Semiconductors are the foundation of all modern electronics. Now, researchers at Linköping University, Sweden, have developed a new method where organic semiconductors can become more conductive with the help of air as a dopant. The study, published in the journal Nature, is a significant step towards future cheap and sustainable organic semiconductors.
Disk-related back pain may one day meet its therapeutic match: Gene therapy delivered by naturally derived nanocarriers that, a new study shows, repairs damaged disks in the spine and lowers pain symptoms in mice.
Prof. Wang Qi’s research group from the Hefei Institutes of Physical Science of the Chinese Academy of Sciences has synthesized iron-and nitrogen-co-doped CoFeP-N nanowires for high-efficiency electrocatalytic water splitting.
To overcome these obstacles, the research team developed a novel pulse irradiation synthesis method that achieves both a low processing temperature and an ultra-short reaction time, surpassing the capabilities of conventional techniques.
With the new method for preparing metal sulfide thin films at low temperatures, these detectors can now achieve higher performance on suitable flexible substrates. This creates exciting possibilities for thermal imaging applications in security monitoring, fire detection, military surveillance, and other fields.
Additionally, the photothermoelectric effect allows for the conversion of invisible infrared light into electrical signals, paving the way for applications in high-speed communications and optical signal processing.
In plants and animals, the basic packaging units of DNA, which carry genetic information, are the so-called nucleosomes. A nucleosome consists of a segment of DNA wound around eight proteins known as histones.
In an important step toward more effective gene therapies for brain diseases, researchers from the Broad Institute of MIT and Harvard have engineered a gene-delivery vehicle that uses a human protein to efficiently cross the blood-brain barrier and deliver a disease-relevant gene to the brain in mice expressing the human protein. Because the vehicle binds to a well-studied protein in the blood-brain barrier, the scientists say it has a good chance of working in patients.
Everything in the universe may be preordained, according to physics.
By Dan Falk
Continue reading “Has Quantum Physics Determined Your Future?” »
Quintessence.
Dive into the cosmic mystery of dark energy with the groundbreaking findings from DESI! Explore how the largest-ever 3D map of the universe challenges our understanding of dark energy and hints at a dynamic cosmos. Discover what this means for the fate of the universe and how it could reshape our view of the cosmos. Join us as we unravel the secrets of the dark universe in this exciting episode!
Continue reading “Largest 3D map the Universe’s Dark Energy May Be Evolving” »
Artificial neural networks (ANNs) show a remarkable pattern when trained on natural data irrespective of exact initialization, dataset, or training objective; models trained on the same data domain converge to similar learned patterns. For example, for different image models, the initial layer weights tend to converge to Gabor filters and color-contrast detectors. Many such features suggest global representation that goes beyond biological and artificial systems, and these features are observed in the visual cortex. These findings are practical and well-established in the field of machines that can interpret literature but lack theoretical explanations.
Localized versions of canonical 2D Fourier basis functions are the most observed universal features in image models, e.g. Gabor filters or wavelets. When vision models are trained on tasks like efficient coding, classification, temporal coherence, and next-step prediction goals, these Fourier features pop up in the model’s initial layers. Apart from this, Non-localized Fourier features have been observed in networks trained to solve tasks where cyclic wraparound is allowed, for example, modular arithmetic, more general group compositions, or invariance to the group of cyclic translations.
Researchers from KTH, Redwood Center for Theoretical Neuroscience, and UC Santa Barbara introduced a mathematical explanation for the rise of Fourier features in learning systems like neural networks. This rise is due to the downstream invariance of the learner that becomes insensitive to certain transformations, e.g., planar translation or rotation. The team has derived theoretical guarantees regarding Fourier features in invariant learners that can be used in different machine-learning models. This derivation is based on the concept that invariance is a fundamental bias that can be injected implicitly and sometimes explicitly into learning systems due to the symmetries in natural data.
Once a buzzword, neuromorphic engineering is gaining traction in the semiconductor industry.
Neuromorphic engineering is finally getting closer to market reality, propelled by the AI/ML-driven need for low-power, high-performance solutions.
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