Computers Math

When it comes to the future of intelligent robots, the first question people ask is often: how many jobs will they make disappear? Whatever the answer, the second question is likely to be: how can I make sure that my job is not among them?
In a study just published in Science Robotics, a team of roboticists from EPFL and economists from the University of Lausanne offers answers to both questions. By combining the scientific and technical literature on robotic abilities with employment and wage statistics, they have developed a method to calculate which of the currently existing jobs are more at risk of being performed by machines in the near future. Additionally, they have devised a method for suggesting career transitions to jobs that are less at risk and require smallest retraining efforts.
“There are several studies predicting how many jobs will be automated by robots, but they all focus on software robots, such as speech and image recognition, financial robo-advisers, chatbots, and so forth. Furthermore, those predictions wildly oscillate depending on how job requirements and software abilities are assessed. Here, we consider not only artificial intelligence software, but also real intelligent robots that perform physical work and we developed a method for a systematic comparison of human and robotic abilities used in hundreds of jobs,” says Prof. Dario Floreano, Director of EPFL’s Laboratory of Intelligent System, who led the study at EPFL.
The key innovation of the study is a new mapping of robot capabilities onto job requirements. The team looked into the European H2020 Robotic Multi-Annual Roadmap (MAR), a strategy document by the European Commission that is periodically revised by robotics experts. The MAR describes dozens of abilities that are required from current robot or may be required by future ones, ranging, organised in categories such as manipulation, perception, sensing, interaction with humans. The researchers went through research papers, patents, and description of robotic products to assess the maturity level of robotic abilities, using a well-known scale for measuring the level of technology development, “technology readiness level” (TRL).
For human abilities, they relied on the O*net database, a widely-used resource database on the US job market, that classifies approximately 1,000 occupations and breaks down the skills and knowledge that are most crucial for each of them
After selectively matching the human abilities from O*net list to robotic abilities from the MAR document, the team could calculate how likely each existing job occupation is to be performed by a robot. Say, for example, that a job requires a human to work at millimetre-level precision of movements. Robots are very good at that, and the TRL of the corresponding ability is thus the highest. If a job requires enough such skills, it will be more likely to be automated than one that requires abilities such as critical thinking or creativity.
The result is a ranking of the 1,000 jobs, with “Physicists” being the ones who have the lowest risk of being replaced by a machine, and “Slaughterers and Meat Packers,” who face the highest risk. In general, jobs in food processing, building and maintenance, construction and extraction appear to have the highest risk.
“The key challenge for society today is how to become resilient against automation” says Prof. Rafael Lalive. who co-led the study at the University of Lausanne. “Our work provides detailed career advice for workers who face high risks of automation, which allows them to take on more secure jobs while re-using many of the skills acquired on the old job. Through this advice, governments can support society in becoming more resilient against automation.”
The authors then created a method to find, for any given job, alternative jobs that have a significantly lower automation risk and are reasonably close to the original one in terms of the abilities and knowledge they require — thus keeping the retraining effort minimal and making the career transition feasible. To test how that method would perform in real life, they used data from the US workforce and simulated thousands of career moves based on the algorithm’s suggestions, finding that it would indeed allow workers in the occupations with the highest risk to shift towards medium-risk occupations, while undergoing a relatively low retraining effort.
The method could be used by governments to measure how many workers could face automation risks and adjust retraining policies, by companies to assess the costs of increasing automation, by robotics manufacturers to better tailor their products to the market needs; and by the public to identify the easiest route to reposition themselves on the job market.
Finally, the authors translated the new methods and data into an algorithm that predicts the risk of automation for hundreds of jobs and suggests resilient career transitions at minimal retraining effort, publicly accessible at https://lis2.epfl.ch/resiliencetorobots. More

Researchers have developed new polymer materials that are ideal for making the optical links necessary to connect chip-based photonic components with board-level circuits or optical fibers. The polymers can be used to easily create interconnects between photonic chips and optical printed circuit boards, the light-based equivalent of electronic printed circuit boards.
“These new materials and the processes they enable could lead to powerful new photonic modules based on silicon photonics,” said research team leader Robert Norwood from the University of Arizona. “They could also be useful for optical sensing or making holographic displays for augmented and virtual reality applications.”
Silicon photonics technology allows light-based components to be integrated onto a tiny chip. Although many of the basic building blocks of silicon photonic devices have been demonstrated, better methods are needed to fabricate the optical connections that link these components together to make more complex systems.
In the journal Optical Materials Express, the researchers report new polymer materials that feature a refractive index that can be adjusted with ultraviolet (UV) light and low optical losses. These materials allow a single-mode optical interconnect to be printed directly into a dry film material using a low cost, high throughput lithography system that is compatible with the CMOS manufacturing techniques used to make chip-based photonic components.
“This technology makes it more practical to fabricate optical interconnects, which can be used to make the Internet — especially the data centers that make it run — more efficient,” said Norwood. “Compared to their electronic counterparts, optical interconnects can increase data throughput while also generating less heat. This reduces power consumption and cooling requirements.”
Replacing wires with light
The research expands on a vinylthiophenol polymer material system known as S-BOC that the investigators developed previously. This material has a refractive index that can be modified using UV illumination. In the new work, the researchers partially fluorinated S-BOC to improve its light efficiency. The new material system, called FS-BOC, exhibits lower optical propagation losses than many other optical interconnect materials. More

Early research into the growing electronic sports (esports) industry highlights a need for better coaching to prevent burnout among professional players.
The study, conducted by researchers at the University of Waterloo, identified several areas, including player fatigue, mental stress and peak performance conditions, that require in-depth research to improve coaching and player performance.
“They burn out because they spend long hours sitting at desks playing and training,” said Bader Sabtan, a systems design engineering PhD student who led the study. “It results in all kinds of problems, from mental health issues to back and wrist injuries.”
In a survey of professional League of Legends teams, it was found there are virtually no standardized coaching approaches or techniques to guide young players.
Instead, players work to remain competitive in the constantly changing team battle video game, one of several with lucrative fan followings around the world, by practicing 12 to 14 hours a day, six days a week.
Professional esports fill stadiums with spectators as players, who average just 18 to 20 years of age, compete at computers while their games are shown on giant screens. One championship event in 2018 drew almost 100 million online viewers.
The researchers focused on League of Legends, which has 47 pro teams in North America, Europe, Korea and China. Players can earn more than $400,000 a year, but rarely have careers that last beyond three or four years.
Coaches who participated in the study unanimously agreed that methods must be developed to make practice more efficient and strategic to reduce the demands on players.
“I was surprised to learn even top professional coaches don’t have systematic training methods,” said Shi Cao, a systems design engineering professor and a member of the Games Institute at Waterloo. “Nothing is supported by scientific evidence or research.
“Just as physiology and kinesiology research supports traditional sports, cognitive psychology and human factors engineering can support mental work like esports,” said Cao, an esports fan and recreational player.
Sabtan can personally relate to the relentless demands on the best players. A few years ago, he was in the top one per cent of League of Legends players and spent up to 50 hours a week practicing to stay there.
“Right now, there is no other option,” Sabtan said. “The required sharpness, game knowledge and reaction speed are only achieved by practicing and repetition, so they just play the game. They don’t have social lives. They don’t have girlfriends or boyfriends. It’s unsustainable.”
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Materials provided by University of Waterloo. Note: Content may be edited for style and length. More

For the first time, researchers have successfully generated strongly nonclassical light using a modular waveguide-based light source. The achievement represents a crucial step toward creating faster and more practical optical quantum computers.
“Our goal is to dramatically improve information processing by developing faster quantum computers that can perform any type of computation without errors,” said research team member Kan Takase from the University of Tokyo. “Although there are several ways to create a quantum computer, light-based approaches are promising because the information processor can operate at room temperature and the computing scale can be easily expanded.”
In the Optica Publishing Group journal Optics Express, a multi-institutional team of researchers from Japan describe the waveguide optical parametric amplifier (OPA) module they created for quantum experiments. Combining this device with a specially designed photon detector allowed them to generate a state of light known as Schrödinger cat, which is a superposition of coherent states.
“Our method for generating quantum light can be used to increase the computing power of quantum computers and to make the information processer more compact,” said Takase. “Our approach outperforms conventional methods, and the modular waveguide OPA is easy to operate and integrate into quantum computers.”
Generating strongly nonclassical light
Continuous wave squeezed light is used to generate the various quantum states necessary to perform quantum computing. For the best computing performance, the squeezed light source must exhibit very low levels of light loss and be broadband, meaning it includes a wide range of frequencies. More

Magnetism, one of the oldest technologies known to humans, is at the forefront of new-age materials that could enable next-generation lossless electronics and quantum computers. Researchers led by Penn State and the university of California, San Diego have discovered a new ‘knob’ to control the magnetic behavior of one promising quantum material, and the findings could pave the way toward novel, efficient and ultra-fast devices.
“The unique quantum mechanical make-up of this material — manganese bismuth telluride — allows it to carry lossless electrical currents, something of tremendous technological interest,” said Hari Padmanabhan, who led the research as a graduate student at Penn State. “What makes this material especially intriguing is that this behavior is deeply connected to its magnetic properties. So, a knob to control magnetism in this material could also efficiently control these lossless currents.”
Manganese bismuth telluride, a 2D material made of atomically thin stacked layers, is an example of a topological insulator, exotic materials that simultaneously can be insulators and conductors of electricity, the scientists said. Importantly, because this material is also magnetic, the currents conducted around its edges could be lossless, meaning they do not lose energy in the form of heat. Finding a way to tune the weak magnetic bonds between the layers of the material could unlock these functions.
Tiny vibrations of atoms, or phonons, in the material may be one way to achieve this, the scientists reported April 8 in the journal Nature Communications.
“Phonons are tiny atomic wiggles — atoms dancing together in various patterns, present in all materials,” Padmanabhan said. “We show that these atomic wiggles can potentially function as a knob to tune the magnetic bonding between the atomic layers in manganese bismuth telluride.”
The scientists at Penn State studied the material using a technique called magneto-optical spectroscopy — shooting a laser onto a sample of the material and measuring the color and intensity of the reflected light, which carries information on the atomic vibrations. The team observed how the vibrations changed as they altered the temperature and magnetic field. More

Testing shortages, long waits for results, and an over-taxed health care system have made headlines throughout the COVID-19 pandemic. These issues can be further exacerbated in small or rural communities in the US and globally. Additionally, respiratory symptoms of COVID-19 such as fever and cough are also associated with the flu, which complicates non-lab diagnoses during certain seasons. A new study by College of Health and Human Services researchers is designed to help identify which symptoms are more likely to indicate COVID during flu season. This is the first study to take seasonality into account.
Farrokh Alemi, principal investigator and professor of Health Administration and Policy, and other Mason researchers predict the probability that a patient has COVID-19, flu, or another respiratory illness prior to testing, depending on the season. This can help clinicians triage patients who are most suspected of having COVID-19.
“When access to reliable COVID testing is limited or test results are delayed, clinicians, especially those who are community-based, are more likely to rely on signs and symptoms than on laboratory findings to diagnose COVID-19,” said Alemi, who observed these challenges at points throughout the pandemic. “Our algorithm can help health care providers triage patient care while they are waiting on lab testing or help prioritize testing if there are testing shortages.”
The findings suggest that community-based health care providers should follow different signs and symptoms for diagnosing COVID depending on the time of year. Outside of flu season, fever is an even stronger predictor of COVID than during flu season. During flu season, a person with a cough is more likely to have the flu than COVID. The study showed that assuming anyone with a fever during flu season has COVID would be incorrect. The algorithm relied on different symptoms for patients in different age and gender. The study also showed that symptom clusters are more important in diagnosis of COVID-19 than symptoms alone.
The algorithms were created by analyzing the symptoms reported by 774 COVID patients in China and 273 COVID patients in the United States. The analysis also included 2,885 influenza and 884 influenza-like illnesses in US patients. “Modeling the Probability of COVID-19 Based on Symptom Screening and Prevalence of Influenza and Influenza-Like Illnesses” was published in Quality Management in Health Care’s April/June 2022 issue. The rest of the research team is also from Mason: Professor of Global Health and Epidemiology Health Amira Roess, Affiliate Faculty Jee Vang, and doctoral candidate Elina Guralnik.
“Though helpful, the algorithms are too complex to expect clinicians to perform these calculations while providing care. The next step is to create an AI, web-based, calculator that can be used in the field. This would allow clinicians to arrive at a presumed diagnosis prior to the visit,” said Alemi. From there, clinicians can make triage decisions on how to care for the patient while waiting for official lab results.
The study does not include any COVID-19 patients without respiratory symptoms, which includes asymptomatic people. Additionally, the study did not differentiate between the first and second week of onset of symptoms, which can vary.
This research was a prototype of how existing data can be used to find signature symptoms of a new disease. The methodology may have relevance beyond this pandemic.
“When there is a new outbreak, collecting data is time consuming. Rapid analysis of existing data can reduce the time to differentiate presentation of new diseases from illnesses with overlapping symptoms. The method in this paper is useful for rapid response to the next pandemic,” said Alemi.
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Materials provided by George Mason University. Original written by Mary Cunningham. Note: Content may be edited for style and length. More

Researchers at The University of Texas MD Anderson Cancer Center have developed a new bioinformatics platform that predicts optimal treatment combinations for a given group of patients based on co-occurring tumor alterations. In retrospective validation studies, the tool selected combinations that resulted in improved patient outcomes across both pre-clinical and clinical studies.
The findings were presented today at the American Association for Cancer Research (AACR) Annual Meeting 2022 by principal investigator Anil Korkut, Ph.D., assistant professor of Bioinformatics and Computational Biology. The study results also were published today in Cancer Discovery.
The platform, called REcurrent Features LEveraged for Combination Therapy (REFLECT), integrates machine learning and cancer informatics algorithms to analyze biological tumor features — including genetic mutations, copy number changes, gene expression and protein expression aberrations — and identify frequent co-occurring alterations that could be targeted by multiple drugs.
“Our ultimate goal is to make precision oncology more effective and create meaningful patient benefit,” Korkut said. “We believe REFLECT may be one of the tools that can help overcome some of the current challenges in the field by facilitating both the discovery and the selection of combination therapies matched to the molecular composition of tumors.”
Targeted therapies have improved clinical outcomes for many patients with cancer, but monotherapies against a single target often lead to treatment resistance. Cancer cells frequently rely on co-occurring alterations, such as mutations in two signaling pathways, to drive tumor progression. Increasing evidence suggests that identifying and targeting both alterations simultaneously could increase durable responses, Korkut explained.
Led by Korkut and postdoctoral fellow Xubin Li, Ph.D., the researchers built and used the REFLECT tool to develop a systematic and unbiased approach to match patients with optimal combination therapies. More

Medical imaging is an important part of modern healthcare, enhancing both the precision, reliability and development of treatment for various diseases. Artificial intelligence has also been widely used to further enhance the process.
However, conventional medical image diagnosis employing AI algorithms require large amounts of annotations as supervision signals for model training. To acquire accurate labels for the AI algorithms — radiologists, as part of the clinical routine, prepare radiology reports for each of their patients, followed by annotation staff extracting and confirming structured labels from those reports using human-defined rules and existing natural language processing (NLP) tools. The ultimate accuracy of extracted labels hinges on the quality of human work and various NLP tools. The method comes at a heavy price, being both labour intensive and time consuming.
An engineering team at the University of Hong Kong (HKU) has developed a new approach “REFERS” (Reviewing Free-text Reports for Supervision), which can cut human cost down by 90%, by enabling the automatic acquisition of supervision signals from hundreds of thousands of radiology reports at the same time. It attains a high accuracy in predictions, surpassing its counterpart of conventional medical image diagnosis employing AI algorithms.
The innovative approach marks a solid step towards realizing generalized medical artificial intelligence. The breakthrough was published in Nature Machine Intelligence in the paper titled “Generalized radiograph representation learning via cross-supervision between images and free-text radiology reports.”
“AI-enabled medical image diagnosis has the potential to support medical specialists in reducing their workload and improving the diagnostic efficiency and accuracy, including but not limited to reducing the diagnosis time and detecting subtle disease patterns,” said Professor YU Yizhou, leader of the team from HKU’s Department of Computer Science under the Faculty of Engineering.
“We believe abstract and complex logical reasoning sentences in radiology reports provide sufficient information for learning easily transferable visual features. With appropriate training, REFERS directly learns radiograph representations from free-text reports without the need to involve manpower in labelling.” Professor Yu remarked.
For training REFERS, the research team uses a public database with 370,000 X-Ray images, and associated radiology reports, on 14 common chest diseases including atelectasis, cardiomegaly, pleural effusion, pneumonia and pneumothorax. The researchers managed to build a radiograph recognition model using 100 radiographs only, and attains 83% accuracy in predictions. When the number was increased to 1,000, their model exhibits amazing performance with an accuracy of 88.2%, which surpasses its counterpart trained with 10,000 radiologist annotations (accuracy at 87.6%). When 10,000 radiographs were used, the accuracy is at 90.1%. In general, an accuracy above 85% in predictions is useful in real-world clinical applications.
REFERS achieves the goal by accomplishing two report-related tasks, i.e., report generation and radiograph-report matching. In the first task, REFERS translates radiographs into text reports by first encoding radiographs into an intermediate representation, which is then used to predict text reports via a decoder network. A cost function is defined to measure the similarity between predicted and real report texts, based on which gradient-based optimization is employed to train the neural network and update its weights.
As for the second task, REFERS first encodes both radiographs and free-text reports into the same semantic space, where representations of each report and its associated radiographs are aligned via contrastive learning.
“Compared to conventional methods that heavily rely on human annotations, REFERS has the ability to acquire supervision from each word in the radiology reports. We can substantially reduce the amount of data annotation by 90% and the cost to build medical artificial intelligence. It marks a significant step towards realizing generalized medical artificial intelligence, ” said the paper’s first author Dr ZHOU Hong-Yu.
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Materials provided by The University of Hong Kong. Note: Content may be edited for style and length. More