LERC: Coordinated Cache Management for Data-Parallel Systems

Memory caches are being aggressively used in today's data-parallel frameworks such as Spark, Tez and Storm. By caching input and intermediate data in memory, compute tasks can witness speedup by orders of magnitude. To maximize the chance of in-memory data access, existing cache algorithms, be it recency- or frequency-based, settle on cache hit ratio as the optimization objective. However, unlike the conventional belief, we show in this paper that simply pursuing a higher cache hit ratio of individual data blocks does not necessarily translate into faster task completion in data-parallel environments. A data-parallel task typically depends on multiple input data blocks. Unless all of these blocks are cached in memory, no speedup will result. To capture this all-or-nothing property, we propose a more relevant metric, called effective cache hit ratio. Specifically, a cache hit of a data block is said to be effective if it can speed up a compute task. In order to optimize the effective cache hit ratio, we propose the Least Effective Reference Count (LERC) policy that persists the dependent blocks of a compute task as a whole in memory. We have implemented the LERC policy as a memory manager in Spark and evaluated its performance through Amazon EC2 deployment. Evaluation results demonstrate that LERC helps speed up data-parallel jobs by up to 37% compared with the widely employed least-recently-used (LRU) policy

Flow Level QoE of Video Streaming in Wireless Networks

The Quality of Experience (QoE) of streaming service is often degraded by frequent playback interruptions. To mitigate the interruptions, the media player prefetches streaming contents before starting playback, at a cost of delay. We study the QoE of streaming from the perspective of flow dynamics. First, a framework is developed for QoE when streaming users join the network randomly and leave after downloading completion. We compute the distribution of prefetching delay using partial differential equations (PDEs), and the probability generating function of playout buffer starvations using ordinary differential equations (ODEs) for CBR streaming. Second, we extend our framework to characterize the throughput variation caused by opportunistic scheduling at the base station, and the playback variation of VBR streaming. Our study reveals that the flow dynamics is the fundamental reason of playback starvation. The QoE of streaming service is dominated by the first moments such as the average throughput of opportunistic scheduling and the mean playback rate. While the variances of throughput and playback rate have very limited impact on starvation behavior.Comment: 14 page

MUBen: Benchmarking the Uncertainty of Pre-Trained Models for Molecular Property Prediction

Large Transformer models pre-trained on massive unlabeled molecular data have shown great success in predicting molecular properties. However, these models can be prone to overfitting during fine-tuning, resulting in over-confident predictions on test data that fall outside of the training distribution. To address this issue, uncertainty quantification (UQ) methods can be used to improve the models' calibration of predictions. Although many UQ approaches exist, not all of them lead to improved performance. While some studies have used UQ to improve molecular pre-trained models, the process of selecting suitable backbone and UQ methods for reliable molecular uncertainty estimation remains underexplored. To address this gap, we present MUBen, which evaluates different combinations of backbone and UQ models to quantify their performance for both property prediction and uncertainty estimation. By fine-tuning various backbone molecular representation models using different molecular descriptors as inputs with UQ methods from different categories, we critically assess the influence of architectural decisions and training strategies. Our study offers insights for selecting UQ and backbone models, which can facilitate research on uncertainty-critical applications in fields such as materials science and drug discovery

SCULPTOR: Skeleton-Consistent Face Creation Using a Learned Parametric Generator

Recent years have seen growing interest in 3D human faces modelling due to its wide applications in digital human, character generation and animation. Existing approaches overwhelmingly emphasized on modeling the exterior shapes, textures and skin properties of faces, ignoring the inherent correlation between inner skeletal structures and appearance. In this paper, we present SCULPTOR, 3D face creations with Skeleton Consistency Using a Learned Parametric facial generaTOR, aiming to facilitate easy creation of both anatomically correct and visually convincing face models via a hybrid parametric-physical representation. At the core of SCULPTOR is LUCY, the first large-scale shape-skeleton face dataset in collaboration with plastic surgeons. Named after the fossils of one of the oldest known human ancestors, our LUCY dataset contains high-quality Computed Tomography (CT) scans of the complete human head before and after orthognathic surgeries, critical for evaluating surgery results. LUCY consists of 144 scans of 72 subjects (31 male and 41 female) where each subject has two CT scans taken pre- and post-orthognathic operations. Based on our LUCY dataset, we learn a novel skeleton consistent parametric facial generator, SCULPTOR, which can create the unique and nuanced facial features that help define a character and at the same time maintain physiological soundness. Our SCULPTOR jointly models the skull, face geometry and face appearance under a unified data-driven framework, by separating the depiction of a 3D face into shape blend shape, pose blend shape and facial expression blend shape. SCULPTOR preserves both anatomic correctness and visual realism in facial generation tasks compared with existing methods. Finally, we showcase the robustness and effectiveness of SCULPTOR in various fancy applications unseen before.Comment: 16 page, 13 fig

Enhanced Gene Transfection Efficacy and Safety Through Granular Hydrogel Mediated Gene Delivery Process

Although gene therapy has made great achievements in both laboratory research and clinical translation, there are still challenges such as limited control of drug pharmacokinetics, acute toxicity, poor tissue retention, insufficient efficacy, and inconsistent clinical translation. Herein, a gene therapy gel is formulated by directly redispersing polyplex nanoparticles into granular hydrogels without any gelation pre-treatment, which provides great convenience for storage, dosing and administration. In vitro studies have shown that use of granular hydrogels can regulate the gene drug release, reduce dose dependent toxicity and help improve transfection efficacy. Moreover, the developed gene therapy gel is easy to operate and can be directly used in vitro to evaluate its synergistic efficacy with various gene delivery systems. As such, it represents a major advance over many conventional excipient-based formulations, and new regulatory strategies for gene therapy may be inspired by it

Mortality Prediction with Adaptive Feature Importance Recalibration for Peritoneal Dialysis Patients

The study aims to develop AICare, an interpretable mortality prediction model, using Electronic Medical Records (EMR) from follow-up visits for End-Stage Renal Disease (ESRD) patients. AICare includes a multi-channel feature extraction module and an adaptive feature importance recalibration module. It integrates dynamic records and static features to perform a personalized health context representation learning. The dataset encompasses 13,091 visits and demographic data of 656 peritoneal dialysis (PD) patients spanning 12 years. An additional public dataset of 4,789 visits from 1,363 hemodialysis (HD) patients is also considered. AI Care outperforms traditional deep learning models in mortality prediction while retaining interpretability. It uncovers mortality-feature relationships, variations in feature importance, and provides reference values. An AI-Doctor interaction system is developed for visualizing patients’ health trajectories and risk indicators

Bilateral heterochronic spontaneous hemothorax caused by pulmonary arteriovenous malformation in a gravid: A case report

Bilateral heterochronic spontaneous hemothorax as a result of pulmonary ateriovenous malformation is a very rarely happened disease. A 34-year-old woman presented major symptoms with right-sided chest pain and shortness of breath. The following contrast-enhanced computed tomographic scan of the chest showed a large amount of fluid in the right thorax with mediastinal shift, but without major vessel injury and 2 small dense opacities in the apical segment of the right lower lobe and in the posterior aspect of the left lower lobe. The patient underwent local resection of the right lower lobe. The pulmonary ateriovenous malformation was further identified by pathological examination. One month after she was discharged home, the symptoms described above recurred. A follow-up computed tomographic scan of the chest showed a large amount of fluid in the left thorax. During the emergency operation, we found a bullous lesion in the left lower lobe and a small blood vessel overlying the lesion that was actively bleeding. As stated above, local resection of the left lower lobe was performed once more. Pathological result was the same as observed previously. There were no postoperative complications and she was discharged from the hospital after two weeks. Two months later, she successfully delivered a healthy female infant. Up to now, regular follow-up observation has shown her to be perfectly asymptomatic