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Category: information science – Page 5

Neuromodulators in the brain act globally at many forms of synaptic plasticity, represented as metaplasticity, which is rarely considered by existing spiking (SNNs) and nonspiking artificial neural networks (ANNs). Here, we report an efficient brain-inspired computing algorithm for SNNs and ANNs, referred to here as neuromodulation-assisted credit assignment (NACA), which uses expectation signals to induce defined levels of neuromodulators to selective synapses, whereby the long-term synaptic potentiation and depression are modified in a nonlinear manner depending on the neuromodulator level. The NACA algorithm achieved high recognition accuracy with substantially reduced computational cost in learning spatial and temporal classification tasks. Notably, NACA was also verified as efficient for learning five different class continuous learning tasks with varying degrees of complexity, exhibiting a markedly mitigated catastrophic forgetting at low computational cost. Mapping synaptic weight changes showed that these benefits could be explained by the sparse and targeted synaptic modifications attributed to expectation-based global neuromodulation.

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Scientists have developed an advanced swarm navigation algorithm for cyborg insects that prevents them from becoming stuck while navigating challenging terrain.

Published in Nature Communications, the new algorithm represents a significant advance in . It could pave the way for applications in , search-and-rescue missions, and infrastructure inspection.

Cyborg insects are real insects equipped with tiny electronic devices on their backs—consisting of various sensors like optical and infrared cameras, a battery, and an antenna for communication—that allow their movements to be remotely controlled for specific tasks.

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Machine learning algorithms significantly impact decision-making in high-stakes domains, necessitating a balance between fairness and accuracy. This study introduces an in-processing, multi-objective framework that leverages the Reject Option Classification (ROC) algorithm to simultaneously optimize fairness and accuracy while safeguarding protected attributes such as age and gender.

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We investigate the properties of a quantum walk which can simulate the behavior of a spin 1/2 particle in a model with an ordinary spatial dimension, and one extra dimension with warped geometry between two branes. Such a setup constitutes a \(1+1\) dimensional version of the Randall–Sundrum model, which plays an important role in high energy physics. In the continuum spacetime limit, the quantum walk reproduces the Dirac equation corresponding to the model, which allows to anticipate some of the properties that can be reproduced by the quantum walk. In particular, we observe that the probability distribution becomes, at large time steps, concentrated near the “low energy” brane, and can be approximated as the lowest eigenstate of the continuum Hamiltonian that is compatible with the symmetries of the model. In this way, we obtain a localization effect whose strength is controlled by a warp coefficient. In other words, here localization arises from the geometry of the model, at variance with the usual effect that is originated from random irregularities, as in Anderson localization. In summary, we establish an interesting correspondence between a high energy physics model and localization in quantum walks.

Anglés-Castillo, A., Pérez, A. A quantum walk simulation of extra dimensions with warped geometry. Sci Rep 12, 1926 (2022). https://doi.org/10.1038/s41598-022-05673-2

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DARPA seeks to revolutionize the practice of anti-money laundering through its A3ML program. A3ML aims to develop algorithms to sift through financial transactions graphs for suspicious patterns, learn new patterns to anticipate future activities, and develop techniques to represent patterns of illicit financial behavior in a concise, machine-readable format that is also easily understood by human analysts. The program’s success hinges on algorithms’ ability to learn a precise representation of how bad actors move money around the world without sharing sensitive data.

DARPA wants to eliminate global money laundering by replacing the current manual, reactive, and expensive analytic practices with agile, algorithmic methods.

Money laundering directly harms American citizens and global interests. Half of North Korea’s nuclear program is funded by laundered funds, according to statements by the White House1, while a federal indictment alleges that money launderers tied to Chinese underground banking are a primary source of financial services for Mexico’s Sinaloa cartel 2.

Despite recent anti-money laundering efforts, the United States (U.S.) still faces challenges in countering money laundering effectively for several reasons. According to Congressional research, money laundering schemes often evade detection and disruption, as anti-money laundering (AML) efforts today rely on manual analysis of large amounts of data and are limited by finite resources and human cognitive processing speed3.

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MicroAlgo Inc. has announced the development of a quantum algorithm it claims significantly enhances the efficiency and accuracy of quantum computing operations. According to a company press release, this advance focuses on implementing a FULL adder operation — an essential arithmetic unit — using CPU registers in quantum gate computers.

The company says this achievement could open new pathways for the design and practical application of quantum gate computing systems. However, it’s important to point out that the company did not cite supporting research papers or third-party validations in the announcement.

Quantum gate computers operate by applying quantum gates to qubits, which are the basic units of quantum information. Unlike classical bits that represent data as either “0” or “1,” qubits can exist in a superposition of probabilistic states, theoretically enabling quantum systems to process specific tasks more efficiently than classical computers. According to the press release, MicroAlgo’s innovation leverages quantum gates and the properties of qubits, including superposition and entanglement, to simulate and perform FULL adder operations.

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As DARPA forges ahead with this new initiative, it raises important questions about the balance between enhancing national security and safeguarding individual privacy and civil liberties.

The potential repercussions of deploying sophisticated algorithms to interpret human behavior could lead to ethical dilemmas and increased scrutiny from civil rights advocates.

In summary, DARPA’s Theory of Mind program is positioned at the intersection of technology and national security, focusing on leveraging machine learning to improve decision-making in complex scenarios.

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For the first time, a framework shows Einstein’s relativity aligns with quantum physics.

Scientists have finally figured out a way to connect the dots between the macroscopic and the microscopic worlds. Their magical equation might provide us answers to questions like why black holes don’t collapse and how quantum gravity works.

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This behavior highlights a critical issue: even systems designed for seemingly harmless tasks can produce unforeseen outcomes when granted enough autonomy.

The challenges posed by AI today are reminiscent of automated trading systems in financial markets. Algorithms designed to optimize trades have triggered flash crashes —sudden, extreme market volatility occurring within seconds, too fast for human intervention to correct.

Similarly, modern AI systems are built to optimize tasks at extraordinary speeds. Without robust controls, their growing complexity and autonomy could unleash consequences no one anticipated—just as automated trading once disrupted financial markets.

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When quantum electrodynamics, the quantum field theory of electrons and photons, was being developed after World War II, one of the major challenges for theorists was calculating a value for the Lamb shift, the energy of a photon resulting from an electron transitioning from one hydrogen hyperfine energy level to another.

The effect was first detected by Willis Lamb and Robert Retherford in 1947, with the emitted photon having a frequency of 1,000 megahertz, corresponding to a photon wavelength of 30 cm and an energy of 4 millionths of an electronvolt—right on the lower edge of the microwave spectrum. It came when the one electron of the hydrogen atom transitioned from the 2P1/2 energy level to the 2S1/2 level. (The leftmost number is the principal quantum number, much like the discrete but increasing circular orbits of the Bohr atom.)

Conventional quantum mechanics didn’t have such transitions, and Dirac’s relativistic Schrödinger equation (naturally called the Dirac equation) did not have such a hyperfine transition either, because the shift is a consequence of interactions with the vacuum, and Dirac’s vacuum was a “sea” that did not interact with real particles.

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