Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference

Latent Diffusion models (LDMs) have achieved remarkable results in synthesizing high-resolution images. However, the iterative sampling process is computationally intensive and leads to slow generation. Inspired by Consistency Models (song et al.), we propose Latent Consistency Models (LCMs), enabling swift inference with minimal steps on any pre-trained LDMs, including Stable Diffusion (rombach et al). Viewing the guided reverse diffusion process as solving an augmented probability flow ODE (PF-ODE), LCMs are designed to directly predict the solution of such ODE in latent space, mitigating the need for numerous iterations and allowing rapid, high-fidelity sampling. Efficiently distilled from pre-trained classifier-free guided diffusion models, a high-quality 768 x 768 2~4-step LCM takes only 32 A100 GPU hours for training. Furthermore, we introduce Latent Consistency Fine-tuning (LCF), a novel method that is tailored for fine-tuning LCMs on customized image datasets. Evaluation on the LAION-5B-Aesthetics dataset demonstrates that LCMs achieve state-of-the-art text-to-image generation performance with few-step inference. Project Page: https://latent-consistency-models.github.io

ChatDB: Augmenting LLMs with Databases as Their Symbolic Memory

Large language models (LLMs) with memory are computationally universal. However, mainstream LLMs are not taking full advantage of memory, and the designs are heavily influenced by biological brains. Due to their approximate nature and proneness to the accumulation of errors, conventional neural memory mechanisms cannot support LLMs to simulate complex reasoning. In this paper, we seek inspiration from modern computer architectures to augment LLMs with symbolic memory for complex multi-hop reasoning. Such a symbolic memory framework is instantiated as an LLM and a set of SQL databases, where the LLM generates SQL instructions to manipulate the SQL databases. We validate the effectiveness of the proposed memory framework on a synthetic dataset requiring complex reasoning. The project website is available at https://chatdatabase.github.io/

Large Trajectory Models are Scalable Motion Predictors and Planners

Motion prediction and planning are vital tasks in autonomous driving, and recent efforts have shifted to machine learning-based approaches. The challenges include understanding diverse road topologies, reasoning traffic dynamics over a long time horizon, interpreting heterogeneous behaviors, and generating policies in a large continuous state space. Inspired by the success of large language models in addressing similar complexities through model scaling, we introduce a scalable trajectory model called State Transformer (STR). STR reformulates the motion prediction and motion planning problems by arranging observations, states, and actions into one unified sequence modeling task. With a simple model design, STR consistently outperforms baseline approaches in both problems. Remarkably, experimental results reveal that large trajectory models (LTMs), such as STR, adhere to the scaling laws by presenting outstanding adaptability and learning efficiency. Qualitative results further demonstrate that LTMs are capable of making plausible predictions in scenarios that diverge significantly from the training data distribution. LTMs also learn to make complex reasonings for long-term planning, without explicit loss designs or costly high-level annotations

Adaptive Online Cache Capacity Optimization via Lightweight Working Set Size Estimation at Scale

Big data applications extensively use cache techniques to accelerate data access. A key challenge for improving cache utilization is provisioning a suitable cache size to fit the dynamic working set size (WSS) and understanding the related item repetition ratio (IRR) of the trace. We propose Cuki, an approximate data structure for efficiently estimating online WSS and IRR for variable-size item access with proven accuracy guarantee. Our solution is cache-friendly, thread-safe, and light-weighted in design. Based on that, we design an adaptive online cache capacity tuning mechanism. Moreover, Cuki can also be adapted to accurately estimate the cache miss ratio curve (MRC) online. We built Cuki as a lightweight plugin of the widely-used distributed file caching system Alluxio. Evaluation results show that Cuki has higher accuracy than four state-of-the-art algorithms by over an order of magnitude and with better stability in performance. The end-to-end data access experiments show that the adaptive cache tuning framework using Cuki reduces the table querying latency by 79% and improves the file reading throughput by 29% on average. Compared with the cutting-edge MRC approach, Cuki uses less memory and improves accuracy by around 73% on average. Cuki is deployed on one of the world’s largest social platforms to run the Presto query workloads

Association of diet and lifestyle factors with semen quality in male partners of Chinese couples preparing for pregnancy

Abstract Background Semen quality significantly influences conception, and its preservation is crucial for couples seeking pregnancy. We investigated dietary and lifestyle risk factors impacting semen quality. Methods A total of 466 males from the Guangzhou Women and Children’s Medical Center’s pre-pregnancy consultation clinic were recruited between January 2021 and March 2023 for inclusion. Semen analysis was performed, and diet and lifestyle data were gathered via questionnaire. Logistic regression was utilized to examine the link between diet, lifestyle variables, and semen quality. Results Smoking worsened progressive sperm motility (38.0% vs. 36.0%, t = 2.262; P = 0.049). Alcohol consumption impaired progressive motility (40.5 ± 17.8% vs. 34.7 ± 16.1%, t = 3.396; P < 0.001) and total motility (56.0% vs. 64.0%; P = 0.001). Using plastic beverage bottles for oil or seasonings lowered sperm concentrations (40.4% vs. 59.0% vs. 65.5%; P = 0.032). A sweet diet correlated with higher total sperm motility (55.0% vs. 60.0%, 62.0% vs. 63.2%; P = 0.017). Higher milk product intake improved sperm concentration (41.610 6 vs. 63.7106 vs. 66.1*106; P = 0.021) and motility (54.5% vs. 56.0% vs. 63.0%; P = 0.033). More frequent egg consumption increased semen volume (3.1 mL vs. 3.8 mL vs. 4.0 mL; P = 0.038). Roughage intake enhanced sperm concentration (160.810 6 vs. 224.6106; P = 0.027), and adequate sleep improved progressive sperm motility rate (35.4% ± 18.2% vs. 40.2 ± 16.3%, F = 3.747; P = 0.024) and total motility (52.7% vs. 61.5%; P = 0.013). The regression model showed that using plastic containers for condiments was a protective factor for semen volume (OR: 0.12; CI 0.03–0.55; P = 0.006), sperm concentration (OR: 0.001, CI 0.00–0.30; P = 0.012), and count (OR: 0.12, CI 0.03–0.48; P = 0.003). Milk and egg consumption were also protective for semen volume (OR: 0.18, CI 0.06–0.51; P = 0.001 and OR: 0.11, CI 0.03–0.55; P = 0.006, respectively), while sufficient sleep benefitted total sperm motility (OR: 0.47, CI 0.24–0.95; P = 0.034). Conclusions Smoking and drinking, type of condiment container, diet preference, sleep duration, and milk, roughage, and egg consumption may reduce semen quality

Homology with Vesicle Fusion Mediator Syntaxin-1a Predicts Determinants of Epimorphin/Syntaxin-2 Function in Mammary Epithelial Morphogenesis*S⃞

We have shown that branching morphogenesis of mammary ductal structures requires the action of the morphogen epimorphin/syntaxin-2. Epimorphin, originally identified as an extracellular molecule, is identical to syntaxin-2, an intracellular molecule that is a member of the extensively investigated syntaxin family of proteins that mediate vesicle trafficking. We show here that, although epimorphin/syntaxin-2 is highly homologous to syntaxin-1a, only epimorphin/syntaxin-2 can stimulate mammary branching morphogenesis. We construct a homology model of epimorphin/syntaxin-2 based on the published structure of syntaxin-1a, and we use this model to identify the structural motif responsible for the morphogenic activity. We identify four residues located within the cleft between helices B and C that differ between syntaxin-1a and epimorphin/syntaxin-2; through site-directed mutagenesis of these four amino acids, we confer the properties of epimorphin for cell adhesion, gene activation, and branching morphogenesis onto the inactive syntaxin-1a template. These results provide a dramatic demonstration of the use of structural information about one molecule to define a functional motif of a second molecule that is related at the sequence level but highly divergent functionally