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Title: I Gave Up Proving the Riemann Hypothesis — and Found Emptiness

The author experienced a tremble realizing the perfection of the zeros in the Riemann Hypothesis.
The Riemann Hypothesis represents a question of prime numbers but also embodies a deeper quest for certainty.
The author contemplated the true nature of reality and how it may be influenced by perception and expectation.
By relinquishing the need to prove and know, the author discovered a sense of clarity in emptiness.

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Medium

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Have We Reshaped Our Approach to Some of Math’s Hardest Problems?

A mathematical exploration journey has led to the discovery of a new diagnostic toolset for deep open problems in mathematics, including the Birch and Swinnerton-Dyer Conjecture and the Navier-Stokes equations.
The study proposes a new summation function over rational points to extract structure and investigate the connection between the number of rational solutions on an elliptic curve and the behavior of an L-function.
The research also applied the same summation-divergence idea to synthetic scale-sensitive velocity fields related to the Navier-Stokes equations, revealing underlying structure in fluid flows, potentially even in turbulent systems.
This work, starting from curiosity and basic questions, led to the development of a versatile toolset that could offer insights into various mathematical and physical domains, emphasizing the importance of curiosity in exploring unsolved problems.

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Medium

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The Hidden Geometry of Emergence: How Structure Arises from Information

SEFA, or Symbolic Emergence Field Analysis, finds structure by combining four features using information theory and self-calibrating to let data decide which features matter most based on their global information content.
SEFA aims to detect emergence, which is the difference between noise and meaning, in various contexts such as neural bursts, social network clusters, and prime number distribution.
It does not rely on fixed thresholds or domain-specific tricks, offering a universal and data-driven approach to reveal hidden order.
SEFA breaks down the process into steps like constructing a base field, capturing the signal's essence with analytic methods, deconstructing geometry, normalizing features, and letting the data dictate importance through self-calibration.
The SEFA score is a composite score that represents structuredness based on multiple informative features agreeing, prioritizing regions with high emergence.
SEFA's weighting scheme is objective, allowing data to determine feature relevance without requiring manual tuning, ensuring robustness and minimizing false positives or biases.
It can be applied to domains beyond number theory, such as EEG burst detection, social network clusters, and even uncovering hidden signals in non-coding DNA.
SEFA's approach involves trusting the data over preconceptions, using entropy to weight features and detect meaningful patterns.
It acknowledges limitations related to data stability, nonstationarity, and computational complexity, emphasizing the importance of cross-validation and critical analysis.
SEFA offers a principled way to uncover meaningful structure in diverse datasets, encouraging curiosity, skepticism, and a deeper understanding of emergence in both data and life.
SEFA is a self-calibrating mathematical method for detecting symbolic emergence in complex data, applicable in various fields beyond number theory due to its domain-agnostic nature.

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Medium

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LLM safety & security: NeurIPS 2024 insights

NeurIPS 2024 highlighted the pivotal role of Large Language Models (LLMs) security in the future of AI, emphasizing the necessity for robust security measures and red-teaming strategies.
Researchers at NeurIPS 2024 explored key breakthroughs in LLM safety, adversarial resilience, and synthetic data generation to address challenges like malicious manipulation of input data.
A curated list of about 100 papers from NeurIPS 2024 focused on LLM safety themes and offered insights into the current research landscape, aiding readers in navigating the vast space of LLM security.
Key research highlights from NeurIPS 2024 included innovative approaches like BackdoorAlign, AutoDefense, WILDTEAMING, DeepInception, GuardFormer, and AnyPrefer to enhance LLM security and alignment.
BackdoorAlign introduced a method to mitigate Fine-tuning based Jailbreak attacks by integrating secret prompts into safety examples, showcasing efficient mitigation of malicious behaviors without performance degradation.
AutoDefense presented a multi-agent framework for LLM defense against jailbreak attacks, enhancing model robustness and instruction-following capabilities by assigning defense roles to LLM agents collaboratively.
WILDTEAMING focused on in-the-wild jailbreak attempts to understand vulnerabilities in LLMs, developing a synthetic safety dataset and automated framework for identifying and mitigating these vulnerabilities.
DeepInception proposed a novel approach to bypass LLM safety measures by inducing virtual scenarios, effectively highlighting potential vulnerabilities that need to be addressed for preventing exploitation of LLMs.
GuardFormer introduced an efficient pretraining approach for safeguarding LLMs against harmful outputs, leveraging synthetic data to train a smaller classifier for superior performance without compromising generalization.
AnyPrefer presented an automatic framework for preference data synthesis, optimizing the target model’s responses based on feedback from a judge model and reward model to generate high-quality preference datasets.

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COSMOS

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New data from NASA’s Juno mission shows winds beneath Jupiter and Io surfaces

NASA's Juno mission has provided new data showing winds beneath the surface of Jupiter and its moon Io.
Using Juno's microwave radiometer (MWR), the mission team discovered evidence of still-warm magma below Io's cooled crust.
Juno also measured Jupiter's atmospheric temperature and found the north polar cap to be about 11°C cooler than surrounding areas with winds exceeding 160 km/h.
The spacecraft tracked massive cyclones on Jupiter, including a central cyclone at the north pole and eight surrounding cyclones that interact and oscillate around the pole.

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Medium

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Martina Solano Soto wants to solve the mysteries of the universe, and MIT is part of her plan

Martina Solano Soto, a 17-year-old from Spain, is passionate about physics and aims to solve big mysteries of the universe. She discovered MIT OpenCourseWare at the age of 14, which has been a significant influence in her journey.
She has delved into quantum physics and various other subjects through MIT Open Learning resources, creating her own syllabus and goals centered around math and physics studies at undergraduate and doctoral levels.
Solano was inspired by Professor Barton Zwiebach's teachings on Quantum Physics and String Theory, and credits MIT Open Learning for providing valuable resources like video lectures, assignments, and lecture notes.
Despite being a high school student, Solano emphasizes the importance of organization and utilizing flexible learning schedules. She continues to pursue her passion for physics, aiming to study particle physics and the cosmos with the help of MIT resources.

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Physicsworld

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Photonic computer chips perform as well as purely electronic counterparts, say researchers

Researchers from Singapore and the US have developed new photonic computer chips that rival electronic chips' performance, integrating with traditional silicon electronics and offering energy-efficient solutions for AI applications.
Traditional electronic computer development based on Moore's law and Dennard scaling is struggling to keep up with the complexity of AI systems, prompting exploration into light-based computation using photons for faster and more efficient operations crucial for AI tasks.
The Singapore-developed hybrid photonic-electronic system, PACE, performs matrix MAC operations efficiently by encoding data elements as binary intensities of light and utilizing optical weight modulators for multiplication and accumulation, achieving swift computation and solving optimization problems effectively.
Meanwhile, researchers at Lightmatter in the US have created a photonic processor capable of executing neural network tasks with high integration, offering speed and energy efficiency advantages in complex AI models like ResNet and BERT, as well as reinforcement learning algorithms.
Both teams utilized standard CMOS processing techniques for fabricating their chips, enabling scalability and manufacturability. The integration of photonic components with electronic systems marks a significant advancement in post-transistor computing, potentially leading to new computing models and applications.
These developments in photonic computer chips could inspire algorithm developers to leverage the low latency and unique benefits of photonic computing for exploring new system architectures and applications based on integrated photonics circuits.

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Physicsworld

206

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Alternate quantum realities: what if Heisenberg stayed at home?

Writer Kevlin Henney discusses his new flash fiction, Heisenberg (not) in Helgoland, which imagines an alternate reality where Werner Heisenberg never visits the island of Helgoland.
Henney explores how flash fiction's brevity allows for the exploration of complex ideas and reflects on the power of this format.
Henney's story will be featured in the Physics World Quantum Briefing 2025, a digital issue launching in May, focusing on the history, mystery, and applications of quantum mechanics.
The image accompanying the article shows Werner Heisenberg in 1933 and Henney discusses quantum computing and the importance of understanding how modern physics influences everyday technologies.

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Physicsworld

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Mathematical genius: celebrating the life and work of Emmy Noether

Emmy Noether, a German mathematician, revolutionized the field of physics despite facing discrimination as a Jewish woman.
Noether's theorem, her profound discovery, is at the heart of modern theoretical physics.
Facing obstacles due to her gender, she pursued advanced mathematics at the University of Göttingen.
Her work on symmetry and invariant theory paved the way for significant contributions to the field.
Noether's collaboration with Einstein on general relativity led to the discovery of conservation laws.
She received support from male colleagues like Hilbert and Einstein in her academic journey.
Despite challenges, she was promoted to a professorship but was not paid for her work.
Dismissed under the Nazi regime, her colleagues advocated for her but she eventually had to leave Germany.
Noether's legacy lives on in the significance of her work and contributions to theoretical physics.
Einstein's Tutor by Lee Phillips familiarizes readers with Noether's importance and her impact on various fields.

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Physicsworld

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Brain region used for speech decoding also supports BCI cursor control

Brain region used for speech decoding also supports BCI cursor control.
BCIs implanted in the brain can help individuals bypass pathways damaged by illness or injury and interact with computers based on neural signals.
Researchers at the University of California, Davis found that the same brain region supporting speech BCI could help in computer cursor control for a person with ALS.
The study suggests that computer cursor control may not be as body-part-specific as previously believed, potentially enabling the development of multi-modal BCIs for people with paralysis.

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Arstechnica

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Physics of the perfect cacio e pepe sauce

Italian physicists have developed a foolproof recipe for the perfect cacio e pepe sauce.
The traditional cheese and pepper sauce is notoriously challenging to make.
The scientists recommend using corn starch to achieve a smooth and creamy sauce.
The balance of starch and cheese is crucial for a successful cacio e pepe.

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Physicsworld

356

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Layer-spintronics makes its debut

Researchers at the Singapore University of Technology and Design (SUTD) have developed an all-electrical method for controlling spin-polarized currents.
By using bilayers of altermagnets, the researchers have created a magnetic-free and tuneable alternative for spintronics.
Spintronics uses the quantum spin of electrons to store and process information, providing advantages of non-volatility and potential energy efficiency.
The new device concept, layer-spintronics, has applications for next-generation memory and logic devices, with the possibility of integrating spintronics with current semiconductor technology.

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Physicsworld

374

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Curiouser and curiouser: delving into quantum Cheshire cats

Quantum Cheshire cats, inspired by Lewis Caroll's Cheshire cat, are quantum phenomena where a particle's property can exist separately from the particle itself, confusing classical intuition.
Aharonov and collaborators proposed the concept of quantum Cheshire cats in 2013, envisioning scenarios where a particle's property appears at a different location from the particle.
Weak measurements, a method introduced in the 1980s, allow for gentle probing of quantum systems to extract information without fully collapsing the system.
Interpreting quantum Cheshire cats involves understanding the disembodiment between a particle and its property, leading to peculiar experimental setups and results.
Aharonov and Popescu's research delves into scenarios where properties propagate in space without a physical carrier, challenging traditional quantum interpretations.
The concept of quantum Cheshire cats sparks interest in the foundational aspects of quantum mechanics and encourages thinking beyond conventional boundaries.
Despite the pragmatic 'shut up and calculate' approach, physicists acknowledge the importance of grappling with the weirdness of quantum mechanics to develop a deeper understanding.
The quantum Cheshire cat concept and related paradoxes aim to push researchers to reevaluate their intuitions about quantum phenomena and further explore the mysteries of quantum mechanics.
This article is part of Physics World's contribution to the 2025 International Year of Quantum Science and Technology, promoting global awareness of quantum physics and its applications.
Stay tuned to Physics World for more coverage on the International Year of Quantum Science and Technology and delve deeper into the intriguing world of quantum phenomena.

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Medium

Newtons Famous Laws

The first law of motion is about inertia, which states that objects will remain at rest or in uniform motion unless acted upon by an external force.
The second law of motion relates force, mass, and acceleration. It states that the acceleration of an object is directly proportional to the force applied and inversely proportional to its mass.
The third law of motion states that for every action, there is an equal and opposite reaction.
Newton's laws of motion are fundamental principles that explain various phenomena, such as the movement of objects and planetary orbits.

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Physicsworld

407

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India must boost investment in quantum technologies to become world leader, says report

India must boost investment in quantum technologies to become world leader, says report
The National Quantum Mission (NQM) of India recommends intensifying efforts in quantum technologies and increasing private investment in the field.
The report suggests improving quantum security, regulation, and making digital infrastructure quantum-safe.
The NQM report focuses on four aspects: quantum computing, communication, sensing and metrology, and materials and devices.

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