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Active particles in anisotropic, viscoelastic fluids experience competing stresses which guide their trajectories. An aligned suspension of particles can trigger a hydrodynamic bend instability, but the elasticity of the fluid can drive particle orientations back towards alignment. To study these competing effects, we examine a dilute suspension of active particles in an Ericksen-Leslie model nematic liquid crystal. For small active Ericksen number or particle concentration the suspension settles to an equilibrium state with uniform alignment. Beyond a critical active Ericksen number or particle concentration the suspension instead can buckle into a steady flowing state. Rather than entering the fully developed roiling state observed in isotropic fluids, however, the development is arrested by fluid elasticity. Arrested states of higher wavenumber emerge at yet higher values of active Ericksen number. If the active particles are motile, traveling waves emerge, including a traveling, oscillatory `thrashing' state. Moment equations are derived, compared to kinetic theory simulations, and analyzed in asymptotic limits which admit exact expressions for the traveling wave speed and both particle orientation and director fields in the arrested state., Comment: 7 figures

Large language models (LLMs) have unlocked a plethora of powerful applications at the network edge, such as intelligent personal assistants. Data privacy and security concerns have prompted a shift towards edge-based fine-tuning of personal LLMs, away from cloud reliance. However, this raises issues of computational intensity and resource scarcity, hindering training efficiency and feasibility. While current studies investigate parameter-efficient fine-tuning (PEFT) techniques to mitigate resource constraints, our analysis indicates that these techniques are not sufficiently resource-efficient for edge devices. To tackle these challenges, we propose Pluto and Charon (PAC), a time and memory efficient collaborative edge AI framework for personal LLMs fine-tuning. PAC breaks the resource wall of personal LLMs fine-tuning with a sophisticated algorithm-system co-design. (1) Algorithmically, PAC implements a personal LLMs fine-tuning technique that is efficient in terms of parameters, time, and memory. It utilizes Parallel Adapters to circumvent the need for a full backward pass through the LLM backbone. Additionally, an activation cache mechanism further streamlining the process by negating the necessity for repeated forward passes across multiple epochs. (2) Systematically, PAC leverages edge devices in close proximity, pooling them as a collective resource for in-situ personal LLMs fine-tuning, utilizing a hybrid data and pipeline parallelism to orchestrate distributed training. The use of the activation cache eliminates the need for forward pass through the LLM backbone,enabling exclusive fine-tuning of the Parallel Adapters using data parallelism. Extensive evaluation based on prototype implementation demonstrates that PAC remarkably outperforms state-of-the-art approaches, achieving up to 8.64x end-to-end speedup and up to 88.16% reduction in memory footprint., Comment: Accepted by The 53rd International Conference on Parallel Processing (ICPP'24)

Collusion between students in online exams is a major problem that undermines the integrity of the exam results. Although there exist methods that use exam data to identify pairs of students who have likely copied each other's answers, these methods are restricted to specific formats of multiple-choice exams. Here we present a statistical algorithm, Q-SID, that efficiently detects groups of students who likely have colluded, i.e., collusion groups, with error quantification. Q-SID uses graded numeric question scores only, so it works for many formats of multiple-choice and non-multiple-choice exams. Q-SID reports two false-positive rates (FPRs) for each collusion group: (1) empirical FPR, whose null data are from 36 strictly proctored exam datasets independent of the user-input exam data and (2) synthetic FPR, whose null data are simulated from a copula-based probabilistic model, which is first fitted to the user-input exam data and then modified to have no collusion. On 34 unproctored exam datasets, including two benchmark datasets with true positives and negatives verified by textural analysis, we demonstrate that Q-SID is a collusion detection algorithm with powerful and robust performance across exam formats, numbers of questions and students, and exam complexity.

Edge computing has recently emerged as a promising paradigm to boost the performance of distributed learning by leveraging the distributed resources at edge nodes. Architecturally, the introduction of edge nodes adds an additional intermediate layer between the master and workers in the original distributed learning systems, potentially leading to more severe straggler effect. Recently, coding theory-based approaches have been proposed for stragglers mitigation in distributed learning, but the majority focus on the conventional workers-master architecture. In this paper, along a different line, we investigate the problem of mitigating the straggler effect in hierarchical distributed learning systems with an additional layer composed of edge nodes. Technically, we first derive the fundamental trade-off between the computational loads of workers and the stragglers tolerance. Then, we propose a hierarchical gradient coding framework, which provides better stragglers mitigation, to achieve the derived computational trade-off. To further improve the performance of our framework in heterogeneous scenarios, we formulate an optimization problem with the objective of minimizing the expected execution time for each iteration in the learning process. We develop an efficient algorithm to mathematically solve the problem by outputting the optimum strategy. Extensive simulation results demonstrate the superiority of our schemes compared with conventional solutions., Comment: The paper has been accepted by IEEE Transactions on Communications

Federated learning (FL) has emerged as a promising paradigm that enables clients to collaboratively train a shared global model without uploading their local data. To alleviate the heterogeneous data quality among clients, artificial intelligence-generated content (AIGC) can be leveraged as a novel data synthesis technique for FL model performance enhancement. Due to various costs incurred by AIGC-empowered FL (e.g., costs of local model computation and data synthesis), however, clients are usually reluctant to participate in FL without adequate economic incentives, which leads to an unexplored critical issue for enabling AIGC-empowered FL. To fill this gap, we first devise a data quality assessment method for data samples generated by AIGC and rigorously analyze the convergence performance of FL model trained using a blend of authentic and AI-generated data samples. We then propose a data quality-aware incentive mechanism to encourage clients' participation. In light of information asymmetry incurred by clients' private multi-dimensional attributes, we investigate clients' behavior patterns and derive the server's optimal incentive strategies to minimize server's cost in terms of both model accuracy loss and incentive payments for both complete and incomplete information scenarios. Numerical results demonstrate that our proposed mechanism exhibits highest training accuracy and reduces up to 53.34% of the server's cost with real-world datasets, compared with existing benchmark mechanisms., Comment: The paper has been accepted by IEEE Transactions on Mobile Computing

The High Energy Photon Source (HEPS), is the first fourth-generation storage ring light source being built in the suburb of Beijing, China. The storage ring was designed with the emittance lower than 60 pm.rad with a circumference of 1.36 km and beam energy of 6 GeV. Its injector contains a 500 MeV S-band Linac and a 454 m booster which was designed as an accumulator at the extraction energy. In the energy ramping control design of HEPS booster, the ramping process was programed to be able to stop and stay at any energy between the injection energy and the extraction energy. This feature enables us to conduct energy-dependent machine studies and ramping curve optimization. The beam commissioning of HEPS Linac finished in June, 2023. And the beam commissioning of booster started in the end of July, 2023. In November 17, main target values proposed in the preliminary design report has been reached. The high-level applications (HLAs) are essential tools for beam commissioning. The development of HLAs, which are based on the framework named Python accelerator physics application set (Pyapas), started in the end of 2021. The HEPS physics team spent more than one year to develop and test the HLAs to meet the requirements of beam commissioning of the booster. Thanks to the modular design, the principle based on physical quantities, and the ability of running simulation models online from the Pyapas, the development efficiency and reliability of the HLAs have been greatly improved. In particular, the principle based on physical quantities allows us to control the beam more intuitively.

In the analysis of spatially resolved transcriptomics data, detecting spatially variable genes (SVGs) is crucial. Numerous computational methods exist, but varying SVG definitions and methodologies lead to incomparable results. We review 33 state-of-the-art methods, categorizing SVGs into three types: overall, cell-type-specific, and spatial-domain-marker SVGs. Our review explains the intuitions underlying these methods, summarizes their applications, and categorizes the hypothesis tests they use in the trade-off between generality and specificity for SVG detection. We discuss challenges in SVG detection and propose future directions for improvement. Our review offers insights for method developers and users, advocating for category-specific benchmarking.

Big data revolutionizes accounting and auditing, offering deep insights but also introducing challenges like data privacy and security. With data from IoT, social media, and transactions, traditional practices are evolving. Professionals must adapt to these changes, utilizing AI and machine learning for efficient data analysis and anomaly detection. Key to overcoming these challenges are enhanced analytics tools, continuous learning, and industry collaboration. By addressing these areas, the accounting and auditing fields can harness big data's potential while ensuring accuracy, transparency, and integrity in financial reporting. Keywords: Big Data, Accounting, Audit, Data Privacy, AI, Machine Learning, Transparency., Comment: 4 pages

This paper addresses the planar finger kinematics for seeking optimized manipulation strategies. The first step is to model based on geometric features of linear and rotation motion so that the robot can select the fingers configurations. This kinematic model considers the motion between hands and object. Based on 2-finger manipulation cases, this model can output the strategies for bimanual manipulation. For executing strategies, the second step is to seek the appropriate values of finger joints according to the ending orientation of fingers. The simulation shows that the computed solutions can complete the relative rotation and linear motion of unknown objects.

In single-cell RNA sequencing (scRNA-seq) analysis, a key challenge is inferring hidden cellular dynamics from static cell snapshots. Various computational methods have been developed to address this, focusing on perspectives like pseudotime trajectories, RNA velocities, and estimating the differentiation potential of cells, often referred to as "cell potency." This review summarizes 14 methods for defining cell potency from scRNA-seq data, categorizing them into average-based, entropy-based, and correlation-based methods based on how they summarize gene expression levels into a potency measure. We highlight the key similarities and differences within and between these categories, offering a high-level intuition for each method. Additionally, we use unified mathematical notations to detail each method's methodology and summarize their usage complexities, including parameters, required inputs, and differences between published descriptions and software implementations. We conclude that cell potency estimation remains an open question without a consensus on the optimal approach, emphasizing the need for benchmark datasets and studies. This review aims to provide a foundation for future benchmark studies, while also addressing the broader challenge of comparing methods that infer cellular dynamics from scRNA-seq data through various perspectives, including pseudotime trajectories, RNA velocities, and cell potency.

Privacy-preserving vector mean estimation is a crucial primitive in federated analytics. Existing practices usually resort to Local Differentiated Privacy (LDP) mechanisms that inject random noise into users' vectors when communicating with users and the central server. Due to the privacy-utility trade-off, the privacy budget has been widely recognized as the bottleneck resource that requires well-provisioning. In this paper, we explore the possibility of privacy budget recycling and propose a novel Chained-DP framework enabling users to carry out data aggregation sequentially to recycle the privacy budget. We establish a sequential game to model the user interactions in our framework. We theoretically show the mathematical nature of the sequential game, solve its Nash Equilibrium, and design an incentive mechanism with provable economic properties. We further derive a differentially privacy-guaranteed protocol to alleviate potential privacy collusion attacks to avoid holistic exposure. Our numerical simulation validates the effectiveness of Chained-DP, showing that it can significantly save privacy budget and lower estimation error compared to the traditional LDP mechanism., Comment: Insufficient completion

Deep learning classifiers are crucial in the age of artificial intelligence. The device-edge-based collaborative inference has been widely adopted as an efficient framework for promoting its applications in IoT and 5G/6G networks. However, it suffers from accuracy degradation under non-i.i.d. data distribution and privacy disclosure. For accuracy degradation, direct use of transfer learning and split learning is high cost and privacy issues remain. For privacy disclosure, cryptography-based approaches lead to a huge overhead. Other lightweight methods assume that the ground truth is non-sensitive and can be exposed. But for many applications, the ground truth is the user's crucial privacy-sensitive information. In this paper, we propose a framework of Roulette, which is a task-oriented semantic privacy-preserving collaborative inference framework for deep learning classifiers. More than input data, we treat the ground truth of the data as private information. We develop a novel paradigm of split learning where the back-end DNN is frozen and the front-end DNN is retrained to be both a feature extractor and an encryptor. Moreover, we provide a differential privacy guarantee and analyze the hardness of ground truth inference attacks. To validate the proposed Roulette, we conduct extensive performance evaluations using realistic datasets, which demonstrate that Roulette can effectively defend against various attacks and meanwhile achieve good model accuracy. In a situation where the non-i.i.d. is very severe, Roulette improves the inference accuracy by 21\% averaged over benchmarks, while making the accuracy of discrimination attacks almost equivalent to random guessing.

In recent years, recommender systems have advanced rapidly, where embedding learning for users and items plays a critical role. A standard method learns a unique embedding vector for each user and item. However, such a method has two important limitations in real-world applications: 1) it is hard to learn embeddings that generalize well for users and items with rare interactions on their own; and 2) it may incur unbearably high memory costs when the number of users and items scales up. Existing approaches either can only address one of the limitations or have flawed overall performances. In this paper, we propose Clustered Embedding Learning (CEL) as an integrated solution to these two problems. CEL is a plug-and-play embedding learning framework that can be combined with any differentiable feature interaction model. It is capable of achieving improved performance, especially for cold users and items, with reduced memory cost. CEL enables automatic and dynamic clustering of users and items in a top-down fashion, where clustered entities jointly learn a shared embedding. The accelerated version of CEL has an optimal time complexity, which supports efficient online updates. Theoretically, we prove the identifiability and the existence of a unique optimal number of clusters for CEL in the context of nonnegative matrix factorization. Empirically, we validate the effectiveness of CEL on three public datasets and one business dataset, showing its consistently superior performance against current state-of-the-art methods. In particular, when incorporating CEL into the business model, it brings an improvement of $+0.6\%$ in AUC, which translates into a significant revenue gain; meanwhile, the size of the embedding table gets $2650$ times smaller.