Aurelia Butler

As every athletic or fashion-conscious person knows, our body image is not always accurate or realistic, but it’s an important piece of information that determines how we function in the world. When you get dressed or play ball, your brain is constantly planning ahead so that you can move your body without bumping, tripping, or falling over.
We humans acquire our body-model as infants, and robots are following suit. A Columbia Engineering team announced today they have created a robot that — for the first time — is able to learn a model of its entire body from scratch, without any human assistance. In a new study published by Science Robotics,, the researchers demonstrate how their robot created a kinematic model of itself, and then used its self-model to plan motion, reach goals, and avoid obstacles in a variety of situations. It even automatically recognized and then compensated for damage to its body.
Robot watches itself like an an infant exploring itself in a hall of mirrors
The researchers placed a robotic arm inside a circle of five streaming video cameras. The robot watched itself through the cameras as it undulated freely. Like an infant exploring itself for the first time in a hall of mirrors, the robot wiggled and contorted to learn how exactly its body moved in response to various motor commands. After about three hours, the robot stopped. Its internal deep neural network had finished learning the relationship between the robot’s motor actions and the volume it occupied in its environment.
“We were really curious to see how the robot imagined itself,” said Hod Lipson, professor of mechanical engineering and director of Columbia’s Creative Machines Lab, where the work was done. “But you can’t just peek into a neural network, it’s a black box.” After the researchers struggled with various visualization techniques, the self-image gradually emerged. “It was a sort of gently flickering cloud that appeared to engulf the robot’s three-dimensional body,” said Lipson. “As the robot moved, the flickering cloud gently followed it.” The robot’s self-model was accurate to about 1% of its workspace.
Self-modeling robots will lead to more self-reliant autonomous systems
The ability of robots to model themselves without being assisted by engineers is important for many reasons: Not only does it save labor, but it also allows the robot to keep up with its own wear-and-tear, and even detect and compensate for damage. The authors argue that this ability is important as we need autonomous systems to be more self-reliant. A factory robot, for instance, could detect that something isn’t moving right, and compensate or call for assistance.
“We humans clearly have a notion of self,” explained the study’s first author Boyuan Chen, who led the work and is now an assistant professor at Duke University. “Close your eyes and try to imagine how your own body would move if you were to take some action, such as stretch your arms forward or take a step backward. Somewhere inside our brain we have a notion of self, a self-model that informs us what volume of our immediate surroundings we occupy, and how that volume changes as we move.”
Self-awareness in robots
The work is part of Lipson’s decades-long quest to find ways to grant robots some form of self-awareness. “Self-modeling is a primitive form of self-awareness,” he explained. “If a robot, animal, or human, has an accurate self-model, it can function better in the world, it can make better decisions, and it has an evolutionary advantage.”
The researchers are aware of the limits, risks, and controversies surrounding granting machines greater autonomy through self-awareness. Lipson is quick to admit that the kind of self-awareness demonstrated in this study is, as he noted, “trivial compared to that of humans, but you have to start somewhere. We have to go slowly and carefully, so we can reap the benefits while minimizing the risks.” More

Medicinal microrobots could help physicians better treat and prevent diseases. But most of these devices are made with synthetic materials that trigger immune responses in vivo. Now, for the first time, researchers reporting in ACS Central Science have used lasers to precisely control neutrophils — a type of white blood cell — as a natural, biocompatible microrobot in living fish. The “neutrobots” performed multiple tasks, showing they could someday deliver drugs to precise locations in the body.
Microrobots currently in development for medical applications would require injections or the consumption of capsules to get them inside an animal or person. But researchers have found that these microscopic objects often trigger immune reactions in small animals, resulting in the removal of microrobots from the body before they can perform their jobs. Using cells already present in the body, such as neutrophils, could be a less invasive alternative for drug delivery that wouldn’t set off the immune system. These white blood cells already naturally pick up nanoparticles and dead red blood cells and can migrate through blood vessels into adjacent tissues, so they are good candidates for becoming microrobots. Previously, researchers have guided neutrophils with lasers in lab dishes, moving them around as “neutrobots.” However, information on whether this approach will work in living animals was lacking. So, Xianchuang Zheng, Baojun Li and colleagues wanted to demonstrate the feasibility of light-driven neutrobots in animals using live zebrafish.
The researchers manipulated and maneuvered neutrophils in zebrafish tails, using focused laser beams as remote optical tweezers. The light-driven microrobot could be moved up to a velocity of 1.3 µm/s, which is three times faster than a neutrophil naturally moves. In their experiments, the researchers used the optical tweezers to precisely and actively control the functions that neutrophils conduct as part of the immune system. For instance, a neutrobot was moved through a blood vessel wall into the surrounding tissue. Another one picked up and transported a plastic nanoparticle, showing its potential for carrying medicine. And when a neutrobot was pushed toward red blood cell debris, it engulfed the pieces. Surprisingly, at the same time, a different neutrophil, which wasn’t controlled by a laser, tried to naturally remove the cellular debris. Because they successfully controlled neutrobots in vivo, the researchers say this study advances the possibilities for targeted drug delivery and precise treatment of diseases.
The authors acknowledge funding from the National Natural Science Foundation of China, the Basic and Applied Basic Research Foundation of Guangdong Province, and the Science and Technology Program of Guangzhou.
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Materials provided by American Chemical Society. Note: Content may be edited for style and length. More

According to ARRS’ American Journal of Roentgenology (AJR), an AI tool for detection of incidental pulmonary embolus (iPE) on conventional contrast-enhanced chest CT examinations had high NPV and moderate PPV for detection, even finding some iPEs missed by radiologists.
“Potential applications of the AI tool include serving as a second reader to help detect additional iPEs or as a worklist triage tool to allow earlier iPE detection and intervention,” wrote lead investigator Kiran Batra from the University of Texas Southwestern Medical Center in Dallas. “Various explanations of misclassifications by the AI tool (both false positives and false negatives) were identified, to provide targets for model improvement.”
Batra and colleagues’ retrospective study included 2,555 patients (1,340 women, 1,215 men; mean age, 53.6 years) who underwent 3,003 conventional contrast-enhanced chest CT examinations between September 2019 and February 2020 at Parkland Health in Dallas, TX. Using an FDA-approved, commercially available AI tool (Aidoc, New York, NY) to detect acute iPE on the images, a vendor-supplied natural language processing algorithm (RepScheme, Tel Aviv, Israel) was then applied to the clinical reports to identify examinations interpreted as positive for iPE.
Ultimately, the commercial AI tool had NPV of 99.8% and PPV of 86.7% for detection of iPE on conventional contrast-enhanced chest CT examinations (i.e., not using CT pulmonary angiography protocols). Of 40 iPEs present in the team’s study sample, 7 were detected only by the clinical reports, and 4 were detected only by AI.
Noting that both the AI tool and clinical reports detected iPEs missed by the other method, “the diagnostic performance of the AI tool did not show significant variation across study subgroups,” the authors of this AJR article added.
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Materials provided by American Roentgen Ray Society. Note: Content may be edited for style and length. More

Researchers at Simon Fraser University have made a crucial breakthrough in the development of quantum technology.
Their research, published in Nature today, describes their observations of over 150,000 silicon ‘T centre’ photon-spin qubits, an important milestone that unlocks immediate opportunities to construct massively scalable quantum computers and the quantum internet that will connect them.
Quantum computing has enormous potential to provide computing power well beyond the capabilities of today’s supercomputers, which could enable advances in many other fields, including chemistry, materials science, medicine and cybersecurity.
In order to make this a reality, it is necessary to produce both stable, long-lived qubits that provide processing power, as well as the communications technology that enables these qubits to link together at scale.
Past research has indicated that silicon can produce some of the most stable and long-lived qubits in the industry. Now the research published by Daniel Higginbottom, Alex Kurkjian, and co-authors provides proof of principle that T centres, a specific luminescent defect in silicon, can provide a ‘photonic link’ between qubits. This comes out of the SFU Silicon Quantum Technology Lab in SFU’s Physics Department, co-led by Stephanie Simmons, Canada Research Chair in Silicon Quantum Technologies and Michael Thewalt, Professor Emeritus.
“This work is the first measurement of single T centresin isolation, and actually, the first measurement of any single spin in silicon to be performed with only optical measurements,” says Stephanie Simmons.
“An emitter like the T centre that combines high-performance spin qubits and optical photon generation is ideal to make scalable, distributed, quantum computers, because they can handle the processing and the communications together, rather than needing to interface two different quantum technologies, one for processing and one for communications,” Simmons says.
In addition, T centres have the advantage of emitting light at the same wavelength that today’s metropolitan fibre communications and telecom networking equipment use.
“With T centres, you can build quantum processors that inherently communicate with other processors,” Simmons says. “When your silicon qubit can communicate by emitting photons (light) in the same band used in data centres and fiber networks, you get these same benefits for connecting the millions of qubits needed for quantum computing.”
Developing quantum technology using silicon provides opportunities to rapidly scale quantum computing. The global semiconductor industry is already able to inexpensively manufacture silicon computer chips at scale, with a staggering degree of precision. This technology forms the backbone of modern computing and networking, from smartphones to the world’s most powerful supercomputers.
“By finding a way to create quantum computing processors in silicon, you can take advantage of all of the years of development, knowledge, and infrastructure used to manufacture conventional computers, rather than creating a whole new industry for quantum manufacturing,” Simmons says. “This represents an almost insurmountable competitive advantage in the international race for a quantum computer.”
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Materials provided by Simon Fraser University. Original written by Erin Brown-John. Note: Content may be edited for style and length. More

Gender-neutral internet searches yield results that nonetheless produce male-dominated output, finds a new study by a team of psychology researchers. Moreover, these search results have an effect on users by promoting gender bias and potentially influencing hiring decisions.
The work, which appears in the journal Proceedings of the National Academy of Sciences (PNAS), is among the latest to uncover how artificial intelligence (AI) can alter our perceptions and actions.
“There is increasing concern that algorithms used by modern AI systems produce discriminatory outputs, presumably because they are trained on data in which societal biases are embedded,” says Madalina Vlasceanu, a postdoctoral fellow in New York University’s Department of Psychology and the paper’s lead author. “As a consequence, their use by humans may result in the propagation, rather than reduction, of existing disparities.”
“These findings call for a model of ethical AI that combines human psychology with computational and sociological approaches to illuminate the formation, operation, and mitigation of algorithmic bias,” adds author David Amodio, a professor in NYU’s Department of Psychology and the University of Amsterdam.
Technology experts have expressed concern that algorithms used by modern AI systems produce discriminatory outputs, presumably because they are trained on data in which societal biases are ingrained.
“Certain 1950s ideas about gender are actually still embedded in our database systems,” Meredith Broussard, author of Artificial Unintelligence: How Computers Misunderstand the World and a professor at NYU’s Arthur L. Carter Journalism Institute, told the Markup earlier this year. More

Superconductivity is a fascinating state of matter in which an electrical current can flow without any resistance. Usually, it can exist in two forms. One is destroyed easily with a magnetic field and has “even parity,” i.e. it has a point symmetric wave function with respect to an inversion point, and one which is stable in magnetic fields applied in certain directions and has “odd parity,” i.e. it has an antisymmetric wave function.
Consequently, the latter should present a characteristic angle dependence of the critical field where superconductivity disappears. But odd-parity superconductivity is rare in nature; only a few materials support this state, and in none of them has the expected angle dependence been observed. In a new publication in PRX, the group by Elena Hassinger and collaborators show that the angle dependence in the superconductor CeRh2As2 is exactly that expected of an odd-parity state.
CeRh2As2 was recently found to exhibit two superconducting states: A low-field state changes into a high-field state at 4 T when a magnetic field is applied along one axis. For varying field directions, we measured the specific heat, magnetic susceptibility, and magnetic torque of this material to obtain the angle dependence of the critical fields. We find that the high-field state quickly disappears when the magnetic field is turned away from the initial axis. These results are in excellent agreement with our model identifying the two states with even- and odd-parity states.
CeRh2As2 presents an extraordinary opportunity to investigate odd-parity superconductivity further. It also allows for testing mechanisms for a transition between two superconducting states, and especially their relation to spin-orbit coupling, multiband physics, and additional ordered states occurring in this material.
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Materials provided by Max Planck Institute for Chemical Physics of Solids. Note: Content may be edited for style and length. More

Immunotherapy is a new cancer treatment that activates the body’s immune system to fight against cancer cells without using chemotherapy or radiotherapy. It has fewer side effects than conventional anticancer drugs because it attacks only cancer cells using the body’s immune system. In addition, because it uses the memory and adaptability of the immune system, patients who have benefited from its therapeutic effects experience sustained anticancer effects.
The recently developed immune checkpoint inhibitor has considerably improved the survival rate of patients with cancer. However, the problem with cancer immunotherapy is that only approximately 30% of cancer patients receive benefits from its therapeutic effect, and the current diagnostic techniques do not accurately predict the patient’s response to the treatment.
Under this circumstance, the research team led by Professor Sanguk Kim (Department of Life Sciences) at POSTECH is gaining attention as they have improved the accuracy of predicting patient response to immune checkpoint inhibitors (ICIs) by using network-based machine learning. The research team discovered new network-based biomarkers by analyzing the clinical results of more than 700 patients with three different cancers (melanoma, gastric cancer, and bladder cancer) and the transcriptome data of the patients’ cancer tissues. By utilizing the network-based biomarkers, the team successfully developed artificial intelligence that could predict the response to anticancer treatment. The team further proved that the treatment response prediction based on the newly discovered biomarkers was superior to that based on conventional anticancer treatment biomarkers including immunotherapy targets and tumor microenvironment markers.
In their previous study, the research team had developed machine learning that could predict drug responses to chemotherapy in patients with gastric or bladder cancer. This study has shown that artificial intelligence using the interactions between genes in a biological network could successfully predict the patient response to not only chemotherapy, but also immunotherapy in multiple cancer types.
This study helps detect patients who will respond to immunotherapy in advance and establish treatment plans, resulting in customized precision medicine with more patients to benefit from cancer treatments. Supported by the POSTECH Medical Device Innovation Center, the Graduate School of Artificial Intelligence, and ImmunoBiome Inc, this study was recently published in Nature Communications, an international peer-reviewed journal.
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Materials provided by Pohang University of Science & Technology (POSTECH). Note: Content may be edited for style and length. More