Researchers Discover Self-Assembled Crystal Structure

 Researchers Discover Self-Assembled Crystal Structures

By Cornell Chronicle,May 2, 2023

Conceptual image showcasing several interaction potential shapes, represented by stems, that will lead to the self-assembly of new low-coordinated crystal structures, represented by flowers.

Cornell Ph.D. student Hillary Pan said the researchers "found new structures that weren’t previously listed in any crystal structure database; these particles are actually assembling into something that nobody had ever seen before."

Cornell University researchers have discovered over 20 never-before-seen self-assembled crystal structures.

Using a targeted computational approach, the researchers looked for previously unknown structures characterized by low particle coordination.

They developed a new functional form for particle interactions that allows all features to be tuned independently.

The researchers observed significant complexity and symmetry within these crystal structures, including clathrates (chemical substances made up of a lattice that traps or contains molecules)

with empty 'cages', and low-symmetry structures.

From Cornell Chronicle

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Wrinkles’ in Time Experience Linked to Heartbeat

Intriguing research.

Wrinkles’ in time experience linked to heartbeat   By James Dean, Cornell Chronicle, March 6, 2023

How long is the present? The answer, Cornell researchers suggest in a new study, depends on your heart.   They found that our momentary perception of time is not continuous but may stretch or shrink with each heartbeat.

The research builds evidence that the heart is one of the brain’s important timekeepers and plays a fundamental role in our sense of time passing – an idea contemplated since ancient times, said Adam K. Anderson, professor in the Department of Psychology and in the College of Human Ecology (CHE).

“Time is a dimension of the universe and a core basis for our experience of self,” Anderson said. “Our research shows that the moment-to-moment experience of time is synchronized with, and changes with, the length of a heartbeat.”

Saeedeh Sadeghi, M.S. ’19, a doctoral student in the field of psychology, is the lead author of “Wrinkles in Subsecond Time Perception are Synchronized to the Heart,” published March 2 in the journal Psychophysiology. Anderson is a co-author with Eve De Rosa, the Mibs Martin Follett Professor in Human Ecology (CHE) and dean of faculty at Cornell, and Marc Wittmann, senior researcher at the Institute for Frontier Areas of Psychology and Mental Health in Germany.

Time perception typically has been tested over longer intervals, when research has shown that thoughts and emotions may distort our sense time, perhaps making it fly or crawl. Sadeghi and Anderson recently reported, for example, that crowding made a simulated train ride seem to pass more slowly.

Such findings, Anderson said, tend to reflect how we think about or estimate time, rather than our direct experience of it in the present moment.

To investigate that more direct experience, the researchers asked if our perception of time is related to physiological rhythms, focusing on natural variability in heart rates. The cardiac pacemaker “ticks” steadily on average, but each interval between beats is a tiny bit longer or shorter than the preceding one, like a second hand clicking at different intervals.

The team harnessed that variability in a novel experiment. Forty-five study participants – ages 18 to 21, with no history of heart trouble – were monitored with electrocardiography, or ECG, measuring heart electrical activity at millisecond resolution. The ECG was linked to a computer, which enabled brief tones lasting 80-180 milliseconds to be triggered by heartbeats. Study participants reported whether tones were longer or shorter relative to others.

The results revealed what the researchers called “temporal wrinkles.” When the heartbeat preceding a tone was shorter, the tone was perceived as longer. When the preceding heartbeat was longer, the sound’s duration seemed shorter.

“These observations systematically demonstrate that the cardiac dynamics, even within a few heartbeats, is related to the temporal decision-making process,” the authors wrote.

The study also showed the brain influencing the heart. After hearing tones, study participants focused attention on the sounds. That “orienting response” changed their heart rate, affecting their experience of time.

“The heartbeat is a rhythm that our brain is using to give us our sense of time passing,” Anderson said. “And that is not linear – it is constantly contracting and expanding.”

The scholars said the connection between time perception and the heart suggests our momentary perception of time is rooted in bioenergetics, helping the brain manage effort and resources based on changing body states including heart rate.

The research shows, Anderson said, that in subsecond intervals too brief for conscious thoughts or feelings, the heart regulates our experience of the present.

“Even at these moment-to-moment intervals, our sense of time is fluctuating,” he said. “A pure influence of the heart, from beat to beat, helps create a sense of time.”  ....  '

AI Tool Guides Users Away from Incendiary Language

Cleaning up language.

AI Tool Guides Users Away from Incendiary Language

By Cornell Chronicle, February 16, 2023

Cornell University researchers have developed an artificial intelligence tool that can track online conversations in real-time, detect when tensions are escalating, and nudge users away from using incendiary language.

The research shows promising signs that conversational forecasting methods within the field of natural language processing could prove useful in helping both moderators and users proactively lessen vitriol and maintain healthy, productive debate forums.

The work is detailed in two papers, "Thread With Caution," and "Proactive Moderation of Online Discussions," presented virtually at the ACM Conference on Computer-Supported Cooperative Work and Social Computing (CSCW).

The first study suggests that AI-powered feedback can be effective in enhancing awareness of existing tension in conversations and guide a user toward language that elevates constructive debate, researchers say.

From Cornell Chronicle

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Autonomous, Wireless Robots Could Dance on a Human Hair

 Antbots, more on micro robots and their potential uses.

Autonomous, Wireless Robots Could Dance on a Human Hair

September 23, 2022

The Antbots incorporate a photovoltaic cell to accept light as power, a tiny integrated circuit for controlling and directing that power, and a set of hinged legs to scoot itself around.

Cornell University researchers have created wireless robots small enough to sit on a human hair that operate autonomously, using light as a power source.

The Antbots combine a photovoltaic cell, an integrated circuit for controlling and directing power, and hinged legs to provide locomotion.

The researchers manufactured the Antbots' integrated circuits using a 180-nanometer silicon fabrication process.

They said said the Antbots could be employed for environmental cleanup and monitoring, targeted drug delivery, cellular monitoring or stimulation, or microscopic surgery.

From TechCrunch   View Full Article  

First 3D Printed Multi Story Home

New directions for efficient assembly become possible

 First Multi-Story 3D-Printed Home Blends Concrete, Wood

Cornell University Chronicle

James Dean, September 14, 2022

Researchers at Cornell University, PERI 3D Construction, contractor CIVE, and other building industry partners are participating in the three-dimensional (3D)-printing of a two-story, single-family home in Houston. The design mixes elements fabricated from 3D-printed concrete with wood framing and minimizes waste, creating structures that are efficient, resilient against weather, and potentially less expensive than homes bult in more traditional ways. The researchers said the construction processes can be scaled up to produce multifamily and mixed-use developments. Said Cornell's Sasa Zivkovic, "Apart from printing technology, the integration of printing with building design and building materials, and the streamlining of construction process are important aspects in the realization of such a project." ... ' 

Tool Uncovers Cancers

 Tool Uncovers Cancer-Driving Structural Variations

Weill Cornell Medicine Newsroom, September 26, 2022

Weill Cornell Medicine researchers created the CSVDriver software to identify cancer-generating structural variants (SVs) from tumor samples via DNA sequence analysis. The software maps and analyzes SV locations in tumor DNA datasets; the researchers applied CSVDriver to a dataset of 2,382 genomes from 32 different cancer types, analyzing the cancer genomes from different organ systems independently. The outcomes verified the likely cancer-producing roles of 47 genes, and suggested 26 other genes as likely cancer drivers. "The general idea here was to model the distribution of background mutations that we would expect for a given cancer type, and then identify, as candidate driver locations, regions where mutations occur more often than expected in a large fraction of patients," said Weill Cornell Medicine's Alexander Martinez-Fundichely.

AI Learns Patterns of Human Language

 Trained and tested from Linguistic textbooks in  languages.  Some surprises in learning cross languages. 

AI Can Learn the Patterns of Human Languages

In MIT News  By Adam Zewe, August 30, 2022

Researchers at Massachusetts Institute of Technology, Cornell University, and McGill University developed an artificial intelligence model that can learn the rules and patterns of human languages automatically, without specific human guidance. The model was trained and tested on problems from linguistic textbooks in 58 different languages that involved word-form changes. The researchers observed that the model could determine a correct set of rules to describe the word-form changes for 60% of the problems. Said Cornell's Kevin Ellis, "One of the things that was most surprising is that we could learn across languages, but it didn't seem to make a huge difference. That suggests two things. Maybe we need better methods for learning across problems. And maybe, if we can't come up with those methods, this work can help us probe different ideas we have about what knowledge to share across problems."  ... 

Researchers Build An Unsupervised Machine Learning Algorithm

 Not understanding it, considerable detail linked to.  

ACM CAREERS

Researchers Build An Unsupervised Machine Learning Algorithm

By Marktechpost, June 21, 2022

A group of Cornell physicists and computer scientists developed an unsupervised machine learning method called X-ray diffraction temperature clustering (X-TEC). This method can automatically extract charge density wave order parameters and detect intraunit cell ordering and its fluctuations from high-volume X-ray diffraction measurements taken at various temperatures. Using X-TEC, the researchers studied the major components of a pyrochlore oxide metal, Cd2Re2O7. 

Their paper, published in the Proceedings of the National Academy of Sciences, demonstrates that machine learning can generate a fair and thorough analysis of such data that combines long-range and short-range structural correlations as a function of temperature.

The researchers believe that the atomic-scale understanding of fluctuations in a complicated quantum substance will pave paths for more scientific discoveries of new phases of matter by employing extensive, information-rich diffraction data.

From Marktechpost

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Tool Predicts Where Coronavirus Binds to Human Proteins

Computational tool for predicting binding sites.

Tool Predicts Where Coronavirus Binds to Human Proteins

Cornell Chronicle, Krishna Ramanujan, November 29, 2021

Cornell University researchers have developed a computational tool for predicting binding sites on the surfaces of human and COVID-19 viral proteins. A user-friendly Web server also provided by the tool’s developers shows all protein structures, so virologists and clinicians can determine whether current drugs, or those under development, will bind to them. "The tool we developed to predict protein-to-protein interfaces is the most accurate, and we can use it to make the most informed predictions for any interactions," said Cornell's Haiyuan Yu. Yu added that the tool also provides structural models for predicting how genetic mutations to proteins in individuals potentially impact viral interactions.

https://news.cornell.edu/stories/2021/11/new-tool-predicts-where-coronavirus-binds-human-proteins

Saving the Grape Crop with Robots and AI

AI linking with sensors for agricultural analysis.

Robots and AI Help Save Multibillion Dollar Grape Crop   By Cornell University, August 18, 2021

 Researchers at Cornell University are using robotics and AI technology to identify grape plants infected with a damaging fungus that attacks wine grapes and other plants.

Adjunct professor Lance Cadle-Davidson developed prototypes of imaging robots as part of a team at the U.S. Department of Agriculture's Agricultural Research Service. The robots could scan grape leaf samples automatically, but researchers were bottlenecked by the need to manually assess thousands of grape leaf samples for evidence of infection.

Assistant research professor Yu Jiang and his team used AI to address the issue, applying deep neural networks to analyze microscopic images of grape leaves.

"It has revolutionized our science," Cadle-Davidson says. "Yu's AI tools actually do a better job of explaining the genetics of these grapes than we can do sitting at a microscope for months at a time doing backbreaking work."

The team describes its work in "Deep Learning-Based Saliency Maps for the Quantification of Grape Powdery Mildew at the Microscopic Level," which won a best paper award at the 2021 American Society of Agricultural and Biological Engineers annual international meeting.

From Cornell University

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Can a Robot Understand a Hug?

Connecting touch and sight sensory interactions. 

Robot Can Understand What a Hug Is  By New Scientist, February 9, 2021

A prototype soft robot with nylon skin developed by researchers at Cornell University is imbued with sensory perception that Cornell's Yuhan Hu said lies somewhere between human touch and sight.

The team stretched the skin over a 1.2-meter (3.9-foot) cylindrical scaffold atop a wheeled platform, with a commercial USB camera for interpreting different types of touch on the nylon.

The Cornell researchers then compiled a database from camera images of humans making one of six interactions with the robot's skin, training a neural network to detect and identify different interactions with up to 92% accuracy.

The team matched simple commands to the gestures, and also showed that with a projector added, the robot could display a user interface on its skin for use as a touchscreen...

Cornell University researchers have created a low-cost method for soft, deformable robots to detect a range of physical interactions, using a USB camera inside the robot to capture shadow movements of hand gestures on the robot's skin and classifying them with machine learning software... 

From New Scientist

Cornell Platform Teaches Nonexperts to use Machine Learning

 Like to see this and it can control model implications.

Platform Teaches Nonexperts to Use ML

Cornell Chronicle, Louis DiPietro, July 28, 2021

An interactive machine learning (ML) platform developed by Cornell University scientists is designed to train nonexperts to use algorithms effectively, efficiently, and ethically. Cornell's Swati Mishra said, "If we design machine learning tools correctly and give enough agency to people to use them, we can ensure their knowledge gets integrated into the machine learning model." Said Cornell's Jeff Rzeszotarski, "While our eventual goal is to help novices become advanced machine-learning users, providing some 'training wheels' through transfer learning can help novices immediately employ machine learning for their own tasks." Added Mishra, “We as researchers and designers have to mitigate user perceptions of what machine learning is. Any interactive tool must help us manage our expectations.”

Teeth Tapping Control

An unexpected direction.

The Next Frontier for Gesture Control: Teeth

IEEE Spectrum, Evan Ackerman, May 5, 2021

Researchers at Cornell University's Smart Computer Interfaces for Future Interactions Lab have developed a prototype wearable system controlled by teeth-tapping gestures. The prototype features an inertial measurement unit (IMU) located behind the bottom of the ear where the jawline begins, and contact microphones that sit against the temporal bone behind the ear. The TeethTap system was capable of identifying and distinguishing 13 different teeth-tapping gestures in a controlled environment with a real-time classification accuracy rate of more than 90%. The researchers found TeethTap worked while study participants were talking, writing, walking, running, eating, or drinking.

Playing Like Humans

Perhaps a useful strategy for other goals involved.    Or a clever 'false flag' at play?   Or mincing a human frailty.  Like the thought of it. 

 Chess engine sacrifices mastery to mimic human play

Cornell Chronicle, Melanie Lefkowitz, January 25, 2021

A team of researchers from Cornell University, Canada's University of Toronto, and Microsoft Research have developed an artificial intelligence chess engine that is trained to play like, rather than beat, humans. The Maia chess engine was taught to mimic human behavior through training on individual human chess moves, instead of the larger problem of winning the game. The researchers found Maia matched human moves within each skill level over 50% of the time, an accuracy rate higher than those of the popular chess engines Stockfish and Leela. Cornell's Jon Kleinberg said, "Our model didn't train itself on the best move; it trained itself on what a human would do. But we had to be very careful—you have to make sure it doesn't search the tree of possible moves too thoroughly, because that would make it too good. It has to just be laser-focused on predicting what a person would do next." ... " 

The Sciences of Reflection

Another example of advanced sensory analysis that can improve 'seeing' in multiple complex  environments. 

Research reflects how AI sees through the looking glass   by Cornell University

AI learns to pick up on unexpected clues to differentiate original images from their reflections, the researchers found. Credit: Cornell University Things are different on the other side of the mirror.

Text is backward. Clocks run counterclockwise. Cars drive on the wrong side of the road. Right hands become left hands.

Intrigued by how reflection changes images in subtle and not-so-subtle ways, a team of Cornell University researchers used artificial intelligence to investigate what sets originals apart from their reflections. Their algorithms learned to pick up on unexpected clues such as hair parts, gaze direction and, surprisingly, beards—findings with implications for training machine learning models and detecting faked images.

"The universe is not symmetrical. If you flip an image, there are differences," said Noah Snavely, associate professor of computer science at Cornell Tech and senior author of the study, "Visual Chirality," presented at the 2020 Conference on Computer Vision and Pattern Recognition, held virtually June 14-19. "I'm intrigued by the discoveries you can make with new ways of gleaning information."   Zhiqui Lin is the paper's first author; co-authors are Abe Davis, assistant professor of computer science, and Cornell Tech postdoctoral researcher Jin Sun.

Differentiating between original images and reflections is a surprisingly easy task for AI, Snavely said—a basic deep learning algorithm can quickly learn how to classify if an image has been flipped with 60% to 90% accuracy, depending on the kinds of images used to train the algorithm. Many of the clues it picks up on are difficult for humans to notice.

For this study, the team developed technology to create a heat map that indicates the parts of the image that are of interest to the algorithm, to gain insight into how it makes these decisions.

They discovered, not surprisingly, that the most commonly used clue was text, which looks different backward in every written language. To learn more, they removed images with text from their data set, and found that the next set of characteristics the model focused on included wrist watches, shirt collars (buttons tend to be on the left side), faces and phones—which most people tend to carry in their right hands—as well as other factors revealing right-handedness. ... "

Search Algorithm Fairness Adjustments

In particular looking at search results.

Algorithm Improves Fairness of Search Results

Cornell Chronicle
Melanie Lefkowitz
August 17, 2020

Cornell University researchers have developed an algorithm to improve the fairness of online search rankings while retaining their utility or relevance. Unfairness stems from search algorithms prioritizing more popular items, which means that the higher a choice appears in the list, the more likely users are to click on and respond to it, reinforcing one item's popularity while others go unnoticed. When seeking the most relevant items, small variations can cause major exposure disparities, because most people select one of the first few listed items. Cornell's Thorsten Joachims said, "We came up with computational tools that let you specify fairness criteria, as well as the algorithm that will provably enforce them." The FairCo tool allocates approximately equal exposure to equally relevant choices and avoids preference for items that are already highly ranked; this can remedy the innate unfairness in current algorithms.  .... "

Security of The Form and Parameters of Neural Nets.

Out of Cornell University an intriguing article that deals with how neural nets react to adversarial attacks in their energy consumption.   As predicted by simulation.   Akin to how you might test a system by giving it questions that you know would take time for a human to do, but are easy for machines.  Doing this repeatedly could reveal indications to the form and parameters of the network involved. Which contains the 'knowledge' involved.  Threats continue to get very innovative.

Sponge Examples: Energy-Latency Attacks on Neural Networks

By Ilia Shumailov, Yiren Zhao, Daniel Bates, Nicolas Papernot, Robert Mullins, Ross Anderson

The high energy costs of neural network training and inference led to the use of acceleration hardware such as GPUs and TPUs. While this enabled us to train large-scale neural networks in datacenters and deploy them on edge devices, the focus so far is on average-case performance. In this work, we introduce a novel threat vector against neural networks whose energy consumption or decision latency are critical. We show how adversaries can exploit carefully crafted sponge examples, which are inputs designed to maximise energy consumption and latency.

We mount two variants of this attack on established vision and language models, increasing energy consumption by a factor of 10 to 200. Our attacks can also be used to delay decisions where a network has critical real-time performance, such as in perception for autonomous vehicles. We demonstrate the portability of our malicious inputs across CPUs and a variety of hardware accelerator chips including GPUs, and an ASIC simulator. We conclude by proposing a defense strategy which mitigates our attack by shifting the analysis of energy consumption in hardware from an average-case to a worst-case perspective.  ... "

Also being discussed at Schneier, where there is some interesting comment going on.

AR and Improved Online Shopping

We spent some time examining this proposition, but did not find that AR provided significant results in engagement and sales, except in very narrow domains. Here new studies of interest with new tech.

AR Can Improve Online Shopping, Study Finds
Cornell Chronicle
E.C. Barrett

Researchers at Cornell University, Iowa State University, and Virginia Polytechnic Institute found that online shopping could be enhanced by allowing consumers to try on garments virtually via Augmented Reality (AR). The goal is to reduce the expense and carbon footprint of bracket shopping, in which shoppers order an item in multiple sizes and colors, and send back those they find unsuitable. The AR system requires a computer, telephone, or tablet screen reflecting the shopper and their physical backgrounds, with selected garments overlaid; study participants assessed the AR garments for size, fit, and performance, followed by physical try-ons. Evaluating the fit in AR was problematic, but shoppers' responses to the AR and actual garments were positive. Cornell's Fatma Baytar said, "We can expect that as these technologies evolve, people will trust online shopping more." ... '

Plant Breeding Towards Goals

Goes along my agriculture and botany threads.    Some exiting things happening in this space.  Most recently my interests have been in horticultural innovation, but the applications are broad.

Professor Thomas Bjorkman studies broccoli in a field Software Helps Plant Breeders Bring Out
Their Best

Cornell Chronicle (NY)
By Melanie Lefkowitz

Cornell University researchers have developed a software program based on a statistical method to standardize evaluations of broccoli, to make plant-breeding decisions more consistent and efficient. Breeders can use the open source RateRvaR software to select desired traits and ask multiple people to perform the same evaluation; the program analyzes that data to determine which traits are more or less important in predicting overall quality. The software also can identify traits that do not seem relevant to overall quality, so breeders can collect less data and still get accurate results. Said Cornell researcher Zachary Stansell, "This approach can standardize evaluations and make them faster and more efficient, and it can also reveal individual biases in how a human might respond to a particular variety of a vegetable or plant."  .....

Chainlink for Smart Contracts

New developments in Smart Contracts.  Addressing some of the difficult issues with the concept of a smart contract.    Good discussion at the link:

Blockchain Smart Contacts Finally Good for Something in the Real World    In MIT Technology Review   By Mike Orcutt

Startup Chainlink has partnered with Cornell University's Initiative for Cryptocurrencies and Contracts to find a reliable way for smart contracts—blockchain-stored computer programs—to connect with real-world events. The concept involves combining smart contracts with real-time "oracle" data feeds so blockchain-based services can interact with events with significantly higher levels of trust. Chainlink's Sergey Nazarov said current oracle services hinder blockchain use because they are centralized and prone to tampering, barring smart contracts' access to real-world data. To overcome this, Chainlink and Cornell developed Town Crier, a "high-trust bridge" between the Ethereum blockchain and HTTPS-enabled online data sources; Town Crier's centerpiece is a program running within an isolated piece of hardware, or secure enclave, that is shielded from attacks while maintaining computation confidentiality. Chainlink's software coordinates decentralized oracle networks harnessing multiple sources of data for smart-contract-based services so that they have no dependence on a single source. ... "