25 papers · ranked by Valyu relevance
Giuseppe Cocco, Albert Guillén Fàbregas, Josep Font‐Segura
This paper shows that the probability that the error exponent of a given code randomly generated from a pairwiseindependent ensemble being smaller than a lower bound on the typical random-coding exponent tends to zero as the codeword length tends to infinity. This lower bound is known to be tight for i.i.d. ensembles…
Jessica Hoth, Macarena Larrain, Gabriele Kaiser
Introduction Mathematics classrooms are typically characterized by considerable heterogeneity with respect to students’ knowledge and skills. Mathematics teachers need to be highly attentive to students’ thinking, learning difficulties, and any misconceptions that they may develop. Identification of potential errors…
David A. W. Soergel
The opportunities for both subtle and profound errors in software and data management are boundless, yet they remain surprisingly underappreciated. Here I estimate that any reported scientific result could very well be wrong if data have passed through a computer, and that these errors may remain largely undetected. It…
Raef Bassily, Yoav Freund
In this paper, we introduce a notion of algorithmic stability called typical stability. When our goal is to release real-valued queries (statistics) computed over a dataset, this notion does not require the queries to be of bounded sensitivity – a condition that is generally assumed under differential privacy [DMNS06…
Ran Tamir (Averbuch), Neri Merhav
Typical random codes (TRCs) in a communication scenario of source coding with side information in the decoder is the main subject of this work. We study the semi-deterministic code ensemble, which is a certain variant of the ordinary random binning code ensemble. In this code ensemble, the relatively small type classes…
Harold Thimbleby, Paul Cairns
Number entry is ubiquitous: it is required in many fields including science, healthcare, education, government, mathematics and finance. People entering numbers are to be expected to make errors, but shockingly few systems make any effort to detect, block or otherwise manage errors. Worse, errors may be ignored but…
Neri Merhav
In continuation to an earlier work, where error exponents of typical random codes were studied in the context of general block coding, with no underlying structure, here we carry out a parallel study on typical random, time–varying trellis codes for general discrete memoryless channels, focusing on a certain range of…
Zhi Chen, Wei Ma, Lingxiao Jiang
AI-driven software development has rapidly advanced with the emergence of software development agents that leverage large language models (LLMs) to tackle complex, repository-level software engineering tasks. These agents go beyond just generation of final code; they engage in multi-step reasoning, utilize various…
Omri Tal, Tat Dat Tran, Jacobus Portegies
We demonstrate an application of a core notion of information theory, that of typical sequences and their related properties, to analysis of population genetic data. Based on the asymptotic equipartition property (AEP) for non-stationary discrete-time sources producing independent symbols, we introduce the concepts of…
Xiaoming Ye, Haibo Liu, Xuebin Xiao, Mo Ling
In several literatures, the authors give a new thinking of measurement theory system based on error non-classification philosophy, which completely overthrows the existing measurement concept system of precision, trueness and accuracy. In this paper, by focusing on the issues of error's regularities and effect…
Edna C. Cieslik, Markus Ullsperger, Martin Gell, Simon B. Eickhoff + 1 more
Brain mechanisms of error processing have often been investigated using response interference tasks and focusing on the posterior medial frontal cortex, which is also implicated in resolving response conflict in general. Thereby, the role other brain regions may play has remained undervalued. Here, activation…
Alex Lee, Joshua Rackers, William Bricker
One of the fundamental limitations of accurately modeling biomolecules like DNA is the inability to perform quantum chemistry calculations on large molecular structures. We present a machine learning model based on an equivariant Euclidean Neural Network framework to obtain quantum-accurate electron densities for…
Tyler J Adkins, Han Zhang, Taraz G Lee
Humans tend to slow down after making an error. A longstanding account of this post-error slowing is that people are simply more cautious. However, accuracy typically does not improve following an error leading some researchers to suggest that an initial ‘orienting’ response may initially impair performance immediately…
Harold Thimbleby, Patrick Oladimeji, Paul Cairns
Number entry is a ubiquitous activity and is often performed in safety- and mission-critical procedures, such as healthcare, science, finance, aviation and in many other areas. We show that Monte Carlo methods can quickly and easily compare the reliability of different number entry systems. A surprising finding is that…
Authors not listed
The rapid growth of worldwide computing power has transformed in silico chemistry into a discipline that is integrated into the daily work of many chemists. Nowadays, researchers find it increasingly straightforward to predict a wide range of molecular properties and chemi- cal processes at reasonable computational…
Simon Iveson, Kevin Galvin
This paper presents a simple relationship for estimating the uncertainty in the overall yield and species recovery when the two-product formula is applied to assays of the feed, product and tailings streams of a steady-state mineral separator. The non-linearity of the two-product formula means the reliability of these…
A. R. Colclough
It has already been noted that there is often confusion about what populations can legitimately be said to contain random errors for the purpose of estimating uncertainties in experimental results. Since the “combination” of uncertainties depends on the identification of the corresponding error types, this is a matter…
Gaurav Panthi, Pratik Mutha
Accurate motor behavior relies on our ability to refine movements based on errors. While sensory prediction errors (SPEs), mismatches between expected and actual sensory feedback, predominantly drive such adaptation, task performance errors (TPEs), or failures in achieving movement goals, also appear to contribute.…
Authors not listed
Material properties calculated using density functional theory (DFT) are often corrected to more closely match experimental values, but the most common correction method has flaws that lead to unphysical results and false positives in the material discovery process. In this work, we show that these flaws stem from the…
Fuqun Huang, Lorenzo Strigini
— As the primary cause of software defects, human error is the key to understanding, and perhaps to predicting and avoiding them. Little research has been done to predict defects on the basis of the cognitive errors that cause them. This paper proposes an approach to predicting software defects, so that they may be…
Hans Kirschner, Jana Tegelbeckers, Daniel Janko, Luisa Göde + 1 more
Sleep deprivation is known to impair cognitive performance, yet its effects on error awareness and subsequent behavioral adjustments remain incompletely understood. Here, we investigated how sleep loss affects the use of subjective performance evaluation to guide post-error adaptations. Thirty healthy adults completed…
Liesbet Van der Borght, Charlotte Desmet, Wim Notebaert
The observation that performance does not improve following errors contradicts the traditional view on error monitoring ([11]; [23]; [22]). However, recent findings suggest that typical laboratory tasks provided us with a narrow window on error monitoring ([15]; [9]). In this study we investigated strategy-use after…
Robert Taylor, Paul M Bays
Observers reproducing elementary visual features from memory after a short delay produce errors consistent with the encoding-decoding properties of neural populations. While inspired by electrophysiological observations of sensory neurons in cortex, the population coding account of these errors is based on a…
Maria H. Rasmussen, Chenru Duan, Heather J. Kulik, Jan Halborg Jensen
With the increasingly more important role of machine learning (ML) models in chemical research, the need for putting a level of confidence to the model predictions naturally arises. Several methods for obtaining uncertainty estimates have been proposed in recent years but consensus on the evaluation of these have yet…
Esther Heid, Charles J. McGill, Florence H. Vermeire, William H. Green
Characterizing uncertainty in machine learning models has recently gained interest in the context of machine learning reliability, robustness, safety, and active learning. Here, we separate the total uncertainty into contributions from noise in the data (aleatoric) and shortcomings of the model (epistemic), further…