Miracle-dz/UniFAR-FTUs huggingface dataset:
Remaining useful life prediction with uncertainty quantification: development of a highly accurate model for rotating machinery

Rotating machinery is essential to modern life, from power generation to transportation and a host of other industrial applications. Since such equipment generally operates under challenging working conditions, which can lead to untimely failures, accurate remaining useful life (RUL) prediction is essential for maintenance planning and to prevent catastrophic failures. In this work, we address current challenges in data-driven RUL prediction for rotating machinery. The challenges revolve around the accuracy and uncertainty quantification of the prediction, and the nonstationarity of the system degradation and RUL estimation given sensor data. We devise a novel architecture and RUL prediction model with uncertainty quantification, termed VisPro, which integrates time-frequency analysis, deep learning image recognition, and nonstationary Gaussian process regression. We analyze and benchmark the results obtained with our model against those of other advanced data-driven RUL prediction models for rotating machinery using the PHM12 bearing vibration dataset. The computational experiments show that (1) the VisPro predictions are highly accurate and provide significant improvements over existing prediction models (three times more accurate than the second-best model), and (2) the RUL uncertainty bounds are valid and informative. We identify and discuss the architectural and modeling choices made that explain this excellent predictive performance of VisPro.
ZSD-YOLO: Zero-Shot YOLO Detection using Vision-Language KnowledgeDistillation

Real-world object sampling produces long-tailed distributions requiring exponentially more images for rare types. Zero-shot detection, which aims to detect unseen objects, is one direction to address this problem. A dataset such as COCO is extensively annotated across many images but with a sparse number of categories and annotating all object classes across a diverse domain is expensive and challenging. To advance zero-shot detection, we develop a Vision-Language distillation method that aligns both image and text embeddings from a zero-shot pre-trained model such as CLIP to a modiﬁed semantic prediction head from a one-stage detector like YOLOv5. With this method, we are able to train an object detector that achieves state-of-the-art accuracy on the COCO zero-shot detection splits with fewer model parameters. During inference, our model can be adapted to detect any number of object classes without additional training. We also ﬁnd that the improvements provided by the scaling of our method are consistent across various YOLOv5 scales. Furthermore, we develop a self-training method that provides a signiﬁcant score improvement without needing extra images nor labels.
SLO beyond the Hardware Isolation Limits

Performance isolation is a keystone for SLO guarantees with shared resources in cloud and datacenter environments. To meet SLO requirements, the state of the art relies on hardware QoS support (e.g., Intel RDT) to allocate shared resources such as last-level caches and memory bandwidth for co-located latency-critical applications. As a result, the number of latency-critical applications that can be deployed on a physical machine is bounded by the hardware allocation capability. Unfortunately, such hardware capability is very limited. For example, Intel Xeon E5 v3 processors support at most four partitions for last-level caches, i.e., at most four applications can have dedicated resource allocation. This paper discusses the feasibility and unexplored challenges of providing SLO guarantees beyond the limits of hardware capability. We present CoCo to show the feasibility and the benefits. CoCo schedules applications to time-share interference-free partitions as a transparent software layer. Our evaluation shows that CoCo outperforms non-partitioned and round-robin approaches by up to 9x and 1.2x.
Meta-brain Models: biologically-inspired cognitive agents

Artificial Intelligence (AI) systems based solely on neural networks or symbolic computation present a representational complexity challenge. While minimal representations can produce behavioral outputs like locomotion or simple decision-making, more elaborate internal representations might offer a richer variety of behaviors. We propose that these issues can be addressed with a computational approach we call meta-brain models. Meta-brain models are embodied hybrid models that include layered components featuring varying degrees of representational complexity. We will propose combinations of layers composed using specialized types of models. Rather than using a generic black box approach to unify each component, this relationship mimics systems like the neocortical-thalamic system relationship of the mammalian brain, which utilizes both feedforward and feedback connectivity to facilitate functional communication. Importantly, the relationship between layers can be made anatomically explicit. This allows for structural specificity that can be incorporated into the model's function in interesting ways. We will propose several types of layers that might be functionally integrated into agents that perform unique types of tasks, from agents that simultaneously perform morphogenesis and perception, to agents that undergo morphogenesis and the acquisition of conceptual representations simultaneously. Our approach to meta-brain models involves creating models with different degrees of representational complexity, creating a layered meta-architecture that mimics the structural and functional heterogeneity of biological brains, and an input/output methodology flexible enough to accommodate cognitive functions, social interactions, and adaptive behaviors more generally. We will conclude by proposing next steps in the development of this flexible and open-source approach.
Generalized transitionless quantum driving for open quantum systems

A general approach for transitionless quantum driving in open quantum systems is introduced. Under the assumption of adiabatic evolution for time-local master equations, we derive the generalized transitionless Lindbladian required to implement a shortcut to adiabaticity in an open system scenario. The general counter-diabatic Lindbladian obtained accounts for a phase freedom, which translates into a set of free parameters throughout the dynamics. We then discuss how our generalized approach allows us to recover the transitionless Lindbladian introduced by G. Vacanti et al. [New J. Phys. 16, 053017 (2014)]. We then show how to engineer timeindependent master equations that provide the same dynamics as the time-dependent master equation provided by the standard transitionless quantum driving in open systems. We illustrate our results by applying them both to the adiabatic Deutsch algorithm under dephasing and to the Landau-Zener Hamiltonian under bit-phase-flip.
Low-J CO Line Ratios from Single-dish CO Mapping Surveys and PHANGS-ALMA

We measure the low-J CO line ratios R 21 ≡ CO (2–1)/CO (1–0), R 32 ≡ CO (3–2)/CO (2–1), and R 31 ≡CO (3–2)/CO (1–0) using whole-disk CO maps of nearby galaxies. We draw CO (2–1) from PHANGS-ALMA, HERACLES, and follow-up IRAM surveys; CO (1–0) from COMING and the Nobeyama CO Atlas of Nearby Spiral Galaxies; and CO (3–2) from the James Clerk Maxwell Telescope Nearby Galaxy Legacy Survey and Atacama Pathfinder Experiment Large APEX Sub-Millimetre Array mapping. All together, this yields 76, 47, and 29 maps of R 21, R 32, and R 31 at 20″ ∼ 1.3 kpc resolution, covering 43, 34, and 20 galaxies. Disk galaxies with high stellar mass, log(M⋆/M⊙)=10.25–11 , and star formation rate (SFR) = 1–5 M ⊙ yr−1, dominate the sample. We find galaxy-integrated mean values and a 16%–84% range of R 21 = 0.65 (0.50–0.83), R 32 = 0.50 (0.23–0.59), and R 31 = 0.31 (0.20–0.42). We identify weak trends relating galaxy-integrated line ratios to properties expected to correlate with excitation, including SFR/M ⋆ and SFR/L CO. Within galaxies, we measure central enhancements with respect to the galaxy-averaged value of ∼ 0.18−0.14+0.09 dex for R 21, 0.27−0.15+0.13 dex for R 31, and 0.08−0.09+0.11 dex for R 32. All three line ratios anticorrelate with galactocentric radius and positively correlate with the local SFR surface density and specific SFR, and we provide approximate fits to these relations. The observed ratios can be reasonably reproduced by models with low temperature, moderate opacity, and moderate densities, in good agreement with expectations for the cold interstellar medium. Because the line ratios are expected to anticorrelate with the CO (1–0)-to-H2 conversion factor, αCO1−0 , these results have general implications for the interpretation of CO emission from galaxies.
Paint4Poem: A Dataset for Artistic Visualization of Classical Chinese Poems

In this work we propose a new task: artistic visualization of classical Chinese poems, where the goal is to generatepaintings of a certain artistic style for classical Chinese poems. For this purpose, we construct a new dataset called Paint4Poem. Thefirst part of Paint4Poem consists of 301 high-quality poem-painting pairs collected manually from an influential modern Chinese artistFeng Zikai. As its small scale poses challenges for effectively training poem-to-painting generation models, we introduce the secondpart of Paint4Poem, which consists of 3,648 caption-painting pairs collected manually from Feng Zikai's paintings and 89,204 poem-painting pairs collected automatically from the web. We expect the former to help learning the artist painting style as it containshis most paintings, and the latter to help learning the semantic relevance between poems and paintings. Further, we analyze Paint4Poem regarding poem diversity, painting style, and the semantic relevance between poems and paintings. We create abenchmark for Paint4Poem: we train two representative text-to-image generation models: AttnGAN and MirrorGAN, and evaluate theirperformance regarding painting pictorial quality, painting stylistic relevance, and semantic relevance between poems and paintings.The results indicate that the models are able to generate paintings that have good pictorial quality and mimic Feng Zikai's style, but thereflection of poem semantics is limited. The dataset also poses many interesting research directions on this task, including transferlearning, few-shot learning, text-to-image generation for low-resource data etc. The dataset is publicly available. (https://github.com/paint4poem/paint4poem)
Blood-derived lncRNAs as potential biomarkers for early cancer diagnosis: The Good, the Bad and the Beauty

Cancer ranks as one of the deadliest diseases worldwide. The high mortality rate associated with cancer is partially due to the lack of reliable early detection methods and/or inaccurate diagnostic tools such as certain protein biomarkers. Cell-free nucleic acids (cfNA) such as circulating long non-coding RNAs (lncRNAs) have recently been proposed as a new class of potential biomarkers that could improve cancer diagnosis. The reported correlation between circulating lncRNA levels and the presence of tumors has triggered a great amount of interest among clinicians and scientists who have been actively investigating their potentials as reliable cancer biomarkers. In this report, we review the progress achieved (the Good) and challenges encountered (the Bad) in the development of circulating lncRNAs as potential biomarkers for early cancer diagnosis. We report and discuss the specificity and sensitivity issues of blood-based lncRNAs currently considered as promising biomarkers for various cancers such as hepatocellular carcinoma, colorectal cancer, gastric cancer and prostate cancer. We also emphasize the potential clinical applications (the Beauty) of circulating lncRNAs both as therapeutic targets and agents, on top of diagnostic and prognostic capabilities. Based on different published works, we finally provide recommendations for investigators who seek to investigate and compare the levels of circulating lncRNAs in the blood of cancer patients compared to healthy subjects by RT-qPCR or Next Generation Sequencing.
Joint Estimation and Inference for Multi-Experiment Networks of High-Dimensional Point Processes

Modern high-dimensional point process data, especially those from neuroscience experiments, often involve observations from multiple conditions and/or experiments. Networks of interactions corresponding to these conditions are expected to share many edges, but also exhibit unique, condition-specific ones. However, the degree of similarity among the networks from different conditions is generally unknown. Existing approaches for multivariate point processes do not take these structures into account and do not provide inference for jointly estimated networks. To address these needs, we propose a joint estimation procedure for networks of high-dimensional point processes that incorporates easy-to-compute weights in order to data-adaptively encourage similarity between the estimated networks. We also propose a powerful hierarchical multiple testing procedure for edges of all estimated networks, which takes into account the data-driven similarity structure of the multi-experiment networks. Compared to conventional multiple testing procedures, our proposed procedure greatly reduces the number of tests and results in improved power, while tightly controlling the family-wise error rate. Unlike existing procedures, our method is also free of assumptions on dependency between tests, offers flexibility on p-values calculated along the hierarchy, and is robust to misspecification of the hierarchical structure. We verify our theoretical results via simulation studies and demonstrate the application of the proposed procedure using neuronal spike train data. Keyword: Joint estimation; Hawkes process; High-dimensional inference; Multiple testing; Family-wise error rate. 1 ar X iv :2 10 9. 11 63 4v 1 [ st at . M E ] 2 3 Se p 20 21
The optimal positive operator-valued measure for state discrimination

Evaluating the amount of information obtained from non-orthogonal quantum states is an important topic in the field of quantum information. The commonly used evaluation method is Holevo bound, which only provides a loose upper bound for quantum measurement. In this paper, we provide a theoretical study of the positive operator-valued measure (POVM) for discriminating nonorthogonal states. We construct a generalized POVM measurement operation, and derive the optimal one for state discrimination by Lagrange multiplier method. With simulation, we find that the optimal POVM measurement provides a tight upper bound for state discrimination, which is significantly lower than that predicted by Holevo bound. The derivation of optimal POVM measurement will play an important role in the security research of quantum key distribution.
Two-Stage Mesh Deep Learning for Automated Tooth Segmentation and Landmark Localization on 3D Intraoral Scans

Accurately segmenting teeth and identifying the corresponding anatomical landmarks on dental mesh models are essential in computer-aided orthodontic treatment. Manually performing these two tasks is time-consuming, tedious, and, more importantly, highly dependent on orthodontists’ experiences due to the abnormality and large-scale variance of patients’ teeth. Some machine learning-based methods have been designed and applied in the orthodontic field to automatically segment dental meshes (e.g., intraoral scans). In contrast, the number of studies on tooth landmark localization is still limited. This paper proposes a two-stage framework based on mesh deep learning (called TS-MDL) for joint tooth labeling and landmark identification on raw intraoral scans. Our TS-MDL first adopts an end-to-end iMeshSegNet method (i.e., a variant of the existing MeshSegNet with both improved accuracy and efficiency) to label each tooth on the downsampled scan. Guided by the segmentation outputs, our TS-MDL further selects each tooth’s region of interest (ROI) on the original mesh to construct a light-weight variant of the pioneering PointNet (i.e., PointNet-Reg) for regressing the corresponding landmark heatmaps. Our TS-MDL was evaluated on a real-clinical dataset, showing promising segmentation and localization performance. Specifically, iMeshSegNet in the first stage of TS-MDL reached an averaged Dice similarity coefficient (DSC) at ${0.964}\pm {0.054}$ , significantly outperforming the original MeshSegNet. In the second stage, PointNet-Reg achieved a mean absolute error (MAE) of ${0}. {597}\pm {0}. {761} \, mm$ in distances between the prediction and ground truth for 66 landmarks, which is superior compared with other networks for landmark detection. All these results suggest the potential usage of our TS-MDL in orthodontics.
RSDet++: Point-Based Modulated Loss for More Accurate Rotated Object Detection

We classify the discontinuity of loss in both five-param and eight-param rotated object detection methods as rotation sensitivity error (RSE) which will result in performance degeneration. We introduce a novel modulated rotation loss to alleviate the problem and a rotation sensitivity detection network (RSDet) which consists of an eight-param single-stage rotated object detector and the modulated rotation loss. Our proposed RSDet has several advantages: 1) it reformulates the rotated object detection problem as predicting the corners of objects while most previous methods employ a five-param-based regression method with different measurement units. 2) modulated rotation loss achieves consistent improvement on both five-param and eight-param rotated object detection methods by solving the discontinuity of loss. To further improve the accuracy of our method on objects smaller than 10 pixels, we introduce a novel RSDet++ which consists of a point-based anchor-free rotated object detector and a modulated rotation loss. Extensive experiments demonstrate the effectiveness of both RSDet and RSDet++, which achieve competitive results on rotated object detection in the challenging benchmarks DOTA-v1.0, DOTA-v1.5, and DOTA-v2.0. We hope the proposed method can provide a new perspective for designing algorithms to solve rotated object detection and pay more attention to tiny objects. The codes and models are available at: https://github.com/yangxue0827/RotationDetection.
Improving Standing Balance Performance through the Assistance of a Mobile Collaborative Robot

This paper presents the design and development of a robotic system to give physical assistance to the elderly or people with neurological disorders such as Ataxia or Parkin-son's. In particular, we propose using a mobile collaborative robot with an interaction-assistive whole-body interface to help people unable to maintain balance. The robotic system consists of an Omni-directional mobile base, a high-payload robotic arm, and an admittance-type interface acting as a support handle while measuring human-sourced interaction forces. The postural balance of the human body is estimated through the projection of the body Center of Mass (CoM) to the support polygon (SP) representing the quasi-static Center of Pressure (CoP). In response to the interaction forces and the tracking of the human posture, the robot can create assistive forces to restore balance in case of its loss. Otherwise, during normal stance or walking, it will follow the user with minimum/no opposing forces through the generation of coupled arm and base movements. As the balance-restoring strategy, we propose two strategies and evaluate them in a laboratory setting on healthy human participants. Quantitative and qualitative results of a 12-subjects experiment are then illustrated and discussed, comparing the performances of the two strategies and the overall system.
GeomGCL: Geometric Graph Contrastive Learning for Molecular Property Prediction

Recently many efforts have been devoted to applying graph neural networks (GNNs) to molecular property prediction which is a fundamental task for computational drug and material discovery. One of major obstacles to hinder the successful prediction of molecular property by GNNs is the scarcity of labeled data. Though graph contrastive learning (GCL) methods have achieved extraordinary performance with insufficient labeled data, most focused on designing data augmentation schemes for general graphs. However, the fundamental property of a molecule could be altered with the augmentation method (like random perturbation) on molecular graphs. Whereas, the critical geometric information of molecules remains rarely explored under the current GNN and GCL architectures. To this end, we propose a novel graph contrastive learning method utilizing the geometry of the molecule across 2D and 3D views, which is named GeomGCL. Specifically, we first devise a dual-view geometric message passing network (GeomMPNN) to adaptively leverage the rich information of both 2D and 3D graphs of a molecule. The incorporation of geometric properties at different levels can greatly facilitate the molecular representation learning. Then a novel geometric graph contrastive scheme is designed to make both geometric views collaboratively supervise each other to improve the generalization ability of GeomMPNN. We evaluate GeomGCL on various downstream property prediction tasks via a finetune process. Experimental results on seven real-life molecular datasets demonstrate the effectiveness of our proposed GeomGCL against state-of-the-art baselines.
Hybrid bacterial cellulose–pectin films for delivery of bioactive molecules

Novel biopolymeric films based on bacterial cellulose (BC) modified with high methoxylated pectin (HMP) were developed for drug delivery. The ability of the films to incorporate an antibiotic, levofloxacin (Levo), was analyzed. Incorporation efficiencies were determined using films with different proportions of HMP (from 0.1% to 2.0%) with a maximum drug payload of 6.23 mg g−1. Characterization studies revealed the existence of a cooperative network between both polymers and deep structural changes in the BC matrix. Besides, the presence of HMP decreased water loss in the BC films from 93% to 75% after 90 min. Additionally, the capacity of the films to incorporate macromolecules was studied using Human Serum Albumin (HSA) as a model protein. The presence of HMP enhanced by more than 3.5 times the encapsulation efficiency (EE) of HSA, and no pH dependence was observed. Release kinetics of both molecules showed hyperbolic profiles with sustained release. In independent experiments, the presence of HMP generated a decrease of around 50% in the release rates of both macromolecules. Additionally, the incorporation of HSA into the BC–HMP matrix modulated the Levo release profile. The antimicrobial activity of Levo released from the BC–HMP–HSA films was confirmed using Staphylococcus aureus. In vitro studies revealed no apparent cytotoxicity of the released compounds in mammalian CHO cells.