Collaborating Authors


Czech Tech Does the Monster Mash: Animating 3D Models in Seconds


Animated films such as Toy Story, The Lion King, and Spirited Away hold cherished childhood memories for people all around the world. While we fondly remember the adorable characters and catchy theme songs, we're less likely to reflect on the complex computer animation processes that enabled these classics. Such processes, especially with 3D animation, are incredibly time-consuming, with intermediate steps involving modelling, defining skeletal joints and deformation parameters, posing, keyframe setting, and much more. Monster Mash, a novel AI-powered 3D modelling and animation tool, aims to make these arduous 3D animation processes a whole lot easier. The proposed framework comes from researchers at Czech Technical University in Prague, Google Research, University of Washington and ETH Zurich, and enables users to create animated 3D models from a single view. Building on the 1999 work Teddy: A Sketching Interface for 3D Freeform Design by Igarashi et al, Monster Mash is described as the first sketch-based tool that enables creating and animating a smooth, consistent 3D model from a single viewpoint within seconds.

Everything AI?


Artificial intelligence is having a growing impact on our daily lives and is also revolutionizing research. ETH Zurich recognizes its responsibility in this area and is striving to promote innovation and trust in this fast-evolving technology. Sometimes a machine takes everyone by surprise. A recent example occurred at the opening event of Scientifica 2019, where ETH robotics specialists had trained a drone to welcome visitors by writing the word "enjoy." At first everything seemed normal as the drone, known as Voliro, began to write.

A deep learning model achieves super-human performance at Gran Turismo Sport


Over the past few decades, research teams worldwide have developed machine learning and deep learning techniques that can achieve human-comparable performance on a variety of tasks. Some of these models were also trained to play renowned board or videogames, such as the Ancient Chinese game Go or Atari arcade games, in order to further assess their capabilities and performance. Researchers at University of Zurich and SONY AI Zurich have recently tested the performance of a deep reinforcement learning-based approach that was trained to play Gran Turismo Sport, the renowned car racing video game developed by Polyphony Digital and published by Sony Interactive Entertainment. Their findings, presented in a paper pre-published on arXiv, further highlight the potential of deep learning techniques for controlling cars in simulated environments. "Autonomous driving at high speed is a challenging task that requires generating fast and precise actions even when the vehicle is approaching its physical limits," Yunlong Song, one of the researchers who carried out the study, told TechXplore.

Artificial intelligence explains hydrogen's behavior on giant planets


The giant planets in our solar system are made mainly of hydrogen, mostly in a liquid state. Near the planets- surface, hydrogen exists in an insulating, molecular form – H 2 – but closer to the center, it takes on a metallic form where individual atoms can move around freely. Professor Michele Ceriotti, who heads the Laboratory of Computational Science and Modelling (COSMO) within EPFL's School of Engineering, along with colleagues from the University of Cambridge and IBM Zurich, have used computer simulations to understand the nature of this elusive transition. That's one reason why scientists study it so much. What makes this phenomenon on giant planets fairly unique – and interesting – is that the transition is between two forms of a liquid state, and not from a liquid to a gaseous or solid state,- says Ceriotti.

Apple launches AI/ML residency program to attract niche experts


As Apple's artificial language and machine learning initiatives continue to expand, its interest in attracting talent has grown -- a theme that's barely under the surface of the company's occasionally updated Machine Learning Research blog. Now Apple is openly seeking to recruit U.S. and European candidates with niche expertise for a yearlong AI/ML residency program, promising immersion and mentoring that will advance their careers. The goal is apparently to find people whose interests aren't necessarily AI/ML specific, then give them the knowledge and tools to apply machine learning and deep learning to their disciplines -- a process that will widen Apple's ability to solve users' problems in those disciplines. Apple says its ideal candidates would come from fields such as cognitive science, psychology, physics, robotics, public health, or computer graphics, but in any case should have programming proficiency and either a graduate degree or equivalent industry experience. Residencies are currently being offered in Cupertino, California; Seattle, Washington; Cambridge, U.K.; Zurich, and "various locations within Germany" for a summer 2021 start date, with assignment descriptions that vary between locations.

A robotic exoskeleton for paraplegics – IAM Network


Combining robotics with artificial intelligence (AI), an Indian health tech startup has developed an exoskeleton used as a robotic arm/leg for paraplegic patients.Arguably the first in the country, the indigenously designed device could be a potential alternative to the expensive products sourced from abroad.The startup, GenElek Technologies, was chosen to represent India at the Powered Exoskeleton Race at Cybathlon 2020 in Zurich before the Covid-19 outbreak forced a reschedule. Two former Indian Army paraplegic soldiers from the Paraplegic Rehabilitation Centre (PRC), Mohali, were to wear the robotic gear and compete with 17 other international teams.Exoskeletons (externally worn robotic support system) make it possible for people with neurological conditions such as paralysis, stroke and spinal cord injury to walk or move better. GenElek's model was to customise its design and tailor it to individual needs, the startup's founder John Ignatius Kujur told DH.So, how does it incorporate artificial intelligence? The data is collected in real time, interpreted and relayed by AI to the cloud. It gets processed in real time by a medical expert monitoring the patient's treatment.Not for amputeesThe device is not for amputees, John explained.

New Products


![Figure][1] Porvair Sciences expanded range of heat-sealing films for microplates and tubes now includes clear, strong (Dura), heavy-duty and gas-permeable products with several variations that add flexibility and compatibility to suit all requirements for sample storage and collection. When heat is applied to these products, a tight seal is formed on polypropylene, polyethylene, polystyrene, polycarbonate, and cyclin olefin copolymer (COC) microplates and tubes. Optically clear films from Porvair are available in a variety of formats, including peelable, pierceable, and strong nonpeelable. These seals are ideal for a wide range of applications, such as imaging, fluorescence, colorimetric assays, and PCR/quantitative PCR. All clear seals are also compatible with devices that use a heat-pressurized lid. The Dura range of durable, foil-based heat-sealing films are ideal for high-temperature applications and room-temperature storage. They come in a variety of formats, including peelable, solvent resistant (including DMSO) and pierceable. All Dura heat-sealing films are autoclavable. Absolute Antibody Ltd., along with the University of Zurich, now offers synthetic nanobodies against the receptor binding domain of SARS-CoV-2, the coronavirus that causes COVID-19. Researchers are exploring the potential of nanobodies as inhalable COVID-19 drugs, which would be easier to administer and could reach patients' lungs faster than other treatments. Absolute Antibody recombinantly produces the SARS-CoV-2 synthetic nanobodies for ensured batch-to-batch reproducibility, high purity, and low endotoxin levels. In addition, we use antibody engineering to fuse the nanobodies to fragment crystallizable (Fc) domains in different species, isotypes, and subtypes for use as serological controls. CELLvo Human Articular Chondrocytes from StemBioSys are low-passage (P0 and P1) cells freshly isolated from young, healthy donors and available for regenerative medicine and basic research. When cultured with CELLvo Matrix, the cells home, attach, and proliferate more rapidly than chondrocytes cultured under standard conditions. They also exhibit similar morphology and phenotype to in vivo chondrocytes, such as a high ratio of type 2 collagen to type 1 collagen. Because the cells and the growth conditions are never exposed to nonhuman proteins, they offer a streamlined pathway to clinical studies. The CELLvo Human Chondrocyte cell culture system delivers improved performance and exceptional research potential to basic and translational studies of osteoarthritis, joint function, chondrocyte differentiation, tissue engineering, and chondrocyte implantation. The FlowSyn Automated Loop Filling (FlowSyn Auto-LF) system is a powerful, highly efficient module for running multiple experiments with multiple reagent inputs under different sets of chemical conditions. This product is ideal for robotic synthesis groups looking to generate combinatorial libraries utilizing the power of flow chemistry. With a choice of two or four channels, this intelligent, simultaneous loop-filling and fraction-collection system saves valuable time by loading the reagent loops for the next reaction while the current run is in progress. Fully integrated wash protocols minimize the risk of cross-contamination. The unit incorporates an automated liquid handler that can accommodate up to 96 samples. The BioTek LogPhase 600 Microbiology Reader is the only four-plate microplate reader built to perform microbial growth curve analysis. Multiple plate measurements increase throughput and reduce the need for costly single-plate absorbance readers that take up valuable bench space. Additionally, highly consistent environmental conditions support high-quality absorbance results without growth curve artifacts, even in bacterial and yeast assays with extended incubation periods. The compact LogPhase 600 features a robust, stringently tested shaking mechanism to ensure that cells remain in suspension and that the hardware will not wear out, even over the course of long-term kinetic assays. Incubation is controlled by several sensors for even heating and includes Condensation Control, which prevents light scatter and artifacts due to condensation. The LogPhase 600 App controls the reader, capturing data and providing powerful analysis with an easy-to-use interface. This reader is ideal for yeast and bacterial growth assays, antimicrobial resistance studies, algal and biofuel research, and food and beverage testing. The T-SPOT Discovery SARS-CoV-2 kit from Oxford Immunotec has been developed to aid in the investigation of the T-cell response to SARS-CoV-2 infection. The kit uses a modified enzyme-linked immunospot (ELISPOT) assay, where samples prepared from peripheral blood are stimulated in vitro by peptide pools from SARS-CoV-2. This restimulation allows SARS-CoV-2–specific T cells to be enumerated. The kit builds on our experience with tuberculosis diagnosis and the assessment of immune response to cytomegalovirus in transplant patients to apply our proprietary T-SPOT technology to the fight against COVID-19. While serology is able to detect antibodies to SARS-CoV-2 in the blood of some individuals after infection, little is currently known about how this confers immunity to COVID-19. The T-SPOT technology goes further than simple serology by interrogating the immune system's T-cell response, and will help researchers measure the strength of that response to SARS-CoV-2. The strength of this response may be linked to protection from reinfection. [1]: pending:yes

A hyperdimensional computing system that performs all core computations in-memory


Hyperdimensional computing (HDC) is an emerging computing approach inspired by patterns of neural activity in the human brain. This unique type of computing can allow artificial intelligence systems to retain memories and process new information based on data or scenarios it previously encountered. Most HDC systems developed in the past only perform well on specific tasks, such as natural language processing (NLP) or time series problems. In a paper published in Nature Electronics, researchers at IBM Research- Zurich and ETH Zurich presented a new HDC system that performs all core computations in-memory and that could be applied to a variety of tasks. "Our work was initiated by the natural fit between the two concepts of in-memory computing and hyperdimensional computing," Abu Sebastian and Abbas Rahimi, the two lead researchers behind the study, told TechXplore.

DeepDream: How Alexander Mordvintsev Excavated the Computer's Hidden Layers


Early in the morning on May 18, 2015, Alexander Mordvintsev made an amazing discovery. He had been having trouble sleeping. Just after midnight, he awoke with a start. He was sure he'd heard a noise in the Zurich apartment where he lived with his wife and child. Afraid that he hadn't locked the door to the terrace, he ran out of the bedroom to check if there was an intruder. All was fine; the terrace door was locked, and there was no intruder.

Artificial Intelligence and Robotics Research is on Oct 26 2020 at Zurich


The IOT consist of computer devices, digital machines, objects and people that are given with unique identifiers. It has an ability to transfer data without human to human interaction or human to computer interaction over a network. The Internet of Things (IoT) refers to the networked interconnection of objects equipped with ubiquitous intelligence, or simply "smart objects". Several endeavours have been made in the last decade to bring together standard modelling languages with generic simulation frameworks.