arXiv:2504.12318v1 Announce Type: cross Abstract: We present a tool AUTONAV that automates the mapping, localization, and path-planning tasks for autonomous navigation of robots. The modular architecture allows easy integration of various algorithms for these tasks for comparison. We present the generated maps and path-plans by AUTONAV in indoor simulation scenarios.
Exploring the Vast Universe: Unveiling the Mysteries of Cosmology
The universe, an infinite expanse of stars, galaxies, and cosmic wonders, has captivated human beings since the dawn of time. Throughout history, humans have looked up at the night sky, wondering about the mysteries that lie beyond our planet. Cosmology, the scientific study of the origin, evolution, and structure of the universe, has emerged as a field of study dedicated to unraveling these enigmas.
Cosmology has come a long way since ancient civilizations first pondered the nature of the cosmos. In ancient Greece, philosophers like Aristotle and Ptolemy proposed geocentric models, with Earth at the center of the universe. However, it wasn’t until the 16th century that Nicolaus Copernicus challenged these beliefs with his heliocentric model, placing the Sun at the center. This groundbreaking idea paved the way for further discoveries and advancements in our understanding of the universe.
One of the most significant breakthroughs in cosmology came in the early 20th century with the development of the Big Bang theory. Proposed by Georges Lemaître and later expanded upon by scientists like Edwin Hubble, this theory suggests that the universe originated from a single, incredibly dense and hot point, and has been expanding ever since. The Big Bang theory not only explains the origin of the universe but also provides a framework for understanding its evolution and structure.
With the advent of powerful telescopes and advanced technology, scientists have been able to explore the universe in unprecedented detail. Telescopes like the Hubble Space Telescope and the upcoming James Webb Space Telescope have allowed us to observe distant galaxies, nebulae, and even the remnants of the early universe. These observations have provided valuable insights into the formation of galaxies, the existence of dark matter and dark energy, and the fate of the universe itself.
One of the most intriguing mysteries in cosmology is the nature of dark matter and dark energy. Dark matter, which cannot be directly observed, is believed to make up a significant portion of the universe’s mass. Its gravitational effects can be observed through its influence on visible matter and the structure of galaxies. Dark energy, on the other hand, is thought to be responsible for the accelerated expansion of the universe. Despite their importance, the true nature of dark matter and dark energy remains elusive, and scientists continue to search for answers.
Cosmology also delves into the concept of black holes, regions of space where gravity is so strong that nothing, not even light, can escape. These cosmic phenomena, once considered purely theoretical, have been observed and studied in recent years. Black holes play a crucial role in shaping the structure of galaxies and are believed to be responsible for some of the most energetic events in the universe, such as gamma-ray bursts and quasars.
As our understanding of cosmology deepens, new questions and mysteries arise. Scientists are now exploring the possibility of multiple universes, parallel dimensions, and the existence of extraterrestrial life. These fascinating concepts push the boundaries of our knowledge and challenge us to think beyond the confines of our own planet.
Cosmology is not only a scientific endeavor but also a philosophical one. It forces us to confront our place in the vastness of the universe and ponder the ultimate questions of existence. What is the purpose of our existence? Are we alone in the cosmos? These questions may never be fully answered, but the pursuit of knowledge and the exploration of the universe will continue to captivate and inspire generations to come.
In conclusion, cosmology is a field of study that seeks to unravel the mysteries of the universe. From the ancient Greeks to modern-day scientists, humans have been driven by a curiosity to understand our place in the cosmos. Through the development of theories like the Big Bang and the exploration of phenomena like dark matter and black holes, we have made significant strides in our understanding of the universe. However, there is still much to discover and explore. The mysteries of cosmology continue to beckon us, inviting us to delve deeper into the vast expanse of the universe and uncover its secrets.
arXiv:2504.11501v1 Announce Type: cross Abstract: This paper presents a proposal for the governance of frontier AI systems through a hybrid public-private system. Private bodies, authorized and overseen by government, provide certifications to developers of frontier AI systems on an opt-in basis. In exchange for opting in, frontier AI firms receive protections from tort liability for customer misuse of their models. Before detailing the proposal, the paper explores more commonly discussed approaches to AI governance, analyzing their strengths and flaws. It also examines the nature of frontier AI governance itself. The paper includes consideration of the political economic, institutional, legal, safety, and other merits and tradeoffs inherent in the governance system it proposes.
Understanding the Potential of Quantum Computing: A Breakthrough in Computing Technology
In the world of technology, quantum computing is emerging as a groundbreaking innovation that has the potential to revolutionize the way we process information. Unlike classical computers, which use bits to represent information as either a 0 or a 1, quantum computers use quantum bits, or qubits, which can represent multiple states simultaneously. This unique property of qubits allows quantum computers to perform complex calculations at an unprecedented speed.
One of the most significant advantages of quantum computing is its ability to solve problems that are currently intractable for classical computers. For example, quantum computers can efficiently factor large numbers, which is the basis of many encryption algorithms used to secure online transactions. This means that quantum computing has the potential to render current encryption methods obsolete, posing both a threat and an opportunity for cybersecurity.
Another area where quantum computing shows immense promise is in the field of optimization. Many real-world problems, such as route optimization, supply chain management, and portfolio optimization, involve finding the best possible solution from a vast number of possibilities. Classical computers struggle to handle these problems efficiently, but quantum computers can explore multiple solutions simultaneously, leading to faster and more accurate results.
Furthermore, quantum computing has the potential to revolutionize drug discovery and material science. The process of discovering new drugs or materials often involves simulating the behavior of molecules and atoms, which is a computationally intensive task. Quantum computers can simulate the quantum behavior of these particles more accurately and efficiently, enabling scientists to accelerate the discovery of new drugs and materials.
Despite its immense potential, quantum computing is still in its early stages of development. Building a practical quantum computer is a complex and challenging task. Qubits are extremely sensitive to environmental disturbances, making it difficult to maintain their fragile quantum states. Additionally, the number of qubits needed to solve complex problems is far beyond the capabilities of current quantum computers.
However, significant progress has been made in recent years. Researchers and tech giants like IBM, Google, and Microsoft are investing heavily in quantum computing research and development. They are exploring different approaches to building quantum computers, such as superconducting circuits, trapped ions, and topological qubits. These advancements bring us closer to realizing the full potential of quantum computing.
In conclusion, quantum computing represents a breakthrough in computing technology with the potential to revolutionize various fields. Its ability to solve complex problems, optimize processes, and accelerate scientific discoveries opens up new possibilities for innovation. While there are still significant challenges to overcome, the progress made in quantum computing research is promising. As we continue to unlock the power of quantum computing, we can expect to witness transformative advancements in various industries, shaping the future of technology.
arXiv:2504.10551v1 Announce Type: cross Abstract: Empirical Risk Minimization (ERM) models often rely on spurious correlations between features and labels during the learning process, leading to shortcut learning behavior that undermines robustness generalization performance. Current research mainly targets identifying or mitigating a single shortcut; however, in real-world scenarios, cues within the data are diverse and unknown. In empirical studies, we reveal that the models rely to varying extents on different shortcuts. Compared to weak shortcuts, models depend more heavily on strong shortcuts, resulting in their poor generalization ability. To address these challenges, we propose MiMu, a novel method integrated with Transformer-based ERMs designed to Mitigate Multiple shortcut learning behavior, which incorporates self-calibration strategy and self-improvement strategy. In the source model, we preliminarily propose the self-calibration strategy to prevent the model from relying on shortcuts and make overconfident predictions. Then, we further design self-improvement strategy in target model to reduce the reliance on multiple shortcuts. The random mask strategy involves randomly masking partial attention positions to diversify the focus of target model other than concentrating on a fixed region. Meanwhile, the adaptive attention alignment module facilitates the alignment of attention weights to the calibrated source model, without the need for post-hoc attention maps or supervision. Finally, extensive experiments conducted on Natural Language Processing (NLP) and Computer Vision (CV) demonstrate the effectiveness of MiMu in improving robustness generalization abilities.