by jsendak | Mar 29, 2025 | Cosmology & Computing
Unveiling the Mysteries of the Cosmos: A Journey into Modern Cosmology
Since the dawn of humanity, we have gazed up at the night sky, pondering the mysteries of the cosmos. The stars, planets, and galaxies have captivated our imaginations, inspiring countless myths, legends, and scientific inquiries. Over the centuries, our understanding of the universe has evolved, and today, modern cosmology stands at the forefront of scientific exploration, offering us a glimpse into the vastness and complexity of the cosmos.
Cosmology, the study of the origin, evolution, and structure of the universe, has come a long way from its humble beginnings. In ancient times, cosmological theories were often based on religious or philosophical beliefs. However, with the advent of the scientific method and technological advancements, our understanding of the cosmos has undergone a revolution.
One of the most groundbreaking discoveries in modern cosmology is the Big Bang theory. Proposed in the early 20th century, this theory suggests that the universe originated from a singularity, a point of infinite density and temperature. Approximately 13.8 billion years ago, this singularity expanded rapidly, giving birth to the universe as we know it. The Big Bang theory not only explains the origin of the universe but also provides insights into its evolution and structure.
Another key concept in modern cosmology is the expansion of the universe. Observations made by astronomers in the early 20th century revealed that galaxies were moving away from each other. This led to the development of the theory of cosmic expansion, which states that the space between galaxies is expanding, causing them to move apart. The discovery of cosmic microwave background radiation, a remnant of the early universe, further supported this theory and provided evidence for the Big Bang.
The study of dark matter and dark energy has also revolutionized our understanding of the cosmos. Dark matter, a mysterious substance that does not interact with light, makes up a significant portion of the universe’s mass. Its existence was first proposed to explain the gravitational effects observed in galaxies. Dark energy, on the other hand, is a hypothetical form of energy that is believed to be responsible for the accelerated expansion of the universe. These enigmatic entities continue to intrigue scientists, and their exploration holds the promise of unraveling more secrets of the cosmos.
Advancements in technology have played a crucial role in the progress of modern cosmology. Telescopes, both ground-based and space-based, have allowed astronomers to observe distant galaxies, study their properties, and gather data to support cosmological theories. The Hubble Space Telescope, launched in 1990, has provided breathtaking images of the universe and has been instrumental in numerous scientific discoveries. Additionally, powerful particle accelerators and detectors have allowed scientists to recreate the conditions of the early universe, shedding light on its fundamental properties.
As our understanding of the cosmos deepens, new questions arise. What is the ultimate fate of the universe? Are there other universes beyond our own? What lies at the center of black holes? These and many more mysteries continue to captivate scientists and drive further exploration.
Modern cosmology is a testament to human curiosity and our relentless pursuit of knowledge. It has transformed our understanding of the universe, challenging our preconceived notions and expanding the boundaries of human comprehension. Through the study of the cosmos, we not only gain insights into the physical nature of the universe but also contemplate our place within it.
In conclusion, modern cosmology has unveiled the mysteries of the cosmos, taking us on a journey from the origin of the universe to its structure and evolution. The Big Bang theory, cosmic expansion, dark matter, and dark energy are just a few of the concepts that have reshaped our understanding of the cosmos. With each new discovery, we inch closer to unraveling the secrets of the universe, reminding us of the infinite wonders that lie beyond our reach.
by jsendak | Mar 28, 2025 | Cosmology & Computing
arXiv:2503.20990v1 Announce Type: cross Abstract: Audio Large Language Models (AudioLLMs) have received widespread attention and have significantly improved performance on audio tasks such as conversation, audio understanding, and automatic speech recognition (ASR). Despite these advancements, there is an absence of a benchmark for assessing AudioLLMs in financial scenarios, where audio data, such as earnings conference calls and CEO speeches, are crucial resources for financial analysis and investment decisions. In this paper, we introduce textsc{FinAudio}, the first benchmark designed to evaluate the capacity of AudioLLMs in the financial domain. We first define three tasks based on the unique characteristics of the financial domain: 1) ASR for short financial audio, 2) ASR for long financial audio, and 3) summarization of long financial audio. Then, we curate two short and two long audio datasets, respectively, and develop a novel dataset for financial audio summarization, comprising the textsc{FinAudio} benchmark. Then, we evaluate seven prevalent AudioLLMs on textsc{FinAudio}. Our evaluation reveals the limitations of existing AudioLLMs in the financial domain and offers insights for improving AudioLLMs. All datasets and codes will be released.
by jsendak | Mar 15, 2025 | Cosmology & Computing
Quantum computing has emerged as a groundbreaking field of research that has the potential to revolutionize the future of technology. With its ability to process vast amounts of data and solve complex problems at an unprecedented speed, quantum computing holds the promise of transforming various industries, from healthcare and finance to cybersecurity and artificial intelligence.
Traditional computers, known as classical computers, use bits to store and process information. These bits represent either a 0 or a 1, forming the basis of binary code. Quantum computers, on the other hand, utilize quantum bits, or qubits, which can represent both 0 and 1 simultaneously, thanks to a phenomenon called superposition. This unique property allows quantum computers to perform multiple calculations simultaneously, exponentially increasing their processing power.
One of the most significant applications of quantum computing lies in the field of cryptography and cybersecurity. As technology advances, so does the sophistication of cyber threats. Classical computers, despite their computational power, struggle to break complex encryption algorithms used to secure sensitive information. Quantum computers, however, have the potential to crack these codes in a matter of seconds, posing a significant challenge to current encryption methods.
On the flip side, quantum computing can also enhance cybersecurity by developing new encryption algorithms that are resistant to quantum attacks. By leveraging the principles of quantum mechanics, quantum cryptography can provide secure communication channels that are virtually impossible to intercept or tamper with. This technology could revolutionize the way we transmit sensitive data, ensuring privacy and security in an increasingly interconnected world.
Another area where quantum computing can make a profound impact is in drug discovery and healthcare. The process of developing new drugs is time-consuming and expensive, often taking years and billions of dollars. Quantum computers can simulate the behavior of molecules and atoms, enabling scientists to analyze and predict their interactions with drugs more accurately. This could significantly accelerate the drug discovery process, leading to the development of more effective treatments for various diseases.
Furthermore, quantum computing can revolutionize machine learning and artificial intelligence. Quantum machine learning algorithms can process and analyze vast amounts of data more efficiently, leading to more accurate predictions and insights. This could have far-reaching implications in fields such as finance, where quantum computers can analyze complex market data and make faster and more accurate trading decisions.
Despite its immense potential, quantum computing is still in its early stages of development. The technology faces numerous challenges, including the need for stable qubits, error correction, and scalability. However, significant progress has been made in recent years, with companies like IBM, Google, and Microsoft investing heavily in quantum research and development.
In conclusion, quantum computing has the potential to revolutionize the future of technology. Its ability to process vast amounts of data and solve complex problems at an unprecedented speed opens up new possibilities in fields such as cryptography, drug discovery, and artificial intelligence. While there are still challenges to overcome, the progress made so far indicates that quantum computing will play a significant role in shaping the future of technology. As researchers continue to explore its potential, we can expect to witness groundbreaking advancements that will transform various industries and pave the way for a new era of innovation.
by jsendak | Mar 14, 2025 | Cosmology & Computing
Understanding the Potential of Quantum Computing: A Revolutionary Breakthrough in Computing Technology
In the ever-evolving world of technology, quantum computing has emerged as a promising and revolutionary breakthrough. Unlike classical computers that rely on bits to process information, quantum computers utilize quantum bits, or qubits, which can exist in multiple states simultaneously. This unique characteristic of qubits allows quantum computers to perform complex calculations at an unprecedented speed, opening up a world of possibilities for solving problems that were previously unsolvable.
One of the most significant advantages of quantum computing is its potential to solve complex problems in a fraction of the time it would take a classical computer. For instance, quantum computers can efficiently factor large numbers, which is a fundamental challenge in cryptography. This ability poses a threat to current encryption methods, as quantum computers could potentially break them, leading to a need for new, quantum-resistant encryption algorithms.
Another area where quantum computing holds immense potential is in optimization problems. Many real-world problems, such as route optimization, supply chain management, and financial portfolio optimization, involve finding the best possible solution from a vast number of possibilities. Classical computers struggle to solve these problems efficiently, but quantum computers can explore all possible solutions simultaneously, significantly reducing the time required to find the optimal solution.
Furthermore, quantum computing has the potential to revolutionize drug discovery and material science. The process of discovering new drugs or designing new materials often involves simulating the behavior of molecules and atoms, which is computationally intensive. Quantum computers can simulate the behavior of these particles accurately, enabling researchers to accelerate the discovery process and potentially find breakthroughs in medicine and materials science.
Additionally, quantum computing has the potential to revolutionize machine learning and artificial intelligence. Quantum machine learning algorithms can process and analyze vast amounts of data more efficiently than classical algorithms. This could lead to advancements in various fields, such as image recognition, natural language processing, and data analysis, ultimately enhancing the capabilities of AI systems.
Despite its immense potential, quantum computing is still in its early stages of development. Building a practical and scalable quantum computer is a significant challenge due to the delicate nature of qubits and the need for precise control and error correction. However, researchers and tech giants like IBM, Google, and Microsoft are investing heavily in quantum computing research, bringing us closer to realizing its full potential.
In conclusion, quantum computing represents a revolutionary breakthrough in computing technology. Its ability to process information at an unprecedented speed and solve complex problems efficiently opens up new possibilities in various fields, including cryptography, optimization, drug discovery, and artificial intelligence. While there are still challenges to overcome, the progress being made in quantum computing research is promising, and it is only a matter of time before we witness the transformative power of this groundbreaking technology.
by jsendak | Mar 14, 2025 | Cosmology & Computing
arXiv:2503.09822v1 Announce Type: cross Abstract: Generative Artificial Intelligence (GenAI), particularly Large Language Models (LLMs), has significantly advanced Natural Language Processing (NLP) tasks, such as Named Entity Recognition (NER), which involves identifying entities like person, location, and organization names in text. LLMs are especially promising for low-resource languages due to their ability to learn from limited data. However, the performance of GenAI models for Nepali, a low-resource language, has not been thoroughly evaluated. This paper investigates the application of state-of-the-art LLMs for Nepali NER, conducting experiments with various prompting techniques to assess their effectiveness. Our results provide insights into the challenges and opportunities of using LLMs for NER in low-resource settings and offer valuable contributions to the advancement of NLP research in languages like Nepali.
by jsendak | Mar 14, 2025 | Cosmology & Computing
arXiv:2503.09871v1 Announce Type: new Abstract: Automatically generating training supervision for embodied tasks is crucial, as manual designing is tedious and not scalable. While prior works use large language models (LLMs) or vision-language models (VLMs) to generate rewards, these approaches are largely limited to simple tasks with well-defined rewards, such as pick-and-place. This limitation arises because LLMs struggle to interpret complex scenes compressed into text or code due to their restricted input modality, while VLM-based rewards, though better at visual perception, remain limited by their less expressive output modality. To address these challenges, we leverage the imagination capability of general-purpose video generation models. Given an initial simulation frame and a textual task description, the video generation model produces a video demonstrating task completion with correct semantics. We then extract rich supervisory signals from the generated video, including 6D object pose sequences, 2D segmentations, and estimated depth, to facilitate task learning in simulation. Our approach significantly improves supervision quality for complex embodied tasks, enabling large-scale training in simulators.
