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Computer Modeling

A New Neural Network Paradigm: Unlocking Memory Retrieval with Input-Driven Plasticity

Listen to the first notes of an old, beloved song. Can you name that tune? If you can, congratulations — it’s a triumph of your associative memory, in which one piece of information (the first few notes) triggers the memory of the entire pattern (the song), without you actually having to hear the rest of the song again. We use this handy neural mechanism to learn, remember, solve problems and generally navigate our reality.

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The human brain has an incredible ability to associate pieces of information and retrieve complete patterns from memory. This associative memory mechanism allows us to learn, remember, and navigate our reality with ease. However, as researchers continue to study and understand this complex process, they are working towards developing artificial intelligence systems that can mimic the brain’s capabilities.

One such system is the Input-Driven Plasticity (IDP) model, proposed by a team of researchers led by Francesco Bullo at UC Santa Barbara and Simone Betteti at the University of Padua in Italy. This new neural network paradigm aims to address the limitations of traditional Hopfield networks and provide a more accurate understanding of how memory retrieval works.

The IDP model is based on the idea that as we experience the world around us, the signals we receive enable us to retrieve memories. According to Bullo, “the stimulus from the external world changes the energy landscape at the same time” – essentially, it simplifies the energy landscape so that no matter what our initial position, we will roll down to the correct memory.

This dynamic mechanism allows the IDP model to be robust to noise and ambiguity in input. The researchers explain that when an individual is gazing at a scene, their gaze shifts between different components of the scene, making it easier for the network to prioritize certain stimuli over others.

While this new neural network paradigm has potential applications in designing future machine learning systems, it also highlights the connection between associative memory systems and large language models like ChatGPT. As Bullo notes, “there’s a lot of potential for the model to be helpful in designing future machine learning systems.”

The IDP model represents an exciting step forward in understanding how memory retrieval works, both in humans and artificial intelligence systems. By unlocking the secrets of this complex process, researchers can develop more accurate and robust models that will enable machines to better understand and interact with the world around them.

The IDP model’s ability to simplify the energy landscape through input-driven plasticity has significant implications for how we design future machine learning systems. As Betteti explains, “once you lock into the input to focus on, the network adjusts itself to prioritize it.” This focus on prioritizing certain stimuli over others is also a key mechanism behind another neural network architecture, the transformer.

In conclusion, the IDP model offers a fresh perspective on how memory retrieval works and has the potential to unlock new capabilities in machine learning systems. By continuing to study this complex process, researchers can develop more accurate and robust models that will enable machines to better understand and interact with the world around them.

Artificial Intelligence

Google’s Deepfake Hunter: Exposing Manipulated Videos with a Universal Detector

AI-generated videos are becoming dangerously convincing and UC Riverside researchers have teamed up with Google to fight back. Their new system, UNITE, can detect deepfakes even when faces aren’t visible, going beyond traditional methods by scanning backgrounds, motion, and subtle cues. As fake content becomes easier to generate and harder to detect, this universal tool might become essential for newsrooms and social media platforms trying to safeguard the truth.

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In an era where manipulated videos can spread disinformation, bully people, and incite harm, researchers at the University of California, Riverside (UCR), have created a powerful new system to expose these fakes. Amit Roy-Chowdhury, a professor of electrical and computer engineering, and doctoral candidate Rohit Kundu, teamed up with Google scientists to develop an artificial intelligence model that detects video tampering – even when manipulations go far beyond face swaps and altered speech.

Their new system, called the Universal Network for Identifying Tampered and synthEtic videos (UNITE), detects forgeries by examining not just faces but full video frames, including backgrounds and motion patterns. This analysis makes it one of the first tools capable of identifying synthetic or doctored videos that do not rely on facial content.

“Deepfakes have evolved,” Kundu said. “They’re not just about face swaps anymore. People are now creating entirely fake videos – from faces to backgrounds – using powerful generative models. Our system is built to catch all of that.”

UNITE’s development comes as text-to-video and image-to-video generation have become widely available online. These AI platforms enable virtually anyone to fabricate highly convincing videos, posing serious risks to individuals, institutions, and democracy itself.

“It’s scary how accessible these tools have become,” Kundu said. “Anyone with moderate skills can bypass safety filters and generate realistic videos of public figures saying things they never said.”

Kundu explained that earlier deepfake detectors focused almost entirely on face cues. If there’s no face in the frame, many detectors simply don’t work. But disinformation can come in many forms. Altering a scene’s background can distort the truth just as easily.

To address this, UNITE uses a transformer-based deep learning model to analyze video clips. It detects subtle spatial and temporal inconsistencies – cues often missed by previous systems. The model draws on a foundational AI framework known as SigLIP, which extracts features not bound to a specific person or object. A novel training method, dubbed “attention-diversity loss,” prompts the system to monitor multiple visual regions in each frame, preventing it from focusing solely on faces.

The result is a universal detector capable of flagging a range of forgeries – from simple facial swaps to complex, fully synthetic videos generated without any real footage. It’s one model that handles all these scenarios,” Kundu said. “That’s what makes it universal.”

The researchers presented their findings at the high-ranking 2025 Conference on Computer Vision and Pattern Recognition (CVPR) in Nashville, Tenn. Their paper, led by Kundu, outlines UNITE’s architecture and training methodology.

While still in development, UNITE could soon play a vital role in defending against video disinformation. Potential users include social media platforms, fact-checkers, and newsrooms working to prevent manipulated videos from going viral.

“People deserve to know whether what they’re seeing is real,” Kundu said. “And as AI gets better at faking reality, we have to get better at revealing the truth.”

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Computational Biology

Quantum Leap Forward: Finnish Researchers Achieve Record-Breaking Qubit Coherence

Aalto University physicists in Finland have set a new benchmark in quantum computing by achieving a record-breaking millisecond coherence in a transmon qubit — nearly doubling prior limits. This development not only opens the door to far more powerful and stable quantum computations but also reduces the burden of error correction.

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The scientific community has made a significant breakthrough in the field of quantum computing, as researchers from Aalto University in Finland have achieved a record-breaking millisecond coherence time for a transmon qubit. This achievement surpasses previous scientifically published records, marking a major leap forward in computational technology.

Longer qubit coherence allows for an extended window of time in which quantum computers can execute error-free operations, enabling more complex quantum computations and reducing the resources needed for quantum error correction. This is a crucial step towards noiseless quantum computing.

The researchers’ findings were published in the prestigious peer-reviewed journal Nature Communications, with the team led by PhD student Mikko Tuokkola. The median reading of half a millisecond also surpasses current recorded readings, making this achievement even more impressive.

Finland’s position at the forefront of quantum science and technology has been cemented through this landmark achievement. The research was conducted by the Quantum Computing and Devices (QCD) group at Aalto University, which is part of the Academy of Finland Centre of Excellence in Quantum Technology (QTF) and the Finnish Quantum Flagship (FQF).

The success reflects the high quality of Micronova cleanrooms at OtaNano, Finland’s national research infrastructure for micro-, nano-, and quantum technologies. Professor Mikko Möttönen, who heads the QCD group, stated that this achievement has strengthened Finland’s standing as a global leader in the field.

To further advance the field, the QCD group has recently opened positions for senior staff members and postdocs to achieve future breakthroughs faster. This commitment to innovation and collaboration will likely lead to even more significant advancements in quantum computing and its applications.

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Chemistry

“Twisted Technology: A Breakthrough in Chiral Metasurfaces Reveals Hidden Images”

Using advanced metasurfaces, researchers can now twist light to uncover hidden images and detect molecular handedness, potentially revolutionizing data encryption, biosensing, and drug safety.

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Imagine a world where technology could reveal hidden secrets just like magic. Scientists have made a breakthrough in creating artificial optical structures called metasurfaces that can control the way they interact with polarized light. This innovation has potential applications in data encryption, biosensing, and quantum technologies.

The team from the Bionanophotonic Systems Laboratory at EPFL’s School of Engineering collaborated with researchers in Australia to create a “chiral design toolkit” that is elegantly simple yet powerful. By varying the orientation of tiny elements called meta-atoms within a 2D lattice, scientists can control the resulting metasurface’s interaction with polarized light.

The innovation was showcased by encoding two different images on a metasurface optimized for the invisible mid-infrared range of the electromagnetic spectrum. The first image of an Australian cockatoo was encoded in the size of the meta-atoms, which represented pixels, and could be decoded with unpolarized light. The second image of the Swiss Matterhorn was encoded using the orientation of the meta-atoms, so that when exposed to circularly polarized light, the metasurface revealed a picture of the iconic mountain.

“This experiment showcased our technique’s ability to produce a dual layer ‘watermark’ invisible to the human eye, paving the way for advanced anticounterfeiting, camouflage and security applications,” says Ivan Sinev, researcher at the Bionanophotonics Systems Lab.

Beyond encryption, the team’s approach has potential applications in quantum technologies, where polarized light is used to perform computations. The ability to map chiral responses across large surfaces could also streamline biosensing.

“We can use chiral metastructures like ours to sense, for example, drug composition or purity from small-volume samples. Nature is chiral, and the ability to distinguish between left- and right-handed molecules is essential, as it could make the difference between a medicine and a toxin,” says Felix Richter, researcher at the Bionanophotonic Systems Lab.

This breakthrough has opened doors to new possibilities in data encryption, biosensing, and quantum technologies. The future of technology is indeed bright, and twisted light just got a whole lot more interesting.

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