23 papers · ranked by Valyu relevance
Zhenyu Cai, Ryan Babbush, Simon C. Benjamin, Suguru Endo + 4 more
'William J. Huggins' 'Ying Li' 'Jarrod R. McClean' 'Thomas E. O’Brien'] For quantum computers to successfully solve real-world problems, it is necessary to tackle the challenge of noise: the errors which occur in elementary physical components due to unwanted or imperfect interactions. The theory of quantum fault…
Xun Jiao, Ruixuan Wang, Fred Y. Lin, Daniel Moore + 1 more
—Graph neural networks (GNNs) have emerged as a promising deep learning (DL) paradigm for analyzing graphstructured data, showing success in various domains such as recommendation systems, social networks, and electronic design automation (EDA). Despite their growing popularity and deployment on modern hardware…
Yihui Quek, Daniel Stilck França, Sumeet Khatri, Johannes Jakob Meyer + 1 more
Quantum error mitigation has been proposed as a means to combat unwanted and unavoidable errors in near-term quantum computing without the heavy resource overheads required by fault-tolerant schemes. Recently, error mitigation has been successfully applied to reduce noise in near-term applications. In this work…
Dayue Qin, Yanzhu Chen, Ying Li
Quantum computing promises advantages over classical computing in many problems. Nevertheless, noise in quantum devices prevents most quantum algorithms from achieving the quantum advantage. Quantum error mitigation provides a variety of protocols to handle such noise using minimal qubit resources . While some of those…
Madaline Kinlay, Wu Yi Zheng, Rosemary Burke, Ilona Juraskova + 4 more
'Lai Mun Ho' 'Hannah Turton' 'Jason Trinh' 'Melissa T. Baysari'] Background Electronic medical record (EMR) systems provide timely access to clinical information and have been shown to improve medication safety. However, EMRs can also create opportunities for error, including system-related errors or errors that were…
Chinmay Hemant Joshi, Anna Dornhaus
The collective behavior of complex systems emerges from the actions and interactions of their individual components. But what if these individuals make mistakes, or deviate from behavior tuned to lead to collective success? Here, we explore the effects of individual errors on collective outcomes. We investigate in…
Authors not listed
This work provides a rigorous theoretical investigation of selective error correction strategies for variational quantum algorithms, with focus on understanding the interplay between error suppression, circuit trainability, and computational resource requirements. We develop a mathematical framework that characterizes…
Connie A. Van der Byl, Harrie Vredenburg
We study errors in organizations to understand and ideally prevent them from reoccurring. In this study we examine mistakes made as an oil company adopted new technology to access untapped reserves. We find that a pre-existing error management culture (EMC) dominated in the organization while error prevention measures…
Mohsen Raji, Mohammad Zaree, Kimia Soroush
—Deep Neural Network (DNN) has achieve great success in solving a wide range of machine learning problems. Recently, they have been deployed in datacenters (potentially for businesscritical or industrial applications) and safety-critical systems such as self-driving cars. So, their correct functionality in the presence…
Varun Totakura, Shayok Chakraborty
Due to the unprecedented success of deep learning, it has become an integral component in several multimedia computing applications in todays world. Unfortunately, deep learning systems are not perfect and can fail, sometimes abruptly, without prior warning or explanation. While reducing the error rate of deep neural…
Ehsan Ahsani-Estahbanati, Vladimir Sergeevich Gordeev, Leila Doshmangir
'Leila Doshmangir'] Background and aim Improving health care quality and ensuring patient safety is impossible without addressing medical errors that adversely affect patient outcomes. Therefore, it is essential to correctly estimate the incidence rates and implement the most appropriate solutions to control and reduce…
Authors not listed
Development of meta-generalized gradient approximations (meta-GGAs) has generally led to more accurate density-functional approximations, albeit ones that have more stringent requirements for the quadrature grids that are used to evaluate the exchange-correlation energy. Here, we demonstrate that grid-induced errors…
Alexander Gutfraind, Vicki Bier
Large language models (LLMs) offer unprecedented and growing capabilities, but also introduce complex safety and security challenges that resist conventional risk management. While conventional probabilistic risk analysis (PRA) requires exhaustive risk enumeration and quantification, the novelty and complexity of these…
Pengyao Ping, Shuquan Su, Xinhui Cai, Tian Lan + 5 more
Although the per-base erring rate of NGS is very low at 0.1% to 0.5%, the percentage/probability of erroneous reads in a short-read sequencing dataset can be as high as 10% to 15% or in the number of millions. Correction of these wrongly sequenced reads to retrieve their huge missing value will improve many downstream…
Fakhradin Ghasemi, Mohammad Babamiri, Zahra Pashootan, Muhammad Junaid Farrukh
'Muhammad Junaid Farrukh'] Medication errors can endanger the health and safety of patients and need to be managed appropriately. This study aimed at developing a new and comprehensive method for estimating the probability of medication errors in hospitals. An extensive literature review was conducted to identify…
Surajit Nandi, Jonas Busk, Peter Bjørn Jørgensen, Tejs Vegge + 1 more
Workflows to predict chemical reaction networks based on density functional theory (DFT) are prone to systematic errors in reaction energy due to the extensive use of cheap DFT exchange-correlation functionals to limit computational cost. Recently, machine learning-based models are increasingly applied to mitigate this…
Mohamed Abdelaal, Christian Hammacher, Harald Schoening
Nowadays, machine learning (ML) plays a vital role in many aspects of our daily life. In essence, building well-performing ML applications requires the provision of high-quality data throughout the entire life-cycle of such applications. Nevertheless, most of the real-world tabular data suffer from different types of…
Naser Al-Fawakhiri, Sarosh Kayani, Samuel D. McDougle
When acquiring a motor skill, learners must practice the skill at a difficulty that is challenging but still manageable to gradually improve their performance. In other words, during training, the learner must experience success as well as failure. Does there exist an optimal proportion of successes and failures to…
Dmitrii Todorov, Maelys Moulin, Ethan Buch, Romain Quentin
We never experience the exact same situation twice. In our dynamic and constantly changing environment, we continuously need to adapt our behavior, either due to external (e.g., a change of wind) or internal factors (e.g., muscle noise). Motor adaptation is the process of recalibration of movements in response to such…
Authors not listed
Bayesian optimization (BO) has become increasingly important for experimental optimization across scientific domains, yet implementing BO pipelines requires significant programming expertise and familiarity with specialized frameworks. This creates a barrier for domain experts who could benefit from BO but lack the…
Authors not listed
A Python script for the systematic, high-throughput analysis of accurate mass data was developed and tested on over 3,000 Supporting Information (SI) PDFs from Organic Letters. For each SI file, quadruplets of molecular formula, measured ion, e.g. [M+Na]+, reported calculated and found masses were extracted and…
Tianhe Wang, Guy Avraham, Jonathan S. Tsay, Richard B. Ivry
In a recent paper^1^ entitled, “An implicit memory of errors limits human sensorimotor adaptation” Albert and colleagues presented a model in which the adaptive response of the sensorimotor system is flexibly modulated by recent experience, or what they refer to as a “memory of errors”. This hypothesis stands in…
Toshiki Kobayashi, Mitsuaki Takemi, Daichi Nozaki
Even experts can sometimes fail while performing fully learned movements. Do such failures suddenly arise, or are there any forecasting signs? It has been reported that the kinematics of the early phase of movements can predict the failure, and brain activity patterns specific to failures are observed just before the…