294 research outputs found
[5-Chloro-2-hydroxy-N′-(2-oxidobenzylidene)benzohydrazidato]dimethyltin(IV)
In the title compound, [Sn(CH3)2(C14H9ClN2O3)], the SnIV ion is coordinated by one N and two O atoms from the tridentate 5-chloro-2-hydroxy-N′-(2-oxidobenzylidene)benzohydrazidate (L) ligand and two methyl groups in a distorted trigonal–bipyramidal geometry. In the ligand, the hydroxy group is involved in an intramolecular O—H⋯N hydrogen bond and the two aromatic rings form a dihedral angle of 5.5 (1)°. In the crystal, weak intermolecular C—H⋯O hydrogen bonds and π–π interactions between the aromatic rings [centroid–centroid distance = 3.816 (3) Å] link the molecules into centrosymmetric dimers
Di-μ-methanolato-κ4 O:O-bis[trichlorido(dimethylformamide-κO)tin(IV)]
The title compound, [Sn2(CH3O)2Cl6(C3H7NO)2], contains two hexacoordinated SnIV atoms symmetrically bridged by two deprotonated methanol ligands, with an inversion center in the middle of the planar Sn2O2 ring. The other sites of the distorted octahedral coordination geometry of the SnIV atom are occupied by three Cl atoms and one O atom from a dimethylformamide molecule. The complex molecules are connected by weak C—H⋯Cl hydrogen bonds into a two-dimensional supramolecular network parallel to (10)
3-Hydroxy-N′-[(E)-3-pyridylmethylidene]-2-naphthohydrazide
The title compound, C17H13N3O2, displays an E configuration about the C=N bond. The mean planes of the pyridine and benzene rings make a dihedral angle of 31.2 (2)°. An intramolecular O—H⋯O hydrogen bond is observed. In the crystal, intermolecular N—H⋯N hydrogen bonding links the molecules into a chain along [101]
μ-2-Aminoterephthalato-κ2 O 1:O 4-bis[triphenyltin(IV)]
The title compound, [Sn2(C6H5)6(C8H5NO4)], contains two triphenyltin groups bridged by a 2-aminoterephthalate ligand. The two SnIV centers have similar distorted tetrahedral coordination geometries. Each SnIV atom is bonded to three phenyl C atoms and one O atom from a carboxylate group. The other O atom of the carboxylate group has a weak interaction with the Sn atom. The amino group is disordered over two sites, with site-occupancy factors of 0.779 (11) and 0.221 (11). Intramolecular N—H⋯O hydrogen bonds are observed
ShareJIT: JIT Code Cache Sharing across Processes and Its Practical Implementation
Just-in-time (JIT) compilation coupled with code caching are widely used to
improve performance in dynamic programming language implementations. These code
caches, along with the associated profiling data for the hot code, however,
consume significant amounts of memory. Furthermore, they incur extra JIT
compilation time for their creation. On Android, the current standard JIT
compiler and its code caches are not shared among processes---that is, the
runtime system maintains a private code cache, and its associated data, for
each runtime process. However, applications running on the same platform tend
to share multiple libraries in common. Sharing cached code across multiple
applications and multiple processes can lead to a reduction in memory use. It
can directly reduce compile time. It can also reduce the cumulative amount of
time spent interpreting code. All three of these effects can improve actual
runtime performance.
In this paper, we describe ShareJIT, a global code cache for JITs that can
share code across multiple applications and multiple processes. We implemented
ShareJIT in the context of the Android Runtime (ART), a widely used,
state-of-the-art system. To increase sharing, our implementation constrains the
amount of context that the JIT compiler can use to optimize the code. This
exposes a fundamental tradeoff: increased specialization to a single process'
context decreases the extent to which the compiled code can be shared. In
ShareJIT, we limit some optimization to increase shareability. To evaluate the
ShareJIT, we tested 8 popular Android apps in a total of 30 experiments.
ShareJIT improved overall performance by 9% on average, while decreasing memory
consumption by 16% on average and JIT compilation time by 37% on average.Comment: OOPSLA 201
Harnessing the Spatial-Temporal Attention of Diffusion Models for High-Fidelity Text-to-Image Synthesis
Diffusion-based models have achieved state-of-the-art performance on
text-to-image synthesis tasks. However, one critical limitation of these models
is the low fidelity of generated images with respect to the text description,
such as missing objects, mismatched attributes, and mislocated objects. One key
reason for such inconsistencies is the inaccurate cross-attention to text in
both the spatial dimension, which controls at what pixel region an object
should appear, and the temporal dimension, which controls how different levels
of details are added through the denoising steps. In this paper, we propose a
new text-to-image algorithm that adds explicit control over spatial-temporal
cross-attention in diffusion models. We first utilize a layout predictor to
predict the pixel regions for objects mentioned in the text. We then impose
spatial attention control by combining the attention over the entire text
description and that over the local description of the particular object in the
corresponding pixel region of that object. The temporal attention control is
further added by allowing the combination weights to change at each denoising
step, and the combination weights are optimized to ensure high fidelity between
the image and the text. Experiments show that our method generates images with
higher fidelity compared to diffusion-model-based baselines without fine-tuning
the diffusion model. Our code is publicly available at
https://github.com/UCSB-NLP-Chang/Diffusion-SpaceTime-Attn.Comment: 20 pages, 16 figure
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