Miracle-dz/UniFAR-FTUs huggingface dataset:
When to Use Graph Side Information in Matrix Completion

We consider a matrix completion problem that leverages graph as side information. One common approach in recently developed efficient algorithms is to take a two-step procedure: (i) clustering communities that form the basis of the graph structure; (ii) exploiting the estimated clusters to perform matrix completion together with iterative local refinement of clustering. A major limitation of the approach is that it achieves the information-theoretic limit on the number of observed matrix entries, promised by maximum likelihood estimation, only when a sufficient amount of graph side information is provided (the quantified measure is detailed later). The contribution of this work is to develop a computationally efficient algorithm that achieves the optimal sample complexity for the entire regime of graph information. The key idea is to make a careful selection for the information employed in the first clustering step, between two types of given information: graph & matrix ratings. Our experimental results conducted both on synthetic and real data confirm the superiority of our algorithm over the prior approaches in the scarce graph information regime.
M2GUDA: Multi-Metrics Graph-Based Unsupervised Domain Adaptation for Cross-Modal Hashing

Cross-modal hashing is a critical but very challenging task that is to retrieve similar samples of one modality via queries of other modalities. To improve the unsupervised cross-modal hashing, domain adaptation techniques can be used to support unsupervised hashing learning by transferring semantic knowledge from labeled source domain to unlabeled target domain. However, there are two problems that cannot be ignored: (1) most of domain adaptation based researches mainly focused on unimodal hashing or cross-modal real value-based retrieval but the study for cross-modal hashing is limited; (2) most existing studies only consider one or two consistency constraints during the domain adaptation learning. To this end, this paper propose a novel end-to-end framework to realize unsupervised domain adaptation for cross-modal hashing. This method, dubbed M$^2$GUDA, including four different consistency constraints: structure consistency, domain consistency, semantic consistency and modality consistency for domain adaptation learning. Besides, to enhance the structure consistency learning, we develop a multi-metrics graph modeling method to capture structure information comprehensively. Extensive experiments are performed on three common used benchmarks to evaluate the effectivity of our method. The results show that our method outperforms several state-of-the-art cross-modal hashing methods.
Neural Full-Rank Spatial Covariance Analysis for Blind Source Separation

This paper describes aneural blind source separation (BSS) method based on amortized variational inference (AVI) of a non-linear generative model of mixture signals. A classical statistical approach to BSS is to fit a linear generative model that consists of spatial and source models representing the inter-channel covariances and power spectral densities of sources, respectively. Although the variational autoencoder (VAE) has successfully been used as a non-linear source model with latent features, it should be pretrained from a sufficient amount of isolated signals. Our method, in contrast, enables the VAE-based source model to be trained only from mixture signals. Specifically, we introduce a neural mixture-to-feature inference model that directly infers the latent features from the observed mixture and integrate it with a neural feature-to-mixture generative model consisting of a full-rank spatial model and a VAE-based source model. All the models are optimized jointly such that the likelihood for the training mixtures is maximized in the framework of AVI. Once the inference model is optimized, it can be used for estimating the latent features of sources included in unseen mixture signals. The experimental results show that the proposed method outperformed the state-of-the-art BSS methods based on linear generative models and was comparable to a method based on supervised learning of the VAE-based sourcemodel.
DOLPHIN: A Framework for the Design and Perceptual Evaluation of Ultrasound Mid-Air Haptic Stimuli

Ultrasound mid-air haptic interfaces can display highly reconfigurable vibrotactile shapes in mid-air for human-computer interaction (HCI) applications. The choice of stimulus shape, spatial, temporal and modulation parameters yields a complex design space, yet relatively little is known about the impact of these design choices on perceived stimulus properties. We define the combination of a spatial discretization of an abstract shape and a set of rules for the temporal display order and intensity modulation of the resulting points as a sampling strategy. We developed DOLPHIN, an open-source framework to aid in designing mid-air stimuli. DOLPHIN allows the study of the impact of rendering parameters on perceived stimulus properties. This platform-agnostic framework standardizes stimulus descriptions as a step toward more replicability and easier communication in the field. It enables reproduction of stimuli between perceptual experiments and ensures stimuli used in applications correspond to those evaluated in prior perceptual studies. We validated DOLPHIN’s usability by conducting a user study assessing the impact of sampling strategy design on curvature discrimination for dynamic mid-air haptic stimuli. The Weber fractions for just-noticeable differences (JNDs) in curvature were found to range between 1 and 1.4, yet no significant effect of the number of spatial sampling points on curvature discrimination was found. This result shows a practical use-case for DOLPHIN and provides insight into rendering mid-air haptic curvature.
A study on optimization design of cosmetics sustainable service system and case-based design on Xie fuchun

With the development of the digital age, the domestic cosmetics industry needs to combine with the needs of users to explore new development paths. This study uses the theory and methods of service design, discusses about the present situation of China time-honored cosmetic brand––Xie Fuchun, through user journey map to find users" pain points and core requirements, thus the design opportunity points were obtained. On this basis, the service blueprint was drawn, the service process of Xie Fuchun was optimized, the "online + offline" service system was built, and the sustainable service system was established, providing effective strategies for Xie Fuchun"s design practice, including online and offline two parts. Online strategies include: 1. increase publicity efforts, 2. AI beauty advisor, 3. facial evaluation report, 4. AR test-cosmetic, 5. establish user profile in customer service management system. And offline strategies mainly include: 1. customer skin test, 2. free trial of cosmetics, 3. participate in cosmetic containers and packaging design, 4. custom Cosmetics Workshop. Based on the strategies above, It has positive practical significance in improving user experience, increasing the stickiness between users and cosmetics brands, and enhancing the sustainable development of domestic cosmetics industry.
Genomic instability in the naturally and prematurely aged myocardium

Significance Accumulation of DNA variants is considered a cause of organ dysfunction during natural aging. Here, we directly assessed the frequency of DNA damage of the murine myocardium during natural aging and in hearts from four mouse models of premature aging. In contrast to the prevailing dogma, our results provide no evidence for an enrichment in variants in the naturally aging heart until 2 y of age in Hutchinson–Gilford Progeria Syndrome hearts or in hearts with reduced telomere lengths. Accumulation of randomly distributed variants was, however, evident in Ercc1 knockout mice with deficient DNA repair machinery and in the intestine, liver, and lung of naturally aging mice. These results launch avenues to dissect the fundamental biological processes underlying cardiac aging. Genomic instability, the unresolved accumulation of DNA variants, is hypothesized as one of the contributors to the natural aging process. We assessed the frequency of unresolved DNA damage reaching the transcriptome of the murine myocardium during the course of natural aging and in hearts from four distinct mouse models of premature aging with established aging-related cardiac dysfunctions. RNA sequencing and variant calling based on total RNA sequencing was compared between hearts from naturally aging mice, mice with cardiomyocyte-specific deficiency of Ercc1, a component of the DNA repair machinery, mice with reduced mitochondrial antioxidant capacity, Tert-deficient mice with reduced telomere length, and a mouse model of human Hutchinson–Gilford progeria syndrome (HGPS). Our results demonstrate that no enrichment in variants is evident in the naturally aging murine hearts until 2 y of age from the HGPS mouse model or mice with reduced telomere lengths. In contrast, a dramatic accumulation of variants was evident in Ercc1 cardiomyocyte-specific knockout mice with deficient DNA repair machinery, in mice with reduced mitochondrial antioxidant capacity, and in the intestine, liver, and lung of naturally aging mice. Our data demonstrate that genomic instability does not evidently contribute to naturally aging of the mouse heart in contrast to other organs and support the contention that the endogenous DNA repair machinery is remarkably active to maintain genomic integrity in cardiac cells throughout life.
Screening Cardiovascular Autonomic Neuropathy in Diabetic Patients With Microvascular Complications Using Machine Learning: A 24-Hour Heart Rate Variability Study

Cardiovascular autonomic neuropathy (CAN) is one of the most overlooked complications associated with diabetes. It is characterized by damage in the autonomic nerves regulating heart rate and vascular compliance. Ewing battery is currently the diagnostic tool of choice but is unable to detect sub-clinical CAN and requires patient cooperation. In addition, appropriate timing (day/night) of CAN diagnostic test was not explored in the past. Therefore, a novel approach is proposed herein to investigate the feasibility of using heart rate variability (HRV) features over 24 hours embedded within machine learning algorithms to provide a complete screening for patients suffering from CAN. 24-hour Holter ECG data were acquired from a Bangladeshi cohort (n = 95 patients [75 Diabetic and 25 healthy]). HRV features were extracted from every 5-minute segment of the HRV signal and used as input to four machine learning algorithms for hourly training and testing. A complete hierarchical step by step diagnosis procedure (4 tests) was developed; namely test 1 to check for being healthy or diabetic; test 2 to check for any microvascular complications (including neuropathy such as CAN, peripheral neuropathy (DPN), nephropathy (NEP), and retinopathy (RET)) or not; test 3 to check for presence of only CAN; test 4 to check for combined or multiple complications along with CAN. The highest levels of performance were achieved with accuracy measures of 85.5% (test 1 - convolutional neural network (CNN)), 98.5% (test 2 - CNN), 98.3% (test 3 - one-class support vector machines (SVM)), and 90.9% (test 4 - random forest). Hours 7:00 AM and 7:00 PM were found to be most significant in the diagnosis of CAN in diabetic patients (test 1, 3, and 4). Early screening of CAN by our proposed models could help primary healthcare centers stratify the risk leading to early treatment in preventing sudden cardiac death due to silent myocardial infarction. The approach is considered to be simple and effective, especially for under-resourced clinical settings.
Design and Control of a Discrete Variable Stiffness Actuator With Instant Stiffness Switch for Safe Human-Robot Interaction

Variable Stiffness Actuators (VSA) have been proposed as an alternative actuation system for manipulators that are utilized for safe physical Human-Robot Interaction (pHRI). However, in the incidents of collision, the need of a fast response in stiffness tuning would rise to ensure safety. In this paper, we present a novel Discrete Variable Stiffness Actuator (DVSA) to be used in a compliant robotic manipulator for safe physical Human-Robot Interaction (pHRI). The novelty of this actuator lies in its design topology which allows the stiffness level to change swiftly among predefined levels without the need of complex stiffness tuning mechanism. Through this topology, three springs in parallel are connected serially between the motor and the link via gear train. The stiffness of the actuator is altered by adding/subtracting the number of involved springs, which can be realized through engagement/disengagement electromagnetic clutches on two of these spring’s shafts. The working principle, and the detailed design of the actuator are illustrated. Moreover, the stiffness model and the dynamic model are presented and discussed thoroughly. In order to validate these mathematical models and achieve optimal control, system identification for the dynamic parameters was performed experimentally on the physical model. Furthermore, the system’s ability of tracking desired trajectory was achieved through the implementation of different control techniques including PID (Proportional-Integral-Derivative), LQR (Linear Quadratic Regulator) and pole placement. The results show the high potential of utilizing the actuator in compliant manipulators. Moreover, DVSA is also characterized for safety in pHRI through Head-Injury Criterion (HIC). Finally, an application of DVSA in human augmentation task (Weight Bearing Task) is presented.
Model-Inspired Deep Detection with Low-Resolution Receivers

The need to recover high-dimensional signals from their noisy low-resolution quantized measurements is widely encountered in communications and sensing. In this paper, we focus on the extreme case of one-bit quantizers, and propose a deep detector network, called LoRD-Net, for signal recovering from one-bit measurements. Our approach relies on a model-aware data-driven architecture, based on a deep unfolding of first-order optimization iterations. LoRD-Net has a task-based architecture dedicated to recovering the underlying signal of interest from the one-bit noisy measurements without requiring prior knowledge of the channel matrix through which the one-bit measurements are obtained. The proposed deep detector has much fewer parameters compared to black-box deep networks due to the incorporation of domain-knowledge in the design of its architecture, allowing it to operate in a data-driven fashion while benefiting from the flexibility, versatility, and reliability of model-based optimization methods. We numerically evaluate the proposed receiver architecture for one-bit signal recovery in wireless communications and demonstrate that the proposed hybrid methodology outperforms both data-driven and model-based state-of-the-art methods, while utilizing small datasets, on the order of merely ~ 500 samples, for training.
HSGMP: Heterogeneous Scene Graph Message Passing for Cross-modal Retrieval

Semantic relationship information is important to the image-text retrieval task. Existing work usually extract relationship information by calculating the relationship value pairwise, which is hardly to find out a meaningful semantic relationship. A more reasonable method is to convert the modal to a scene graph, thereby explicitly modeling the relationship. Scene graph is a kind of graph data structure modeling the scene of modality. There are two concept in a scene graph, object and relationship. In image modal, object indicates the image region and relationship represents the predicate of the image regions. In text modal, object indicates the entity and relationship represents the association between entities, also known as semantic relationship. In image-text retrieval task, both object and relationship are important, and a key challenge is to obtain semantic information. In this paper, image and text are represented as two kinds of scene graphs: visual scene graph and textual scene graph, and then they are combined into Heterogeneous Scene Graph(HSG). By explicitly modeling relationships using directed graph, the information can be passed edge-wise. To further extract semantic information, we introduce the metapath, which can extract specific semantic information on specified path. Moreover, we propose Heterogeneous Message Passing(HMP) to communicate information on the metapath. After the message passing, the similarity of two modalities can be represented as the similarity of the graphs. Experiment shows that the model achieve competitive results on Flickr30K and MSCOCO, which indicates that our approach has advantages in image-text retrieval.
Rational Design of Nonbonded Point Charge Models for Highly Charged Metal Cations with Lennard-Jones 12-6 Potential

Here, we developed nonbonded point charge models using a simple Lennard-Jones (LJ) 12-6 potential for highly charged metal cations (18 trivalent and 6 tetravalent ions) for use with 11 water models of TIP3P, OPC3, SPC/E, SPC/Eb, TIP3P-FB, a99SB-disp, TIP4P-Ew, OPC, TIP4P/2005, TIP4P-D, and TIP4P-FB. The designed models simultaneously reproduce the hydration free energy (HFE) and ion-oxygen distance (IOD) in the first hydration shell with an error within 1 kcal/mol and 0.01 Å on average, respectively, and yield reasonable coordination numbers for most cations. Such performance is equivalent to the previously reported point charge models using a more complex 12-6-4 LJ-type potential, while the LJ R parameters of our models are much close to Shannon's revised effective ion radii than that of the 12-6-4 models. Our designed models overestimate the diffusion constants of several trivalent ions by 5-68%. The performance in predicting osmotic coefficients of trivalent chlorides in aqueous solution depends on the salt type. A calibration of cation-anion interacting LJ parameters reproduces the experimental osmotic coefficients of an AlCl3 solution at 0.2-3.0 mol/L. The effectiveness of our new models is further demonstrated by simulating a metalloprotein system with four force field/water combinations. This work facilitates accurate modeling of metal-containing systems by a variety of force fields and water models in aqueous solution.
In-field cotton detection algorithm based on the dual-path feature extraction

Abstract. The complex distribution, mutual occlusion, and scale difference greatly increase the difficulty of cotton detection in the wild. To reduce the omission ratio and raise the detection accuracy of cotton, a dual-path feature extraction (DPFE) cotton detection algorithm is proposed. It consists of a DPFE convolutional neural network, a multi-path feature fusion module, and a multi-scale prediction module. First, the algorithm uses the Darknet network as the main path for feature extraction. At the same time, the double downsampling feature map of the main path is enhanced by a proposed feature enhancement module—spatial pyramid convolution. Then a four-layer convolutional neural structure is designed as the auxiliary path for feature extraction. Finally, multiple feature information is incorporated to locate and recognize cotton with a higher accuracy. In addition, we collected and labeled a cotton dataset with 168 high-resolution images, including 4922 cotton instances for research. The experimental results demonstrate that the DPFE algorithm increases the average detection precision by 9.55% and the recall rate by 13.69%, compared with the traditional algorithm.
Products and technologies for treating patients with evidence-based pressure ulcers.

OBJECTIVE to identify products/technologies for treating patients with pressure ulcers with an evidence level 1. METHOD this is an integrative literature review. A survey of studies was carried out using the United States National Library of Medicine Portal, Scientific Electronic Library Online, Virtual Health Library, National Library of Medicine(®), The Cumulative Index to Nursing and Allied Health Literature, Latin American and Caribbean in Health Sciences, Nursing Database. RESULTS sixteen articles were selected with level of evidence 1. The findings were categorized into five categories: Topical therapy to promote healing; Alternative therapy to promote healing; Topical therapy to promote debridement; Topical therapy to minimize lesion contamination; Topical therapy to reduce lesion size. FINAL CONSIDERATIONS the 17 products/technologies identified favor/fast healing, debridement, minimize contamination and reduce lesion size to accelerate healing.
Integration of the saliency-based seeds generation and random walks with restart for image segmentation

Abstract. In the task of interactive image segmentation, the user initially indicates some pixels as target seeds and background seeds and propagates the seeds’ labels to the rest of the image. We propose an equidistant seed generation method for interactive image segmentation that combines saliency detection and random walk (RW) with restart with optimal restarting probability. Our method includes two stages: first, we utilize the saliency detection method to obtain the initial target region and generate the initial seeds. Then we relabel the seeds using a Gaussian mixture model with limited user interaction to obtain accurate seeds. The relabeled seeds can limit the range of object; therefore, in the second stage, we can select an optimal restarting probability by calculating the area of the RW that exceeds the limited range of the object seeds. Our method can generate effective seeds and thus reduce the dependence on user interaction. Furthermore, using the optimal restarting probability, we can obtain higher segmentation accuracy. Extensive experiments on the Berkeley segmentation dataset, GrabCut dataset, and MSRA1000 dataset demonstrate that our method can reduce the dependence on user interaction and achieve better performance than the latest interactive image segmentation methods, such as RW, RW with restart, normalized RW, sub-Markov RW, and label propagation through complex networks.
Constructing Knowledge Graphs for Online Collaborative Programming

This study aimed to automatically construct knowledge graphs for online collaborative programming. We proposed several models and developed a system to construct knowledge graphs based on online discussion texts and the target knowledge graph for the C programming language. Our system included two main modules, namely, entity recognition and relation extraction. We proposed an innovative approach for recognizing knowledge entities, which included sequence tagging, text classification, and keyword matching. The extraction of relationships among knowledge entities was performed through queries of the target knowledge graph. The six kinds of knowledge graphs could be automatically generated through our method, including the activated and unactivated knowledge graphs of each student, each group, and each class. The accuracy of entity recognition reached 87.27%. The accuracies of relation extraction for students, groups, and the class reached 89.7%, 90.4%, and 90.2%, respectively. This study is very promising and significant for both teachers and practitioners to provide interventions and personalized learning services based on the constructed knowledge graphs.