Computers Math

For the first time, U.S. and European physicists have found a way to switch the topological state of a quantum material on and off.
Because they are extremely stable and have immutable features that cannot be erased or lost to quantum decoherence, topological states play an important role in materials research and quantum computing. In a study published in Nature Communications, researchers from Rice University, Austria’s Vienna University of Technology (TU Wien), Los Alamos National Laboratory and the Netherlands’ Radboud University described their method of using a magnetic field to activate and deactivate a topological state in a strongly correlated metal.
“Topological properties are usually found in insulating materials with weak electron correlations,” said Rice study co-author Qimiao Si, a member of theRice Quantum Initiative and director of theRice Center for Quantum Materials (RCQM). “The material we study is metallic and is strongly correlated.”
Strongly correlated quantum materials are those where the interactions of billions upon billions of electrons give rise to collective behaviors like unconventional superconductivity or electrons that behave as if they have more than 1,000 times their normal mass. Though physicists have studiedtopological materials for decades, they have only recently begun investigating topological metals that host strongly correlated interactions.
Si, a theoretical physicist, has long collaborated with the study’s corresponding author, Silke Bühler-Paschen at TU Wien’s Institute of Solid State Physics. Si and Bühler-Paschen’s research groups previously made notable discoveries on topological states in strongly correlated quantum materials. In late 2017, Si’s theoretical group found a metallic topological state caused by the quintessential example of strong-correlation physics called theKondo effect, and Bühler-Paschen’s experimental group observed the state in a composite material made of cerium, bismuth and palladium. The two teams named the strongly correlated state of matter a Weyl-Kondo semimetal.
In the new study, Bühler-Paschen’s team found small impurities or external disturbances did not bring about a dramatic change in the material’s topological properties, but the application of a laboratory-scale external magnetic field could. More

The amount of energy used for computing is climbing at an exponential rate. Business intelligence and consulting firm Enerdata reports that information, communication and technology accounts for 5% to 9% of total electricity consumption worldwide.
If growth continues unabated, computing could demand up to 20% of the world’s power generation by 2030. With power grids already under strain from weather-related events and the economy transitioning from fossil fuel to renewables, engineers desperately need to flatten computing’s energy demand curve.
Members of Jon Ihlefeld’s multifunctional thin film group are doing their part. They are investigating a material system that will allow the semiconductor industry to co-locate computation and memory on a single chip.
“Right now we have a computer chip that does its computing activities with a little bit of memory on it,” said Ihlefeld, associate professor of materials science and engineering and electrical and computer engineering at the University of Virginia School of Engineering and Applied Science.
Every time the computer chip wants to talk to memory the larger memory bank, it sends a signal down the line, and that requires energy. The longer the distance, the more energy it takes. Today the distance can be quite far — up to several centimeters.
“In a perfect world, we would get them in direct contact with each other,” Ihlefeld said. More

Cerebral aneurysms appear in 5% to 8% of the general population. The blood vessel rupture and resultant blood leakage within the brain can lead to severe stroke or fatal consequences. Over one quarter of patients who experience a hemorrhagic stroke die before reaching a health care facility.
Predicting the rupture of aneurysms is crucial for medical prevention and treatment. In Physics of Fluids, by AIP Publishing, researchers from the Sree Chitra Tirunal Institute for Medical Sciences and Technology, Trivandrum, and the Indian Institute of Technology Madras, developed a patient-specific mathematical model to examine what aneurysm parameters influence rupture risk prior to surgery.
Aneurysms occur when the weakest point of a blood vessel thins, expands, and, after a certain limit, bursts. In the case of cerebral aneurysms such as internal carotid artery bifurcation aneurysm, blood leaks into the intracranial cavity
“Since clinicians encounter these aneurysms at various growth stages, it motivated us to analyze internal carotid artery aneurysms in a systematic manner,” said B. Jayanand Sudhir, of the Sree Chitra Tirunal Institute for Medical Sciences and Technology. “The current study is a sincere and systematic attempt to address the dynamics of blood flow at various stages to understand the initiation, progression, and rupture risk.”
The team examined the aspect ratio and size ratio of aneurysms, which describe the shape and size characteristics of the bulge in a holistic manner. As these parameters increase and the aneurysm expands, the stress applied against the aneurysm walls and the time blood spends within the aneurysm increase. This leads the probability of rupture to rise.
Patient-specific computed tomography scans are fed into the model, which reconstructs the geometry and blood flow of the aneurysm. It then uses mathematical equations to describe the fluid flow, generating information about the blood vessel walls and blood flow patterns.
“This was feasible due to the access we had to the national supercomputing cluster for performing the computational fluid dynamics-based simulations,” said S.V. Patnaik of the Indian Institute of Technology Madras.
“The novelty of this work lies in close collaboration and amalgamation of expertise from clinical and engineering backgrounds,” said Sudhir. “The aneurysm models were of different shapes, which helped us build and understand the complexity of flow structures in multilobed cerebral aneurysms.”
Multilobed aneurysms, which include more than one balloonlike pocket of expanding blood, contained more complex blood flow structures than their single-lobed counterparts.
The authors hope to transform the rupture risk predictions into a user-friendly software to help clinicians and neurosurgeons prioritize and manage high-risk patients. They plan to use the model to assess the effectiveness of different treatment options for aneurysms.
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Materials provided by American Institute of Physics. Note: Content may be edited for style and length. More

Electronic gaming can precipitate life-threatening cardiac arrhythmias in susceptible children whose predisposition may have been previously unrecognized, according to a new report in Heart Rhythm, the official journal of the Heart Rhythm Society, the Cardiac Electrophysiology Society, and the Pediatric & Congenital Electrophysiology Society, published by Elsevier. The investigators documented an uncommon, but distinct pattern among children who lose consciousness while playing electronic (video) games.
“Video games may represent a serious risk to some children with arrhythmic conditions; they might be lethal in patients with predisposing, but often previously unrecognized arrhythmic conditions,” explained lead investigator Claire M. Lawley, MBBS, PhD, The Heart Centre for Children, Sydney Children’s Hospitals Network, Sydney, Australia. “Children who suddenly lose consciousness while electronic gaming should be assessed by a heart specialist as this could be the first sign of a serious heart problem.”
The investigators performed a systematic review of literature and initiated a multisite international outreach effort to identify cases of children with sudden loss of consciousness while playing video games. Across the 22 cases they found, multiplayer war gaming was the most frequent trigger. Some children died following a cardiac arrest. Subsequent diagnoses of several heart rhythm conditions put the children at continuing risk. Catecholaminergic polymorphic ventricular tachycardia (CPVT) and congenital long QT syndrome (LQTS) types 1 and 2 were the most common underlying causes.
There was a high incidence of potentially relevant genetic variants (63%) among the patients, which has significant implications for their families. In some cases, the investigation of a child who lost consciousness during video gaming led to many family members being diagnosed with an important familial heart rhythm problem. “Families and healthcare teams should think about safety precautions around electronic gaming in children who have a condition where dangerous fast heart rhythms are a risk,” noted Dr. Lawley.
The investigators attributed adrenergic stimulation related to the emotionally charged electronic gaming environment as the pathophysiological basis for this phenomenon. Electronic gaming is not always the “safe alternative” to competitive sports it is often considered. At the time of the cardiac incidents, many of the patients were in excited states, having just won or lost games, or were engaging in conflict with companions.
“We already know that some children have heart conditions that can put them at risk when playing competitive sports, but we were shocked to discover that some patients were having life-threatening blackouts during video gaming,” added co-investigator Christian Turner, MBBS, The Heart Centre for Children, Sydney Children’s Hospitals Network, Sydney, Australia. “Video gaming was something I previously thought would be an alternative ‘safe activity.’ This is a really important discovery. We need to ensure everyone knows how important it is to get checked out when someone has had a blacking out episode in these circumstances.”
The study notes that while this phenomenon is not a common occurrence, it is becoming more prevalent. “Having looked after children with heart rhythm problems for more than 25 years, I was staggered to see how widespread this emerging presentation is, and to find that a number of children had even died from it. All of the collaborators are keen to publicize this phenomenon so our colleagues across the globe can recognize it and protect these children and their families,” noted co-investigator of the study, Jonathan Skinner, MBChB, MD, also from Sydney.
As an accompanying editorial Daniel Sohinki, MD, MSc, Department of Cardiology, Augusta University, Augusta, GA, USA, and coauthors pointed out that, “exertion should be understood to encompass activities outside of traditional competitive athletics. Appropriate counseling regarding the risks of intense video gameplay should be targeted in children with a pro-arrhythmic cardiac diagnosis, and in any child with a history of exertional syncope of undetermined etiology. Further, any future screening programs aimed at identifying athletes at risk for malignant arrhythmias should encompass athletes being considered for participation in eSports.”
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Working alongside robots may contribute to job burnout and workplace incivility, but self-affirmation techniques could help alleviate fears about being replaced by these machines, according to research published by the American Psychological Association.
Researchers found that workers in the U.S. and parts of Asia feel job insecurity from robots, even in industries where robots aren’t being used, and those fears may not be justified, said lead researcher Kai Chi Yam, PhD, an associate professor of management at the National University of Singapore.
“Some economists theorize that robots are more likely to take over blue-collar jobs faster than white-collar jobs,” Yam said. “However, it doesn’t look like robots are taking over that many jobs yet, at least not in the United States, so a lot of these fears are rather subjective.”
Researchers conducted experiments and analyzed data from participants in the U.S., Singapore, India and Taiwan. The study was published online in the Journal of Applied Psychology.
Working with industrial robots was linked to greater reports of burnout and workplace incivility in an experiment with 118 engineers employed by an Indian auto manufacturing company.
An online experiment with 400 participants found that self-affirmation exercises, where people are encouraged to think positively about themselves and their uniquely human characteristics, may help lessen workplace robot fears. Participants wrote about characteristics or values that were important to them, such as friends and family, a sense of humor or athletics.
“Most people are overestimating the capabilities of robots and underestimating their own capabilities,” Yam said.
Fears about job insecurity from robots are common. The researchers analyzed data about the prevalence of robots in 185 U.S. metropolitan areas along with the overall use of popular job recruiting sites in those areas (LinkedIn, Indeed, etc.). Areas with the most prevalent rates of robots also had the highest rates of job recruiting site searches, even though unemployment rates weren’t higher in those areas. The researchers theorized that people in these areas may have felt more job insecurity because of robots, but that there could be other reasons, such as people seeking new careers or feeling dissatisfied with their current jobs.
Another experiment comprised 343 parents of students at the National University of Singapore who were randomly assigned to three groups. One group read an article about the use of robots in businesses, the second group read a general article about robots, and the third read an unrelated article. Then the participants were surveyed about their job insecurity concerns, with the first group reporting significantly higher levels of job insecurity than the two other groups.
While some people may have legitimate concerns about losing their jobs to robots, some media coverage may be unnecessarily heightening fears among the general public, Yam said.
“Media reports on new technologies like robots and algorithms tend to be apocalyptic in nature, so people may develop an irrational fear about them,” he said.
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Artificial intelligence models that interpret medical images hold the promise to enhance clinicians’ ability to make accurate and timely diagnoses, while also lessening workload by allowing busy physicians to focus on critical cases and delegate rote tasks to AI.
But AI models that lack transparency about how and why a diagnosis is made can be problematic. This opaque reasoning — also known “black box” AI — can diminish clinician trust in the reliability of the AI tool and thus discourage its use. This lack of transparency could also mislead clinicians into over-trusting the tool’s interpretation.
In the realm of medical imaging, one way to create more understandable AI models and to demystify AI decision-making have been saliency assessments — an approach that uses heat maps to pinpoint whether the tool is correctly focusing only on the relevant pieces of a given image or homing in on irrelevant parts of it.
Heat maps work by highlighting areas on an image that influenced the AI model’s interpretation. This could help human physicians see whether the AI model focuses on the same areas as they do or is mistakenly focusing on irrelevant spots on an image.
But a new study, published in Nature Machine Intelligence on Oct. 10, shows that for all their promise, saliency heat maps may not be yet ready for prime time.
The analysis, led by Harvard Medical School investigator Pranav Rajpurkar, Matthew Lungren of Stanford, and Adriel Saporta of New York University, quantified the validity of seven widely used saliency methods to determine how reliably and accurately they could identify pathologies associated with 10 conditions commonly diagnosed on X-ray, such as lung lesions, pleural effusion, edema, or enlarged heart structures. To ascertain performance, the researchers compared the tools’ performance against human expert judgment. More

New research highlights a growing market in AI-powered recruitment tools, used to process high volumes of job applicants, that claim to bypass human bias and remove discrimination from hiring. •These AI tools reduce race and gender to trivial data points, and often rely on personality analysis that is “automated pseudoscience,” according to Cambridge researchers. Academics have also teamed up with computing students to debunk use of AI in recruitment by building a version of the kinds of software increasingly used by HR teams. It demonstrates how random changes in clothing or lighting give radically different personality readings that could prove make-or-break for a generation of job seekers.
Recent years have seen the emergence of AI tools marketed as an answer to lack of diversity in the workforce, from use of chatbots and CV scrapers to line up prospective candidates, through to analysis software for video interviews.
Those behind the technology claim it cancels out human biases against gender and ethnicity during recruitment, instead using algorithms that read vocabulary, speech patterns and even facial micro-expressions to assess huge pools of job applicants for the right personality type and “culture fit.”
However, in a new report published in Philosophy and Technology, researchers from Cambridge’s Centre for Gender Studies argue these claims make some uses of AI in hiring little better than an “automated pseudoscience” reminiscent of physiognomy or phrenology: the discredited beliefs that personality can be deduced from facial features and skull shape.
They say it is a dangerous example of “technosolutionism”: turning to technology to provide quick fixes for deep-rooted discrimination issues that require investment and changes to company culture.
In fact, the researchers have worked with a team of Cambridge computer science undergraduates to debunk these new hiring techniques by building an AI tool modelled on the technology, available at: https://personal-ambiguator-frontend.vercel.app/
The ‘Personality Machine’ demonstrates how arbitrary changes in facial expression, clothing, lighting and background can give radically different personality readings — and so could make the difference between rejection and progression for a generation of job seekers vying for graduate positions. More

Rare diseases are often difficult to diagnose and predicting the best course of treatment can be challenging for clinicians. Investigators from the Mahmood Lab at Brigham and Women’s Hospital, a founding member of the Mass General Brigham healthcare system, have developed a deep learning algorithm that can teach itself to learn features which can then be used to find similar cases in large pathology image repositories. Known as SISH (Self-Supervised Image search for Histology), the new tool acts like a search engine for pathology images and has many potential applications, including identifying rare diseases and helping clinicians determine which patients are likely to respond to similar therapies. A paper introducing the self-teaching algorithm is published in Nature Biomedical Engineering.
“We show that our system can assist with the diagnosis of rare diseases and find cases with similar morphologic patterns without the need for manual annotations, and large datasets for supervised training,” said senior author Faisal Mahmood, PhD, in the Brigham’s Department of Pathology. “This system has the potential to improve pathology training, disease subtyping, tumor identification, and rare morphology identification.”
Modern electronic databases can store an immense amount of digital records and reference images, particularly in pathology through whole slide images (WSIs). However, the gigapixel size of each individual WSI and the ever-increasing number of images in large repositories, means that search and retrieval of WSIs can be slow and complicated. As a result, scalability remains a pertinent roadblock for efficient use.
To solve this issue, researchers at the Brigham developed SISH, which teaches itself to learn feature representations which can be used to find cases with analogous features in pathology at a constant speed regardless of the size of the database.
In their study, the researchers tested the speed and ability of SISH to retrieve interpretable disease subtype information for common and rare cancers. The algorithm successfully retrieved images with speed and accuracy from a database of tens of thousands of whole slide images from over 22,000 patient cases, with over 50 different disease types and over a dozen anatomical sites. The speed of retrieval outperformed other methods in many scenarios, including disease subtype retrieval, particularly as the image database size scaled into the thousands of images. Even while the repositories expanded in size, SISH was still able to maintain a constant search speed.
The algorithm, however, has some limitations including a large memory requirement, limited context awareness within large tissue slides and the fact that it is limited to a single imaging modality.
Overall, the algorithm demonstrated the ability to efficiently retrieve images independent of repository size and in diverse datasets. It also demonstrated proficiency in diagnosis of rare disease types and the ability to serve as a search engine to recognize certain regions of images that may be relevant for diagnosis. This work may greatly inform future disease diagnosis, prognosis, and analysis.
“As the sizes of image databases continue to grow, we hope that SISH will be useful in making identification of diseases easier,” said Mahmood. “We believe one important future direction in this area is multimodal case retrieval which involves jointly using pathology, radiology, genomic and electronic medical record data to find similar patient cases.”
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