22 papers · ranked by Valyu relevance
Sophie K. Herbst, Jonas Obleser
Can human listeners use strictly implicit temporal contingencies in auditory input to form temporal predictions, and if so, how are these predictions represented endogenously? To assess this question, we implicitly manipulated foreperiods in an auditory pitch discrimination task. Unbeknownst to participants, the pitch…
Diogo Pacheco, Marcos Oliveira, Zexun Chen, Hugo Barbosa + 3 more
'Brooke Foucault Welles' 'Gourab Ghoshal' 'Ronaldo Menezes'] Human travelling behaviours are markedly regular, to a large extent, predictable, and mostly driven by biological necessities (e.g. sleeping, eating) and social constructs (e.g. school schedules, synchronisation of labour). Not surprisingly, such…
Joseph Sollini, Katarina C Poole, Dominic Blauth-Muszkowski, Jennifer K. Bizley
The cochlea decomposes sounds into separate frequency channels, from which the auditory brain must reconstruct the auditory scene. To do this the auditory system must make decisions about which frequency information should be grouped together, and which should remain distinct. Two key cues for grouping are temporal…
Disheng Tang, Wenbo Du, Louis Shekhtman, Yijie Wang + 3 more
'Xianbin Cao' 'Gang Yan'] Title: Abstract Links in most real networks often change over time. Such temporality of links encodes the ordering and causality of interactions between nodes and has a profound effect on network dynamics and function. Empirical evidence has shown that the temporal nature of links in many…
V. Jurczyk, V. Mittelstädt, K. Fröber
Task performance improves when the required tasks are predicted by the preceding time intervals, suggesting that participants form time-based task expectancies. In the present study, we pursued the question whether temporal predictability of tasks can also influence task choice. For this purpose, we conducted three…
Melissa R. Beck, S. Lee Hong, Amanda E. van Lamsweerde, Justin M. Ericson + 1 more
'Justin M. Ericson' 'Suliann Ben Hamed'] Responses are quicker to predictable stimuli than if the time and place of appearance is uncertain. Studies that manipulate target predictability often involve overt cues to speed up response times. However, less is known about whether individuals will exhibit faster response…
Disheng Tang, Wenbo Du, Louis Shekhtman, Yijie Wang + 3 more
'Xianbin Cao' 'Gang Yan'] 1School of Electronic and Information Engineering, Beihang University, Beijing 100191, China; 2School of Physics Science and Engineering, Tongji University, Shanghai 200092, China; 3National Engineering Laboratory of Big Data Application Technologies of Comprehensive Transportation, Beijing…
Camila Silveira Agostino, Herman Hinrichs, Toemme Noesselt
Predicting future events is a fundamental cognitive ability which often depends on the volatility of the environment. Previous studies on apparent motion reported that when the brain is confronted with low levels of predictability, activity in low-level sensory areas is increased, including primary visual cortex.…
En Xu, Yilin Bi, Hongwei Hu, Xin Chen + 4 more
a Department of Electronic Engineering, Tsinghua University, Beijing, 100084, China b CompleX Lab, University of Electronic Science and Technology of China, Chengdu, 611731, China c Department of Statistics and Data Science, Southern University of Science and Technology, Shenzhen, 518055, China d School of Computer…
Laura Broeker, Andrea Kiesel, Stefanie Aufschnaiter, Harald E. Ewolds + 7 more
Other than enhancing predictability by structuring events or tasks, recent accounts have investigated the impact of interval durations between tasks, assuming that the temporal distribution of tasks may carry information about which task will occur. To investigate whether participants adapt to regularities of waiting…
Michelle L. Eisenberg, Jeffrey M. Zacks, Shaney Flores
The ability to predict what is going to happen in the near future is integral for daily functioning. Previous research suggests that predictability varies over time, with increases in prediction error at those moments that people perceive as boundaries between meaningful events. These moments also tend to be points of…
Alexandra Bezbochina, Elizaveta Stavinova, Anton Kovantsev, Petr Chunaev + 1 more
'Petr Chunaev' 'Mohammad Reza Rahimi Tabar'] Driven by the variety of available measures intended to estimate predictability of diverse objects such as time series and network links, this paper presents a comprehensive overview of the existing literature in this domain. Our overview delves into predictability from two…
Sahand Karimi-Arpanahi, S. Ali Pourmousavi, Nariman Mahdavi
Title: Summary Decision-making in the power systems domain often relies on predictions of renewable generation. While sophisticated forecasting methods have been developed to improve the accuracy of such predictions, their accuracy is limited by the inherent predictability of the data used. However, the predictability…
Wei Zhong Goh, Varun Ursekar, Marc W. Howard
In recent years it has become clear that the brain maintains a temporal memory of recent events stretching far into the past. This paper presents a neurally-inspired algorithm to use a scale-invariant temporal representation of the past to predict a scale-invariant future. The result is a scale-invariant estimate of…
Takayuki Katsuki, Takayuki Osogami, Akira Koseki, Masaki Ono + 3 more
'Michiharu Kudo' 'Masaki Makino' 'Atsushi Suzuki'] Abstract—This paper proposes a method for modeling event sequences with ambiguous timestamps, a time-discounting convolution. Unlike in ordinary time series, time intervals are not constant, small time-shifts have no significant effect, and inputting timestamps or time…
Samuel Genheden, Agnes Mårdh, Gustav Lahti, Ola Engkvist + 2 more
We present machine learning models for predicting the chemical context for Buchwald-Hartwig coupling reactions. Using reaction data from in-house electronic lab notebooks, we train two models: one based on single-label data and one based on multi-label data. Both models show excellent top-3 accuracy around 90%, which…
Authors not listed
Kinetic modeling is essential for predicting changes in food quality during processing and storage. This study evaluates the application of physics-informed neural networks (PINN) for food kinetic modeling, integrating kinetic insights into neural network frameworks. Based on three case studies, namely seed drying…
Authors not listed
Accurate prediction of chemical reaction yields remains essential for accelerating synthesis optimization, yet current machine learning models face critical limitations in capturing temporal dynamics, providing calibrated uncertainty estimates, and explicitly modeling reactant-to-product transformations. Here we…
Authors not listed
Accurately modeling the dynamics of open quantum systems is critical for advancing quantum technologies, yet traditional methods often struggle with balancing accuracy and efficiency. Machine learning (ML) offers a promising alternative, particularly through recursive models that predict system evolution based on the…
Fuzhan Rahmanian, Robert M. Lee, Dominik Linzner, Kathrin Michel + 4 more
Predicting and monitoring battery life early and across chemistries is a significant challenge due to the plethora of degradation paths, form factors, and electrochemical testing protocols. Existing models typically translate poorly across different electrode, electrolyte, and additive materials, mostly require a fixed…
Tianfan Jin, Brett M Savoie
Contemporary machine learning algorithms have largely succeeded in automating the development of mathematical models from data. Although this is a striking accomplishment, it leaves unaddressed the multitude of scenarios, especially across the chemical sciences and engineering, where deductive, rather than inductive…
Authors not listed
A framework for catalysis based on categorical aperture selection rather than temporal acceleration is presented. Traditional catalysis theory describes catalysts as agents that accelerate reactions by lowering activation energies, implicitly treating time as the fundamental variable and reaction rate enhancement as…