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768 results for “reinforcement”
FIGURES 10–11 in Larval morphology of Paroster Sharp, 1882 (Coleoptera: Dytiscidae: Hydroporinae): reinforcement of the hypothesis of monophyletic origin and discussion of phenotypic accommodation to a hypogaeic environment
FIGURES 10–11. Paroster darlotensis (first-instar larva), metathoracic leg: (10) anterior aspect; (11) posterior aspect. CO, coxa; FE, femur; PT, pretarsus; TA, tarsus; TI, tibia; TR, trochanter. Setae PT1 and PT2 not represented. Numbers and lowercase letters refer to primary setae and pores, respectively. Scale bar = 0.10 mm.
FIGURES 15–16 in Larval morphology of Paroster Sharp, 1882 (Coleoptera: Dytiscidae: Hydroporinae): reinforcement of the hypothesis of monophyletic origin and discussion of phenotypic accommodation to a hypogaeic environment
FIGURES 15–16. Paroster darlotensis (third-instar larva): (15) head capsule, dorsal aspect (not all secondary setae represented); (16) abdominal segment eight and proximal portion of urogomphi, dorsal aspect. Scale bars = 0.50 mm.
Dataset for "Meta-reinforcement learning via orbitofrontal cortex"
<p>Neural and behavioral dataset for <a href="https://doi.org/10.1038/s41593-023-01485-3">"Meta-reinforcement learning via orbitofrontal cortex" published in Nature Neuroscience</a>.<br><br>The zip file includes 3 folders: "Imaging", "Inactivation", "paAIP2"</p> <p><em><strong>"Imaging"</strong></em><br>Datasets are separated into different folders based on the mouse name (e.g. RH365) and imaging plane (deeper or shallower). Each data file in .npz format contains the following variables:<br><strong>zSR_readystart</strong>: OFC neural population activity. Deconvolved calcium signals are aligned to a ready cue onset and saved in 3d tensor format ([Neuron ID] x [Frames] x [Trials]). Neural activity of 10 sec before and 14 sec after the ready cue onset is included.<br><strong>SR_fps</strong>: Frame rate of neural activity data (frame per sec).<br><strong>pre_ready_sec</strong>: Pre-ready onset time (10 sec) included in zSR_readystart.<br><strong>post_ready_sec</strong>: Post-ready onset time (14 sec) included in zSR_readystart.<br><strong>pre_ready_frame</strong>: Pre-ready onset frames included in zSR_readystart. The frame number corresponds to pre_ready_sec.<br><strong>post_ready_frame</strong>: Post-ready onset frames included in zSR_readystart. The frame number corresponds to post_ready_sec.<br><strong>Ready2Ans_frameSR</strong>: Frame number from ready cue onset to go cue. This can be used to re-align activity to go cue onset.<br><strong>Ready2Choice_frameSR</strong>: Frame number from ready cue onset to choice. This can be used to re-align activity to choice onset.<br><strong>Trials4Use</strong>: Trial numbers (begins from 0) with good imaging quality. Use only these trials for neural activity analyses.<br><strong>a</strong>: Actions of mice during behavioral session. 1: right choice, 2: left choice, 3: alarm trial, 4: miss trial<br><strong>R</strong>: Reward outcomes during behavioral session. 1: reward, 0: no-reward<br><strong>rprob</strong>: Reward assignment probability on right side in each trial.<br><strong>lprob</strong>: Reward assignment probability on left side in each trial.</p> <p><em><strong>"Inactivation"</strong></em><br>Datasets for optogenetic OFC inactivation experiments. Datasets are separated into different folders based on experiment type (Bilateral or Unilateral OFC inactivation), mouse type (e.g. ChrimsonR, tdTomato control), illumination type (e.g. iti_2sec, ready), and the mouse name (e.g. RH895). Each data file in .npz format contains the following variables:<br><strong>a</strong>: Actions of mice during behavioral session. 1: right choice, 2: left choice, 3: alarm trial, 4: miss trial<br><strong>R</strong>: Reward outcomes during behavioral session. 1: reward, 0: no-reward<br><strong>Opto</strong>: 1: inactivation trials, 0: masking light only<br><strong>rprob</strong>: Reward assignment probability on right side in each trial.<br><strong>lprob</strong>: Reward assignment probability on left side in each trial.</p> <p><em><strong>"paAIP2"</strong></em><br>Datasets for OFC plasticity suppression experiments. Datasets are separated into different folders based on experiment type (Suppression at expert or during training), mouse type (e.g. paAIP2 or Control), and the mouse name (e.g. RH923). Each data file in .npz format contains the following variables:<br><strong>a</strong>: Actions of mice during behavioral session. 1: right choice, 2: left choice, 3: alarm trial, 4: miss trial<br><strong>R</strong>: Reward outcomes during behavioral session. 1: reward, 0: no-reward<br><strong>rprob</strong>: Reward assignment probability on right side in each trial.<br><strong>lprob</strong>: Reward assignment probability on left side in each trial.</p> <p>Details of the datasets and experimental conditions are described in the publication (<a href="https://doi.org/10.1038/s41593-023-01485-3">Hattori et al., Meta-reinforcement learning via orbitofrontal cortex, Nature Neuroscience, 2023</a>).</p> <p><br><em>There was a report that Mac users may get "extra bytes at beginning" error when trying to unzip the file. A solution to this problem was to run "zip -FFv foo.zip --out fixed.zip" where foo.zip is the path of the file downloaded from Zenodo. Then, run "unzip fixed.zip".</em></p>
Personalize E-Commerce Product Recommendations Based on User Behavior Using Reinforced Learning Algorithms
<p><span>The development of a personalized and adaptive e-commerce product recommendation system will be developed using the Reinforcement Learning algorithm in this study. Initial data is extremely promising in its ability to raise sales conversion: 30% of the products added to the cart are never purchased. Additionally, there is a strong correlation of 0.8 between viewed versus purchased products. Data collection was from 447 Indonesian respondents over a period of June to July 2024. It was collected using an online questionnaire that measures recommendation quality, satisfaction, and ease of use with purposive sampling. Partial Least Squares Structural Equation Modeling was done on the data analysis. From that, it has been found that system quality is positively related to the accuracy, novelty, and diversity of the recommendation. The results further show how this would lead to an improved user experience, satisfaction, and sales conversion with the reinforcement learning-based system. These findings give insight into developing efficient adaptive recommendation systems on e-commerce platforms and open opportunities for further research. </span></p>
Creep behavior of short glass fiber reinforced poly(butylene terephthalate) and its prediction based on the matrix creep using an elementary volume approach
<p><span>The creep compliances of short glass fiber reinforced poly(butylene terephthalate) composites (SFRC PBT) plates processed by conventional injection molding and push pull processing were determined parallel and perpendicular to the flow direction using a tensile creep stand. The fiber weight contents of the plates were 0, 20 and 30 %. Tensile test bars were taken out of the plates parallel and perpendicular to the flow direction to determine short-term mechanical properties, fiber length distribution and fiber orientation distribution necessary for subsequent modelling. An elementary volume approach was used to calculate the longitudinal and transverse creep compliances in the main axes system showing that their time dependencies are governed by the creep of the PBT matrix only. As the plates hardly exhibit any fiber orientation in the thickness direction, the components of the 2D compliance tensor were transformed according to RM Jones’ <em>Mechanics of composite materials</em> to take into account the fiber misalignment in SFRC. This transformation introduces the unknown shear modulus G<sub>12</sub> to the components of the transformed compliances tensor. This problem could be overcome as G<sub>12</sub> can be expressed in terms of the transverse compliance J<sub>22</sub> and a shear correction factor within the EV approach. Comparing the predicted creep compliances to measured ones resulted in an underestimation of 15 to 30 % parallel and 5 to 15 % perpendicular to the flow direction. This underestimation can be attributed to a large extent to a non-perfect fiber matrix adhesion. Furthermore, SEM pictures of fracture surfaces show different failure behavior parallel to the fiber axis and perpendicular to it. Thus, the fiber matrix adhesion seems to depend on the direction of stress with respect to the fiber axes. The time range of well correspondence of matrix creep and composites creep can be expressed by the creep time limit which decreases exponentially with increasing creep stresses.</span></p>
Figure 5 in Terrestrial invertebrates surviving San Ambrosio island's ecological catastrophe reinforce biogeographic affinities between the Juan Fernández and Desventuradas Islands
Figure 5. Insects. (a) Dermestes (Dermestinus) maculatus (Dermestidae), (b) Kuschelinus insularis (Carabidae) Endemic, (c) Pachystylus sp. 2 (Curculionidae), (d) Toxotarsus ambrosianus (Calliphoridae) Endemic, (e) Nysius sp. (Lygaeidae), and (f) Trimerotropis ochraceipennis (Acrididae).
Figure 1 in Terrestrial invertebrates surviving San Ambrosio island's ecological catastrophe reinforce biogeographic affinities between the Juan Fernández and Desventuradas Islands
Figure 1. Desventuradas Islands. Located in the Southeastern Pacific the Desventuradas Islands (Red box) are composed of two islands: to the west San Félix (26°17'S; 80°05'W) and to the east San Ambrosio (26°20'S; 79°53'W) (Yellow box). Map generated with images from www.esri.com.
Figure 2 in Terrestrial invertebrates surviving San Ambrosio island's ecological catastrophe reinforce biogeographic affinities between the Juan Fernández and Desventuradas Islands
Figure 2. Deforestation of the Thamnoseris lacerata forest. (a) Historical picture taken around 1980 showing the extent of the T. lacerata forest on San Ambrosio. Photo credit: "Isla San Ambrosio, 1980" In: Chile a color, Geografía. Colección Biblioteca Antártica, Vol. I. pag. 437. Direction and production: Isabel Margarita Aguirre and other authors. Editorial Antártica 1983, Santiago de Chile. (b) Picture taken in September 2018, showing the same approximate location of a deforested landscape on San Ambrosio. Photo credit: Lukas Mekis / Island Conservation.
Automated inference of sequences of operations in RESTful APIs with deep reinforcement learning - replication package
<p>This directory contains the replication package of the article entitled "Automated inference of sequences of operations in RESTful APIs with deep reinforcement learning". Please note that this is NOT a real publication or scientific report, but a project created for the purpose of learning the basic concepts of research by emulating the process of creating an article.</p>
Dataset for Xia et al.: Reproductive isolation via divergent genital morphology due to cascade reinforcement in Ohomopterus ground beetles
<p><span>Secondary contact between incipient species and selection against maladaptive hybridization can drive reinforcement between populations in contact and result in reproductive character displacement (RCD). Resultant divergence in mating traits within a species may generate downstream reproductive isolation between populations with </span><span>d</span><span>isplaced and non-displaced traits, referred to as the cascade reinforcement hypothesis. We examined this hypothesis using three allopatric populations of the ground beetle </span><span>Carabus maiyasanus</span><span> with</span><span> a genital lock-and-key system. This species shows RCD in male and female genital morphologies in populations in contact with the sister species C. iwawakianus. In a reciprocal mating experiment using three allopatric populations with differences in male and female genital sizes, insemination</span><span>failure increased as the difference in genital size increased. Based on the reproductive isolation index, insemination failure w</span><span>as the major postmating-prezygotic isolation barrier, at least in one population pair with comparable total isolation to those of other species pairs. By contrast, there was</span><span> only incomplete premating isolation among populations</span><span>. </span><span>These results suggest that RCD in genital morphologies drives incipient allopatric speciation, supporting the cascade reinforcement hypothesis. These findings provide insight into the roles of interspecific interactions and subsequent trait diversification in speciation processes</span><span>.</span></p>
Mutation Testing of Deep Reinforcement Learning Based on Real Faults
<p>Trained agents to be used in the replication package of the paper "<em>Mutation Testing of Deep Reinforcement Learning Based on Real Faults</em>" accepted to the International Conference on Software Testing (ICST) 2023. The replication package is at https://github.com/FlowSs/RLMutation.</p>
Data publication: Simulation of macroscopic boundary value problems using phenomenological material model for steel fiber reinforced high performance concrete (HPC)
<p>This data set contains all necessary inputs for the Simulation of macroscopic boundary value problems using phenomenological material model, including boundary conditions, material parameters and numerical results. The discretization is realized in terms of the finite element method. </p>
Data for "Nanoparticle reinforced medium entropy CoCrFeNi produced by laser powder bed fusion: Microstructure evolution"
<p>Nanoparticle-reinforced metallic composites produced via laser powder bed fusion (LPBF) offer an economically feasible approach for obtaining high-strength near-net shaped critical components in automotive and aviation industries. This study investigates the equiatomic medium entropy alloy (MEA) CoCrFeNi manufactured by LPBF, incorporating two types of reinforcing particles, titanium nitride (TiN) and titanium oxide (TiO2), with varying sizes and volume concentrations. In this paper, we focus on analyzing the microstructure and texture evolution of all alloys, alongside examining the dissolution, precipitation and phase transitioning of the particles. TiN nanoparticles dissolve in the melt pool and uniformly precipitate as TiO2, forming novel core-shell nanoparticles resistant to coarsening. </p> <p>Here we share the STEM raw data files that were used in the analysis. We also share a general image analysis routine that used python based modules to measure the particle sizes and their volume fraction.</p> <p>In brief, we used the following steps for several images of each sample:<br>(a) Threshold the equalized grayscale image to create a binary image.<br>(b) Calculate the area fraction of the cleaned binary image.<br>(c) Detect contours in the binary image.<br>(d) Extract properties of circles from the contours, such as scaled diameter, and area.<br>(e) Draw circles on the original image using the detected contours.</p> <p><br>For TiN/5/800 samples containing multiple square-shaped particles, we assess the area of these squares and subsequently determine the diameter of a circle possessing an equivalent area.</p> <p>We also share the the raw file and the jupyter notebook for the 4DSTEM experiment conducted on the core-shell nanoparticle.</p>
Dataset for Automated Unit Test Generation via Chain of Thought Prompt and Reinforcement Learning
<p>This is the replication package including three types datasets: training dataset with CoT prompts, reward dataset for training reward model, rl dataset for optimizing policy model. The training dataset includes filter_test_cot_rule_50k.csv, filter_train_cot_rule_50k.csv, and filter_valid_cot_rule_50k.csv. These three datasets includes multiple fields (i.e., src_fm, intention, plan, elaboration, gpt_test, src_fm_cot_gpt, target, src_fm_fc_ms_ff,src_fm_intention,src_fm_plan,src_fm_elaboration,idx,rule_cot,rule_cot_nlp,combine_cot,src_fm_rule_cot_nlp,src_fm_cot_nlp_gpt,gpt_cot_filter,src_fm_plan_intention). The reward dataset includes test_athena.json, train_athena.json, and valid_athena.json three files. The rl dataset includes three files: filter_test_cot_gpt_rl.csv, filter_train_cot_gpt_rl.csv, filter_valid_cot_gpt_rl.csv. These files include mulitple fields: src_fm,intention,plan,elaboration,gpt_test,src_fm_cot_gpt,target,src_fm_fc_ms_ff,src_fm_intention,src_fm_plan,src_fm_elaboration,gpt_cot_filter.</p>
Replication of manuscript entitled "Tropical eastern Pacific cooling trend reinforced by human activity"
<p>NCAR Command Language (NCL) codes and data for replicating main figures in a manuscript published in npj Climate and Atmospheric Science</p>
DATABASE_Performance of nodal regions of reinforced concrete (review 2024)
<p>DATABASE corresponding to the article "Performance of nodal regions of RC corner frames subjected to opening bending moments" (2024)</p> <p> </p>
Data of "Ultra-thin Strain Hardening Cementitious Composite (SHCC) layer in reinforced concrete cover zone for crack width control"
Open the record for dataset details and reuse information.
Impact Compressive Properties of Polyurethane Foams with 3D Continuous Fibre Reinforcement
<p>Flexible and rigid polyurethane foams are used in applications subjected to impact loads. Integrating a three-dimensional, continuous fibre reinforcement meets high requirements for stiffness and strength. A fundamental comparison of the impact compressive properties was carried out for contributing to a reasonable choice between these variants. A rigid and flexible foam with a bulk density of 85 kg/m<sup>3</sup> each was investigated with and without reinforcement by a warp-knitted spacer fabric. The material testing was conducted using a drop weight tester in accordance with a German standard. Three impacts are applied by a hammer with a flat surface larger than the area of the sample plate. Different impact energies were investigated and the deformation behaviour was analysed using a high-speed camera. Much higher energy absorptions at low deformations were found with rigid foam. Peak stresses and decelerations were lower with flexible foam if the load did not densify the material. Reinforcing the foams leads to an anisotropic deformation and improves the impact compressive properties, especially in the case of the flexible variant. The four materials analysed had a similar rebound and largely maintained their performance when subjected to the repeated impact load.</p>
Exploring Clustering Techniques for Effective Reinforcement Learning based Personalization for Health and Wellbeing/Database 1
<p>The first Dataset of the five runs done. If you would like to have access to any or all of the others please send an e-mail to the authors.</p>
Dataset: Dopamine neurons projecting to medial shell of the nucleus accumbens drive heroin reinforcement.
<p>This dataset contains the data presented in the paper Dopamine neurons projecting to medial shell of the nucleus accumbens drive heroin reinforcement. (doi: 10.7554/eLife.39945 )</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.