Computers Math

Previously, very little was known about online gaming behaviour based on the actual games played and how career interests are reflected in what people play. To examine this correlation, in collaboration with Game Academy Ltd, Surrey researchers investigated the gaming behaviour of 16,033 participants to explore how the hobby could support video game players’ future career planning and professional training.
The participants played a different number of games on Steam — a video game digital distribution service and storefront. Researchers studied the 800 most-played games and only included participants for whom they had access to gender and job details.
Researchers discovered that IT professionals and engineers played puzzle-platform games, which possibly enhance their spatial skills. People in managerial roles showed an interest in action roleplay games where organisational and planning skills are involved and engineering professionals were associated with strategy games which often require problem-solving and spatial skills. There were apparent gender differences too — females preferred playing single-player games, whereas males preferred playing shooting games.
Dr Anna-Stiina Wallinheimo, lead author of the study, Cognitive Psychologist, and Postdoctoral Research Fellow at the University of Surrey’s Centre for Translation Studies (CTS) said:
“In recruitment processes, the best candidates may be missed because organisations do not consider the soft skills that have been gained through non-work activities (for example, online gaming). As a result of our research, we believe applicants’ online gaming experiences should be highlighted because these acquired soft skills can really help to develop their all-round strengths for the job at hand.”
Dr Anesa Hosein, co-author of the study and Associate Professor in Higher Education at the University of Surrey said:
“By understanding to what extent career interests are reflected in game playing, we may be able to demonstrate more clearly how these align with career interests and encourage employers to understand the value of the soft skills associated with gaming. Our research could also inspire game developers to work on honing these soft skills more closely in their design. Furthermore, places of learning, such as universities, could allow students to reflect and incorporate gaming as part of their career development and consider how gaming can be included in the curriculum to enhance alignment between students’ learning, career aspirations and extra-curricular gaming interests.”
This research was published in SAGE Journals.
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In recent years, research and development on quantum computers has made considerable progress. Quantum chemical calculations for electronic structures of atoms and molecules are attracting great attention as one of the most promising applications of quantum computers. In order to utilize quantum chemical calculations for chemistry and related fields, it is essential to develop geometry optimization methods for finding the most stable structure of molecules. The geometry optimization requires calculations of energy derivatives with respect to nuclear coordinates of molecules.
The finite difference method is one approach for energy derivative calculations. On a classical computer, calculations based on this method for one-dimensional systems require at least two evaluations of the energy. Previous research has shown that a quantum computer, in contrast, requires only a single query to calculate the energy derivatives based on the finite difference method, regardless of the number of degrees of freedom. However, quantum circuits relevant to quantum algorithms capable of performing energy derivative calculations have not been implemented.
A research group including Dr. Kenji Sugisaki, Professor Kazunobu Sato, and Professor Emeritus Takeji Takui from the Graduate School of Science at Osaka Metropolitan University has successfully extended the quantum phase difference estimation algorithm, a general quantum algorithm for the direct calculations of energy gaps, to enable the direct calculation of energy differences between two different molecular geometries. This allows for the computation, based on the finite difference method, of energy derivatives with respect to nuclear coordinates in a single calculation.
Furthermore, the research group has applied the developed energy derivative calculations to execute geometry optimizations of H2, LiH, BeH2, and N2 molecules without calculating the total energies, demonstrating the usefulness of the developed method. The group also discussed how quantum circuits can be assembled according to different degrees of freedom of the molecules.
This research is the latest in a series of the researchers’ articles on quantum chemical calculations on quantum computers. “Our latest findings bring us one step closer to applying quantum chemical calculations on a quantum computer to real-world problems,” said Dr. Sugisaki. “Since energy derivative calculations are used for not only molecular geometry optimizations but also various calculations for molecular properties, the application of our method is expected to play a very important role in a wide range of related fields, such as in silico drug discovery/design and materials development.”
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Materials provided by Osaka Metropolitan University. Note: Content may be edited for style and length. More

Imagine walking into an ice cream shop and scanning your options. A sugar cone with one scoop is $3. A second scoop comes out to $4, but for just 50 cents more, you can get a large waffle cone with three scoops. Some people may not want that much ice cream. But for many, it’s hard to pass up a good deal.
Adding a third option to make something else (usually the higher priced item) more attractive is a common marketing strategy. But since the 1980s, scholars have been debating whether this attraction effect via a “decoy option” actually works in real-life settings.
Findings from a new, in-depth study bolster the argument that decoy options can shift consumer preferences. It’s also the first to test this approach in digital markets where billions of people make choices every day.
The researchers randomly assigned 4,000 participants to eight experiments based on reward-based crowdfunding campaigns, including one on Kickstarter with a real Swiss watchmaking company. Their findings, published in Information Systems Research, show the attraction effect shifted preferences from a low-priced to a high-priced reward by as much as 28%.
“That’s a significant jump that can turn an entrepreneur’s project into a reality,” said Abhay Mishra, associate professor of information systems and business analytics at Iowa State University and co-author of the study. “Our findings can help inform artists, innovators and creatives to make smarter, more effective reward menus to reach their fundraising goals.”
In reward-based crowdfunding markets, entrepreneurs set a fundraising target with a deadline for their project. Backers choose from several reward menu options, pledging a certain amount of money to receive a product or sample of the to-be-funded project. Whether the entrepreneur gets any of the funding and the backers receive their rewards depends on the project successfully reaching the fundraising goal by the set date. More

Microscopic materials analysis is essential to achieve desirable performance in next-generation nanoelectronic devices, such as low power consumption and high speeds. However, the magnetic materials involved in such devices often exhibit incredibly complex interactions between nanostructures and magnetic domains. This, in turn, makes functional design challenging.
Traditionally, researchers have performed a visual analysis of the microscopic image data. However, this often makes the interpretation of such data qualitative and highly subjective. What is lacking is a causal analysis of the mechanisms underlying the complex interactions in nanoscale magnetic materials.
In a recent breakthrough published in Scientific Reports, a team of researchers led by Prof. Masato Kotsugi from Tokyo University of Science, Japan succeeded in automating the interpretation of the microscopic image data. This was achieved using an “extended Landau free energy model” that the team developed using a combination of topology, data science, and free energy. The model could illustrate the physical mechanism as well as the critical location of the magnetic effect, and proposed an optimal structure for a nano device. The model used physics-based features to draw energy landscapes in the information space, which could be applied to understand the complex interactions at the nanoscales in a wide variety of materials.
“Conventional analysis are based on a visual inspection of microscope images, and the relationships with the material function are expressed only qualitatively, which is a major bottleneck for material design. Our extended Landau free energy model enables us to identify the physical origin and location of the complex phenomena within these materials. This approach overcomes the explainability problem faced by deep learning, which, in a way, amounts to reinventing new physical laws,” Prof. Kotsugi explains. This work was supported by KAKENHI, JSPS, and the MEXT-Program for Creation of Innovative Core Technology for Power Electronics Grant.
When designing the model, the team made use of the state-of-art technique in the fields of topology and data science to extend the Landau free energy model. This led to a model that enabled a causal analysis of the magnetization reversal in nanomagnets. The team then carried out an automated identification of the physical origin and visualization of the original magnetic domain images.
Their results indicated that the demagnetization energy near a defect gives rise to a magnetic effect, which is responsible for the “pinning phenomenon.” Further, the team could visualize the spatial concentration of energy barriers, a feat that had not been achieved until now. Finally, the team proposed a topologically inverse design of recording devices and nanostructures with low power consumption.
The model proposed in this study is expected to contribute to a wide range of applications in the development of spintronic devices, quantum information technology, and Web 3.
“Our proposed model opens up new possibilities for optimization of magnetic properties for material engineering. The extended method will finally allow us to clarify ‘why’ and ‘where’ the function of a material is expressed. The analysis of material functions, which used to rely on visual inspection, can now be quantified to make precise functional design possible,” concludes an optimistic Prof. Kotsugi.
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A team of engineers at UC Santa Cruz has developed a new method for remote automation of the growth of cerebral organoids — miniature, three-dimensional models of brain tissue grown from stem cells. Cerebral organoids allow researchers to study and engineer key functions of the human brain with a level of accuracy not possible with other models. This has implications for understanding brain development and the effects of pharmaceutical drugs for treating cancer or other diseases.
In a new study published in the journal Nature Scientific Reports, researchers from the UCSC Braingeneers group detail their automated, internet-connected microfluidics system, called “Autoculture.” The system precisely delivers feeding liquid to individual cerebral organoids in order to optimize their growth without the need for human interference with the tissue culture.
Cerebral organoids require a high level of expertise and consistency to maintain the precise conditions for cell growth over weeks or months. Using an automated system, as demonstrated in this study, can eliminate disturbance to cell culture growth caused by human interference or error, provide more robust results, and allow more scientists access to opportunities to conduct research with human brain models.
Autoculture also addresses variation that arises in organoid growth due to “batch effect” issues, where organoids grown at different times or at different labs under similar conditions may vary just because of the complexity of their growth. Using this uniform, automated system can reduce variation and allow researchers to better compare and validate their results.
“One of the big challenges is that these cultures are not very reproducible, and in part it’s not surprising because these are months-long experiments. You have to change media every couple of days and try to treat these cultures uniformly, which is extremely challenging,” said Sofie Salama, an acting professor of molecular, cellular and developmental biology at UCSC and an author on the study.
Unique design
Autoculture uses a microfluidic chip designed by the researchers, spearheaded by Associate Professor of Electrical and Computer Engineering Mircea Teodorescu and Biomolecular Engineering Ph.D. student Spencer Seiler. Their novel chips, created from a unique bi-layer mold, have tiny wells and channels for delivering minute amounts of liquid to the organoid, which allow the scientists to have a high level of control over nutrient concentrations and byproducts. Overall, the system uses mostly off-the-shelf, low-cost components, making it accessible and modular. More

Combining machine learning with multimodal electrochemical sensing can significantly improve the analytical performance of biosensors, according to new findings from a Penn State research team. These improvements may benefit noninvasive health monitoring, such as testing that involves saliva or sweat. The findings were published this month in Analytica Chimica Acta.
The researchers developed a novel analytical platform that enabled them to selectively measure multiple biomolecules using a single sensor, saving space and reducing complexity as compared to the usual route of using multi-sensor systems. In particular, they showed that their sensor can simultaneously detect small quantities of uric acid and tyrosine — two important biomarkers associated with kidney and cardiovascular diseases, diabetes, metabolic disorders, and neuropsychiatric and eating disorders — in sweat and saliva, making the developed method suitable for personalized health monitoring and intervention.
Many biomarkers have similar molecular structures or overlapping electrochemical signatures, making it difficult to detect them simultaneously. Leveraging machine learning for measuring multiple biomarkers can improve the accuracy and reliability of diagnostics and as a result improve patient outcomes, according to the researchers. Further, sensing using the same device saves resources and biological sample volumes needed for tests, which is critical with clinical samples with scarce amounts.
“We developed a new approach to improve the performance of electrochemical biosensors by combining machine learning with multimodal measurement,” said Aida Ebrahimi, Thomas and Sheila Roell Early Career Assistant Professor of Electrical Engineering and assistant professor of biomedical engineering. “Using our optimized machine learning architecture, we could detect biomolecules in amounts 100 times lower than what conventional sensing methods can do.”
The researchers’ methodology features a hardware/software system that enables them to automatically gather and process information based on a machine learning model that is trained to identify biomolecules in biological fluids such as saliva and sweat, which are common choices for noninvasive health monitoring.
“The machine learning-powered electrochemical diagnostic approach presented in this paper may find broader application in multiplexed biochemical sensing,” said Vinay Kammarchedu, 2022-23 Milton and Albertha Langdon Memorial Graduate Fellow in Electrical Engineering at Penn State and first author on the paper. “For example, this method can be extended to a variety of other molecules, including food and water toxins, drugs and neurochemicals that are challenging to detect simultaneously using conventional electrochemical methods.”
In their ongoing work, the researchers are applying this approach on such neurochemicals, which are difficult to detect due to similarities in their molecular structure and overlapping electrochemical signatures. More

Researchers at The University of Texas at Dallas and Novartis Pharmaceuticals Corp. have devised a computer-based platform for drug discovery that could make the process more effective, more efficient and less costly.
Dr. Baris Coskunuzer, professor of mathematical sciences at UT Dallas, and his colleagues developed an approach based on topological data analysis to screen thousands of possible drug candidates virtually and narrow the compound candidates considerably to those that are most fit for laboratory and clinical testing.
The researchers will present their findings at the 36th Conference on Neural Information Processing Systems, which will be held Nov. 28 through Dec. 9 in New Orleans.
Typically, the early phases of drug discovery involve researchers identifying a biological target, such as a protein associated with a disease of interest. The next step is to screen libraries of thousands of potential chemical compounds that might be effective or could be modified to affect the target to alleviate the disease’s cause or symptoms. The most promising candidates move on to the lengthy and expensive process of laboratory and clinical testing and regulatory approval.
“The drug-discovery process can take 10 to 15 years and cost a billion dollars,” Coskunuzer said. “Drug companies want a more cost-effective way to do this. They want to find the most promising compounds at the beginning of the process so they’re not wasting time testing dead ends.
“We have provided a completely new method of virtual screening that is computationally efficient and ranks compounds based on how likely they are to work.”
While virtual screening of libraries of chemical compounds is not new, Coskunuzer said his group’s approach significantly outperforms other state-of-the-art methods on large data sets. More

Cornell University researchers have created an interface that allows users to handwrite and sketch within computer code — a challenge to conventional coding, which typically relies on typing.
The pen-based interface, called Notate, lets users of computational, digital notebooks open drawing canvases and handwrite diagrams within lines of traditional, digitized computer code.
Powered by a deep learning model, the interface bridges handwritten and textual programming contexts: notation in the handwritten diagram can reference textual code and vice versa. For instance, Notate recognizes handwritten programming symbols, like “n,” and then links them up to their typewritten equivalents.
“A system like this would be great for data science, specifically with sketching plots and charts that then inter-operate with textual code,” said Ian Arawjo, lead author of the paper and doctoral student in the field of information science. “Our work shows that the current infrastructure of programming is actually holding us back. People are ready for this type of feature, but developers of interfaces for typing code need to take note of this and support images and graphical interfaces inside code.”
Arawjo also said the work demonstrates a new path forward by introducing artificial intelligence-powered, pen-based coding at a time when drawing tablets are becoming more widely used.
“Tools like Notate are important because they open us up to new ways to think about what programming is, and how different tools and representational practices can change that perspective,” said Tapan Parikh, associate professor of information science and paper co-author.
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Materials provided by Cornell University. Original written by Louis DiPietro. Note: Content may be edited for style and length. More