[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