Aurelia Butler

Scientists have developed a small robot to understand how ants teach one another.
The team built the robot to mimic the behaviour of rock ants that use one-to-one tuition, in which an ant that has discovered a much better new nest can teach the route there to another individual.
The findings, published in the Journal of Experimental Biology today, confirm that most of the important elements of teaching in these ants are now understood because the teaching ant can be replaced by a machine.
Key to this process of teaching is tandem running where one ant literally leads another ant quite slowly along a route to the new nest. The pupil ant learns the route sufficiently well that it can find its own way back home and then lead a tandem-run with another ant to the new nest, and so on.
Prof Nigel Franks of Bristol’s School of Biological Sciences said: “Teaching is so important in our own lives that we spend a great deal of time either instructing others or being taught ourselves. This should cause us to wonder whether teaching actually occurs among non-human animals. And, in fact, the first case in which teaching was demonstrated rigorously in any other animal was in an ant.” The team wanted to determine what was necessary and sufficient in such teaching. If they could build a robot that successfully replaced the teacher, this should show that they largely understood all the essential elements in this process.
The researchers built a large arena so there was an appreciable distance between the ants’ old nest, which was deliberately made to be of low quality, and a new much better one that ants could be led to by a robot. A gantry was placed atop the arena to move back and forth with a small sliding robot attached to it, so that the scientists could direct the robot to move along either straight or wavy routes. Attractive scent glands, from a worker ant, were attached to the robot to give it the pheromones of an ant teacher. More

Managers need to make a consistent impression in order to motivate and inspire people, and that applies even more to video communication than to other digital channels. That is the result of a study by researchers at Karlsruhe Institute of Technology (KIT). They investigated the influence that charismatic leadership tactics used in text, audio and video communication channels have on employee performance. They focused on mobile work and the gig economy, in which jobs are flexibly assigned to freelancers via online platforms.
Since the onset of the Covid-19 pandemic, more and more people are working partly or entirely from home or in mobile work arrangements. At the same time, the so-called gig economy is growing. It involves the flexible assignment of short-term work to freelancers or part-time, low-wage staff via online platforms. Both trends are accelerating the digitalization of work. However, compared to face-to-face conversation between people in the same place, communication through digital channels offers fewer opportunities to motivate people and show charisma. This presents new challenges for managers. The impact of charismatic leadership tactics (CLTs) and the choice of communications channel (text, audio or video) on staff performance is the subject of a study by Petra Nieken, professor of human resource management at the Institute of Management at KIT. The study has been published in the journal The Leadership Quarterly.
Charismatic Leadership Tactics Can Be Learned and Objectively Observed
A charismatic leadership style can be learned; researchers speak of charismatic leadership tactics, which include verbal, paraverbal and non-verbal means such as metaphors, anecdotes, contrasts, rhetorical questions, pitch and tone of voice, and gestures. CLTs can be objectively observed and measured. They can be selectively changed in randomized controlled trials. “Managers can use the entire range of CLTs in face-to-face meetings. Digital communication reduces the opportunities to signal charisma,” says Nieken. “Depending on the communication channel, visual and/or acoustic cues can be missing. The question is whether people’s performance suffers as a result or if they adjust their expectations to the selected channel.”
In the first part of her study, Nieken conducted a field test with text, audio and video communication channels in which a task description was presented neutrally in one case and with the use of as many CLTs as possible in the other. In the neutral case, video messages led to lower performance than did audio and text messages. In contrast, there were no significant differences in performance in the CLT case. “The results show a positive correlation between video communication and charismatic communication; the charismatic video led to better performance than the neutral video,” explains Nieken. “So we can conclude that it’s most important for managers to convey a consistent impression when they use the video channel.”
Traditional Charisma Questionnaires Do Not Predict Staff Performance
In the second part of her study, Nieken had the different cases assessed with traditional questionnaires like the Multifactor Leadership Questionnaire (MLQ) and compared the results with those from the first part. Charisma noted in the questionnaires correlated with the use of CLTs but not with staff performance. “Traditional questionnaires like the MLQ are not suitable for predicting how people will perform in mobile work situations, working from home or in the gig economy,” concludes Nieken.
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Materials provided by Karlsruher Institut für Technologie (KIT). Note: Content may be edited for style and length. More

A team of researchers at Carnegie Mellon University believe they have developed the first AI pilot that enables autonomous aircraft to navigate a crowded airspace.
The artificial intelligence can safely avoid collisions, predict the intent of other aircraft, track aircraft and coordinate with their actions, and communicate over the radio with pilots and air traffic controllers. The researchers aim to develop the AI so the behaviors of their system will be indistinguishable from those of a human pilot.
“We believe we could eventually pass the Turing Test,” said Jean Oh, an associate research professor at CMU’s Robotics Institute (RI) and a member of the AI pilot team, referring to the test of an AI’s ability to exhibit intelligent behavior equivalent to a human.
To interact with other aircraft as a human pilot would, the AI uses both vision and natural language to communicate its intent with other aircraft, whether piloted or not. This behavior leads to safe and socially compliant navigation. Researchers achieved this implicit coordination by training the AI on data collected at the Allegheny County Airport and the Pittsburgh-Butler Regional Airport that included air traffic patterns, images of aircraft and radio transmissions.
The AI uses six cameras and a computer vision system to detect nearby aircraft in a manner similar to that of a human pilot. Its automatic speech recognition function uses natural language processing techniques to both understand incoming radio messages and communicate with pilots and air traffic controllers using speech.
Advancement in autonomous aircraft will broaden opportunities for drones, air taxis, helicopters and other aircraft to operate — moving people and goods, inspecting infrastructure, treating fields to protect crops, and monitoring for poaching or deforestation — often without a pilot behind the controls. These aircraft will have to fly, however, in an airspace already crowded with small airplanes, medical helicopters and more. More

While it’s long been understood that predicting outcomes in patients with cancer requires considering many factors, such as patient history, genes and disease pathology, clinicians struggle with integrating this information to make decisions about patient care. A new study from researchers from the Mahmood Lab at Brigham and Women’s Hospital reveals a proof-of-concept model that uses artificial intelligence (AI) to combine multiple types of data from different sources to predict patient outcomes for 14 different types of cancer. Results are published in Cancer Cell.
Experts depend on several sources of data, like genomic sequencing, pathology, and patient history, to diagnose and prognosticate different types of cancer. While existing technology enables them to use this information to predict outcomes, manually integrating data from different sources is challenging and experts often find themselves making subjective assessments.
“Experts analyze many pieces of evidence to predict how well a patient may do,” said Faisal Mahmood, PhD, an assistant professor in the Division of Computational Pathology at the Brigham and associate member of the Cancer Program at the Broad Institute of Harvard and MIT. “These early examinations become the basis of making decisions about enrolling in a clinical trial or specific treatment regimens. But that means that this multimodal prediction happens at the level of the expert. We’re trying to address the problem computationally.”
Through these new AI models, Mahmood and colleagues uncovered a means to integrate several forms of diagnostic information computationally to yield more accurate outcome predictions. The AI models demonstrate the ability to make prognostic determinations while also uncovering the predictive bases of features used to predict patient risk — a property that could be used to uncover new biomarkers.
Researchers built the models using The Cancer Genome Atlas (TCGA), a publicly available resource containing data on many different types of cancer. They then developed a multimodal deep learning-based algorithm which is capable of learning prognostic information from multiple data sources. By first creating separate models for histology and genomic data, they could fuse the technology into one integrated entity that provides key prognostic information. Finally, they evaluated the model’s efficacy by feeding it data sets from 14 cancer types as well as patient histology and genomic data. Results demonstrated that the models yielded more accurate patient outcome predictions than those incorporating only single sources of information.
This study highlights that using AI to integrate different types of clinically informed data to predict disease outcomes is feasible. Mahmood explained that these models could allow researchers to discover biomarkers that incorporate different clinical factors and better understand what type of information they need to diagnose different types of cancer. The researchers also quantitively studied the importance of each diagnostic modality for individual cancer types and the benefit of integrating multiple modalities.
The AI models are also capable of elucidating pathologic and genomic features that drive prognostic predictions. The team found that the models used patient immune responses as a prognostic marker without being trained to do so, a notable finding given that previous research shows that patients whose tumors elicit stronger immune responses tend to experience better outcomes.
While this proof-of-concept model reveals a newfound role for AI technology in cancer care, this research is only a first step in implementing these models clinically. Applying these models in the clinic requires incorporating larger data sets and validating on large independent test cohorts. Going forward, Mahmood aims to integrate even more types of patient information, such as radiology scans, family histories, and electronic medical records, and eventually bring the model to clinical trials.
“This work sets the stage for larger health care AI studies that combine data from multiple sources,” said Mahmood. “In a broader sense, our findings emphasize a need for building computational pathology prognostic models with much larger datasets and downstream clinical trials to establish utility.”
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Materials provided by Brigham and Women’s Hospital. Note: Content may be edited for style and length. More

Newly developed artificial intelligence (AI) programs accurately predicted the role of DNA’s regulatory elements and three-dimensional (3D) structure based solely on its raw sequence, according to two recent studies in Nature Genetics. These tools could eventually shed new light on how genetic mutations lead to disease and could lead to new understanding of how genetic sequence influences the spatial organization and function of chromosomal DNA in the nucleus, said study author Jian Zhou, Ph.D., Assistant Professor in the Lyda Hill Department of Bioinformatics at UTSW.
“Taken together, these two programs provide a more complete picture of how changes in DNA sequence, even in noncoding regions, can have dramatic effects on its spatial organization and function,” said Dr. Zhou, a member of the Harold C. Simmons Comprehensive Cancer Center, a Lupe Murchison Foundation Scholar in Medical Research, and a Cancer Prevention and Research Institute of Texas (CPRIT) Scholar.
Only about 1% of human DNA encodes instructions for making proteins. Research in recent decades has shown that much of the remaining noncoding genetic material holds regulatory elements — such as promoters, enhancers, silencers, and insulators — that control how the coding DNA is expressed. How sequence controls the functions of most of these regulatory elements is not well understood, Dr. Zhou explained.
To better understand these regulatory components, he and colleagues at Princeton University and the Flatiron Institute developed a deep learning model they named Sei, which accurately sorts these snippets of noncoding DNA into 40 “sequence classes” or jobs — for example, as an enhancer for stem cell or brain cell gene activity. These 40 sequence classes, developed using nearly 22,000 data sets from previous studies studying genome regulation, cover more than 97% of the human genome. Moreover, Sei can score any sequence by its predicted activity in each of the 40 sequence classes and predict how mutations impact such activities.
By applying Sei to human genetics data, the researchers were able to characterize the regulatory architecture of 47 traits and diseases recorded in the UK Biobank database and explain how mutations in regulatory elements cause specific pathologies. Such capabilities can help gain a more systematic understanding of how genomic sequence changes are linked to diseases and other traits. The findings were published this month.
In May, Dr. Zhou reported the development of a different tool, called Orca, which predicts the 3D architecture of DNA in chromosomes based on its sequence. Using existing data sets of DNA sequences and structural data derived from previous studies that revealed the molecule’s folds, twists, and turns, Dr. Zhou trained the model to make connections and evaluated the model’s ability to predict structure at various length scales.
The findings showed that Orca predicted DNA structures both small and large based on their sequences with high accuracy, including for sequences carrying mutations associated with various health conditions including a form of leukemia and limb malformations. Orca also enabled the researchers to generate new hypotheses about how DNA sequence controls its local and large-scale 3D structure.
Dr. Zhou said that he and his colleagues plan to use Sei and Orca, which are both publicly available on web servers and as open-source code, to further explore the role of genetic mutations in causing the molecular and physical manifestations of diseases — research that could eventually lead to new ways to treat these conditions.
The Orca study was supported by grants from CPRIT (RR190071), the National Institutes of Health (DP2GM146336), and the UT Southwestern Endowed Scholars Program in Medical Science.
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Materials provided by UT Southwestern Medical Center. Note: Content may be edited for style and length. More

When users want to send data over the internet faster than the network can handle, congestion can occur — the same way traffic congestion snarls the morning commute into a big city.
Computers and devices that transmit data over the internet break the data down into smaller packets and use a special algorithm to decide how fast to send those packets. These congestion control algorithms seek to fully discover and utilize available network capacity while sharing it fairly with other users who may be sharing the same network. These algorithms try to minimize delay caused by data waiting in queues in the network.
Over the past decade, researchers in industry and academia have developed several algorithms that attempt to achieve high rates while controlling delays. Some of these, such as the BBR algorithm developed by Google, are now widely used by many websites and applications.
But a team of MIT researchers has discovered that these algorithms can be deeply unfair. In a new study, they show there will always be a network scenario where at least one sender receives almost no bandwidth compared to other senders; that is, a problem known as starvation cannot be avoided.
“What is really surprising about this paper and the results is that when you take into account the real-world complexity of network paths and all the things they can do to data packets, it is basically impossible for delay-controlling congestion control algorithms to avoid starvation using current methods,” says Mohammad Alizadeh, associate professor of electrical engineering and computer science (EECS).
While Alizadeh and his co-authors weren’t able to find a traditional congestion control algorithm that could avoid starvation, there may be algorithms in a different class that could prevent this problem. Their analysis also suggests that changing how these algorithms work, so that they allow for larger variations in delay, could help prevent starvation in some network situations. More