Aurelia Butler

Many computer systems people interact with on a daily basis require knowledge about certain aspects of the world, or models, to work. These systems have to be trained, often needing to learn to recognize objects from video or image data. This data often contains superfluous content that reduces the accuracy of models. So researchers found a way to incorporate natural hand gestures into the teaching process. This way, users can more easily teach machines about objects, and the machines can also learn more effectively.
You’ve probably heard the term machine learning before, but are you familiar with machine teaching? Machine learning is what happens behind the scenes when a computer uses input data to form models that can later be used to perform useful functions. But machine teaching is the somewhat less explored part of the process, of how the computer gets its input data to begin with. In the case of visual systems, for example ones that can recognize objects, people need to show objects to a computer so it can learn about them. But there are drawbacks to the ways this is typically done that researchers from the University of Tokyo’s Interactive Intelligent Systems Laboratory sought to improve.
“In a typical object training scenario, people can hold an object up to a camera and move it around so a computer can analyze it from all angles to build up a model,” said graduate student Zhongyi Zhou. “However, machines lack our evolved ability to isolate objects from their environments, so the models they make can inadvertently include unnecessary information from the backgrounds of the training images. This often means users must spend time refining the generated models, which can be a rather technical and time-consuming task. We thought there must be a better way of doing this that’s better for both users and computers, and with our new system, LookHere, I believe we have found it.”
Zhou, working with Associate Professor Koji Yatani, created LookHere to address two fundamental problems in machine teaching: firstly, the problem of teaching efficiency, aiming to minimize the users’ time, and required technical knowledge. And secondly, of learning efficiency — how to ensure better learning data for machines to create models from. LookHere achieves these by doing something novel and surprisingly intuitive. It incorporates the hand gestures of users into the way an image is processed before the machine incorporates it into its model, known as HuTics. For example, a user can point to or present an object to the camera in a way that emphasizes its significance compared to the other elements in the scene. This is exactly how people might show objects to each other. And by eliminating extraneous details, thanks to the added emphasis to what’s actually important in the image, the computer gains better input data for its models.
“The idea is quite straightforward, but the implementation was very challenging,” said Zhou. “Everyone is different and there is no standard set of hand gestures. So, we first collected 2,040 example videos of 170 people presenting objects to the camera into HuTics. These assets were annotated to mark what was part of the object and what parts of the image were just the person’s hands. LookHere was trained with HuTics, and when compared to other object recognition approaches, can better determine what parts of an incoming image should be used to build its models. To make sure it’s as accessible as possible, users can use their smartphones to work with LookHere and the actual processing is done on remote servers. We also released our source code and data set so that others can build upon it if they wish.”
Factoring in the reduced demand on users’ time that LookHere affords people, Zhou and Yatani found that it can build models up to 14 times faster than some existing systems. At present, LookHere deals with teaching machines about physical objects and it uses exclusively visual data for input. But in theory, the concept can be expanded to use other kinds of input data such as sound or scientific data. And models made from that data would benefit from similar improvements in accuracy too.
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Quantum dots are clusters of some 1,000 atoms which act as one large ‘super-atom’. It is possible to accurately design the electronic properties of these dots just by changing their size. However, to create functional devices, a large number of dots have to be combined into a new material. During this process, the properties of the dots are often lost. Now, a team led by University of Groningen professor of Photophysics and Optoelectronics, Maria Antonietta Loi, has succeeded in making a highly conductive optoelectronic metamaterial through self-organization. The metamaterial is described in the journal Advanced Materials, published on 29 October.
Quantum dots of PbSe (lead selenide) or PbS (lead sulphide) can convert shortwave infrared light into an electrical current. This is a useful property for making detectors, or a switch for telecommunications. ‘However, a single dot does not make a device. And when dots are combined, the assembly often loses the unique optical properties of individual dots, or, if they do maintain them, their capacity to transport charge carriers becomes very poor’, explains Loi. ‘This is because it is difficult to create an ordered material from the dots.’
Ordered layer
Working with colleagues from the Zernike Institute for Advanced Materials at the Faculty of Science and Engineering, University of Groningen, Loi experimented with a method that allows the production of a metamaterial from a colloidal solution of quantum dots. These dots, each about five to six nanometres in size, show a very high conductivity when assembled in an ordered array, while maintaining their optical properties.
‘We knew from the literature that dots can self-organize into a two-dimensional, ordered layer. We wanted to expand this to a 3D material’, says Loi. To achieve this, they filled small containers with a liquid that acted as a ‘mattress’ for the colloidal quantum dots. ‘By injecting a small amount onto the surface of the liquid, we created a 2D material. Then, adding a bigger volume of quantum dots turned out to produce an ordered 3D material.’
Superlattice
The dots are not submersed in the liquid, but self-orient on the surface to achieve a low energy state. ‘The dots have a truncated cubic shape, and when they are put together, they form an ordered structure in three dimensions; a superlattice, where the dots act like atoms in a crystal’, explains Loi. This superlattice that is composed by the quantum dot super atoms displays the highest electron mobility reported for quantum dot assemblies.
Detectors
It took special equipment to ascertain what the new metamaterial looks like. The team used an electron microscope which has atomic resolution to show the details of the material. They also ‘imaged’ the large-scale structure of the material using a technique called Grazing-incidence small-angle X-ray scattering. ‘Both techniques are available at the Zernike Institute, thanks to my colleagues Bart Kooi and Giuseppe Portale, respectively, which was a great help’, says Loi.
Measurements of the electronic properties of the material show that it closely resembles that of a bulk semiconductor, but with the optical properties of the dots. Thus, the experiment paves the way to create new metamaterials based on quantum dots. The sensitivity of the dots used in the present study to infrared light could be used to create optical switches for telecommunication devices. ‘And they might also be used in infrared detectors for night-vision and autonomous driving.’
ERC Grant
Loi is extremely pleased with the results of the experiments: ‘People have been dreaming of achieving this since the 1980s. That is how long attempts have been made to assemble quantum dots into functional materials. The control of the structure and the properties we have achieved was beyond our wildest expectations.’ Loi is now working on understanding and improving the technology to build extended superlattices from quantum dots, but is also planning to do so with other building blocks, for which she was recently awarded an Advanced Grant from the European Research Council. ‘Our next step is to improve the technique in order to make the materials more perfect and fabricate photodetectors with them.’
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Engineers at MIT and the University of Tokyo have produced centimeter-scale structures, large enough for the eye to see, that are packed with hundreds of billions of hollow aligned fibers, or nanotubes, made from hexagonal boron nitride.
Hexagonal boron nitride, or hBN, is a single-atom-thin material that has been coined “white graphene” for its transparent appearance and its similarity to carbon-based graphene in molecular structure and strength. It can also withstand higher temperatures than graphene, and is electrically insulating, rather than conductive. When hBN is rolled into nanometer-scale tubes, or nanotubes, its exceptional properties are significantly enhanced.
The team’s results, published today in the journal ACS Nano, provide a route toward fabricating aligned boron nitride nanotubes (A-BNNTs) in bulk. The researchers plan to harness the technique to fabricate bulk-scale arrays of these nanotubes, which can then be combined with other materials to make stronger, more heat-resistant composites, for instance to shield space structures and hypersonic aircraft.
As hBN is transparent and electrically insulating, the team also envisions incorporating the BNNTs into transparent windows and using them to electrically insulate sensors within electronic devices. The team is also investigating ways to weave the nanofibers into membranes for water filtration and for “blue energy” — a concept for renewable energy in which electricity is produced from the ionic filtering of salt water into fresh water.
Brian Wardle, professor of aeronautics and astronautics at MIT, likens the team’s results to scientists’ decades-long, ongoing pursuit of manufacturing bulk-scale carbon nanotubes.
“In 1991, a single carbon nanotube was identified as an interesting thing, but it’s been 30 years getting to bulk aligned carbon nanotubes, and the world’s not even fully there yet,” Wardle says. “With the work we’re doing, we’ve just short-circuited about 20 years in getting to bulk-scale versions of aligned boron nitride nanotubes.”
Wardle is the senior author of the new study, which includes lead author and MIT research scientist Luiz Acauan, former MIT postdoc Haozhe Wang, and collaborators at the University of Tokyo. More

Stake out in Pennsylvania’s Cook State Forest at the right time of year and you can see one of nature’s great light shows: swarms of fireflies that synchronize their flashes like strings of Christmas lights in the dark.
A new study by Pitt mathematicians shows that math borrowed from neuroscience can describe how swarms of these unique insects coordinate their light show, capturing key details about how they behave in the wild.
“This firefly has a quick sequence of flashes, and then a big pause before the next burst,” said Jonathan Rubin, professor and chair of the Department of Mathematics in the Kenneth P. Dietrich School of Arts and Sciences. “We knew a good framework for modeling this that could capture a lot of the features, and we were curious how far we could push it.”
Male fireflies produce a glow from their abdomens to call out to potential mates, sending out blinking patterns in the dark to woo females of their own species. Synchronous fireflies of the species Photinus carolinus take it a step further, coordinating their blinking throughout entire swarms. It’s a rare trait — there are only a handful of such species in North America — and the striking lights they produce draw crowds to locations where the insects are known to gather.
They’ve also attracted the interest of mathematicians seeking to understand how they synchronize their blinks. It’s just one example of how synchronization can evolve from randomness, a process that has intrigued mathematicians for centuries. One famous example from the 1600s showed that pendulum clocks hung next to one another synchronize through vibrations that travel through the wall, and the same branch of math can be used to describe everything from the action of intestines to audience members clapping.
“Synchrony is important for a lot of things, good and bad,” said co-author Bard Ermentrout, distinguished professor of mathematics in the Dietrich School. “Physicists, mathematicians, we’re all interested in synchronization.”
To crack the fireflies’ light show, the Pitt team used a more complex model called an “elliptic burster” that’s used to describe the behavior of brain cells. The duo, along with then-undergrad Madeline McCrea (A&S ’22) published details of their model Oct. 26 in the Journal of the Royal Society Interface. More

Most deaths from melanoma — the most lethal form of skin cancer — occur in patients who were initially diagnosed with early-stage melanoma and then later experienced a recurrence that is typically not detected until it has spread or metastasized.
A team led by investigators at Massachusetts General Hospital (MGH) recently developed an artificial intelligence-based method to predict which patients are most likely to experience a recurrence and are therefore expected to benefit from aggressive treatment. The method was validated in a study published in npj Precision Oncology.
Most patients with early-stage melanoma are treated with surgery to remove cancerous cells, but patients with more advanced cancer often receive immune checkpoint inhibitors, which effectively strengthen the immune response against tumor cells but also carry significant side effects.
“There is an urgent need to develop predictive tools to assist in the selection of high-risk patients for whom the benefits of immune checkpoint inhibitors would justify the high rate of morbid and potentially fatal immunologic adverse events observed with this therapeutic class,” says senior author Yevgeniy R. Semenov, MD, an investigator in the Department of Dermatology at MGH.
“Reliable prediction of melanoma recurrence can enable more precise treatment selection for immunotherapy, reduce progression to metastatic disease and improve melanoma survival while minimizing exposure to treatment toxicities.”
To help achieve this, Semenov and his colleagues assessed the effectiveness of algorithms based on machine learning, a branch of artificial intelligence, that used data from patient electronic health records to predict melanoma recurrence.
Specifically, the team collected 1,720 early-stage melanomas — 1,172 from the Mass General Brigham healthcare system (MGB) and 548 from the Dana-Farber Cancer Institute (DFCI) — and extracted 36 clinical and pathologic features of these cancers from electronic health records to predict patients’ recurrence risk with machine learning algorithms. Algorithms were developed and validated with various MGB and DFCI patient sets, and tumor thickness and rate of cancer cell division were identified as the most predictive features.
“Our comprehensive risk prediction platform using novel machine learning approaches to determine the risk of early-stage melanoma recurrence reached high levels of classification and time to event prediction accuracy,” says Semenov. “Our results suggest that machine learning algorithms can extract predictive signals from clinicopathologic features for early-stage melanoma recurrence prediction, which will enable the identification of patients who may benefit from adjuvant immunotherapy.”
Additional Mass General co-authors include Ahmad Rajeh, Michael R. Collier, Min Seok Choi, Munachimso Amadife, Kimberly Tang, Shijia Zhang, Jordan Phillips, Nora A. Alexander, Yining Hua, Wenxin Chen, Diane, Ho, Stacey Duey, and Genevieve M. Boland.
This work was supported by the Melanoma Research Alliance, the National Institutes of Health, the Department of Defense, and the Dermatology Foundation.
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Scientists at the Max Planck Institute for the Science of Light have managed for the first time to create a unidirectional device that significantly increases the quality of a special class of transmitted signals in optical communications: optical vortices. By transmitting selective optical vortex modes exclusively unidirectionally, the developed device largely reduces detrimental backscattering to a minimum. The scientists emphasize the great practical utility of their discovery in many optical systems, with applications ranging from mode division multiplexed communications, optical tweezers, vortex lasers to quantum manipulation systems.
Optical communication can be improved by increasing the amount of optical information transmitted. This can be achieved by using multiplexed channels such as using many optical wavelengths, different polarization states or multiple time slots. In the last decade, optical spatial modes, which are the eigenfields in the waveguides, are widely exploited to further improve the communication capacity due to the little crosstalk between orthogonal spatial modes.
In classical communication as well as in quantum communication, the use of vortex modes in multiplexing methods has proven to be advantageous. This special mode set possesses a helical optical phase distribution and allows an additional degree of freedom for multiplexing optical signals. Devices like vortex generators, lasers and signal amplifiers were demon-strated and are in great demand.
A limiting effect on the applicability is that there has not yet been a device that permits transmission of certain vortex modes in one direction but not the opposite one. However, just this kind of device — a so-called optical vortex isolator — is of crucial importance for the improvement of signal quality and purity. The particular difficulty in developing such a device is a fundamental principle of optics: reciprocity. It requires a symmetrical response of a transmission channel when the source and observation points are interchanged.
Researchers succeed in building an optical vortex insulator
Now, a team at the Max Planck Institute for the Science of Light led by Xinglin Zeng, Philip Russell and Birgit Stiller, achieved a breakthrough that makes this possible: They used sound waves that propagate only in one direction to break the light transmission reciprocity for chosen vortex modes. The effect of so-called topology-selective Brillouin-Mandelstam scattering in chiral photonic crystal fibre allows for a unidirectional interaction of vortex-carrying light waves with traveling sound waves. A specific optical vortex can be strongly suppressed or amplified with a well-designed control light. The experimental results published in Science Advances show a significant vortex isolation rate, preventing random backscattering and signal degradation in the system.
“This is the first nonreciprocal system for vortex modes, which opens up a new perspective in nonreciprocal optics — the same physical effect can happen not only on the fundamental modes but also on higher-order modes” says Xinglin Zeng, the first author of this paper. “The light-driven optical vortex isolator will have great impact on the applications such as optical communications, quantum information processing, optical tweezers, and fiber lasers. I find the possibility of selective manipulation of vortex modes solely by light and sound waves a very fascinating concept” says Birgit Stiller, the leader of the Quantum Optoacoustics Research Group.
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Self-driving cars, like the human drivers that preceded them, need to see what’s around them to avoid obstacles and drive safely.
The most sophisticated autonomous vehicles typically use lidar, a spinning radar-type device that acts as the eyes of the car. Lidar provides constant information about the distance to objects so the car can decide what actions are safe to take.
But these eyes, it turns out, can be tricked.
New research reveals that expertly timed lasers shined at an approaching lidar system can create a blind spot in front of the vehicle large enough to completely hide moving pedestrians and other obstacles. The deleted data causes the cars to think the road is safe to continue moving along, endangering whatever may be in the attack’s blind spot.
This is the first time that lidar sensors have been tricked into deleting data about obstacles.
The vulnerability was uncovered by researchers from the University of Florida, the University of Michigan and the University of Electro-Communications in Japan. The scientists also provide upgrades that could eliminate this weakness to protect people from malicious attacks. More

Lithium-ion batteries are ubiquitous in the world of electric vehicles. However, a significant challenge encountered with their use is their low battery life and slow charging capability. Recent studies suggest two-dimensional (2D) nanomaterials to be a strong candidate for enhancing their performance. Recently, a collaborative research team from Japan and India demonstrated the efficacy of using 2D titanium diboride nanosheets in lithium-ion batteries. Their findings could have far-reaching consequences in the field of electric vehicles and other electronics.
As the electric vehicle (EV) industry is advancing, so are the efforts in the research and development of superior lithium (Li)-ion batteries to power these vehicles. Exploring and expanding rapid charge-discharge technology and extended battery life are critical challenges in their development. A few factors, such as the diffusion of Li ions, characteristics of the electrode-electrolyte interface, and electrode porosity, can help overcome these issues achieve extreme fast charging and ultralong life.
In recent years, two-dimensional (2D) nanomaterials, which are thin sheet-like structures with a thickness of a few nanometers, have emerged as potential anode materials for Li-ion batteries. These nanosheets possess a high aspect ratio and high density of active sites, which enables fast charging and superior cycling performance. In particular, 2D nanomaterials based on transition-metal diborides (or TMDs) have piqued the interest of researchers. TMDs have been found to have a high rate and long cycling stability for Li ion storage, owing to their honeycomb planes of boron and multivalent transition-metal atoms.
Recently, a group of scientists led by Prof. Noriyoshi Matsumi from the Japan Advanced Institute of Science and Technology (JAIST) and Prof. Kabeer Jasuja from the Indian Institite of Technology (IIT) Gandhinagar set out to further explore the potential of TMDs for energy storage. The team conducted the first experimental study on the storage potential of titanium diboride (TiB2)-based hierarchical nanosheets (THNS) as an anode material for Li-ion batteries. The team comprised Rajashekar Badam, former Senior Lecturer at JAIST; Akash Varma, former M.S. Course Student at JAIST; Koichi Higashimine, Technical Specialist at JAIST and Asha Liza James, Ph.D. Student at IIT Gandhinagar. Their study was published in ACS Applied Nano Materials and made available online on September 19, 2022.
The THNS were developed by oxidizing TiB2 powder with hydrogen peroxide, followed by centrifuging and freeze-drying the solution. “What makes our work stand out is the scalability of the method developed for synthesizing these TiB2 nanosheets. For any nanomaterial to translate into a tangible technology, scalability is the limiting factor. Our synthesis method only requires stirring and no sophisticated equipment. This is on account of the dissolution and recrystallization behavior exhibited by TiB2, a serendipitous discovery that makes this work a promising bridge from lab to the field,” explains Prof. Kabeer.
Thereafter, the team constructed an anodic Li-ion half-cell using the THNS as active anode material. The team studied the charge-storage characteristics of the THNS-based anodes.
The team found that the THNS-based anode showed a high discharge capacity of 380 mAh/g with a current density of just 0.025 A/g. Furthermore, they saw that a discharge capacity of 174 mAh/g could be obtained for a high current density of 1 A/g, with a charge time of 10 min and a capacity retention of 89.7% after 1,000 cycles. Additionally, the THNS-based Li-ion anode could sustain very high current rates, in the order of 15 to 20 A/g facilitating ultrafast charging in about 9 to 14 seconds. Under the high current rate, with a capacity retention greater than 80% was observed after 10,000 cycles.
The results of this study indicate the suitability of the 2D TiB2 nanosheets as a candidate for fast-charging and long-life Li-ion batteries. They also highlight the advantage of nano-scaling bulk materials, like TiB2, to attain promising properties, including pseudocapacitive charge storage, excellent high-rate capability, and superior cyclability. Explaining the potential long-term effects of their research, Prof. Matsumi says, “Such quick-charging technology can accelerate the diffusion of EVs and significantly decrease waiting times for charging various mobile electronic devices. We hope our findings can stimulate more research in this field, which can eventually lead to the convenience of EV users, lesser air pollution in cities, and less stressful mobile life in order to enhance the productivity of our society.”
Here’s hoping that we soon see this remarkable technology being used in EVs and other electronic devices. More