Miracle-dz/UniFAR-FTUs huggingface dataset:
Robotic Inspection and Characterization of Subsurface Defects on Concrete Structures Using Impact Sounding

Impact-sounding (IS) and impact-echo (IE) are well-developed non-destructive evaluation (NDE) methods that are widely used for inspections of concrete structures to ensure the safety and sustainability. However, it is a tedious work to collect IS and IE data along grid lines covering a large target area for characterization of subsurface defects. On the other hand, data processing is very complicated that requires domain experts to interpret the results. To address the above problems, we present a novel robotic inspection system named as Impact-Rover to automate the data collection process and introduce data analytics software to visualize the inspection result allowing regular non-professional people to understand. The system consists of three modules: 1) a robotic platform with vertical mobility to collect IS and IE data in hard-to-reach locations, 2) vision-based positioning module that fuses the RGB-D camera, IMU and wheel encoder to estimate the 6-DOF pose of the robot, 3) a data analytics software module for processing the IS data to generate defect maps. The Impact-Rover hosts both IE and IS devices on a sliding mechanism and can perform move-stop-sample operations to collect multiple IS and IE data at adjustable spacing. The robot takes samples much faster than the manual data collection method because it automatically takes the multiple measurements along a straight line and records the locations. This paper focuses on reporting experimental results on IS. We calculate features and use unsupervised learning methods for analyzing the data. By combining the pose generated by our vision-based localization module and the position of the head of the sliding mechanism we can generate maps of possible defects. The results on concrete slabs demonstrate that our impact-sounding system can effectively reveal shallow defects.
Chemical abundances in the outskirts of nearby galaxy groups measured with joint Suzaku and Chandra observations

We report results from deep Suzaku and mostly snapshot Chandra observations of four nearby galaxy groups: MKW4, Antlia, RXJ1159+5531, and ESO3060170. Their peak temperatures vary over 2–3 keV, making them the smallest systems with gas properties constrained to their viral radii. The average Fe abundance in the outskirts (R > 0.25R 200 ) of their intragroup medium (IGrM) is 𝑍 Fe = 0 . 309 ± 0 . 018 𝑍 (cid:12) with 𝜒 2 = 14 for 12 degrees of freedom, which is remarkably uniform and strikingly similar to that of massive galaxy clusters, and is fully consistent with the numerical predictions from the IllustrisTNG cosmological simulation. Our results support an early-enrichment scenario among galactic systems over an order of magnitude in mass, even before their formation. When integrated out to R 200 , we start to see a tension between the measured Fe content in ICM and what is expected from supernovae yields. We further constrain their O, Mg, Si, S, and Ni abundances. The abundance ratios of those elements relative to Fe are consistent with the predictions (if available) from IllustrisTNG. Their Type Ia supernovae fraction varies between 14%–21%. A pure core collapsed supernovae enrichment at group outskirts can be ruled out. Their cumulative iron-mass-to-light ratios within R 200 are half that of the Perseus cluster, which may imply that galaxy groups do not retain all of their enriched gas due to their shallower gravitational potential wells, or that groups and clusters may have diﬀerent star formation histories.
Sketched Reality: Sketching Bi-Directional Interactions Between Virtual and Physical Worlds with AR and Actuated Tangible UI

This paper introduces Sketched Reality, an approach that combines AR sketching and actuated tangible user interfaces (TUI) for bi-directional sketching interaction. Bi-directional sketching enables virtual sketches and physical objects to “affect” each other through physical actuation and digital computation. In the existing AR sketching, the relationship between virtual and physical worlds is only one-directional — while physical interaction can affect virtual sketches, virtual sketches have no return effect on the physical objects or environment. In contrast, bi-directional sketching interaction allows the seamless coupling between sketches and actuated TUIs. In this paper, we employ tabletop-size small robots (Sony Toio) and an iPad-based AR sketching tool to demonstrate the concept. In our system, virtual sketches drawn and simulated on an iPad (e.g., lines, walls, pendulums, and springs) can move, actuate, collide, and constrain physical Toio robots, as if virtual sketches and the physical objects exist in the same space through seamless coupling between AR and robot motion. This paper contributes a set of novel interactions and a design space of bi-directional AR sketching. We demonstrate a series of potential applications, such as tangible physics education, explorable mechanism, tangible gaming for children, and in-situ robot programming via sketching.
Anomaly segmentation model for defects detection in electroluminescence images of heterojunction solar cells

Efficient defect detection in solar cell manufacturing is crucial for stable green energy technology manufacturing. This paper presents a deep-learning-based automatic detection model SeMaCNN for classification and semantic segmentation of electroluminescent images for solar cell quality evaluation and anomalies detection. The core of the model is an anomaly detection algorithm based on Mahalanobis distance that can be trained in a semi-supervised manner on imbalanced data with small number of digital electroluminescence images with relevant defects. This is particularly valuable for prompt model integration into the industrial landscape. The model has been trained with the on-plant collected dataset consisting of 68 748 electroluminescent images of heterojunction solar cells with a busbar grid. Our model achieves the accuracy of 92.5%, F1 score 95.8%, recall 94.8%, and precision 96.9% within the validation subset consisting of 1049 manually annotated images. The model was also tested on the open ELPV dataset and demonstrates stable performance with accuracy 94.6% and F1 score 91.1%. The SeMaCNN model demonstrates a good balance between its performance and computational costs, which make it applicable for integrating into quality control systems of solar cell manufacturing.
Personalizing or Not: Dynamically Personalized Federated Learning with Incentives

Personalized federated learning (FL) facilitates collaborations between multiple clients to learn personalized models without sharing private data. The mechanism mitigates the statistical heterogeneity commonly encountered in the system, i.e., non-IID data over different clients. Existing personalized algorithms generally assume all clients volunteer for personalization. However, potential participants might still be reluctant to personalize models since they might not work well. In this case, clients choose to use the global model instead. To avoid making unrealistic assumptions, we introduce the personalization rate, measured as the fraction of clients willing to train personalized models, into federated settings and propose DyPFL. This dynamically personalized FL technique incentivizes clients to participate in personalizing local models while allowing the adoption of the global model when it performs better. We show that the algorithmic pipeline in DyPFL guarantees good convergence performance, allowing it to outperform alternative personalized methods in a broad range of conditions, including variation in heterogeneity, number of clients, local epochs, and batch sizes.
Is Your Model Sensitive? SPEDAC: A New Resource for the Automatic Classification of Sensitive Personal Data

In recent years, there has been an exponential growth of applications, including dialogue systems, that handle sensitive personal information. This has brought to light the extremely important issue of personal data protection in virtual environments. Sensitive information detection (SID) covers different domains and languages in literature. However, if we refer to the personal data domain, the absence of a shared standard benchmark makes comparison with the state-of-the-art difficult for this task. To fill this gap, we introduce and release SPEDAC, a new annotated resource for the identification of sensitive personal data categories in the English language. SPEDAC enables the evaluation of computational models for three different SID subtasks with increasing levels of complexity. SPEDAC 1 regards binary classification, a model has to detect if a sentence contains sensitive information or not; in SPEDAC 2 we collected labeled sentences using 5 categories that relate to macro-domains of personal information; in SPEDAC 3, the labeling is fine-grained and includes 61 personal data categories. We conduct an extensive evaluation of the resource using different state-of-the-art-classifiers. The results show that SPEDAC is challenging, particularly with regard to fine-grained classification. Classifiers based on the transformer architectures achieve good results on SPEDAC 1 and 2 but have difficulties to discern among fine-grained classes in SPEDAC 3.
Semantic Decomposition Network With Contrastive and Structural Constraints for Dental Plaque Segmentation

Segmenting dental plaque from images of medical reagent staining provides valuable information for diagnosis and the determination of follow-up treatment plan. However, accurate dental plaque segmentation is a challenging task that requires identifying teeth and dental plaque subjected to semantic-blur regions (i.e., confused boundaries in border regions between teeth and dental plaque) and complex variations of instance shapes, which are not fully addressed by existing methods. Therefore, we propose a semantic decomposition network (SDNet) that introduces two single-task branches to separately address the segmentation of teeth and dental plaque and designs additional constraints to learn category-specific features for each branch, thus facilitating the semantic decomposition and improving the performance of dental plaque segmentation. Specifically, SDNet learns two separate segmentation branches for teeth and dental plaque in a divide-and-conquer manner to decouple the entangled relation between them. Each branch that specifies a category tends to yield accurate segmentation. To help these two branches better focus on category-specific features, two constraint modules are further proposed: 1) contrastive constraint module (CCM) to learn discriminative feature representations by maximizing the distance between different category representations, so as to reduce the negative impact of semantic-blur regions on feature extraction; 2) structural constraint module (SCM) to provide complete structural information for dental plaque of various shapes by the supervision of an boundary-aware geometric constraint. Besides, we construct a large-scale open-source Stained Dental Plaque Segmentation dataset (SDPSeg), which provides high-quality annotations for teeth and dental plaque. Experimental results on SDPSeg datasets show SDNet achieves state-of-the-art performance.
Scholastic: Graphical Human-AI Collaboration for Inductive and Interpretive Text Analysis

Interpretive scholars generate knowledge from text corpora by manually sampling documents, applying codes, and refining and collating codes into categories until meaningful themes emerge. Given a large corpus, machine learning could help scale this data sampling and analysis, but prior research shows that experts are generally concerned about algorithms potentially disrupting or driving interpretive scholarship. We take a human-centered design approach to addressing concerns around machine-assisted interpretive research to build Scholastic, which incorporates a machine-in-the-loop clustering algorithm to scaffold interpretive text analysis. As a scholar applies codes to documents and refines them, the resulting coding schema serves as structured metadata which constrains hierarchical document and word clusters inferred from the corpus. Interactive visualizations of these clusters can help scholars strategically sample documents further toward insights. Scholastic demonstrates how human-centered algorithm design and visualizations employing familiar metaphors can support inductive and interpretive research methodologies through interactive topic modeling and document clustering.
Instance Image Retrieval by Learning Purely From Within the Dataset

Quality feature representation is key to instance image retrieval. To attain it, existing methods usually resort to a deep model pre-trained on benchmark datasets or even fine-tune the model with a task-dependent labelled auxiliary dataset. Although achieving promising results, this approach is restricted by two issues: 1) the domain gap between benchmark datasets and the dataset of a given retrieval task; 2) the required auxiliary dataset cannot be readily obtained. In light of this situation, this work looks into a different approach which has not been well investigated for instance image retrieval previously: {can we learn feature representation \textit{specific to} a given retrieval task in order to achieve excellent retrieval? } Our finding is encouraging. By adding an object proposal generator to generate image regions for self-supervised learning, the investigated approach can successfully learn feature representation specific to a given dataset for retrieval. This representation can be made even more effective by boosting it with image similarity information mined from the dataset. As experimentally validated, such a simple ``self-supervised learning + self-boosting'' approach can well compete with the relevant state-of-the-art retrieval methods. Ablation study is conducted to show the appealing properties of this approach and its limitation on generalisation across datasets.
Backward Stochastic Differential Equations (BSDEs) Using Infinite-Dimensional Martingales with Subdifferential Operator

In this paper, we focus on a family of backward stochastic differential equations (BSDEs) with subdifferential operators that are driven by infinite-dimensional martingales. We shall show that the solution to such infinite-dimensional BSDEs exists and is unique. The existence and uniqueness of the solution are established using Yosida approximations. Furthermore, as an application of the main result, we shall show that the backward stochastic partial differential equation driven by infinite-dimensional martingales with a continuous linear operator has a unique solution under the special condition that the Ft-progressively measurable generator F of the model we proposed in this paper equals zero.
Progressive Interpretation Synthesis: Interpreting Task Solving by Quantifying Previously Used and Unused Information

Abstract A deep neural network is a good task solver, but it is difficult to make sense of its operation. People have different ideas about how to interpret its operation. We look at this problem from a new perspective where the interpretation of task solving is synthesized by quantifying how much and what previously unused information is exploited in addition to the information used to solve previous tasks. First, after learning several tasks, the network acquires several information partitions related to each task. We propose that the network then learns the minimal information partition that supplements previously learned information partitions to more accurately represent the input. This extra partition is associated with unconceptualized information that has not been used in previous tasks. We manage to identify what unconceptualized information is used and quantify the amount. To interpret how the network solves a new task, we quantify as meta-information how much information from each partition is extracted. We implement this framework with the variational information bottleneck technique. We test the framework with the MNIST and the CLEVR data set. The framework is shown to be able to compose information partitions and synthesize experience-dependent interpretation in the form of meta-information. This system progressively improves the resolution of interpretation upon new experience by converting a part of the unconceptualized information partition to a task-related partition. It can also provide a visual interpretation by imaging what is the part of previously unconceptualized information that is needed to solve a new task.
Dimensions of Motion: Monocular Prediction through Flow Subspaces

We introduce a way to learn to estimate a scene representation from a single image by predicting a low-dimensional subspace of optical flow for each training example, which encompasses the variety of possible camera and object movement. Supervision is provided by a novel loss which measures the distance between this predicted flow subspace and an observed optical flow. This provides a new approach to learning scene representation tasks, such as monocular depth prediction or instance segmentation, in an unsupervised fashion using in-the-wild input videos without requiring camera poses, intrinsics, or an explicit multi-view stereo step. We evaluate our method in multiple settings, including an indoor depth prediction task where it achieves comparable performance to recent methods trained with more supervision. Our project page is at https://dimensions-of-motion.github. io/.
An effective field theory for gravitating spinning objects in the post-Newtonian scheme

An effective field theory for gravitating spinning objects in the post-Newtonian approximation is formulated in the context of the binary inspiral problem. We aim at an effective action, where all field modes below the orbital scale are integrated out. We spell out the relevant degrees of freedom, in particular the rotational ones, and the associated symmetries. Building on these symmetries, we introduce the minimal coupling part of the point particle action in terms of gauge rotational variables. We then proceed to construct the spin-induced nonminimal couplings, where we obtain the leading order couplings to all orders in spin for the first time. We specify to a gauge for the rotational variables, where the unphysical degrees of freedom are eliminated already from the Feynman rules, and all the orbital field modes are conveniently integrated out. The equations of motion of spin are then directly obtained via a proper variation of the action, and they take on a simple form. We implement this effective field theory for spin to derive all spin dependent potentials up to next-to-leading order to quadratic level in spin, namely up to the third post-Newtonian order for rapidly rotating compact objects. For the implementations we use the nonrelativistic gravitational field decomposition, which is found here to eliminate higher-loop Feynman diagrams also in spin dependent sectors, and facilitates derivations. Finally, the corresponding Hamiltonians are also straightforwardly obtained from the potentials derived via this formulation. Thus, the formulation is ideal for the treatment of further higher order spin dependent sectors.
Nitric oxide‐soluble guanylyl cyclase pathway as a contributor to age‐related memory impairment in Drosophila

Age‐related changes in the transcriptome lead to memory impairment. Several genes have been identified to cause age‐dependent memory impairment (AMI) by changes in their expression, but genetic screens to identify genes critical for AMI have not been performed. The fruit fly is a useful model for studying AMI due to its short lifespan and the availability of consistent techniques and environments to assess its memory ability. We generated a list of candidate genes that act as AMI regulators by performing a comprehensive analysis of RNAsequencing data from young and aged fly heads and genome‐wide RNAi screening data to identify memory‐regulating genes. A candidate screen using temporal and panneuronal RNAi expression was performed to identify genes critical for AMI. We identified the guanylyl cyclase β‐subunit at 100B (gycβ) gene, which encodes a subunit of soluble guanylyl cyclase (sGC), the only intracellular nitric oxide (NO) receptor in fruit flies, as a negative regulator of AMI. RNAi knockdown of gycβ in neurons and NO synthase (NOS) in glia or neurons enhanced the performance of intermediate‐term memory (ITM) without apparent effects on memory acquisition. We also showed that pharmacological inhibition of sGC and NOS enhanced ITM in aged individuals, suggesting the possibility that age‐related enhancement of the NO‐sGC pathway causes memory impairment.
N-Stearoylethanolamine Inhibits Integrin-Mediated Activation, Aggregation, and Adhesion of Human Platelets

N-stearoylethanolamine (NSE), a lipid mediator that belongs to the N-acylethanolamine (NAE) family, has anti-inflammatory, antioxidant, and membranoprotective actions. In contrast to other NAEs, NSE does not interact with cannabinoid receptors. The exact mechanism of its action remains unclear. The aim of this study is to evaluate the action of NSE on activation, aggregation, and adhesion of platelets that were chosen as a model of cellular response. Aggregation of platelets was measured to analyze the action of NSE (10−6–10−10 M) on platelet reactivity. Changes in granularity and shape of resting platelets and platelets stimulated with ADP in the presence of NSE were monitored by flow cytometry, and platelet deganulation was monitored by spectrofluorimetry. In vivo studies were performed using obese insulin-resistant rats. Binding of fibrinogen to the GPIIb/IIIa receptor was estimated using indirect ELISA and a scanning electron microscopy (SEM). It was found that NSE inhibits the activation and aggregation of human platelets. Our results suggest that NSE may decrease the activation and subsequent aggregation of platelets induced by ristocetin, epinephrine, and low doses of ADP. NSE also reduced the binding of fibrinogen to GPIIb/IIIa on activated platelets. These effects could be explained by the inhibition of platelet activation mediated by integrin receptors: the GPIb-IX-V complex for ristocetin-induced activation and GPIIb/IIIa when epinephrine and low doses of ADP were applied. The anti-platelet effect of NSE complements its anti-inflammatory effect and allows us to prioritize studies of NSE as a potent anti-thrombotic agent. SIGNIFICANCE STATEMENT N-stearoylethanolamine (NSE) was shown to possess inhibitory action on platelet activation, adhesion, and aggregation. The mechanism of inhibition possibly involves integrin receptors. This finding complements the known anti-inflammatory effects of NSE.