Bioinspired trajectory modulation for effective slip control in robot manipulation.

Ensuring a stable grasp during robotic manipulation is essential for dexterous and reliable performance. Traditionally, slip control has relied on grip force modulation. Here we show that trajectory modulation provides an effective alternative for slip prevention in certain robotic manipulation tasks.

Embodied large language models enable robots to complete complex tasks in unpredictable environments.

Completing complex tasks in unpredictable settings challenges robotic systems, requiring a step change in machine intelligence. Sensorimotor abilities are considered integral to human intelligence. Thus, biologically inspired machine intelligence might usefully combine artificial intelligence with robotic sensorimotor capabilities.

Camera trap surveys of Atlantic Forest mammals: A data set for analyses considering imperfect detection (2004-2020).

Camera traps became the main observational method of a myriad of species over large areas. Data sets from camera traps can be used to describe the patterns and monitor the occupancy, abundance, and richness of wildlife, essential information for conservation in times of rapid climate and land-cover changes. Habitat loss and poaching are responsible for historical population losses of mammals in the Atlantic Forest biodiversity hotspot, especially for medium to large-sized species.

Artificial intelligence and illusions of understanding in scientific research.

Scientists are enthusiastically imagining ways in which artificial intelligence (AI) tools might improve research. Why are AI tools so attractive and what are the risks of implementing them across the research pipeline? Here we develop a taxonomy of scientists' visions for AI, observing that their appeal comes from promises to improve productivity and objectivity by overcoming human shortcomings. But proposed AI solutions can also exploit our cognitive limitations, making us vulnerable to illusions of understanding in which we believe we understand more about the world than we actually do.

Emergent behaviour and neural dynamics in artificial agents tracking odour plumes.

Tracking an odour plume to locate its source under variable wind and plume statistics is a complex task. Flying insects routinely accomplish such tracking, often over long distances, in pursuit of food or mates. Several aspects of this remarkable behaviour and its underlying neural circuitry have been studied experimentally.

Prediction of mechanistic subtypes of Parkinson's using patient-derived stem cell models.

Parkinson's disease is a common, incurable neurodegenerative disorder that is clinically heterogeneous: it is likely that different cellular mechanisms drive the pathology in different individuals. So far it has not been possible to define the cellular mechanism underlying the neurodegenerative disease in life. We generated a machine learning-based model that can simultaneously predict the presence of disease and its primary mechanistic subtype in human neurons.

Three types of incremental learning.

Incrementally learning new information from a non-stationary stream of data, referred to as 'continual learning', is a key feature of natural intelligence, but a challenging problem for deep neural networks. In recent years, numerous deep learning methods for continual learning have been proposed, but comparing their performances is difficult due to the lack of a common framework. To help address this, we describe three fundamental types, or 'scenarios', of continual learning: task-incremental, domain-incremental and class-incremental learning.

Dual Use of Artificial Intelligence-powered Drug Discovery.

An international security conference explored how artificial intelligence (AI) technologies for drug discovery could be misused for de novo design of biochemical weapons. A thought experiment evolved into a computational proof.

A context-aware deconfounding autoencoder for robust prediction of personalized clinical drug response from cell-line compound screening.

Accurate and robust prediction of patient-specific responses to a new compound is critical to personalized drug discovery and development. However, patient data are often too scarce to train a generalized machine learning model. Although many methods have been developed to utilize cell-line screens for predicting clinical responses, their performances are unreliable owing to data heterogeneity and distribution shift.

Pollen data from bottom sediments of a tundra lake in the Yerkuta River basin on the Yamal Peninsula.

This article extends the findings of our previous research "Preliminary reconstruction of climate changes and vegetation cover inferred from pollen study of the arctic lake bottom sediments from the southwestern part of the Yamal Peninsula" (G.R. Nigamatzyanova, N.

Deep learning-based robust positioning for all-weather autonomous driving.

Interest in autonomous vehicles (AVs) is growing at a rapid pace due to increased convenience, safety benefits and potential environmental gains. Although several leading AV companies predicted that AVs would be on the road by 2020, they are still limited to relatively small-scale trials. The ability to know their precise location on the map is a challenging prerequisite for safe and reliable AVs due to sensor imperfections under adverse environmental and weather conditions, posing a formidable obstacle to their widespread use.

Advancing COVID-19 diagnosis with privacy-preserving collaboration in artificial intelligence.

Artificial intelligence provides a promising solution for streamlining COVID-19 diagnoses; however, concerns surrounding security and trustworthiness impede the collection of large-scale representative medical data, posing a considerable challenge for training a well-generalized model in clinical practices. To address this, we launch the Unified CT-COVID AI Diagnostic Initiative (UCADI), where the artificial intelligence (AI) model can be distributedly trained and independently executed at each host institution under a federated learning framework without data sharing. Here we show that our federated learning framework model considerably outperformed all of the local models (with a test sensitivity/specificity of 0.

Cognitive functioning and social media: Has technology changed us?

Social media use and its effects on mood have been well researched. However, social media use and its effects on cognition are not as well known. Based on the research studies available, this study hypothesized that those categorized as participating in high social media use would have lower ability to effectively inhibit irrelevant information and higher ability for working memory.

Impact of COVID-19 pandemic on mobility in ten countries and associated perceived risk for all transport modes.

The restrictive measures implemented in response to the COVID-19 pandemic have triggered sudden massive changes to travel behaviors of people all around the world. This study examines the individual mobility patterns for all transport modes (walk, bicycle, motorcycle, car driven alone, car driven in company, bus, subway, tram, train, airplane) before and during the restrictions adopted in ten countries on six continents: Australia, Brazil, China, Ghana, India, Iran, Italy, Norway, South Africa and the United States. This cross-country study also aims at understanding the predictors of protective behaviors related to the transport sector and COVID-19.

Unsupervised Resolution of Histomorphologic Heterogeneity in Renal Cell Carcinoma Using a Brain Tumor-Educated Neural Network.

Purpose: Applications of deep learning to histopathology have proven capable of expert-level performance, but approaches have largely focused on supervised classification tasks requiring context-specific training and deployment. More generalizable workflows that can be easily shared across subspecialties could help accelerate and broaden adoption. Here, we hypothesized that histology-optimized feature representations, generated by a convolutional neural network (CNN) during supervised learning, are transferable and can resolve meaningful differences in large-scale, discovery-type unsupervised analyses.

From fighting COVID-19 pandemic to tackling sustainable development goals: An opportunity for responsible information systems research.

The recent outbreak of the COVID-19 pandemic has posed a significant threat to the healthy lives and well-being of billions of people worldwide. As the world begins to open up from lockdowns and enters an unprecedented state of vulnerability, or what many have called "the new normal", it makes sense to reflect on what we have learned, revisit our fundamental assumptions, and start charting the way forward to contribute to building a sustainable world. In this essay, we argue that despite its significant damage to human lives and livelihoods, the coronavirus pandemic presents an excellent opportunity for the human family to act in solidarity and turn this crisis into an impetus to achieve the United Nation's (UN) Sustainable Development Goals (SDG).

The Transformative Potential of Disruptions: A Viewpoint.

I engage with the impact of disruptions on my work life, and consider the transformative potential that these disruptions offer. I focus on four parts of my life: as a researcher, teacher, administrator and editor. In each, I examine the nature of the disruption and the way I deal with it.

WASP (Write a Scientific Paper): Structuring a scientific paper.

Writing and publishing scientific papers have become requisites for all scientists (researchers and academics alike) to maintain their professional career. The prospects of writing a scientific paper are often regarded as somewhat daunting to the uninitiated. However a universal, well established structure format known as "IMRAD": i.

Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning.

Having accurate, detailed, and up-to-date information about the location and behavior of animals in the wild would improve our ability to study and conserve ecosystems. We investigate the ability to automatically, accurately, and inexpensively collect such data, which could help catalyze the transformation of many fields of ecology, wildlife biology, zoology, conservation biology, and animal behavior into "big data" sciences. Motion-sensor "camera traps" enable collecting wildlife pictures inexpensively, unobtrusively, and frequently.

A biodiversity hotspot losing its top predator: The challenge of jaguar conservation in the Atlantic Forest of South America.

The jaguar is the top predator of the Atlantic Forest (AF), which is a highly threatened biodiversity hotspot that occurs in Brazil, Paraguay and Argentina. By combining data sets from 14 research groups across the region, we determine the population status of the jaguar and propose a spatial prioritization for conservation actions. About 85% of the jaguar's habitat in the AF has been lost and only 7% remains in good condition.

Twelve tips for getting your manuscript published.

The author shares twelve practical tips on how to navigate the process of getting a manuscript published. These tips, which apply to all fields of academic writing, advise that during the initial preparation phase authors should: (1) plan early to get it out the door; (2) address authorship and writing group expectations up front; (3) maintain control of the writing; (4) ensure complete reporting; (5) use electronic reference management software; (6) polish carefully before they submit; (7) select the right journal; and (8) follow journal instructions precisely. Rejection after the first submission is likely, and when this occurs authors should (9) get it back out the door quickly, but first (10) take seriously all reviewer and editor suggestions.