Computers Math

Policy-makers are faced with an exceptional challenge: how to reduce harm caused by firearms while maintaining citizens’ right to bear arms and protect themselves. This is especially true as the Supreme Court has hobbled New York State regulations restricting who can carry a concealed weapon.
While meaningful legislation requires an understanding of how access to firearms is associated with different outcomes of harm, this knowledge also calls for accurate, highly-resolved data on firearm possession, data that is presently unavailable due to a lack of a comprehensive national firearm ownership registry.
Newly published research from data scientist and firearm proliferation researcher Maurizio Porfiri, Institute Professor at the NYU Tandon School of Engineering, and co-authors Roni Barak Ventura, a post-doctoral researcher at Porfiri’s Dynamical Systems Lab, and Manuel Ruiz Marin of the Universidad Politécnica de Cartagena, Spain describe a spatio-temporal model to predict trends in firearm prevalence on a state-by-state level by fusing data from two available proxies — background checks per capita and suicides committed with a firearm in a given state. The study “A spatiotemporal model of firearm ownership in the United States,” in the Cell Press journal Patterns, details how by calibrating their results with yearly survey data, the team determined that the two proxies can be simultaneously considered to draw precise information regarding firearm ownership.
Porfiri, who in 2020 received one of the first newly authorized NSF federal grants for $2 million to study the “firearm ecosystem” in the U.S., has spent the last few years exploring gun acquisition trends and how they relate to and are influenced by a number of factors, from media coverage of mass shootings, to the influence of the sitting President.
“There is very limited knowledge on when and where guns are acquired in the country, and even less is known regarding future ownership trends,” said Porfiri, professor of mechanical and aerospace, biomedical, and civil and urban engineering and incoming director of the Center for Urban Science and Progress (CUSP) at NYU Tandon. “Prior studies have largely relied on the use of a single, select proxy to make some inference of gun prevalence, typically within simple correlation schemes. Our results show that there is a need to combine proxies of sales and violence to draw precise inferences on firearm prevalence.” He added that most research aggregates the measure counts within states and does not consider interference between states or spill-over effects.
Their study shows how their model can be used to better understand the relationships between media coverage, mass shootings, and firearm ownership, uncovering causal associations that are masked when the proxies are used individually.
While the researchers found, for example, that media coverage of firearm control is causally associated with firearm ownership, they discovered that their model generating a strong firearm ownership profile for a state was a strong predictor of mass shootings in that state.
“The potential link between mass shootings and firearm purchases is a unique contribution of our model,” said Ruiz Marin. “Such a link can only be detected by scratching the surface on the exact gun counts in the country.”
“We combined publicly available data variables into one measure of ownership. Because it has a spatial component, we could also track gun flow from one state to another based on political and cultural similarities,” said Barak-Ventura, adding that the spatial component of the work is novel. “Prior studies looked at a correlation of two variables such as increasing background checks and an increase in gun violence.”
Barak-Ventura said the team is now using their model to explore which policies are effective in reducing death by guns in a state and surrounding regions, and how the relationship between gun ownership and violent outcomes is disrupted by different legislation.
The research was supported by the National Science Foundation and by RAND’s National Collaborative on Gun Violence Research through a postdoctoral fellowship award. Roni Barak Ventura’s work was supported by a scholarship from the Mitsui USA Foundation. This study was also part of the collaborative activities carried out under the programs of the region of Murcia (Spain) as well as the Ministerio de Ciencia, Innovación y Universidades. More

In a step toward robots that can learn on the fly like humans do, a new approach expands training data sets for robots that work with soft objects like ropes and fabrics, or in cluttered environments.
Developed by robotics researchers at the University of Michigan, it could cut learning time for new materials and environments down to a few hours rather than a week or two.
In simulations, the expanded training data set improved the success rate of a robot looping a rope around an engine block by more than 40% and nearly doubled the successes of a physical robot for a similar task.
That task is among those a robot mechanic would need to be able to do with ease. But using today’s methods, learning how to manipulate each unfamiliar hose or belt would require huge amounts of data, likely gathered for days or weeks, says Dmitry Berenson, U-M associate professor of robotics and senior author of a paper presented today at Robotics: Science and Systems in New York City.
In that time, the robot would play around with the hose — stretching it, bringing the ends together, looping it around obstacles and so on — until it understood all the ways the hose could move.
“If the robot needs to play with the hose for a long time before being able to install it, that’s not going to work for many applications,” Berenson said. More

The Universidad Carlos III de Madrid (UC3M) is conducting research that analyses antibiotic resistance patterns with the aim of finding trends that can help decide which treatment to apply to each type of patient and stop the spread of bacteria. This study, recently published in the scientific journal Nature Communications, has been carried out together with the University of Exeter, the University of Birmingham (both in the United Kingdom) and the Westmead Hospital in Sydney (Australia).
In order to observe a bacterial pathogen’s resistance to an antibiotic in clinical environments, a measure called MIC (Minimum Inhibitory Concentration) is used, which is the minimum concentration of antibiotic capable of inhibiting bacterial growth. The greater the MIC of a bacterium against an antibiotic, the greater its resistance.
However, most public databases only contain the frequency of resistant pathogens, which is aggregated data calculated from MIC measurements and predefined resistance thresholds. “For example, for a given pathogen, the antibiotic resistance threshold may be 4: if a bacterium has an MIC of 16, it is considered resistant and is counted when calculating the resistance frequency,” says Pablo Catalán, lecturer and researcher in the UC3M Mathematics Department and author of the study. In this regard, the resistance reports that are carried out nationally and by organisations such as the WHO are prepared using this aggregated resistance frequency data.
To conduct this research, the team has analysed a database which is ground-breaking, as it contains raw data on antibiotic resistance. This database, called ATLAS, is managed by Pfizer and has been public since 2018. The working group led by UC3M has compared the information of 600,000 patients from over 70 countries and has used machine learning methods (a type of artificial intelligence technique) to extract resistance evolution patterns.
By analysing this data, the research team has discovered that there are resistance evolution patterns that can be detected when using the raw data (MIC), but which are undetectable using the aggregated data. “A clear example of this is a pathogen whose MIC is slowly increasing over time, but below the resistance threshold. Using this frequency data we wouldn’t be able to say anything, since the resistance frequency remains constant. However, by using MIC data we can detect such a case and be on alert. In the paper, we discuss several clinically relevant cases which have these characteristics. Furthermore, we are the first team to describe this database in depth,” says Catalán.
This study makes it possible to design antibiotic treatments that are more effective in controlling infections and curbing the rise of resistance which causes many clinical problems. “The research uses mathematical ideas to find new ways of extracting antibiotic resistance patterns from 6.5 million data points,” concludes the research author.
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Sensing with levitated nanoparticles has so far been limited by the precision of position measurements. Now, researchers at the University of Innsbruck led by Tracy Northup, have demonstrated a new method for optical interferometry in which light scattered by a particle is reflected by a mirror. This opens up new possibilities for using levitated particles as sensors, in particular, in quantum regimes.
Levitated nanoparticles are promising tools for sensing ultra-weak forces of biological, chemical or mechanical origin and even for testing the foundations of quantum physics. However, such applications require precise position measurement. Researchers at the Department of Experimental Physics of the University of Innsbruck, Austria, have now demonstrated a new technique that boosts the efficiency with which the position of a sub-micron levitated object is detected. “Typically, we measure a nanoparticle’s position with a technique called optical interferometry, in which part of the light emitted by a nanoparticle is compared with the light from a reference laser,” says Lorenzo Dania, a PhD student in Tracy Northup’s research group. “A laser beam, however, has a much different shape than the light pattern emitted by a nanoparticle, known as dipole radiation.” That shape difference currently limits the measurement precision.
Self-interference method
The new technique demonstrated by Tracy Northup, a professor at the University of Innsbruck, and her team resolves this limitation by replacing the laser beam with the light of the particle reflected by a mirror. The technique builds on a method to track barium ions that has been developed in recent years by Rainer Blatt, also of the University of Innsbruck, and his team. Last year, researchers from the two teams proposed to extend this method to nanoparticles. Now, using a nanoparticle levitated in an electromagnetic trap, the researchers showed that this method outperformed other state-of-the-art detection techniques. The result opens up new possibilities for using levitated particles as sensors — for example, to measure tiny forces — and for bringing the particles’ motion into realms described by quantum mechanics.
Financial support for the research was provided, among others, by the European Union as well as by the Austrian Science Fund FWF, the Austrian Academy of Sciences and the Austrian Federal Ministry of Education, Science and Research.
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For humans, finding a lost wallet buried under a pile of items is pretty straightforward — we simply remove things from the pile until we find the wallet. But for a robot, this task involves complex reasoning about the pile and objects in it, which presents a steep challenge.
MIT researchers previously demonstrated a robotic arm that combines visual information and radio frequency (RF) signals to find hidden objects that were tagged with RFID tags (which reflect signals sent by an antenna). Building off that work, they have now developed a new system that can efficiently retrieve any object buried in a pile. As long as some items in the pile have RFID tags, the target item does not need to be tagged for the system to recover it.
The algorithms behind the system, known as FuseBot, reason about the probable location and orientation of objects under the pile. Then FuseBot finds the most efficient way to remove obstructing objects and extract the target item. This reasoning enabled FuseBot to find more hidden items than a state-of-the-art robotics system, in half the time.
This speed could be especially useful in an e-commerce warehouse. A robot tasked with processing returns could find items in an unsorted pile more efficiently with the FuseBot system, says senior author Fadel Adib, associate professor in the Department of Electrical Engineering and Computer Science and director of the Signal Kinetics group in the Media Lab.
“What this paper shows, for the first time, is that the mere presence of an RFID-tagged item in the environment makes it much easier for you to achieve other tasks in a more efficient manner. We were able to do this because we added multimodal reasoning to the system — FuseBot can reason about both vision and RF to understand a pile of items,” adds Adib.
Joining Adib on the paper are research assistants Tara Boroushaki, who is the lead author; Laura Dodds; and Nazish Naeem. The research will be presented at the Robotics: Science and Systems conference. More

The BirdNET app, a free machine-learning powered tool that can identify over 3,000 birds by sound alone, generates reliable scientific data and makes it easier for people to contribute citizen-science data on birds by simply recording sounds.
An article publishing June 28thin the open access journal PLOS Biology by Connor Wood and colleagues in the K. Lisa Yang Center for Conservation Bioacoustics at the Cornell Lab of Ornithology, U.S. suggests that the BirdNET app lowers the barrier to citizen science because it doesn’t require bird-identification skills to participate. Users simply listen for birds and tap the app to record. BirdNET uses artificial intelligence to automatically identify the species by sound and captures the recording for use in research.
“Our guiding design principles were that we needed an accurate algorithm and a simple user interface,” said study co-author Stefan Kahl in the Yang Center at the Cornell Lab, who led the technical development. “Otherwise, users would not return to the app.” The results exceeded expectations: Since its launch in 2018, more than 2.2 million people have contributed data.
To test whether the app could generate reliable scientific data, the authors selected four test cases in which conventional research had already provided robust answers. Their results show that BirdNET app data successfully replicated known patterns of song dialects in North American and European songbirds and accurately mapped a bird migration on both continents.
Validating the reliability of the app data for research purposes was the first step in what they hope will be a long-term, global research effort — not just for birds, but ultimately for all wildlife and indeed entire soundscapes. Data used in the four test cases is publicly available, and the authors are working on making the entire dataset open.
“The most exciting part of this work is how simple it is for people to participate in bird research and conservation,” Wood adds. “You don’t need to know anything about birds, you just need a smartphone, and the BirdNET app can then provide both you and the research team with a prediction for what bird you’ve heard. This has led to tremendous participation worldwide, which translates to an incredible wealth of data. It’s really a testament to an enthusiasm for birds that unites people from all walks of life.”
The BirdNET app is part of the Cornell Lab of Ornithology’s suite of tools, including the educational Merlin Bird ID app and citizen-science apps eBird, NestWatch, and Project FeederWatch, which together have generated more than 1 billion bird observations, sounds, and photos from participants around the world for use in science and conservation.
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Particle scattering is an important test of the quantum properties of atoms and larger molecules. While electrons have historically dominated these experiments, their positively charged antimatter counterparts? — ?positrons? — ?can be used in promising applications when the negatively charged particles aren’t suitable.
A new paper published in EPJ D examines the scattering of positrons from rare gas atoms stuffed inside the fullerenes — so-called “rare gas endohedrals.” The paper is authored by Km Akanksha Dubey from the Indian Institute of Technology Patna, Patna, Bihta, India, and Marcelo Ciappina, Guangdong Technion-Israel Institute of Technology, Shantou, China.
“Our focus was to investigate positron scattering processes with rare gas endohedrals. As a reference to the endohedral system, we also considered positron scattering from bare C60 targets,” Ciappina says. “”In our study, we chose rare gas atoms for encapsulation inside carbon 60 (C60), as they are probably the most popular and studied endohedrals. Rare gas endohedrals are very stable formations; the encapsulated atoms find their equilibrium position at almost the geometrical centre of the C60.”
The study builds upon the findings of previous studies involving the collision of positrons with giant targets like C60 and rare gas endohedrals. The major difference being that the resonance scattering with different sizes of the encaged atoms is elucidated in comparison to the bare C60 scattering; resonances are also tested under the different scattering fields of the projectile-target complex.
“To our surprise, resonance formations in the rare gas endohedrals are altered as compared to the case of positron-C60 collision, despite the dominant scattering field in positron scattering being repulsive in nature,” Ciappina says. The resonances at the lower energy are significantly affected by various scattering fields considered alternatively.
“Thus, scattering resonances in the positron scattering find their natural abode in the C60 and rare gas endohedrals, and the resonance states can be favourably manipulated by keeping the rare gas atoms inside it.”
With insights into many aspects of such collision processes, potential applications for the findings of the paper could range from fields such as positron beam spectroscopy and the investigation of nanomaterials.
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As the world fights climate change, will the increasingly widespread use of artificial intelligence (AI) be a help or a hindrance? In a paper published this week in Nature Climate Change, a team of experts in AI, climate change, and public policy present a framework for understanding the complex and multifaceted relationship of AI with greenhouse gas emissions, and suggest ways to better align AI with climate change goals.
“AI affects the climate in many ways, both positive and negative, and most of these effects are poorly quantified,” said David Rolnick, Assistant Professor of Computer Science at McGill University and a Core Academic Member of Mila — Quebec AI Institute, who co-authored the paper. “For example, AI is being used to track and reduce deforestation, but AI-based advertising systems are likely making climate change worse by increasing the amount that people buy.”
The paper divides the impacts of AI on greenhouse gas emissions into three categories: 1) Impacts from the computational energy and hardware used to develop, train, and run AI algorithms, 2) immediate impacts caused by the applications of AI — such as optimizing energy use in buildings (which decreases emissions) or accelerating fossil fuel exploration (which increases emissions), and 3) system-level impacts caused by the ways in which AI applications affect behaviour patterns and society more broadly, such as via advertising systems and self-driving cars.
“Climate change should be a key consideration when developing and assessing AI technologies,” said Lynn Kaack, Assistant Professor of Computer Science and Public Policy at the Hertie School, and lead author on the report. “We find that those impacts that are easiest to measure are not necessarily those with the largest impacts. So, evaluating the effect of AI on the climate holistically is important.”
AI’s impacts on greenhouse gas emissions — a matter of choice
The authors emphasize the ability of researchers, engineers, and policymakers to shape the impacts of AI, writing that its “… ultimate effect on the climate is far from predestined, and societal decisions will play a large role in shaping its overall impacts.” For example, the paper notes that AI-enabled autonomous vehicle technologies can help lower emissions if they are designed to facilitate public transportation, but they can increase emissions if they are used in personal cars and result in people driving more.
The researchers also note that machine learning expertise is often concentrated among a limited set of actors. This raises potential challenges with respect to the governance and implementation of machine learning in the context of climate change, since it may create or widen the digital divide, or shift power from public to large private entities by virtue of who controls relevant data or intellectual capital.
“The choices that we make implicitly as technologists can matter a lot,” said Prof. Rolnick. “Ultimately, AI for Good shouldn’t just be about adding beneficial applications on top of business as usual, it should be about shaping all the applications of AI to achieve the impact we want to see.”
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