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Fig. 10. Camponotus keihitoi Forel, 1913 in Two new species of the genus Camponotus Mayr, 1861 (Hymenoptera: Formicidae) with five new records from India
Fig. 10. Camponotus keihitoi Forel, 1913, major worker (PUAC T82). A. Head in full face view. B. Body in profile view. C. Body in dorsal view.
Fig. 35 in Two new species of the genus Camponotus Mayr, 1861 (Hymenoptera: Formicidae) with five new records from India
Fig. 35. Head in full face view. A. Camponotus variegatus infuscus Forel, 1892. B. Camponotus barbatus taylori Forel, 1892. Scale bars = 1 mm.
Fig. 40 in Two new species of the genus Camponotus Mayr, 1861 (Hymenoptera: Formicidae) with five new records from India
Fig. 40. Gaster in dorsal view. A. Camponotus sp. 101. B. Camponotus habereri Forel, 1911. Scale bars = 1 mm.
Fig. 16. Camponotus simoni Emery, 1893 in Two new species of the genus Camponotus Mayr, 1861 (Hymenoptera: Formicidae) with five new records from India
Fig. 16. Camponotus simoni Emery, 1893, major worker (PUAC T123). A. Head in full face view. B. Body in profile view. C. Body in dorsal view.
Fig. 29 in Two new species of the genus Camponotus Mayr, 1861 (Hymenoptera: Formicidae) with five new records from India
Fig. 29. Head in full face view. A. Camponotus angusticollis (Jerdon, 1851).B. Camponotus angusticollis sanguinolentus Forel, 1895. Scale bars = 1 mm.
Fig. 24 in Two new species of the genus Camponotus Mayr, 1861 (Hymenoptera: Formicidae) with five new records from India
Fig. 24. Clypeus in full face view. A. Camponotus rufoglaucus (Jerdon, 1851). B. Camponotus mendax Forel, 1895. Scale bars: A = 1 mm; B = 0.5 mm.
Fig. 41 in Two new species of the genus Camponotus Mayr, 1861 (Hymenoptera: Formicidae) with five new records from India
Fig. 41. Mesosoma in dorsal view. A. Camponotus carin Forel, 1889. B. Camponotus thraso Forel, 1893. Scale bars = 1 mm.
Fig. 4 in Two new species of the genus Camponotus Mayr, 1861 (Hymenoptera: Formicidae) with five new records from India
Fig. 4. Camponotus sholensis sp. nov., paratype, minor worker (PUAC T33). A. Head in full face view. B. Body in profile view. C. Body in dorsal view.
Fig. 3 in Two new species of the genus Camponotus Mayr, 1861 (Hymenoptera: Formicidae) with five new records from India
Fig. 3. Camponotus sholensis sp. nov., holotype, major worker (PUAC T27). A. Head in full face view. B. Body in profile view. C. Body in dorsal view.
Fig. 2 in Two new species of the genus Camponotus Mayr, 1861 (Hymenoptera: Formicidae) with five new records from India
Fig. 2. Illustrations of measurements for species of Camponotus Mayr, 1861. A. Head in full-face view. B. Body in lateral view. C. Body in dorsal view. Abbreviations: see Material and methods.
Fig. 5 in Two new species of the genus Camponotus Mayr, 1861 (Hymenoptera: Formicidae) with five new records from India
Fig. 5. Camponotus sholensis sp. nov., paratype, gyne (PUAC T 45). A. Head in full face view. B. Body in profile view. C. Body in dorsal view.
A Two Level Neural Approach Combining Off-Chip Prediction with Adaptive Prefetch Filtering
<p>To alleviate the performance and energy overheads of contemporary applications with large data footprints, we propose the Two Level Perceptron (TLP) predictor, a neural mechanism that effectively combines predicting whether an access will be off-chip with adaptive prefetch filtering at the first-level data cache (L1D). TLP is composed of two connected microarchitectural perception predictors, named First Level Predictor (FLP) and Second Level Predictor (SLP). FLP performs accurate off-chip prediction by using several program features based on virtual addresses and a novel selective delay component. The novelty of SLP relies on leveraging off-chip prediction to drive L1D prefetch filtering by using physical addresses and the FLP prediction as features. TLP constitutes the first hardware proposal targeting both off-chip prediction and prefetch filtering using a multi-level perception hardware approach. TLP only requires 7KB of storage. To demonstrate the benefits of TLP we compare its performance with state-of-the-art approaches using off-chip prediction and prefetch filtering on a wide range of single-core and multi-core workloads. Our experiments show that TLP reduces the average DRAM transactions by 30.7% and 17.7%, as compared to a baseline using state-of-the-art cache prefetchers but no off-chip prediction mechanism, across the single-core and multi-core workloads, respectively, while recent work significantly increases DRAM transactions. As a result, TLP achieves geometric mean performance speedups of 6.2% and 11.8% across single-core and multi-core workloads, respectively. In addition, our evaluation demonstrates that TLP is effective independently of the L1D prefetching logic.</p>
A Two Level Neural Approach Combining Off-Chip Prediction with Adaptive Prefetch Filtering
<p>To alleviate the performance and energy overheads of contemporary applications with large data footprints, we propose the Two Level Perceptron (TLP) predictor, a neural mechanism that effectively combines predicting whether an access will be off-chip with adaptive prefetch filtering at the first-level data cache (L1D). TLP is composed of two connected microarchitectural perception predictors, named First Level Predictor (FLP) and Second Level Predictor (SLP). FLP performs accurate off-chip prediction by using several program features based on virtual addresses and a novel selective delay component. The novelty of SLP relies on leveraging off-chip prediction to drive L1D prefetch filtering by using physical addresses and the FLP prediction as features. TLP constitutes the first hardware proposal targeting both off-chip prediction and prefetch filtering using a multi-level perception hardware approach. TLP only requires 7KB of storage. To demonstrate the benefits of TLP we compare its performance with state-of-the-art approaches using off-chip prediction and prefetch filtering on a wide range of single-core and multi-core workloads. Our experiments show that TLP reduces the average DRAM transactions by 30.7% and 17.7%, as compared to a baseline using state-of-the-art cache prefetchers but no off-chip prediction mechanism, across the single-core and multi-core workloads, respectively, while recent work significantly increases DRAM transactions. As a result, TLP achieves geometric mean performance speedups of 6.2% and 11.8% across single-core and multi-core workloads, respectively. In addition, our evaluation demonstrates that TLP is effective independently of the L1D prefetching logic.</p>
FIGURE 10 in (Decapoda, Caridea, Hippolytidae), with the designation of a new genus and description of two new species from the Indo-West Pacific. Zootaxa, 2768, 32-54.
FIGURE 10. Alcyonohippolyte commensalis (Kemp, 1925) comb. nov., A–D, G, ovigerous female (pcl 2.5 mm); E, female (pcl. 1.8 mm); F, ovigerous female (plc. 2.1 mm) (QM W33475): A, B, carapace; C, telson; D, antennule; E–G, pereiopod II.
Fig. 4 in Rhinobatos sainsburyi n.sp. and Aptychotrema timorensis n.sp. -Two New Shovelnose Rays (Batoidea: Rhinobatidae) from the Eastern Indian Ocean
Fig. 4. Radiograph of the chondrocranium of the holotype of Rhinobatos sainsburyi n.sp. (CSIRO H.4041-04).
Fig. 2 in Rhinobatos sainsburyi n.sp. and Aptychotrema timorensis n.sp. -Two New Shovelnose Rays (Batoidea: Rhinobatidae) from the Eastern Indian Ocean
Fig. 2. Radiograph of the chondrocranium of the holotype of Aptychotrema timorensis n.sp. (CSIRO CA1258).
Figs 7-9 in Two new species of Molophilus CURTIS from Australia (Diptera, Limoniidae)
Figs 7-9: Molophilus (Molophilus) mediolobatus nov.sp., male hypopygium, (7-8) ventral view; (9) lateral view. Abbreviations as used in fig. 7: ae, aedeagus; ge, gonocoxite; ig, inner gonostylus; ml, medial lobe; og, outer gonostylus; t9, tergite 9; vl, ventral lobe.
Figs 1-3 in Two new species of Molophilus CURTIS from Australia (Diptera, Limoniidae)
Figs 1-3: Molophilus (Molophilus) drepanon nov.sp., male: (1) head and antenna, lateral; (2) thorax, dorsal; (3) thorax, lateral.
Figure 3 in Two new species of the genus Pahamunaya Schmid (Trichoptera: Polycentropodidae) from Vietnam Tatiana I. Arefina-Armitage and Brian J. Armitage
Figure 3. Pahamunaya khoii Oláh and Johanson, male. A) Head, dorsal. Genitalia: B) Lateral; C) Dorsal; D) Ventral; E) Left inferior appendage ventrolateral; F) Phallic apparatus, lateral.
Figure 1 in Two new species of the genus Pahamunaya Schmid (Trichoptera: Polycentropodidae) from Vietnam Tatiana I. Arefina-Armitage and Brian J. Armitage
Figure 1. Pahamunaya talon sp. n., male. A) Fore- and hind wings. Genitalia: B) Lateral; C) Dorsal; D) Ventral; E) Phallic apparatus, lateral.
ScienceDex guides
Understand access before you commit
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.