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Despite recent advances in artificial cilia technologies, the application of metachrony, which is the collective wavelike motion by cilia moving out-of-phase, has been severely hampered by difficulties in controlling closely packed artificial cilia at micrometer length scales. Moreover, there has been no direct experimental proof yet that a metachronal wave in combination with fully reciprocal ciliary motion can generate significant microfluidic flow on a micrometer scale as theoretically predicted. In this study, using an in-house developed precise micro-molding technique, we have fabricated closely packed magnetic artificial cilia that can generate well-controlled metachronal waves. We studied the effect of pure metachrony on fluid flow by excluding all symmetry-breaking ciliary features. Experimental and simulation results prove that net fluid transport can be generated by metachronal motion alone, and the effectiveness is strongly dependent on cilia spacing. This technique not only offers a biomimetic experimental platform to better understand the mechanisms underlying metachrony, it also opens new pathways towards advanced industrial applications.

The fast development of flexible and wearable electronics increases the demand for flexible secondary batteries, and the emerging high-performance K-ion batteries (KIBs) have shown immense promise for the flexible electronics due to the abundant and cost-effective potassium resources. However, the implementation of flexible cathodes for KIBs is hampered by the critical issues of low capacity, rapid capacity decay with cycles, and limited initial Coulombic efficiency. To address these pressing issues, a freestanding K-rich iron hexacyanoferrate/carbon cloth (KFeHCF/CC) electrode is designed and fabricated by cathodic deposition. This innovative binder-free and self-supporting KFeHCF/CC electrode not only provides continuous conductive channels for electrons, but also accelerates the diffusion of potassium ions through the active electrode-electrolyte interface. Moreover, the nanosized potassium iron hexacyanoferrate particles limit particle fracture and pulverization to preserve the structure and stability during cycling. As a result, the K-rich KFeHCF/CC electrode shows a reversible discharging capacity of 110.1 mAh g-1 at 50 mA g-1 after 100 cycles in conjunction with capacity retention of 92.3% after 1000 cycles at 500 mA g-1. To demonstrate the commercial feasibility, a flexible tubular KIB is assembled with the K-rich KFeHCF/CC electrode, and excellent flexibility, capacity, and stability are observed. In this study, a flexible and freestanding K-rich KFeHCF/CC electrode is prepared by cathodic deposition to form the cathode in a flexible K-ion battery (KIB). The flexible and wearable tubular KIB constructed with the potassium-rich iron hexacyanoferrate cathode shows excellent flexibility, high capacity, and good stability. The results show that the electrode materials have a large potential in flexible potassium-ion energy storage devices.image

BACKGROUND: Circulating total insulin-like growth factor-I (IGF-I) is an established risk factor for prostate cancer. However, only a small proportion of circulating IGF-I is free or readily dissociable from IGF-binding proteins (its bioavailable form), and few studies have investigated the association of circulating free IGF-I with prostate cancer risk. METHODS: We analyzed data from 767 prostate cancer cases and 767 matched controls nested within the European Prospective Investigation into Cancer and Nutrition cohort, with an average of 14-years (interquartile range = 2.9) follow-up. Matching variables were study center, length of follow-up, age, and time of day and fasting duration at blood collection. Circulating free IGF-I concentration was measured in serum samples collected at recruitment visit (mean age 55 years old; standard deviation = 7.1) using an enzyme-linked immunosorbent assay (ELISA). Conditional logistic regressions were performed to examine the associations of free IGF-I with risk of prostate cancer overall and subdivided by time to diagnosis (≤ 14 and > 14 years), and tumor characteristics. RESULTS: Circulating free IGF-I concentrations (in fourths and as a continuous variable) were not associated with prostate cancer risk overall (odds ratio [OR] = 1.00 per 0.1 nmol/L increment, 95% CI: 0.99, 1.02) or by time to diagnosis, or with prostate cancer subtypes, including tumor stage and histological grade. CONCLUSIONS: Estimated circulating free IGF-I was not associated with prostate cancer risk. Further research may consider other assay methods that estimate bioavailable IGF-I to provide more insight into the well-substantiated association between circulating total IGF-I and subsequent prostate cancer risk.

Cilia are slender, hair-like cell protrusions that are present ubiquitously in the natural world. They perform essential functions, such as generating fluid flow, propulsion, and feeding, in organisms ranging from protozoa to the human body. The coordinated beating of cilia, which results in wavelike motions known as metachrony, has fascinated researchers for decades for its role in functions such as flow generation and mucus transport. Inspired by nature, researchers have explored diverse materials for the fabrication of artificial cilia and developed several methods to mimic the metachronal motion observed in their biological counterparts. In this review, we will introduce the different types of metachronal motion generated by both biological and artificial cilia, the latter including pneumatically, photonically, electrically, and magnetically driven artificial cilia. Furthermore, we review the possible applications of metachronal motion by artificial cilia, focusing on flow generation, transport of mucus, particles, and droplets, and microrobotic locomotion. The overall aim of this review is to offer a comprehensive overview of the metachronal motions exhibited by diverse artificial cilia and the corresponding practical implementations. Additionally, we identify the potential future directions within this field. These insights present an exciting opportunity for further advancements in this domain.

Validating and controlling safety-critical systems in uncertain environments necessitates probabilistic reachable sets of future state evolutions. The existing methods of computing probabilistic reachable sets normally assume that the uncertainties are independent of the state. However, this assumption falls short in many real-world applications, where uncertainties are state-dependent, referred to as contextual uncertainties. This paper formulates the problem of computing probabilistic reachable sets of stochastic nonlinear states with contextual uncertainties by seeking minimum-volume polynomial sublevel sets with contextual chance constraints. The formulated problem cannot be solved by the existing sample-based approximation method since the existing methods do not consider the conditional probability densities. To address this, we propose a consistent sample approximation of the original problem by leveraging the conditional density estimation and resampling. The obtained approximate problem is a tractable optimization problem. Additionally, we prove the almost uniform convergence of the proposed sample-based approximation, showing that it gives the optimal solution almost consistently with the original ones. Through a numerical example, we evaluate the effectiveness of the proposed method against existing approaches, highlighting its capability to significantly reduce the bias inherent in sample-based approximation without considering a conditional probability density.

Low-rank adaptation (LoRA) and its variants are widely employed in fine-tuning large models, including large language models for natural language processing and diffusion models for computer vision. This paper proposes a generalized framework called SuperLoRA that unifies and extends different LoRA variants, which can be realized under different hyper-parameter settings. Introducing grouping, folding, shuffling, projecting, and tensor factoring, SuperLoRA offers high flexibility compared with other LoRA variants and demonstrates superior performance for transfer learning tasks especially in the extremely few-parameter regimes., Comment: 33 pages, 29 figures

High-level synthesis (HLS) is a design flow that leverages modern language features and flexibility, such as complex data structures, inheritance, templates, etc., to prototype hardware designs rapidly. However, exploring various design space parameters can take much time and effort for hardware engineers to meet specific design specifications. This paper proposes a novel framework called AutoHLS, which integrates a deep neural network (DNN) with Bayesian optimization (BO) to accelerate HLS hardware design optimization. Our tool focuses on HLS pragma exploration and operation transformation. It utilizes integrated DNNs to predict synthesizability within a given FPGA resource budget. We also investigate the potential of emerging quantum neural networks (QNNs) instead of classical DNNs for the AutoHLS pipeline. Our experimental results demonstrate up to a 70-fold speedup in exploration time., Comment: 5 pages, 6 figures, MWSCAS 2023

Personalized text-to-image (T2I) models not only produce lifelike and varied visuals but also allow users to tailor the images to fit their personal taste. These personalization techniques can grasp the essence of a concept through a collection of images, or adjust a pre-trained text-to-image model with a specific image input for subject-driven or attribute-aware guidance. Yet, accurately capturing the distinct visual attributes of an individual image poses a challenge for these methods. To address this issue, we introduce OSTAF, a novel parameter-efficient one-shot fine-tuning method which only utilizes one reference image for T2I personalization. A novel hypernetwork-powered attribute-focused fine-tuning mechanism is employed to achieve the precise learning of various attribute features (e.g., appearance, shape or drawing style) from the reference image. Comparing to existing image customization methods, our method shows significant superiority in attribute identification and application, as well as achieves a good balance between efficiency and output quality.

Personalized federated learning becomes a hot research topic that can learn a personalized learning model for each client. Existing personalized federated learning models prefer to aggregate similar clients with similar data distribution to improve the performance of learning models. However, similaritybased personalized federated learning methods may exacerbate the class imbalanced problem. In this paper, we propose a novel Dynamic Affinity-based Personalized Federated Learning model (DA-PFL) to alleviate the class imbalanced problem during federated learning. Specifically, we build an affinity metric from a complementary perspective to guide which clients should be aggregated. Then we design a dynamic aggregation strategy to dynamically aggregate clients based on the affinity metric in each round to reduce the class imbalanced risk. Extensive experiments show that the proposed DA-PFL model can significantly improve the accuracy of each client in three real-world datasets with state-of-the-art comparison methods.

In this paper, we propose \textbf{UniCode}, a novel approach within the domain of multimodal large language models (MLLMs) that learns a unified codebook to efficiently tokenize visual, text, and potentially other types of signals. This innovation addresses a critical limitation in existing MLLMs: their reliance on a text-only codebook, which restricts MLLM's ability to generate images and texts in a multimodal context. Towards this end, we propose a language-driven iterative training paradigm, coupled with an in-context pre-training task we term ``image decompression'', enabling our model to interpret compressed visual data and generate high-quality images.The unified codebook empowers our model to extend visual instruction tuning to non-linguistic generation tasks. Moreover, UniCode is adaptable to diverse stacked quantization approaches in order to compress visual signals into a more compact token representation. Despite using significantly fewer parameters and less data during training, Unicode demonstrates promising capabilities in visual reconstruction and generation. It also achieves performances comparable to leading MLLMs across a spectrum of VQA benchmarks., Comment: 14 pages, 2 figures, 11 tables

When completing knowledge-intensive tasks, humans sometimes need not just an answer but also a corresponding reference passage for auxiliary reading. Previous methods required obtaining pre-segmented article chunks through additional retrieval models. This paper explores leveraging the parameterized knowledge stored during the pre-training phase of large language models (LLMs) to independently recall reference passage from any starting position. We propose a two-stage framework that simulates the scenario of humans recalling easily forgotten references. Initially, the LLM is prompted to recall document title identifiers to obtain a coarse-grained document set. Then, based on the acquired coarse-grained document set, it recalls fine-grained passage. In the two-stage recall process, we use constrained decoding to ensure that content outside of the stored documents is not generated. To increase speed, we only recall a short prefix in the second stage, then locate its position to retrieve a complete passage. Experiments on KILT knowledge-sensitive tasks have verified that LLMs can independently recall reference passage location in various task forms, and the obtained reference significantly assist downstream tasks.

Active learning (AL) in open set scenarios presents a novel challenge of identifying the most valuable examples in an unlabeled data pool that comprises data from both known and unknown classes. Traditional methods prioritize selecting informative examples with low confidence, with the risk of mistakenly selecting unknown-class examples with similarly low confidence. Recent methods favor the most probable known-class examples, with the risk of picking simple already mastered examples. In this paper, we attempt to query examples that are both likely from known classes and highly informative, and propose a \textit{Bidirectional Uncertainty-based Active Learning} (BUAL) framework. Specifically, we achieve this by first pushing the unknown class examples toward regions with high-confidence predictions with our proposed \textit{Random Label Negative Learning} method. Then, we propose a \textit{Bidirectional Uncertainty sampling} strategy by jointly estimating uncertainty posed by both positive and negative learning to perform consistent and stable sampling. BUAL successfully extends existing uncertainty-based AL methods to complex open-set scenarios. Extensive experiments on multiple datasets with varying openness demonstrate that BUAL achieves state-of-the-art performance.

Coordinate-based neural implicit representation or implicit fields have been widely studied for 3D geometry representation or novel view synthesis. Recently, a series of efforts have been devoted to accelerating the speed and improving the quality of the coordinate-based implicit field learning. Instead of learning heavy MLPs to predict the neural implicit values for the query coordinates, neural voxels or grids combined with shallow MLPs have been proposed to achieve high-quality implicit field learning with reduced optimization time. On the other hand, lightweight field representations such as linear grid have been proposed to further improve the learning speed. In this paper, we aim for both fast and high-quality implicit field learning, and propose TaylorGrid, a novel implicit field representation which can be efficiently computed via direct Taylor expansion optimization on 2D or 3D grids. As a general representation, TaylorGrid can be adapted to different implicit fields learning tasks such as SDF learning or NeRF. From extensive quantitative and qualitative comparisons, TaylorGrid achieves a balance between the linear grid and neural voxels, showing its superiority in fast and high-quality implicit field learning.

A multimodal large language model (MLLM) agent can receive instructions, capture images, retrieve histories from memory, and decide which tools to use. Nonetheless, red-teaming efforts have revealed that adversarial images/prompts can jailbreak an MLLM and cause unaligned behaviors. In this work, we report an even more severe safety issue in multi-agent environments, referred to as infectious jailbreak. It entails the adversary simply jailbreaking a single agent, and without any further intervention from the adversary, (almost) all agents will become infected exponentially fast and exhibit harmful behaviors. To validate the feasibility of infectious jailbreak, we simulate multi-agent environments containing up to one million LLaVA-1.5 agents, and employ randomized pair-wise chat as a proof-of-concept instantiation for multi-agent interaction. Our results show that feeding an (infectious) adversarial image into the memory of any randomly chosen agent is sufficient to achieve infectious jailbreak. Finally, we derive a simple principle for determining whether a defense mechanism can provably restrain the spread of infectious jailbreak, but how to design a practical defense that meets this principle remains an open question to investigate. Our project page is available at https://sail-sg.github.io/Agent-Smith/., Comment: ICML 2024

For small privacy parameter $\epsilon$, $\epsilon$-differential privacy (DP) provides a strong worst-case guarantee that no membership inference attack (MIA) can succeed at determining whether a person's data was used to train a machine learning model. The guarantee of DP is worst-case because: a) it holds even if the attacker already knows the records of all but one person in the data set; and b) it holds uniformly over all data sets. In practical applications, such a worst-case guarantee may be overkill: practical attackers may lack exact knowledge of (nearly all of) the private data, and our data set might be easier to defend, in some sense, than the worst-case data set. Such considerations have motivated the industrial deployment of DP models with large privacy parameter (e.g. $\epsilon \geq 7$), and it has been observed empirically that DP with large $\epsilon$ can successfully defend against state-of-the-art MIAs. Existing DP theory cannot explain these empirical findings: e.g., the theoretical privacy guarantees of $\epsilon \geq 7$ are essentially vacuous. In this paper, we aim to close this gap between theory and practice and understand why a large DP parameter can prevent practical MIAs. To tackle this problem, we propose a new privacy notion called practical membership privacy (PMP). PMP models a practical attacker's uncertainty about the contents of the private data. The PMP parameter has a natural interpretation in terms of the success rate of a practical MIA on a given data set. We quantitatively analyze the PMP parameter of two fundamental DP mechanisms: the exponential mechanism and Gaussian mechanism. Our analysis reveals that a large DP parameter often translates into a much smaller PMP parameter, which guarantees strong privacy against practical MIAs. Using our findings, we offer principled guidance for practitioners in choosing the DP parameter., Comment: Accepted at PPAI-24: AAAI Workshop on Privacy-Preserving Artificial Intelligence

Traditional applications of natural language processing (NLP) in healthcare have predominantly focused on patient-centered services, enhancing patient interactions and care delivery, such as through medical dialogue systems. However, the potential of NLP to benefit inexperienced doctors, particularly in areas such as communicative medical coaching, remains largely unexplored. We introduce ``ChatCoach,'' an integrated human-AI cooperative framework. Within this framework, both a patient agent and a coaching agent collaboratively support medical learners in practicing their medical communication skills during consultations. Unlike traditional dialogue systems, ChatCoach provides a simulated environment where a human doctor can engage in medical dialogue with a patient agent. Simultaneously, a coaching agent provides real-time feedback to the doctor. To construct the ChatCoach system, we developed a dataset and integrated Large Language Models such as ChatGPT and Llama2, aiming to assess their effectiveness in communicative medical coaching tasks. Our comparative analysis demonstrates that instruction-tuned Llama2 significantly outperforms ChatGPT's prompting-based approaches., Comment: NA

Learning with noisy labels can significantly hinder the generalization performance of deep neural networks (DNNs). Existing approaches address this issue through loss correction or example selection methods. However, these methods often rely on the model's predictions obtained from the softmax function, which can be over-confident and unreliable. In this study, we identify the translation invariance of the softmax function as the underlying cause of this problem and propose the \textit{Dirichlet-based Prediction Calibration} (DPC) method as a solution. Our method introduces a calibrated softmax function that breaks the translation invariance by incorporating a suitable constant in the exponent term, enabling more reliable model predictions. To ensure stable model training, we leverage a Dirichlet distribution to assign probabilities to predicted labels and introduce a novel evidence deep learning (EDL) loss. The proposed loss function encourages positive and sufficiently large logits for the given label, while penalizing negative and small logits for other labels, leading to more distinct logits and facilitating better example selection based on a large-margin criterion. Through extensive experiments on diverse benchmark datasets, we demonstrate that DPC achieves state-of-the-art performance. The code is available at https://github.com/chenchenzong/DPC.

Existing 3D-aware portrait synthesis methods can generate impressive high-quality images while preserving strong 3D consistency. However, most of them cannot support the fine-grained part-level control over synthesized images. Conversely, some GAN-based 2D portrait synthesis methods can achieve clear disentanglement of facial regions, but they cannot preserve view consistency due to a lack of 3D modeling abilities. To address these issues, we propose 3D-SSGAN, a novel framework for 3D-aware compositional portrait image synthesis. First, a simple yet effective depth-guided 2D-to-3D lifting module maps the generated 2D part features and semantics to 3D. Then, a volume renderer with a novel 3D-aware semantic mask renderer is utilized to produce the composed face features and corresponding masks. The whole framework is trained end-to-end by discriminating between real and synthesized 2D images and their semantic masks. Quantitative and qualitative evaluations demonstrate the superiority of 3D-SSGAN in controllable part-level synthesis while preserving 3D view consistency.

Adaptive bitrate (ABR) using conventional codecs cannot further modify the bitrate once a decision has been made, exhibiting limited adaptation capability. This may result in either overly conservative or overly aggressive bitrate selection, which could cause either inefficient utilization of the network bandwidth or frequent re-buffering, respectively. Neural representation for video (NeRV), which embeds the video content into neural network weights, allows video reconstruction with incomplete models. Specifically, the recovery of one frame can be achieved without relying on the decoding of adjacent frames. NeRV has the potential to provide high video reconstruction quality and, more importantly, pave the way for developing more flexible ABR strategies for video transmission. In this work, a new framework, named Evolutional Video streaming Adaptation via Neural representation (EVAN), which can adaptively transmit NeRV models based on soft actor-critic (SAC) reinforcement learning, is proposed. EVAN is trained with a more exploitative strategy and utilizes progressive playback to avoid re-buffering. Experiments showed that EVAN can outperform existing ABRs with 50% reduction in re-buffering and achieve nearly 20% ., Comment: accepted by ICME (conference)

Temporal knowledge graph (TKG) reasoning has two settings: interpolation reasoning and extrapolation reasoning. Both of them draw plenty of research interest and have great significance. Methods of the former de-emphasize the temporal correlations among facts sequences, while methods of the latter require strict chronological order of knowledge and ignore inferring clues provided by missing facts of the past. These limit the practicability of TKG applications as almost all of the existing TKG reasoning methods are designed specifically to address either one setting. To this end, this paper proposes an original Temporal PAth-based Reasoning (TPAR) model for both the interpolation and extrapolation reasoning. TPAR performs a neural-driven symbolic reasoning fashion that is robust to ambiguous and noisy temporal data and with fine interpretability as well. Comprehensive experiments show that TPAR outperforms SOTA methods on the link prediction task for both the interpolation and the extrapolation settings. A novel pipeline experimental setting is designed to evaluate the performances of SOTA combinations and the proposed TPAR towards interpolation and extrapolation reasoning. More diverse experiments are conducted to show the robustness and interpretability of TPAR., Comment: To appear in ACL 2024 main conference

Approximate Computing (AC) has emerged as a promising technique for achieving energy-efficient architectures and is expected to become an effective technique for reducing the electricity cost for cloud service providers (CSP). However, the potential misuse of AC has not received adequate attention, which is a coming crisis behind the blueprint of AC. Driven by the pursuit of illegal financial profits, untrusted CSPs may deploy low-cost AC devices and deceive clients by presenting AC services as promised accurate computing products, while falsely claiming AC outputs as accurate results. This misuse of AC will cause both financial loss and computing degradation to cloud clients. In this paper, we define this malicious attack as DisHonest Approximate Computing (DHAC) and analyze the technical challenges faced by clients in detecting such attacks. To address this issue, we propose two golden model free detection methods: Residual Class Check (RCC) and Forward-Backward Check (FBC). RCC provides clients a low-cost approach to infer the residual class to which a legitimate accurate output should belong. By comparing the residual class of the returned result, clients can determine whether a computing service contains any AC elements. FBC detects potential DHAC by computing an invertible check branch using the intermediate values of the program. It compares the values before entering and after returning from the check branch to identify any discrepancies. Both RCC and FBC can be executed concurrently with real computing tasks, enabling real-time DHAC detection with current inputs. Our experimental results show that both RCC and FBC can detect over 96%-99% of DHAC cases without misjudging any legitimate accurate results., Comment: 12 pages, 9 figures

Piano audio-to-score transcription (A2S) is an important yet underexplored task with extensive applications for music composition, practice, and analysis. However, existing end-to-end piano A2S systems faced difficulties in retrieving bar-level information such as key and time signatures, and have been trained and evaluated with only synthetic data. To address these limitations, we propose a sequence-to-sequence (Seq2Seq) model with a hierarchical decoder that aligns with the hierarchical structure of musical scores, enabling the transcription of score information at both the bar and note levels by multi-task learning. To bridge the gap between synthetic data and recordings of human performance, we propose a two-stage training scheme, which involves pre-training the model using an expressive performance rendering (EPR) system on synthetic audio, followed by fine-tuning the model using recordings of human performance. To preserve the voicing structure for score reconstruction, we propose a pre-processing method for **Kern scores in scenarios with an unconstrained number of voices. Experimental results support the effectiveness of our proposed approaches, in terms of both transcription performance on synthetic audio data in comparison to the current state-of-the-art, and the first experiment on human recordings., Comment: 8 pages, 5 figures, accepted by IJCAI 2024 - AI, Arts & Creativity Track

Clickbaits are surprising social posts or deceptive news headlines that attempt to lure users for more clicks, which have posted at unprecedented rates for more profit or commercial revenue. The spread of clickbait has significant negative impacts on the users, which brings users misleading or even click-jacking attacks. Different from fake news, the crucial problem in clickbait detection is determining whether the headline matches the corresponding content. Most existing methods compute the semantic similarity between the headlines and contents for detecting clickbait. However, due to significant differences in length and semantic features between headlines and contents, directly calculating semantic similarity is often difficult to summarize the relationship between them. To address this problem, we propose a prompt-tuning method for clickbait detection via text summarization in this paper, text summarization is introduced to summarize the contents, and clickbait detection is performed based on the similarity between the generated summary and the contents. Specifically, we first introduce a two-stage text summarization model to produce high-quality news summaries based on pre-trained language models, and then both the headlines and new generated summaries are incorporated as the inputs for prompt-tuning. Additionally, a variety of strategies are conducted to incorporate external knowledge for improving the performance of clickbait detection. The extensive experiments on well-known clickbait detection datasets demonstrate that our method achieved state-of-the-art performance.

Generalized Class Discovery (GCD) aims to dynamically assign labels to unlabelled data partially based on knowledge learned from labelled data, where the unlabelled data may come from known or novel classes. The prevailing approach generally involves clustering across all data and learning conceptions by prototypical contrastive learning. However, existing methods largely hinge on the performance of clustering algorithms and are thus subject to their inherent limitations. Firstly, the estimated cluster number is often smaller than the ground truth, making the existing methods suffer from the lack of prototypes for comprehensive conception learning. To address this issue, we propose an adaptive probing mechanism that introduces learnable potential prototypes to expand cluster prototypes (centers). As there is no ground truth for the potential prototype, we develop a self-supervised prototype learning framework to optimize the potential prototype in an end-to-end fashion. Secondly, clustering is computationally intensive, and the conventional strategy of clustering both labelled and unlabelled instances exacerbates this issue. To counteract this inefficiency, we opt to cluster only the unlabelled instances and subsequently expand the cluster prototypes with our introduced potential prototypes to fast explore novel classes. Despite the simplicity of our proposed method, extensive empirical analysis on a wide range of datasets confirms that our method consistently delivers state-of-the-art results. Specifically, our method surpasses the nearest competitor by a significant margin of 9.7% within the Stanford Cars dataset and 12x clustering efficiency within the Herbarium 19 dataset. We will make the code and checkpoints publicly available at https://github.com/xjtuYW/PNP.git., Comment: 9 pages, 7 figures

Biaxial motion control systems are used extensively in manufacturing and printing industries. To improve throughput and reduce machine cost, lightweight materials are being proposed in structural components but may result in higher flexibility in the machine links. This flexibility is often position dependent and compromises precision of the end effector of the machine. To address the need for improved contouring accuracy in industrial machines with position-dependent structural flexibility, this paper introduces a novel contouring error-bounded control algorithm for biaxial switched linear systems. The proposed algorithm utilizes model predictive control to guarantee the satisfaction of state, input, and contouring error constraints for any admissible mode switching. In this paper, the switching signal remains unknown to the controller, although information about the minimum time the system is expected to stay in a specific mode is considered to be available. The proposed algorithm has the property of recursive feasibility and ensures the stability of the closed-loop system. The effectiveness of the proposed method is demonstrated by applying it to a high-fidelity simulation of a dual-drive industrial laser machine. The results show that the contouring error is successfully bounded within the given tolerance.

In recent years, reinforcement learning (RL) has emerged as a valuable tool in drug design, offering the potential to propose and optimize molecules with desired properties. However, striking a balance between capabilities, flexibility, reliability, and efficiency remains challenging due to the complexity of advanced RL algorithms and the significant reliance on specialized code. In this work, we introduce ACEGEN, a comprehensive and streamlined toolkit tailored for generative drug design, built using TorchRL, a modern RL library that offers thoroughly tested reusable components. We validate ACEGEN by benchmarking against other published generative modeling algorithms and show comparable or improved performance. We also show examples of ACEGEN applied in multiple drug discovery case studies. ACEGEN is accessible at \url{https://github.com/acellera/acegen-open} and available for use under the MIT license.

Text-conditioned image-to-video generation (TI2V) aims to synthesize a realistic video starting from a given image (e.g., a woman's photo) and a text description (e.g., "a woman is drinking water."). Existing TI2V frameworks often require costly training on video-text datasets and specific model designs for text and image conditioning. In this paper, we propose TI2V-Zero, a zero-shot, tuning-free method that empowers a pretrained text-to-video (T2V) diffusion model to be conditioned on a provided image, enabling TI2V generation without any optimization, fine-tuning, or introducing external modules. Our approach leverages a pretrained T2V diffusion foundation model as the generative prior. To guide video generation with the additional image input, we propose a "repeat-and-slide" strategy that modulates the reverse denoising process, allowing the frozen diffusion model to synthesize a video frame-by-frame starting from the provided image. To ensure temporal continuity, we employ a DDPM inversion strategy to initialize Gaussian noise for each newly synthesized frame and a resampling technique to help preserve visual details. We conduct comprehensive experiments on both domain-specific and open-domain datasets, where TI2V-Zero consistently outperforms a recent open-domain TI2V model. Furthermore, we show that TI2V-Zero can seamlessly extend to other tasks such as video infilling and prediction when provided with more images. Its autoregressive design also supports long video generation., Comment: CVPR 2024

Requirements elicitation, a critical, yet time-consuming and challenging step in product development, often fails to capture the full spectrum of user needs. This may lead to products that fall short of expectations. This paper introduces a novel framework that leverages Large Language Models (LLMs) to automate and enhance the requirements elicitation process. LLMs are used to generate a vast array of simulated users (LLM agents), enabling the exploration of a much broader range of user needs and unforeseen use cases. These agents engage in product experience scenarios, through explaining their actions, observations, and challenges. Subsequent agent interviews and analysis uncover valuable user needs, including latent ones. We validate our framework with three experiments. First, we explore different methodologies for diverse agent generation, discussing their advantages and shortcomings. We measure the diversity of identified user needs and demonstrate that context-aware agent generation leads to greater diversity. Second, we show how our framework effectively mimics empathic lead user interviews, identifying a greater number of latent needs than conventional human interviews. Third, we showcase that LLMs can be used to analyze interviews, capture needs, and classify them as latent or not. Our work highlights the potential of using LLM agents to accelerate early-stage product development, reduce costs, and increase innovation.

The diverse cell types of an organ have a highly structured organization to enable their efficient and correct function. To fully appreciate gene functions in a given cell type, one needs to understand how much, when and where the gene is expressed. Classic bulk RNA sequencing and popular single cell sequencing destroy cell structural organization and fail to provide spatial information. However, the spatial location of gene expression or of the cell in a complex tissue provides key clues to comprehend how the neighboring genes or cells cross talk, transduce signals and work together as a team to complete the job. The functional requirement for the spatial content has been a driving force for rapid development of the spatial transcriptomics technologies in the past few years. Here, we present an overview of current spatial technologies with a special focus on the commercially available or currently being commercialized technologies, highlight their applications by category and discuss experimental considerations for a first spatial experiment.

Introduction: Parkinson's disease and type 2 diabetes have both elements of local amyloid depositions in their pathogenesis. In Parkinson's disease, alpha-synuclein (aSyn) forms insoluble Lewy bodies and Lewy neurites in brain neurons, and in type 2 diabetes, islet amyloid polypeptide (IAPP) comprises the amyloid in the islets of Langerhans. In this study, we assessed the interaction between aSyn and IAPP in human pancreatic tissues, both ex vivo and in vitro. Material and Methods: The antibody-based detection techniques, proximity ligation assay (PLA), and immuno-TEM were used for co-localization studies. Bifluorescence complementation (BiFC) was used for interaction studies between IAPP and aSyn in HEK 293 cells. The Thioflavin T assay was used for studies of cross-seeding between IAPP and aSyn. ASyn was downregulated with siRNA, and insulin secretion was monitored using TIRF microscopy. Results: We demonstrate intracellular co-localization of aSyn with IAPP, while aSyn is absent in the extracellular amyloid deposits. ASyn reactivity is present in the secretory granules of beta-cells and some alpha-cells in human islets. The BiFC-expression of aSyn/aSyn and IAPP/IAPP in HEK293 cells resulted in 29.3% and 19.7% fluorescent cells, respectively, while aSyn/IAPP co-expression resulted in & SIM;10% fluorescent cells. Preformed aSyn fibrils seeded IAPP fibril formation in vitro, but adding preformed IAPP seeds to aSyn did not change aSyn fibrillation. In addition, mixing monomeric aSyn with monomeric IAPP did not affect IAPP fibril formation. Finally, the knockdown of endogenous aSyn did not affect beta cell function or viability, nor did overexpression of aSyn affect beta cell viability. Discussion: Despite the proximity of aSyn and IAPP in beta-cells and the detected capacity of preformed aSyn fibrils to seed IAPP in vitro, it is still an open question if an interaction between the two molecules is of pathogenic significance for type 2 diabetes.

This paper investigates the resource allocation and computation offloading problem for multi-access edge computing (MEC) systems, where multiple mobile users (MUs) equipped with multiple antennas access the base station in a non-orthogonal multiple access manner. We jointly optimize the offloading ratio, computational frequency and transmit precoding matrix of each MU to minimize the total energy consumption of all MUs while satisfying the latency constraints. The problem is formulated as a non-convex optimization problem and a two-layer iterative method is proposed to solve the problem efficiently with low complexity. Specifically, we first decompose the original problem into several subproblems, and then sequentially solve these subproblems in an alternative fashion. Furthermore, we also discuss the optimal decoding order of MUs under two different scenarios. Firstly, when the MUs' channel conditions are similar, by deriving closed-form expressions for energy consumptions of all MUs, we prove that the optimal decoding order is only determined by the latency requirements. On the other hand, when the MUs' channel conditions are different, we show that the optimal decoding order is determined by both the channel conditions and the latency requirements. As such, we propose a metric aiming to balance the effects of channel conditions and latency requirements on the MUs' decoding order. Simulation results validate the convergence of the proposed method and demonstrate its superiority over benchmark algorithms., QC 20230824

Biological cilia, hairlike organelles on cell surfaces, often exhibit collective wavelike motion known as metachrony, which helps generating fluid flow. Inspired by nature, researchers have developed artificial cilia as microfluidic actuators, exploring several methods to mimic the metachrony. However, reported methods are difficult to miniaturize because they require either control of individual cilia properties or the generation of a complex external magnetic field. We introduce a concept that generates metachronal motion of magnetic artificial cilia (MAC), even though the MAC are all identical, and the applied external magnetic field is uniform. This is achieved by integrating a paramagnetic substructure in the substrate underneath the MAC. Uniquely, we can create both symplectic and antiplectic metachrony by changing the relative positions of MAC and substructure. We demonstrate the flow generation of the two metachronal motions in both high and low Reynolds number conditions. Our research marks a significant milestone by breaking the size limitation barrier in metachronal artificial cilia. This achievement not only showcases the potential of nature-inspired engineering but also opens up a host of exciting opportunities for designing and optimizing microsystems with enhanced fluid manipulation capabilities.

Evolution has produced natural systems that generate motion and sense external stimuli at the micro- and nanoscales. At extremely small scales, the intricate motions and large deformations shown by these biosystems are due to a tipping balance between their structural compliance and the actuating force generated in them. Artificially mimicking such ingenious systems for scientific and engineering applications has been approached through the development and use of different smart materials mostly limited to microscale dimensions. To push the application range down to the nanoscale, we developed a material preparation process that yields a library of nanomagnetic elastomers with high magnetic particle concentrations. Through this process, we have realized a material with the highest magnetic-to-elastic force ratio, as is shown by an extensive mechanical and magnetic characterization of the materials. Furthermore, we have fabricated and actuated micro- and nanostructures mimicking cilia, demonstrating the extreme compliance and responsiveness of the developed materials.

In cardiac fibrosis, in response to stress or injury, cardiac fibroblasts deposit excessive amounts of collagens which contribute to the development of heart failure. The biochemical stimuli in this process have been extensively studied, but the influence of cyclic deformation on the fibrogenic behavior of cardiac fibroblasts in the ever-beating heart is not fully understood. In fact, most investigated mechanotransduction pathways in cardiac fibroblasts seem to ultimately have profibrotic effects, which leaves an important question in cardiac fibrosis research unanswered: how do cardiac fibroblasts stay quiescent in the ever-beating human heart? In this study, we developed a human cardiac fibrosis-on-a-chip platform and utilized it to investigate if and how cyclic strain affects fibrogenic signaling. The pneumatically actuated platform can expose engineered tissues to controlled strain magnitudes of 0–25% – which covers the entire physiological and pathological strain range in the human heart – and to biochemical stimuli and enables high-throughput screening of multiple samples. Microtissues of human fetal cardiac fibroblasts (hfCF) embedded in gelatin methacryloyl (GelMA) were 3D-cultured on this platform and exposed to strain conditions which mimic the healthy human heart. The results provide evidence of an antifibrotic effect of the applied strain conditions on cardiac fibroblast behavior, emphasizing the influence of biomechanical stimuli on the fibrogenic process and giving a detailed overview of the mechanosensitive pathways and genes involved, which can be used in the development of novel therapies against cardiac fibrosis.

Wang Y, Liu Y, Du X, Ma H, Yao J. Onco Targets Ther. 2020;13:1909-1919. We, the Editors and Publisher of OncoTargets and Therapy, have retracted the following article. Since publication, concerns have been raised about the integrity of the data in the article. This includes the duplication of images from Figures 2 and 4. Specifically, The western blot image for Figure 2B, MCF7/ADR, MDR1 appears to have been duplicated with the image for Figure 4C, MCF7/ADR, p62, which has been flipped. The image for Figure 4B, MCF7/ADR, siPTEN, has been duplicated with the image for Figure 4D, MCF/ADR, BBR. The western blot image for Figure 4C, MCF7/ADR, GAPDH, has been duplicated with the image for Figure 4E, MCF7/ADR, GAPDH. When approached for an explanation, the authors did not respond to our queries, nor did they provide original data for their study. As the findings could not be verified the Editor and Publisher made the decision to retract the article and the authors were notified of this. We have been informed in our decision-making by our editorial policies and COPE guidelines. The retracted article will remain online to maintain the scholarly record, but it will be digitally watermarked on each page as ‘Retracted’.

Ye Wang,1,* Fenxiang Li,1,* Jian Liu,2 Jing Liu,3 Pei Qin,4 Jiayi Zhang,5 Yingtao Zhang,5 Shuning Wu6 1Department of Epidemiology, School of Medicine, Jinan University, Guangzhou, People’s Republic of China; 2Department of General Surgery, Maternal and Child Health Hospital, Qingyuan, Guangdong, People’s Republic of China; 3Intensive Care Unit 1, The First Affiliated Hospital, Jinan University, Guangdong, Guangzhou, People’s Republic of China; 4Shenzhen Qianhai Shekou Free Trade Zone Hospital, Shenzhen, People’s Republic of China; 5Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, People’s Republic of China; 6Department of Thyroid and Breast Surgery, Shenzhen Qianhai Shekou Free Trade Zone Hospital, Shenzhen, People’s Republic of China*These authors contributed equally to this workCorrespondence: Yingtao Zhang, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, 510000, People’s Republic of China, Tel +86 15918805867, Email zhangyt9@mail2.sysu.edu.cn Shuning Wu, Thyroid and Breast Surgical Department, Shenzhen Qianhai Shekou Free Trade Zone Hospital, Shenzhen, 518067, People’s Republic of China, Tel +86 13480209989, Email jessica540wu@outlook.comPurpose: This study aims to identify common COVID-19 symptoms and asymptomatic infection rates during the epidemic in China. We also introduce the concepts of “Time-point asymptomatic rate” and “Period asymptomatic rate”.Object and Methods: A questionnaire survey was conducted online from December 2022 to January 5, 2023, collecting demographic characteristics, laboratory results, clinical symptoms, lifestyle and vaccination history. Statistical methods were used to analyze symptom characteristics, associated factors, and patterns during an 8-day observation period. Numerical variables were described by median M (Q1-Q3) or mean and standard deviation (). Categorical variables are described by frequency (N), ratio (%) or rate (%).

Traditional federated learning (FL) algorithms, such as FedAvg, fail to handle non-i.i.d data because they learn a global model by simply averaging biased local models that are trained on non-i.i.d data, therefore failing to model the global data distribution. In this paper, we present a novel Bayesian FL algorithm that successfully handles such a non-i.i.d FL setting by enhancing the local training task with an auxiliary task that explicitly estimates the global data distribution. One key challenge in estimating the global data distribution is that the data are partitioned in FL, and therefore the ground-truth global data distribution is inaccessible. To address this challenge, we propose an expectation-propagation-inspired probabilistic neural network, dubbed federated neural propagation (FedNP), which efficiently estimates the global data distribution given non-i.i.d data partitions. Our algorithm is sampling-free and end-to-end differentiable, can be applied with any conventional FL frameworks and learns richer global data representation. Experiments on both image classification tasks with synthetic non-i.i.d image data partitions and real-world non-i.i.d speech recognition tasks demonstrate that our framework effectively alleviates the performance deterioration caused by non-i.i.d data. Copyright © 2023, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.

Viewpoint.

Diabetes Mellitus (DM) is a metabolic disorder characterized by hyperglycemia. Over the years, scientists have identified many factors that may have causal relationships with DM development. Identified factors are either genetic or environmental, and they may promote or prevent DM development. This review discusses various factors that are involved in the molecular pathogenesis, development, and therapeutic strategies of type 2 diabetes. DM is caused by interactions between multiple factors and triggers. Altered metabolic pathways and cellular functions, primarily in organs involved in glucose metabolisms, such as the pancreas and liver, often result in metabolic dysfunction, leading to DM. Additionally, abnormal levels of some factors, the presence of some pathogens, or the use of some types of medicine, such as immuno-inflammatory mediators, glucagon, apolipoprotein E4, chromogranin-A, exosomes, vitamin D, viruses, glucocorticoid medication, and antipsychotic drugs, may play roles in the development of DM. Some of these factors and mechanisms are well-studied, while others are more controversial and have contradicting experimental results. Further research is needed to confirm the roles of these factors in DM and fully understand how they contribute to DM development. Numerous medications for diabetics have been developed to help alleviate the symptoms of hyperglycemia and its complications. Several types of small compounds or peptide drugs with anti-diabetic effects can decrease blood glucose levels, improve insulin resistance, and inhibit key enzymes involved in the development and progression of diabetes. Here, we review the commonly used effective antidiabetic drugs, including the most recent innovative ones, such as GLP-1R/GIPR and GLP-1R/GCGR agonists, and Chinese medicine.

Multi-media communications facilitate global interaction among people. However, despite researchers exploring cross-lingual translation techniques such as machine translation and audio speech translation to overcome language barriers, there is still a shortage of cross-lingual studies on visual speech. This lack of research is mainly due to the absence of datasets containing visual speech and translated text pairs. In this paper, we present \textbf{AVMuST-TED}, the first dataset for \textbf{A}udio-\textbf{V}isual \textbf{Mu}ltilingual \textbf{S}peech \textbf{T}ranslation, derived from \textbf{TED} talks. Nonetheless, visual speech is not as distinguishable as audio speech, making it difficult to develop a mapping from source speech phonemes to the target language text. To address this issue, we propose MixSpeech, a cross-modality self-learning framework that utilizes audio speech to regularize the training of visual speech tasks. To further minimize the cross-modality gap and its impact on knowledge transfer, we suggest adopting mixed speech, which is created by interpolating audio and visual streams, along with a curriculum learning strategy to adjust the mixing ratio as needed. MixSpeech enhances speech translation in noisy environments, improving BLEU scores for four languages on AVMuST-TED by +1.4 to +4.2. Moreover, it achieves state-of-the-art performance in lip reading on CMLR (11.1\%), LRS2 (25.5\%), and LRS3 (28.0\%)., Comment: https://github.com/Exgc/AVMuST-TED

We construct a strong PUF with provable security against ML attacks on both classical and quantum computers. The security is guaranteed by the cryptographic hardness of learning decryption functions of public-key cryptosystems, and the hardness of the learning-with-errors (LWE) problem defined on integer lattices. We call our construction the lattice PUF. We construct lattice PUF with a physically obfuscated key and an LWE decryption function block. To allow deployments in different scenarios, we demonstrate designs with different latency-area trade-offs. A compact design uses a highly serialized LFSR and LWE decryption function, while a latency-optimized design uses an unrolled LFSR and a parallel datapath. We prototype lattice PUF designs with $2^{136}$ challenge-response pairs (CRPs) on a Spartan 6 FPGA. In addition to theoretical security guarantee, we evaluate empirical resistance to the various leading ML techniques: the prediction error remains above $49.76\%$ after $1$ million training CRPs. The resource-efficient design requires only $45$ slices for the PUF logic proper, and $351$ slices for a fuzzy extractor. The latency-optimized design achieves a $148X$ reduction in latency, at a $10X$ increase in PUF hardware utilization. The mean uniformity of PUF responses is $49.98\%$, the mean uniqueness is $50.00\%$, and the mean reliability is $1.26\%$.

Recent advances in generative adversarial networks (GANs) have led to remarkable achievements in face image synthesis. While methods that use style-based GANs can generate strikingly photorealistic face images, it is often difficult to control the characteristics of the generated faces in a meaningful and disentangled way. Prior approaches aim to achieve such semantic control and disentanglement within the latent space of a previously trained GAN. In contrast, we propose a framework that a priori models physical attributes of the face such as 3D shape, albedo, pose, and lighting explicitly, thus providing disentanglement by design. Our method, MOST-GAN, integrates the expressive power and photorealism of style-based GANs with the physical disentanglement and flexibility of nonlinear 3D morphable models, which we couple with a state-of-the-art 2D hair manipulation network. MOST-GAN achieves photorealistic manipulation of portrait images with fully disentangled 3D control over their physical attributes, enabling extreme manipulation of lighting, facial expression, and pose variations up to full profile view.

Atmospheric ammonia has been hazardous to the environment and human health for decades. Current inventories are usually constructed in a bottom-up manner and subject to uncertainties and incapable of reproducing the spatiotemporal characteristics of ammonia emission. Satellite measurements, for example, Infrared Atmospheric Sounder Interferometer (IASI) and Cross-Track Infrared Sounder, which provide global coverage of ammonia distribution, have gained popularity in ammonia emission estimation through data assimilation methods. However, satellite-based emission inversion studies on China are limited. In this study, we propose a four-dimensional ensemble variational-based ammonia emission inversion system to optimize ammonia emissions in China. It was developed by assimilating the IASI ammonia retrievals onboard Meteorological Operational satellite A and B into a chemical transport model Goddard Earth Observing System Chemical model (GEOS-Chem). Monthly inversion experiments were conducted in April, July, and October 2016 to test the performance. The inversion result indicated that the prior inventory from the MEIC model captured ammonia spreads in general; however, it heterogeneously underrated the emission intensity. The increments obtained in the assimilation were as high as 50% in North, East, and Northwest China. The posterior emission inventory presented a regional emission flux consistent with relevant studies. Driven by the optimized source estimate, GEOS-Chem provides superior results than using the prior in the evaluation of the assimilated IASI retrievals and the surface ammonia concentration measured by the ground-based Ammonia Monitoring Network in China., Mathematical Physics

Music rearrangement is a common music practice of reconstructing and reconceptualizing a piece using new composition or instrumentation styles, which is also an important task of automatic music generation. Existing studies typically model the mapping from a source piece to a target piece via supervised learning. In this paper, we tackle rearrangement problems via self-supervised learning, in which the mapping styles can be regarded as conditions and controlled in a flexible way. Specifically, we are inspired by the representation disentanglement idea and propose Q&A, a query-based algorithm for multi-track music rearrangement under an encoder-decoder framework. Q&A learns both a content representation from the mixture and function (style) representations from each individual track, while the latter queries the former in order to rearrange a new piece. Our current model focuses on popular music and provides a controllable pathway to four scenarios: 1) re-instrumentation, 2) piano cover generation, 3) orchestration, and 4) voice separation. Experiments show that our query system achieves high-quality rearrangement results with delicate multi-track structures, significantly outperforming the baselines., Comment: Accepted by IJCAI 2023 Special Track for AI the Arts and Creativity

Automatic Pronunciation Assessment (APA) is vital for computer-assisted language learning. Prior methods rely on annotated speech-text data to train Automatic Speech Recognition (ASR) models or speech-score data to train regression models. In this work, we propose a novel zero-shot APA method based on the pre-trained acoustic model, HuBERT. Our method involves encoding speech input and corrupting them via a masking module. We then employ the Transformer encoder and apply k-means clustering to obtain token sequences. Finally, a scoring module is designed to measure the number of wrongly recovered tokens. Experimental results on speechocean762 demonstrate that the proposed method achieves comparable performance to supervised regression baselines and outperforms non-regression baselines in terms of Pearson Correlation Coefficient (PCC). Additionally, we analyze how masking strategies affect the performance of APA., Comment: Accepted to Interspeech 2023

The development of general-domain neural machine translation (NMT) methods has advanced significantly in recent years, but the lack of naturalness and musical constraints in the outputs makes them unable to produce singable lyric translations. This paper bridges the singability quality gap by formalizing lyric translation into a constrained translation problem, converting theoretical guidance and practical techniques from translatology literature to prompt-driven NMT approaches, exploring better adaptation methods, and instantiating them to an English-Chinese lyric translation system. Our model achieves 99.85%, 99.00%, and 95.52% on length accuracy, rhyme accuracy, and word boundary recall. In our subjective evaluation, our model shows a 75% relative enhancement on overall quality, compared against naive fine-tuning (Code available at https://github.com/Sonata165/ControllableLyricTranslation)., Comment: Accepted by ACL 2023. Camera-ready version

Scientific literature understanding tasks have gained significant attention due to their potential to accelerate scientific discovery. Pre-trained language models (LMs) have shown effectiveness in these tasks, especially when tuned via contrastive learning. However, jointly utilizing pre-training data across multiple heterogeneous tasks (e.g., extreme multi-label paper classification, citation prediction, and literature search) remains largely unexplored. To bridge this gap, we propose a multi-task contrastive learning framework, SciMult, with a focus on facilitating common knowledge sharing across different scientific literature understanding tasks while preventing task-specific skills from interfering with each other. To be specific, we explore two techniques -- task-aware specialization and instruction tuning. The former adopts a Mixture-of-Experts Transformer architecture with task-aware sub-layers; the latter prepends task-specific instructions to the input text so as to produce task-aware outputs. Extensive experiments on a comprehensive collection of benchmark datasets verify the effectiveness of our task-aware specialization strategy, where we outperform state-of-the-art scientific pre-trained LMs. Code, datasets, and pre-trained models can be found at https://scimult.github.io/., Comment: 17 pages; Accepted to Findings of EMNLP 2023 (Project Page: https://scimult.github.io/)