Fragmented Data: Coding for Resilience in DNA Storage
As DNA emerges as a promising medium for long-term data archiving, new coding schemes are vital to overcome the challenges of fragment reassembly and data corruption.
Science
Geometric Cryptography: A New Shield Against Quantum Threats
Researchers are exploring the power of differential geometry and algebraic topology to build cryptographic systems resilient to attacks from quantum computers.
Fortifying Trust in a Post-Quantum World
A new architectural approach integrates post-quantum cryptography to secure the software supply chain against future quantum-based attacks.
Beyond Malignancy: Reframing Cancer as a Survival Response
A new theory proposes that cancer isn’t driven by inherent aggression, but by a desperate struggle for stability in the face of cellular crisis.
Smarter Caching: Optimizing Data Delivery with Novel Packing Techniques
A new caching scheme dramatically improves transmission rates by intelligently organizing and delivering data to multiple users simultaneously.
Sensing and Communication: Finding the Sweet Spot
A new analysis reveals the fundamental limits of simultaneously transmitting data and sensing the environment, paving the way for more efficient wireless systems.
Modeling CuTe’s Complex Behavior: A Density Functional Showdown
A new computational study reveals that the r²SCAN functional offers a significant improvement in accurately predicting charge density wave instabilities and lattice dynamics in the material CuTe.
Unlocking the Secrets of Baryons: A New Look at Diquark Mass
A lattice QCD study provides crucial insights into the fundamental building blocks of matter by precisely determining the mass of scalar diquarks and characterizing their interactions.
Broadcasting Data: How Codebook Diversity Boosts Reliability
New research reveals how strategically diversifying data codebooks improves the efficiency of broadcasting information across noisy channels.
Smarter Aggregation: Minimizing Communication Costs in Polynomial Computing
![Compressed sensing, via the exploitation of orthogonality conditions detailed in Theorem 1 and minimized in Theorem 2, achieves feasibility with fewer responses-specifically [latex]N \leq d(K-1)[/latex]-than baseline individual decoding schemes, which require at least [latex]N \geq d(K-1) + 1[/latex] responses to ensure a viable solution, thereby demonstrating a fundamental efficiency gain in data acquisition.](https://arxiv.org/html/2601.10028v1/x1.png)
New research defines the limits of efficient data aggregation, revealing how to dramatically reduce the number of responses needed for weighted polynomial computations.