Deep Learning (DL), Federated Learning (FL), IoT, Blockchain, Natural Language Processing (NLP), and large language models (LLMs) in optimizing ICT processes within smart cities. We aim to spotlight the vast potential of these technologies as foundational elements that technically strengthen the realization and advancement of smart cities, underscoring their significance in driving innovation within this transformative urban milieu. Our discourse culminates with an exploration of the formidable challenges that DL, FL, IoT, Blockchain, NLP, and LLMs face within these contexts, and we offer insights into potential future directions.

The modern world relies on and is driven by software. Embedded systems, command-line interfaces and user interface (UI) software are present across systems all around the world. The ease of use coupled with their intuitive nature has allowed for UI systems to become a staple as a crucial tool in modern software and beyond. UI systems serve as a visually appealing packaging of function calls and event handlers, allowing for complex event pipelines and data flows to be abstracted by buttons, text fields, menus, etc. The evolutions made in large language models (LLMs) over the past year have exhibited true "cognitive" potential. This potent ability has unveiled innumerable new opportunities to revolutionize the way our contemporary software systems are expected to operate. In this paper, we explore our vision and progress toward developing a UI architectural paradigm which employs a multimodal engine powered by LLMs and state-of-the-art transformer models. This framework aims to abstract monotonous UI interactions with prompting mechanisms that serve as "cognitively aware", powering automated functional calling and data flow pipelines, which translate to full speech-based intelligence control over visual UI systems.

We apply a state-of-the-art membership inference attack (MIA) to systematically test the practical privacy vulnerability of fine-tuning large image classification models.We focus on understanding the properties of data sets and samples that make them vulnerable to membership inference. In terms of data set properties, we find a strong power law dependence between the number of examples per class in the data and the MIA vulnerability, as measured by true positive rate of the attack at a low false positive rate. For an individual sample, large gradients at the end of training are strongly correlated with MIA vulnerability.