As the importance of high-throughput screening (HTS) continues to grow due to its value in early stage drug discovery and data generation for training machine learning models, there is a growing need for robust methods for pre-screening compounds to identify and prevent false-positive hits. Small, colloidally aggregating molecules are one of the primary sources of false-positive hits in high-throughput screens, making them an ideal candidate to target for removal from libraries using predictive pre-screening tools. However, a lack of understanding of the causes of molecular aggregation introduces difficulty in the development of predictive tools for detecting aggregating molecules. Herein, we present an examination of the molecular features differentiating datasets of aggregating and non-aggregating molecules, as well as a machine learning approach to predicting molecular aggregation. Our method uses explainable graph neural networks and counterfactuals to reliably predict and explain aggregation, giving additional insights and design rules for future screening. The integration of this method in HTS approaches will help combat false positives, providing better lead molecules more rapidly and thus accelerating drug discovery cycles.

Researchers often rely on humans to code (label, annotate, etc.) large sets of texts. This kind of human coding forms an important part of social science research, yet the coding process is both resource intensive and highly variable from application to application. In some cases, efforts to automate this process have achieved human-level accuracies, but to achieve this, these attempts frequently rely on thousands of hand-labeled training examples, which makes them inapplicable to small-scale research studies and costly for large ones. Recent advances in a specific kind of artificial intelligence tool - language models (LMs) - provide a solution to this problem. Work in computer science makes it clear that LMs are able to classify text, without the cost (in financial terms and human effort) of alternative methods. To demonstrate the possibilities of LMs in this area of political science, we use GPT-3, one of the most advanced LMs, as a synthetic coder and compare it to human coders. We find that GPT-3 can match the performance of typical human coders and offers benefits over other machine learning methods of coding text. We find this across a variety of domains using very different coding procedures. This provides exciting evidence that language models can serve as a critical advance in the coding of open-ended texts in a variety of applications.

An emerging paradigm is being embraced in the conceptualization of future planetary exploration missions. Ambitious objectives and increasingly demanding mission constraints stress the importance associated with faster surface mobility. Driving speeds approaching or surpassing 1 m/s have been rarely used and their effect on performance is today unclear. This study presents experimental evidence and preliminary observations on the impact that increasing velocity has on the tractive performance of planetary rovers. Single-wheel driving tests were conducted using two different metallic, grousered wheels-one rigid and one flexible-over two different soils, olivine sand and CaCO3-based silty soil. Experiments were conducted at speeds between 0.01-1 m/s throughout an ample range of slip ratios (5-90%). Three performance metrics were evaluated: drawbar pull coefficient, wheel sinkage, and tractive efficiency. Results showed similar data trends among all the cases investigated. Drawbar pull and tractive efficiency considerably decreased for speeds beyond 0.2 m/s. Wheel sinkage, unlike what published evidence suggested, increased with increasing velocities. The flexible wheel performed the best at 1m/s, exhibiting 2 times higher drawbar pull and efficiency with 18% lower sinkage under low slip conditions. Although similar data trends were obtained, a different wheel-soil interactive behavior was observed when driving over the different soils. Overall, despite the performance reduction experienced at higher velocities, a speed in the range of 0.2-0.3 m/s would enable 5-10 times faster traverses, compared to current rovers driving capability, while only diminishing drawbar pull and efficiency by 7%. The measurements collected and the analysis presented here lay the groundwork for initial stages in the development of new locomotion subsystems for planetary surface exploration. At the same time...

Designing cyber defense systems to account for cognitive biases in human decision making has demonstrated significant success in improving performance against human attackers. However, much of the attention in this area has focused on relatively simple accounts of biases in human attackers, and little is known about adversarial behavior or how defenses could be improved by disrupting attacker's behavior. In this work, we present a novel model of human decision-making inspired by the cognitive faculties of Instance-Based Learning Theory, Theory of Mind, and Transfer of Learning. This model functions by learning from both roles in a security scenario: defender and attacker, and by making predictions of the opponent's beliefs, intentions, and actions. The proposed model can better defend against attacks from a wide range of opponents compared to alternatives that attempt to perform optimally without accounting for human biases. Additionally, the proposed model performs better against a range of human-like behavior by explicitly modeling human transfer of learning, which has not yet been applied to cyber defense scenarios. Results from simulation experiments demonstrate the potential usefulness of cognitively inspired models of agents trained in attack and defense roles and how these insights could potentially be used in real-world cybersecurity.

Ever since the discovery of x-rays, considerable breakthroughs have been made using them as a probe of matter, from testing models of the atom to solving the structure of deoxyribonucleic acid (DNA). Over the last few decades with the proliferation of synchrotron x-ray sources around the world, the application to many scientific fields has progressed tremendously and allowed studies of complicated structures and phenomena like protein dynamics and crystallography [1, 2], electronic structures of strongly correlated materials [3, 4], and a wide variety of elementary excitations [5, 6]. With the the development of the next generation of light sources, especially the x-ray free electron lasers (X-FEL) [7, 8], not only have discoveries accelerated, but completely novel techniques have been developed and new fields of science have emerged, such as laboratory astrophysics [9, 10, 11, 12] and single particle diffractive imaging [13, 14, 15]. Among these emerging techniques brought by X-FELs, the development of x-ray photon fluctuation spectroscopy (XPFS) holds particular relevance for condensed matter and material physics [16]. XPFS is a unique and powerful approach that opens up numerous opportunities to probe ultrafast dynamics of timescales corresponding to the µeV to meV-energy level. As the high-level coherence of the x-ray beam encodes subtle changes in the system at these timescales, XPFS is capable of investigating fluctuations of elementary excitations, such as that of the spin [17]. The fluctuation spectra collected using this method can be directly related back to correlation functions derived from Hamiltonians [18, 19], yielding invaluable experimental insights for theoretical developments and deeper understandings of the underlying physics.

Spoken Question Answering (QA) is a key feature of voice assistants, usually backed by multiple QA systems. Users ask questions via spontaneous speech which can contain disfluencies, errors, and informal syntax or phrasing. This is a major challenge in QA, causing unanswered questions or irrelevant answers, and leading to bad user experiences. We analyze failed QA requests to identify core challenges: lexical gaps, proposition types, complex syntactic structure, and high specificity. We propose a Semantic Question Reformulation (SURF) model offering three linguistically-grounded operations (repair, syntactic reshaping, generalization) to rewrite questions to facilitate answering. Offline evaluation on 1M unanswered questions from a leading voice assistant shows that SURF significantly improves answer rates: up to 24% of previously unanswered questions obtain relevant answers (75%). Live deployment shows positive impact for millions of customers with unanswered questions; explicit relevance feedback shows high user satisfaction.

Sign up to receive our weekly newsletter of the best New Yorker podcasts. Sam Altman, the C.E.O. of OpenAI, which created ChatGPT, says that artificial intelligence is a powerful tool that will streamline human work and quicken the pace of scientific advancement. But ChatGPT has both enthralled and terrified us, and even some of A.I.'s pioneers are freaked out by the technology and how quickly it has advanced. David Remnick talks with Altman, and with the computer scientist Yoshua Bengio, who won the prestigious Turing Award for his work in 2018, but recently signed an open letter calling for a moratorium on some A.I. research until regulation can be implemented. The stakes, Bengio says, are high: "I believe there is a non-negligible risk that this kind of technology, in the short term, could disrupt democracies."

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Extremist rebels in eastern Congo killed at least nine people with knives and guns, a civil society organization said Friday. The attack happened Thursday evening on the Kyondo-Kyavinyonge road in North Kivu province, said Meleki Mulala the coordinator for the Congolese civil society group for the Ruwenzori sector. Civilians were taken from their homes before they were killed, and many homes were looted, he said.

On Tuesday morning, the merchants of artificial intelligence warned once again about the existential might of their products. Hundreds of AI executives, researchers, and other tech and business figures, including OpenAI CEO Sam Altman and Bill Gates, signed a one-sentence statement written by the Center for AI Safety declaring that "mitigating the risk of extinction from AI should be a global priority alongside other societal-scale risks such as pandemics and nuclear war." Those 22 words were released following a multi-week tour in which executives from OpenAI, Microsoft, Google, and other tech companies called for limited regulation of AI. They spoke before Congress, in the European Union, and elsewhere about the need for industry and governments to collaborate to curb their product's harms--even as their companies continue to invest billions in the technology. Several prominent AI researchers and critics told me that they're skeptical of the rhetoric, and that Big Tech's proposed regulations appear defanged and self-serving.

Sam Altman, the CEO of artificial intelligence lab OpenAI, told a Senate panel he welcomes federal regulation on the technology "to mitigate" its risks. President Biden told hundreds of U.S. Air Force Academy graduation attendees on Thursday that scientists are warning about the capabilities of artificial intelligence. "I met in the Oval Office, in my office, with 12 leading -- no, excuse me, eight leading scientists -- in the area of AI," he said, speaking at Falcon Stadium in Colorado. "Some are very worried that AI can actually overtake human thinking and planning," Biden noted. "So we've got a lot to deal with."