In-vitro blood purification using tiny pinch holographic optical tweezers based on deep learning

In-vitro blood purification is essential to a wide range of medical treatments, requiring fine-grained analysis and precise separation of blood components. Despite existing methods that can extract specific components from blood by size or by magnetism, there is not yet a general approach to efficiently filter blood components on demand. In this work, we introduce the first programmable non-contact blood purification system for accurate blood component detection and extraction. To accurately identify different cells and artificial particles in the blood, we collected and annotated a new blood component object detection dataset and trained a collection of deep-learning-based object detectors upon it. To precisely capture and extract desired blood components, we fabricated a microfluidic chip and set up a customized holographic optical tweezer to trap and move cells/particles in the blood. Empirically, we demonstrate that our proposed system can perform real-time blood fractionation with high precision reaching up to 96.89%, as well as high efficiency. Its scalability and flexibility open new research directions in blood treatment

Question Selection for Multimodal Code Search Synthesis Using Probabilistic Version Spaces

Searching the occurrences of specific code patterns (code search) is a common task in software engineering, and programming by example (PBE) techniques have been applied to ease customizing code patterns. However, previous PBE tools only synthesize programs meeting the input-output examples, which may not always align with the user intent. To bridge this gap, this paper proposes Excalibur, a multi-modal (example and natural language description) and interactive synthesizer for code search. Excalibur ensures that the generated programs are correct for the provided examples (soundness) and include the user-intended program (bounded completeness). Furthermore, Excalibur helps the user identify the user-intended program through question-answer interaction. To minimize the required interaction efforts, question selection is crucial. To improve question selection for code search, we propose probabilistic version spaces (ProbVS), in which the user-intended program’s probability is high and others are low. ProbVS combines traditional version spaces for compactly representing extensive programs and large language models (on the user-provided natural language description) for adjusting programs’ probabilities to align with users’ intents. Extensive experiments on a benchmark of 44 tasks demonstrated the effectiveness of Excalibur and ProbVS and demystified how ProbVS affects probability distributions and how the configurable parameters affect ProbVS

Mediating Proximate Care in Transnational Families in Sweden and the UK: Language Practices and Institutional Processes

The paper advances our understanding of care in transnational families by exploring how proximate family members engage in care within two institutional contexts, a school and a hospital. It considers how care processes and outcomes are shaped by the transnational character of families and by the related power dynamics inherent within families and institutions. It does so by studying language and literacy practices that people engage in when they act as language brokers and literacy mediators for family members who are accessing care. Working with two families in the United Kingdom and Sweden, our analysis draws on fieldnotes, interviews with caregivers, and interactional data. We describe the language and literacy practices and interactional events associated with our participants' institutional encounters, relating them to individuals' intersecting positionalities. Analysis demonstrates the ways in which these practices enable them to challenge inequalities inherent in health and educational systems. © 2025 The Author(s). Population, Space and Place published by John Wiley & Sons Ltd

A Tale of Two DL Cities: When Library Tests Meet Compiler

Deep Learning (DL) compilers typically load a DL model and optimize it with intermediate representation. Existing DL compiler testing techniques mainly focus on model optimization stages, but rarely explore bug detection at the model loading stage. Effectively testing the model loading stage requires covering diverse usages of each DL operator from various DL libraries, which shares a common objective with DL library testing, indicating that the embedded knowledge in DL library tests is beneficial for testing the model loading stage of DL compilers. With this idea, we propose Opera to migrate the knowledge embedded in DL library tests to test the model loading stage. Opera constructs diverse tests from various tests for DL libraries (including the tests documented in DL libraries and those generated by recent fuzzers). In total, we considered three sources of tests in DL libraries for migration. In addition, it incorporates a diversity-based test prioritization strategy to migrate and execute those tests that are more likely to detect diverse bugs earlier. We then used eight frontends from three DL compilers (e.g., TVM, TensorRT, and OpenVINO) for evaluation. OPERA detected 170 previously unknown bugs in total, 90 of which have been confirmed/fixed by developers, demonstrating the effectiveness of such the migration-based idea. The test prioritization strategy in OPERA improves testing efficiency with migrated tests by 11.9%∼47.4% on average compared to general test prioritization strategies

Data-driven management of on-demand transportation services

On-demand transportation services, such as ride-sourcing, bike-sharing, and food delivery, have rapidly expanded globally, transforming the way people access transportation and services. These platforms are centrally managed, enabling real-time data collection and analysis of supply and demand. However, despite the efficiency these platforms offer, they face significant operational challenges due to the dynamic nature of demand, spatial-temporal imbalances, and the need for accurate, real-time decision-making. Traditional methods based on historical data often fail to account for the inherent uncertainty in usage patterns, limiting the long-term effectiveness of operational strategies. This thesis addresses these challenges by proposing data-driven methods to improve the operational efficiency of on-demand services. The central approach integrates historical data and real-time information to predict demand more accurately, account for uncertainties, and optimize resource allocation. By leveraging the rich data sources available through GPS and smartphone applications, platforms can develop forward-looking strategies that enhance system performance. The research is structured around two key phases: First, historical data is used to predict future market conditions, enabling accurate demand forecasting and the development of models that capture usage patterns and uncertainties. Second, these predictive insights are combined with real-time data to guide platform operations proactively. This allows for the implementation of dynamic operational strategies, such as incentive mechanisms, repositioning techniques, and dynamic pricing, to optimize supply allocation and balance demand, addressing the spatial-temporal imbalances that often hinder service efficiency and user satisfaction. The thesis consists of four studies that address these challenges. The first study introduces a deep learning model for usage prediction and reliability analysis, aiming to enhance forecasting accuracy and account for uncertainties in demand. The second study investigates the integration of autonomous vehicles (AVs) into ride-sourcing services for short-distance trips, leveraging differential pricing and expanding supply with AVs to balance supply and demand. The third study focuses on optimizing supply distribution in ebike-sharing systems through battery swapping and rebalancing services. Finally, the fourth study explores incentive mechanisms to encourage participation in food delivery services, thereby increasing capacity and improving the system's ability to meet fluctuating demand.</p

Evaluating the influence of relic neutrinos on galaxy cluster evolution with cosmological simulations

In this thesis, the effects of massive relic neutrinos on the two-point correlation functions of galaxy clusters are studied. By performing large-volume cluster formation simulations with relic neutrinos incorporated, large simulated samples of clusters are generated to study the sensitivity of cluster correlations to neutrino mass. The number of simulated clusters is comparable to the data expected to be observed by DESI and EUCLID, and our results serve as predictions for these surveys. An extended ΛCDM model with refitted parameters that includes Ων is used, as well as a novel method that treats Ων as a perturbation. We find that correlations are enhanced with increasing neutrino mass by the Kaiser bias predicted for a Gaussian random field at fixed cluster mass limits [1], while cluster abundance is reduced which corresponds to rarer peaks of an overdensity field. The evolution of cluster number density under various relic neutrino masses is predicted by our simulations, and compared with SDSS and HSC survey data. These results collectively provide comprehensive sensitivity to relic neutrino mass anticipated for ongoing high-redshift surveys.</p

EnsDiff : ensemble precipitation nowcasting with diffusion

Operational numerical weather prediction precipitation nowcasting usually considers forecast reliability by utilizing an ensemble of model forecasts. Existing data-driven methods often optimize MSE deterministically or resort to probabilistic forecasting with generative models. However, they only emphasize the optimization of the point forecast metrics, which makes it challenging to balance the trade-off between the optimization of accuracy and uncertainty. Human experts can hardly make an appropriate decision with an ensemble forecast when forecast calibration and sharpness are not considered. In this thesis, we propose EnsDiff, which models the probability distribution to produce ensemble diffusion predictions. Not only does it outperform the SOTA model on a proper scoring rule, Continuous Ranked Probability Score (CRPS), but it also outperforms other models on the deterministic metrics. Extensive experiments show that EnsDiff can enhance probabilistic, deterministic skills, and perceptual quality, outperforming state-of-the-art models.</p