LIVIVO - Search results -

Search results

Result 1 - 10 of total 19

Search options

Book ; Online: Tri-Level Model for Hybrid Renewable Energy Systems

Hosseini, Eghbal

2023

Abstract: In practical scenarios, addressing real-world challenges often entails the incorporation of diverse renewable energy sources, such as solar, energy storage systems, and greenhouse gas emissions. The core purpose of these interconnected systems is to ... ...

Abstract	In practical scenarios, addressing real-world challenges often entails the incorporation of diverse renewable energy sources, such as solar, energy storage systems, and greenhouse gas emissions. The core purpose of these interconnected systems is to optimize a multitude of factors and objectives concurrently. Hence, it is imperative to formulate models that comprehensively cover all these objectives. This paper introduces tri-level mathematical models for Hybrid Renewable Energy Systems (HRESs), offering a framework to concurrently tackle diverse objectives and decision-making levels within the realm of renewable energy integration. The proposed model seeks to maximize the efficiency of solar PV, enhance the performance of energy storage systems, and minimize greenhouse gas emissions.
Keywords	Electrical Engineering and Systems Science - Systems and Control
Publishing date	2023-12-05
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

Full text online

Full text

Inter-library loan at ZB MED

Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

Book ; Online: Large language models implicitly learn to straighten neural sentence trajectories to construct a predictive representation of natural language

Hosseini, Eghbal A. / Fedorenko, Evelina

2023

Abstract: Predicting upcoming events is critical to our ability to interact with our environment. Transformer models, trained on next-word prediction, appear to construct representations of linguistic input that can support diverse downstream tasks. But how does a ...

Abstract	Predicting upcoming events is critical to our ability to interact with our environment. Transformer models, trained on next-word prediction, appear to construct representations of linguistic input that can support diverse downstream tasks. But how does a predictive objective shape such representations? Inspired by recent work in vision (Henaff et al., 2019), we test a hypothesis about predictive representations of autoregressive transformers. In particular, we test whether the neural trajectory of a sentence becomes progressively straighter as it passes through the network layers. The key insight is that straighter trajectories should facilitate prediction via linear extrapolation. We quantify straightness using a 1-dimensional curvature metric, and present four findings in support of the trajectory straightening hypothesis: i) In trained models, the curvature decreases from the early to the deeper layers of the network. ii) Models that perform better on the next-word prediction objective exhibit greater decreases in curvature, suggesting that this improved ability to straighten sentence trajectories may be the driver of better language modeling performance. iii) Given the same linguistic context, the sequences that are generated by the model have lower curvature than the actual continuations observed in a language corpus, suggesting that the model favors straighter trajectories for making predictions. iv) A consistent relationship holds between the average curvature and the average surprisal of sentences in the deep model layers, such that sentences with straighter trajectories also have lower surprisal. Importantly, untrained models do not exhibit these behaviors. In tandem, these results support the trajectory straightening hypothesis and provide a possible mechanism for how the geometry of the internal representations of autoregressive models supports next word prediction. Comment: 37th Conference on Neural Information Processing Systems (NeurIPS 2023). 20 pages, 5 main figures, 7 supplementary figures
Keywords	Computer Science - Computation and Language ; Computer Science - Artificial Intelligence
Subject code	401
Publishing date	2023-11-05
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

Full text online

Full text

Inter-library loan at ZB MED

Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

Article ; Online: Artificial Neural Network Language Models Predict Human Brain Responses to Language Even After a Developmentally Realistic Amount of Training.

Hosseini, Eghbal A / Schrimpf, Martin / Zhang, Yian / Bowman, Samuel / Zaslavsky, Noga / Fedorenko, Evelina

Neurobiology of language (Cambridge, Mass.)

2024 Volume 5, Issue 1, Page(s) 43–63

Abstract: Artificial neural networks have emerged as computationally plausible models of human language processing. A major criticism of these models is that the amount of training data they receive far exceeds that of humans during language learning. Here, we use ...

Abstract	Artificial neural networks have emerged as computationally plausible models of human language processing. A major criticism of these models is that the amount of training data they receive far exceeds that of humans during language learning. Here, we use two complementary approaches to ask how the models' ability to capture human fMRI responses to sentences is affected by the amount of training data. First, we evaluate GPT-2 models trained on 1 million, 10 million, 100 million, or 1 billion words against an fMRI benchmark. We consider the 100-million-word model to be developmentally plausible in terms of the amount of training data given that this amount is similar to what children are estimated to be exposed to during the first 10 years of life. Second, we test the performance of a GPT-2 model trained on a 9-billion-token dataset to reach state-of-the-art next-word prediction performance on the human benchmark at different stages during training. Across both approaches, we find that (i) the models trained on a developmentally plausible amount of data already achieve near-maximal performance in capturing fMRI responses to sentences. Further, (ii) lower perplexity-a measure of next-word prediction performance-is associated with stronger alignment with human data, suggesting that models that have received enough training to achieve sufficiently high next-word prediction performance also acquire representations of sentences that are predictive of human fMRI responses. In tandem, these findings establish that although
Language	English
Publishing date	2024-04-01
Publishing country	United States
Document type	Journal Article
ISSN	2641-4368
ISSN (online)	2641-4368
DOI	10.1162/nol_a_00137
Database	MEDical Literature Analysis and Retrieval System OnLINE

Order via subito

This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

Inter-library loan at ZB MED

Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

Book ; Online: WhisBERT

Wolf, Lukas / Tuckute, Greta / Kotar, Klemen / Hosseini, Eghbal / Regev, Tamar / Wilcox, Ethan / Warstadt, Alex

Multimodal Text-Audio Language Modeling on 100M Words

2023

Abstract: Training on multiple modalities of input can augment the capabilities of a language model. Here, we ask whether such a training regime can improve the quality and efficiency of these systems as well. We focus on text--audio and introduce Whisbert, which ... ...

Abstract	Training on multiple modalities of input can augment the capabilities of a language model. Here, we ask whether such a training regime can improve the quality and efficiency of these systems as well. We focus on text--audio and introduce Whisbert, which is inspired by the text--image approach of FLAVA (Singh et al., 2022). In accordance with Babylm guidelines (Warstadt et al., 2023), we pretrain Whisbert on a dataset comprising only 100 million words plus their corresponding speech from the word-aligned version of the People's Speech dataset (Galvez et al., 2021). To assess the impact of multimodality, we compare versions of the model that are trained on text only and on both audio and text simultaneously. We find that while Whisbert is able to perform well on multimodal masked modeling and surpasses the Babylm baselines in most benchmark tasks, it struggles to optimize its complex objective and outperform its text-only Whisbert baseline. Comment: Published at the BabyLM Challenge, a shared task co-sponsored by CMCL 2023 and CoNLL 2023, hosted by EMNLP 2023
Keywords	Computer Science - Computation and Language ; Computer Science - Artificial Intelligence
Subject code	410
Publishing date	2023-12-05
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

Full text online

Full text

Inter-library loan at ZB MED

Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

Article ; Online: Reinforcement regulates timing variability in thalamus.

Wang, Jing / Hosseini, Eghbal / Meirhaeghe, Nicolas / Akkad, Adam / Jazayeri, Mehrdad

eLife

2020 Volume 9

Abstract: Learning reduces variability but variability can facilitate learning. This paradoxical relationship has made it challenging to tease apart sources of variability that degrade performance from those that improve it. We tackled this question in a context- ... ...

Abstract	Learning reduces variability but variability can facilitate learning. This paradoxical relationship has made it challenging to tease apart sources of variability that degrade performance from those that improve it. We tackled this question in a context-dependent timing task requiring humans and monkeys to flexibly produce different time intervals with different effectors. We identified two opposing factors contributing to timing variability: slow memory fluctuation that degrades performance and reward-dependent exploratory behavior that improves performance. Signatures of these opposing factors were evident across populations of neurons in the dorsomedial frontal cortex (DMFC), DMFC-projecting neurons in the ventrolateral thalamus, and putative target of DMFC in the caudate. However, only in the thalamus were the performance-optimizing regulation of variability aligned to the slow performance-degrading memory fluctuations. These findings reveal how variability caused by exploratory behavior might help to mitigate other undesirable sources of variability and highlight a potential role for thalamocortical projections in this process.
MeSH term(s)	Adolescent ; Adult ; Aged ; Animals ; Behavior ; Brain Mapping ; Cues ; Female ; Frontal Lobe/physiology ; Humans ; Learning/physiology ; Macaca mulatta ; Male ; Middle Aged ; Models, Neurological ; Motor Activity ; Reward ; Task Performance and Analysis ; Thalamus/physiology ; Time Perception/physiology ; Young Adult
Language	English
Publishing date	2020-12-01
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	2687154-3
ISSN	2050-084X ; 2050-084X
ISSN (online)	2050-084X
ISSN	2050-084X
DOI	10.7554/eLife.55872
Database	MEDical Literature Analysis and Retrieval System OnLINE

Order via subito

Article ; Online: (with research data) The decay of motor adaptation to novel movement dynamics reveals an asymmetry in the stability of motion state-dependent learning.

Hosseini, Eghbal A / Nguyen, Katrina P / Joiner, Wilsaan M

PLoS computational biology

2017 Volume 13, Issue 5, Page(s) e1005492

Abstract: Motor adaptation paradigms provide a quantitative method to study short-term modification of motor commands. Despite the growing understanding of the role motion states (e.g., velocity) play in this form of motor learning, there is little information on ... ...

Abstract	Motor adaptation paradigms provide a quantitative method to study short-term modification of motor commands. Despite the growing understanding of the role motion states (e.g., velocity) play in this form of motor learning, there is little information on the relative stability of memories based on these movement characteristics, especially in comparison to the initial adaptation. Here, we trained subjects to make reaching movements perturbed by force patterns dependent upon either limb position or velocity. Following training, subjects were exposed to a series of error-clamp trials to measure the temporal characteristics of the feedforward motor output during the decay of learning. The compensatory force patterns were largely based on the perturbation kinematic (e.g., velocity), but also showed a small contribution from the other motion kinematic (e.g., position). However, the velocity contribution in response to the position-based perturbation decayed at a slower rate than the position contribution to velocity-based training, suggesting a difference in stability. Next, we modified a previous model of motor adaptation to reflect this difference and simulated the behavior for different learning goals. We were interested in the stability of learning when the perturbations were based on different combinations of limb position or velocity that subsequently resulted in biased amounts of motion-based learning. We trained additional subjects on these combined motion-state perturbations and confirmed the predictions of the model. Specifically, we show that (1) there is a significant separation between the observed gain-space trajectories for the learning and decay of adaptation and (2) for combined motion-state perturbations, the gain associated to changes in limb position decayed at a faster rate than the velocity-dependent gain, even when the position-dependent gain at the end of training was significantly greater. Collectively, these results suggest that the state-dependent adaptation associated with movement velocity is relatively more stable than that based on position.
MeSH term(s)	Adaptation, Physiological/physiology ; Computational Biology ; Female ; Humans ; Learning/physiology ; Male ; Movement/physiology ; Psychomotor Performance/physiology ; Task Performance and Analysis
Language	English
Publishing date	2017-05-08
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	2193340-6
ISSN	1553-7358 ; 1553-734X
ISSN (online)	1553-7358
ISSN	1553-734X
DOI	10.1371/journal.pcbi.1005492
Database	MEDical Literature Analysis and Retrieval System OnLINE

Order via subito

Article ; Online: COVID-19 Optimizer Algorithm, Modeling and Controlling of Coronavirus Distribution Process.

Hosseini, Eghbal / Ghafoor, Kayhan Zrar / Sadiq, Ali Safaa / Guizani, Mohsen / Emrouznejad, Ali

IEEE journal of biomedical and health informatics

2020 Volume 24, Issue 10, Page(s) 2765–2775

Abstract: The emergence of novel COVID-19 is causing an overload on public health sector and a high fatality rate. The key priority is to contain the epidemic and reduce the infection rate. It is imperative to stress on ensuring extreme social distancing of the ... ...

Abstract	The emergence of novel COVID-19 is causing an overload on public health sector and a high fatality rate. The key priority is to contain the epidemic and reduce the infection rate. It is imperative to stress on ensuring extreme social distancing of the entire population and hence slowing down the epidemic spread. So, there is a need for an efficient optimizer algorithm that can solve NP-hard in addition to applied optimization problems. This article first proposes a novel COVID-19 optimizer Algorithm (CVA) to cover almost all feasible regions of the optimization problems. We also simulate the coronavirus distribution process in several countries around the globe. Then, we model a coronavirus distribution process as an optimization problem to minimize the number of COVID-19 infected countries and hence slow down the epidemic spread. Furthermore, we propose three scenarios to solve the optimization problem using most effective factors in the distribution process. Simulation results show one of the controlling scenarios outperforms the others. Extensive simulations using several optimization schemes show that the CVA technique performs best with up to 15%, 37%, 53% and 59% increase compared with Volcano Eruption Algorithm (VEA), Gray Wolf Optimizer (GWO), Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), respectively.
MeSH term(s)	Algorithms ; Betacoronavirus ; COVID-19 ; Computational Biology ; Computer Simulation ; Coronavirus Infections/epidemiology ; Coronavirus Infections/prevention & control ; Coronavirus Infections/transmission ; Humans ; Models, Biological ; Pandemics/prevention & control ; Pandemics/statistics & numerical data ; Pneumonia, Viral/epidemiology ; Pneumonia, Viral/prevention & control ; Pneumonia, Viral/transmission ; SARS-CoV-2
Keywords	covid19
Language	English
Publishing date	2020-07-28
Publishing country	United States
Document type	Evaluation Study ; Journal Article
ZDB-ID	2695320-1
ISSN	2168-2208 ; 2168-2194
ISSN (online)	2168-2208
ISSN	2168-2194
DOI	10.1109/JBHI.2020.3012487
Database	MEDical Literature Analysis and Retrieval System OnLINE

In stock of ZB MED Cologne/Königswinter

Zs.A 5483: Show issues

Location:
Je nach Verfügbarkeit (siehe Angabe bei Bestand)
bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
Jg. 1995 - 2021: Lesesall (2.OG)
ab Jg. 2022: Lesesaal (EG)

Order via subito

Details ▾
- See ZB MED holdings
- Order with fees

Article ; Online: Novel metaheuristic based on multiverse theory for optimization problems in emerging systems.

Hosseini, Eghbal / Ghafoor, Kayhan Zrar / Emrouznejad, Ali / Sadiq, Ali Safaa / Rawat, Danda B

Applied intelligence (Dordrecht, Netherlands)

2020 Volume 51, Issue 6, Page(s) 3275–3292

Abstract: Finding an optimal solution for emerging cyber physical systems (CPS) for better efficiency and robustness is one of the major issues. Meta-heuristic is emerging as a promising field of study for solving various optimization problems applicable to ... ...

Abstract	Finding an optimal solution for emerging cyber physical systems (CPS) for better efficiency and robustness is one of the major issues. Meta-heuristic is emerging as a promising field of study for solving various optimization problems applicable to different CPS systems. In this paper, we propose a new meta-heuristic algorithm based on Multiverse Theory, named MVA, that can solve NP-hard optimization problems such as non-linear and multi-level programming problems as well as applied optimization problems for CPS systems. MVA algorithm inspires the creation of the next population to be very close to the solution of initial population, which mimics the nature of parallel worlds in multiverse theory. Additionally, MVA distributes the solutions in the feasible region similarly to the nature of big bangs. To illustrate the effectiveness of the proposed algorithm, a set of test problems is implemented and measured in terms of feasibility, efficiency of their solutions and the number of iterations taken in finding the optimum solution. Numerical results obtained from extensive simulations have shown that the proposed algorithm outperforms the state-of-the-art approaches while solving the optimization problems with large feasible regions.
Language	English
Publishing date	2020-11-11
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	1479519-X
ISSN	1573-7497 ; 0924-669X
ISSN (online)	1573-7497
ISSN	0924-669X
DOI	10.1007/s10489-020-01920-z
Database	MEDical Literature Analysis and Retrieval System OnLINE

Full text online

Accessible to users with ZB MED library card

Order via subito

Details ▾
- Full text online
- Order with fees

Article ; Online: COVID-19 Optimizer Algorithm, Modeling and Controlling of Coronavirus Distribution Process

Hosseini, Eghbal / Ghafoor, Kayhan / Sadiq, Ali / Guizani, Mohsen / Emrouznejad, Ali

2020

Abstract	The emergence of novel COVID-19 is causing an overload on public health sector and a high fatality rate. The key priority is to contain the epidemic and reduce the infection rate. It is imperative to stress on ensuring extreme social distancing of the entire population and hence slowing down the epidemic spread. So, there is a need for an efficient optimizer algorithm that can solve NP-hard in addition to applied optimization problems. This article first proposes a novel COVID-19 optimizer Algorithm (CVA) to cover almost all feasible regions of the optimization problems. We also simulate the coronavirus distribution process in several countries around the globe. Then, we model a coronavirus distribution process as an optimization problem to minimize the number of COVID-19 infected countries and hence slow down the epidemic spread. Furthermore, we propose three scenarios to solve the optimization problem using most effective factors in the distribution process. Simulation results show one of the controlling scenarios outperforms the others. Extensive simulations using several optimization schemes show that the CVA technique performs best with up to 15%, 37%, 53% and 59% increase compared with Volcano Eruption Algorithm (VEA), Gray Wolf Optimizer (GWO), Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), respectively.
Keywords	covid19
Language	English
Publishing date	2020-10-01
Publishing country	uk
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

Full text online

Full text

Inter-library loan at ZB MED

Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

Article ; Online: Flexible Sensorimotor Computations through Rapid Reconfiguration of Cortical Dynamics.

Remington, Evan D / Narain, Devika / Hosseini, Eghbal A / Jazayeri, Mehrdad

Neuron

2018 Volume 98, Issue 5, Page(s) 1005–1019.e5

Abstract: Neural mechanisms that support flexible sensorimotor computations are not well understood. In a dynamical system whose state is determined by interactions among neurons, computations can be rapidly reconfigured by controlling the system's inputs and ... ...

Abstract	Neural mechanisms that support flexible sensorimotor computations are not well understood. In a dynamical system whose state is determined by interactions among neurons, computations can be rapidly reconfigured by controlling the system's inputs and initial conditions. To investigate whether the brain employs such control mechanisms, we recorded from the dorsomedial frontal cortex of monkeys trained to measure and produce time intervals in two sensorimotor contexts. The geometry of neural trajectories during the production epoch was consistent with a mechanism wherein the measured interval and sensorimotor context exerted control over cortical dynamics by adjusting the system's initial condition and input, respectively. These adjustments, in turn, set the speed at which activity evolved in the production epoch, allowing the animal to flexibly produce different time intervals. These results provide evidence that the language of dynamical systems can be used to parsimoniously link brain activity to sensorimotor computations.
MeSH term(s)	Animals ; Cerebral Cortex/physiology ; Cognition ; Electroencephalography ; Female ; Frontal Lobe/physiology ; Macaca mulatta ; Male ; Neural Networks, Computer ; Neurons/physiology ; Sensorimotor Cortex/physiology ; Systems Analysis ; Task Performance and Analysis ; Time Factors
Language	English
Publishing date	2018-06-07
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	808167-0
ISSN	1097-4199 ; 0896-6273
ISSN (online)	1097-4199
ISSN	0896-6273
DOI	10.1016/j.neuron.2018.05.020
Database	MEDical Literature Analysis and Retrieval System OnLINE

Full text online

Accessible to users with ZB MED library card

In stock of ZB MED Cologne/Königswinter

Zs.A 2496: Show issues			Location: Je nach Verfügbarkeit (siehe Angabe bei Bestand) bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular Jg. 1995 - 2021: Lesesall (2.OG) ab Jg. 2022: Lesesaal (EG)
Zs.MG 92: Show issues

Order via subito

Details ▾

To top

Your last searches

Full text online

More links

Kategorien

Inter-library loan at ZB MED

Full text online

More links

Kategorien

Inter-library loan at ZB MED

More links

Kategorien

Order via subito

Inter-library loan at ZB MED

Full text online

More links

Kategorien

Inter-library loan at ZB MED

More links

Kategorien

Order via subito

More links

Kategorien

Order via subito

More links

Kategorien

In stock of ZB MED Cologne/Königswinter

Order via subito

Full text online

More links

Kategorien

Order via subito

Full text online

More links

Kategorien

Inter-library loan at ZB MED

Full text online

More links

Kategorien

In stock of ZB MED Cologne/Königswinter

Order via subito