A digital scheduling framework combining machine learning, optimization and blockchain technology could reduce carbon emissions at a lower cost in regional energy systems, according to research in the International Journal of Environment and Pollution. The system cut the marginal emission-reduction cost—the additional expense of removing one more ton of carbon—by about 12% when the carbon quota was restricted to half the system’s baseline emissions.A digital scheduling framework combining machine learning, optimization and blockchain technology could reduce carbon emissions at a lower cost in regional energy systems, according to research in the International Journal of Environment and Pollution. The system cut the marginal emission-reduction cost—the additional expense of removing one more ton of carbon—by about 12% when the carbon quota was restricted to half the system’s baseline emissions.Energy & Green Tech[#item_full_content]
In project CONVOLVE, researchers successfully used new chip-design techniques to develop powerful AI computing chips close to where data is generated and used, often referred to as edge computing.In project CONVOLVE, researchers successfully used new chip-design techniques to develop powerful AI computing chips close to where data is generated and used, often referred to as edge computing.Machine learning & AI[#item_full_content]
Researchers in the Department of Electrical and Computer Engineering of the Faculty of Engineering and the Centre for Advanced Semiconductors and Integrated Circuits (CASIC) at the University of Hong Kong (HKU) have made a breakthrough in brain-inspired computing. In collaboration with Hewlett Packard Labs, the team has developed a memristor chip that overcomes a long-standing limit on the capacity of “associative memory,” the brain-like ability to recall complete information from a partial cue, while keeping it reliable even when a large fraction of the hardware fails.Researchers in the Department of Electrical and Computer Engineering of the Faculty of Engineering and the Centre for Advanced Semiconductors and Integrated Circuits (CASIC) at the University of Hong Kong (HKU) have made a breakthrough in brain-inspired computing. In collaboration with Hewlett Packard Labs, the team has developed a memristor chip that overcomes a long-standing limit on the capacity of “associative memory,” the brain-like ability to recall complete information from a partial cue, while keeping it reliable even when a large fraction of the hardware fails.Hardware[#item_full_content]
A research team led by principal researcher Sang-Chul Lee of the Division of Nanotechnology at DGIST has developed AI technology that automatically identifies and compensates for user groups whose recommendation performance deteriorates significantly after user data is deleted.A research team led by principal researcher Sang-Chul Lee of the Division of Nanotechnology at DGIST has developed AI technology that automatically identifies and compensates for user groups whose recommendation performance deteriorates significantly after user data is deleted.Machine learning & AI[#item_full_content]
A research group comprising Associate Professor Yoshihiro Nakata and Taisei Mogi from the Graduate School of Informatics and Engineering at The University of Electro-Communications (UEC), Japan, and Mari Saito of Sony Corporation has developed MOFU (MOrphing Fluffy Unit), a mobile robot capable of whole-body expansion and contraction. The group investigated how whole-body expansion-contraction affects perceived animacy, or the extent to which the robot is perceived as lifelike. In addition to expansion-contraction and locomotion, MOFU was designed with features including quiet operation and a soft, fluffy exterior. The study was published in PLOS ONE.A research group comprising Associate Professor Yoshihiro Nakata and Taisei Mogi from the Graduate School of Informatics and Engineering at The University of Electro-Communications (UEC), Japan, and Mari Saito of Sony Corporation has developed MOFU (MOrphing Fluffy Unit), a mobile robot capable of whole-body expansion and contraction. The group investigated how whole-body expansion-contraction affects perceived animacy, or the extent to which the robot is perceived as lifelike. In addition to expansion-contraction and locomotion, MOFU was designed with features including quiet operation and a soft, fluffy exterior. The study was published in PLOS ONE.[#item_full_content]
Hydrogen could help store renewable energy and power fuel cells, but storing it remains difficult because hydrogen gas has a low density under everyday conditions. Compressing it to high pressure or cooling it into a liquid requires substantial energy and specialized equipment. Solid materials that absorb or adsorb hydrogen offer an alternative, but finding materials that store enough hydrogen, release it under useful conditions, work quickly and survive repeated use remains a complex challenge.Hydrogen could help store renewable energy and power fuel cells, but storing it remains difficult because hydrogen gas has a low density under everyday conditions. Compressing it to high pressure or cooling it into a liquid requires substantial energy and specialized equipment. Solid materials that absorb or adsorb hydrogen offer an alternative, but finding materials that store enough hydrogen, release it under useful conditions, work quickly and survive repeated use remains a complex challenge.Energy & Green Tech[#item_full_content]
An editorial by Charles Branas and Bruce Levine proposes norms for the use of AI in science.An editorial by Charles Branas and Bruce Levine proposes norms for the use of AI in science.Machine learning & AI[#item_full_content]
Mistral announced Tuesday that it had raised three billion euros ($3.5 billion) in what it described as the largest-ever European tech fundraising, valuing the French AI firm at more than 21 billion euros.Mistral announced Tuesday that it had raised three billion euros ($3.5 billion) in what it described as the largest-ever European tech fundraising, valuing the French AI firm at more than 21 billion euros.Machine learning & AI[#item_full_content]
Reconstructing a three-dimensional object or environment from multiple photographs is a central problem in computer vision. The resulting models support applications such as robotics, augmented and virtual reality, digital twins, and cultural heritage preservation. An effective reconstruction system must represent both the visual appearance of a scene and the geometry of its surfaces. Achieving these goals simultaneously, however, remains challenging.Reconstructing a three-dimensional object or environment from multiple photographs is a central problem in computer vision. The resulting models support applications such as robotics, augmented and virtual reality, digital twins, and cultural heritage preservation. An effective reconstruction system must represent both the visual appearance of a scene and the geometry of its surfaces. Achieving these goals simultaneously, however, remains challenging.[#item_full_content]
Reconstructing a three-dimensional object or environment from multiple photographs is a central problem in computer vision. The resulting models support applications such as robotics, augmented and virtual reality, digital twins, and cultural heritage preservation. An effective reconstruction system must represent both the visual appearance of a scene and the geometry of its surfaces. Achieving these goals simultaneously, however, remains challenging.Reconstructing a three-dimensional object or environment from multiple photographs is a central problem in computer vision. The resulting models support applications such as robotics, augmented and virtual reality, digital twins, and cultural heritage preservation. An effective reconstruction system must represent both the visual appearance of a scene and the geometry of its surfaces. Achieving these goals simultaneously, however, remains challenging.Computer Sciences[#item_full_content]
