Reviewerly/exHarmony huggingface dataset:
Map3D: Registration-Based Multi-Object Tracking on 3D Serial Whole Slide Images

There has been a long pursuit for precise and reproducible glomerular quantification on renal pathology to leverage both research and practice. When digitizing the biopsy tissue samples using whole slide imaging (WSI), a set of serial sections from the same tissue can be acquired as a stack of images, similar to frames in a video. In radiology, the stack of images (e.g., computed tomography) are naturally used to provide 3D context for organs, tissues, and tumors. In pathology, it is appealing to do a similar 3D assessment. However, the 3D identification and association of large-scale glomeruli on renal pathology is challenging due to large tissue deformation, missing tissues, and artifacts from WSI. In this paper, we propose a novel Multi-object Association for Pathology in 3D (Map3D) method for automatically identifying and associating large-scale cross-sections of 3D objects from routine serial sectioning and WSI. The innovations of the Multi-Object Association for Pathology in 3D (Map3D) method are three-fold: (1) the large-scale glomerular association is formed as a new multi-object tracking (MOT) perspective; (2) the quality-aware whole series registration is proposed to not only provide affinity estimation but also offer automatic kidney-wise quality assurance (QA) for registration; (3) a dual-path association method is proposed to tackle the large deformation, missing tissues, and artifacts during tracking. To the best of our knowledge, the Map3D method is the first approach that enables automatic and large-scale glomerular association across 3D serial sectioning using WSI. Our proposed method Map3D achieved MOTA = 44.6, which is 12.1% higher than the non-deep learning benchmarks.
Generalised Pattern Search Based on Covariance Matrix Diagonalisation

Abstract Pattern Search is a family of gradient-free direct search methods for numerical optimisation problems. The characterising feature of pattern search methods is the use of multiple directions spanning the problem domain to sample new candidate solutions. These directions compose a matrix of potential search moves, that is the pattern. Although some fundamental studies theoretically indicate that various directions can be used, the selection of the search directions remains an unaddressed problem. The present article proposes a procedure for selecting the directions that guarantee high convergence/high performance of pattern search. The proposed procedure consists of a fitness landscape analysis to characterise the geometry of the problem by sampling points and selecting those whose objective function values are below a threshold. The eigenvectors of the covariance matrix of this distribution are then used as search directions for the pattern search. Numerical results show that the proposed method systematically outperforms its standard counterpart and is competitive with modern complex direct search and metaheuristic methods.
Construction and Analysis of SHA-256 Compression Function Based on Chaos S-Box

To further improve the security of SHA-256, a compression function construction scheme based on chaotic S-box is proposed. Through the reasonable design of the iteration mode and using the nonlinearity, confusion, and anti-difference of chaotic S-box, the relationship between the plaintext and the hash value becomes more complicated and unpredictable, by the diffusion of the linear transformation P-box, the dependence between the plaintext and the hash value is further improved. The compression function is applied to the SHA-256 Hash Algorithm to test and analyze the distribution, sensitivity, confusion, diffusion, and collision resistance, and compared with the original SHA-256 and the mainstream Hash Algorithms. The results show that the proposed scheme can effectively improve the collision resistance of the SHA-256 Hash Algorithm and enhance the stability of confusion and diffusion with fewer cryptographic components and lower operational consumption to provide more reliable security guarantee for its application.
A Lightweight Formalism for Reference Lifetimes and Borrowing in Rust

Rust is a relatively new programming language that has gained significant traction since its v1.0 release in 2015. Rust aims to be a systems language that competes with C/C++. A claimed advantage of Rust is a strong focus on memory safety without garbage collection. This is primarily achieved through two concepts, namely, reference lifetimes and borrowing . Both of these are well-known ideas stemming from the literature on region-based memory management and linearity / uniqueness . Rust brings both of these ideas together to form a coherent programming model. Furthermore, Rust has a strong focus on stack-allocated data and, like C/C++ but unlike Java, permits references to local variables. Type checking in Rust can be viewed as a two-phase process: First, a traditional type checker operates in a flow-insensitive fashion; second, a borrow checker enforces an ownership invariant using a flow-sensitive analysis. In this article, we present a lightweight formalism that captures these two phases using a flow-sensitive type system that enforces “ type and borrow safety .” In particular, programs that are type and borrow safe will not attempt to dereference dangling pointers. Our calculus core captures many aspects of Rust, including copy- and move-semantics, mutable borrowing, reborrowing, partial moves, and lifetimes. In particular, it remains sufficiently lightweight to be easily digested and understood and, we argue, still captures the salient aspects of reference lifetimes and borrowing. Furthermore, extensions to the core can easily add more complex features (e.g., control-flow, tuples, method invocation). We provide a soundness proof to verify our key claims of the calculus. We also provide a reference implementation in Java with which we have model checked our calculus using over 500B input programs. We have also fuzz tested the Rust compiler using our calculus against 2B programs and, to date, found one confirmed compiler bug and several other possible issues.
Eigen Selection in Spectral Clustering: A Theory-Guided Practice

Based on a Gaussian mixture type model of K components, we derive eigen selection procedures that improve the usual spectral clustering algorithms in high-dimensional settings, which typically act on the top few eigenvectors of an affinity matrix (e.g., X⊤X) derived from the data matrix X. Our selection principle formalizes two intuitions: (i) eigenvectors should be dropped when they have no clustering power; (ii) some eigenvectors corresponding to smaller spiked eigenvalues should be dropped due to estimation inaccuracy. Our selection procedures lead to new spectral clustering algorithms: ESSC for K = 2 and GESSC for K > 2. The newly proposed algorithms enjoy better stability and compare favorably against canonical alternatives, as demonstrated in extensive simulation and multiple real data studies. Supplementary materials for this article are available online.
Incorporating prior knowledge from counterfactuals into knowledge graph reasoning

Knowledge graph reasoning aims to find the missing links in knowledge graphs and is an important fundamental task. Existing methods mostly reason end-to-end and ignore the prior knowledge in the knowledge graph. In this paper, we attempt to mine prior knowledge from the knowledge graph based on counterfactuals and to use the prior knowledge to enhance the model. Specifically, we begin by constructing counterfactuals to assign a weight for each relation as prior knowledge and then perform reasoning based on both prior knowledge and reinforcement learning. This approach combines the advantages of prior knowledge and neural networks. Experiments on three large datasets show that the prior knowledge extracted from counterfactuals is effective in improving the multi-hop reasoning model. Prior knowledge also has the advantage of being path-length independent, which mitigates the performance degradation in multi-hop reasoning when the reasoning path is excessively long.
Cloud-based Signature Validation Using CNN Inception-ResNet Architecture

Signature validation is a critical component of biometric security systems, especially those used in high-risk sectors like finance or legal. To reduce the cases of document falsification and forgery, a method of conducting digital signature verification has been devised. The developed system makes use of the Inception-ResNet neural network model to create a cloud-based system that conducts signature validation via image processing techniques. Upon testing, the developed system obtained an overall accuracy percentage of 85%, as well as False Acceptance Rate (FAR) and False Rejection Rate (FRR) of 20.83% and 6.25%, respectively. Upon conducting a comparison test with similar existing methodologies, it was concluded by the proponents that the developed system possessed better accuracy as well as lower error rates than previous research regarding signature validation, making the prototype a viable alternative to current verification methods. It was also recommended by the proponents that in further studies on the topic, more standardized references should be trained in the neural network model to further improve system accuracy.
Generation of photonic matrix product states with Rydberg atomic arrays

We show how one can deterministically generate photonic matrix product states with high bond and physical dimensions with an atomic array if one has access to a Rydberg-blockade mechanism. We develop both a quantum gate and an optimal control approach to universally control the system and analyze the photon retrieval efficiency of atomic arrays. Comprehensive modeling of the system shows that our scheme is capable of generating a large number of entangled photons. We further develop a multi-port photon emission approach that can efficiently distribute entangled photons into free space in several directions, which can become a useful tool in future quantum networks.
Transfer Learning Technique with VGG-16 for Near-Infrared Facial Expression Recognition

Abstract In this paper, we investigate a deep learning vgg-16 network architecture for facial expression recognition under active near-infrared illumination condition and background. In particular, we consider the concept of transfer learning whereby features learned from high resolution images of huge datasets can be used to train a model of relatively small dataset without loosing the generalization ability. The pre-trained vgg-16 network architecture with transfer learning technique has been trained and validated on the Oulu-CASIA NIR dataset comprising of six (6) distinct facial expressions, and average test accuracy of 98.11% was achieved. The validation on our test data using the confusion, the precision, and the recall matrix reveals that our method achieves better results in comparison with the other methods in the literature.
Application of multivariate geostatistical simulation and fractal analysis for detection of rare-earth element geochemical anomalies in the Esfordi phosphate mine, Central Iran

The present study was conducted to determine different geochemical anomalies of rare-earth elements (REEs) using a combined approach of stepwise factor analysis (SFA), sequential Gaussian simulation (SGS), and concentration–area (C-A) fractal modeling based on surface lithogeochemical samples obtained from the Esfordi phosphate mine (Central Iran). The Esfordi mine is one of the important mines in the Bafq metallogenic zone due to average and maximum grades of 0.5 and 1.7%, respectively for REEs. With SFA operating in two steps, the REEs were placed in the first factor of the second stage (F1-2). Then, SGS and C-A fractal modeling were performed on F1-2 factor scores for classification of anomalies. A log-ratio matrix was used to evaluate the correlation of these results with anomalous lithogeochemical samples, as well as to determine the relationship of anomalies with rock types and mineralized units and, finally, to validate the results of the SGS–fractal modeling. The results confirmed an appropriate correlation between F1-2 anomalies and high concentration in further rock samples. The main anomalies were found to have good correlation with an apatite–iron unit and in general with other apatite-bearing units based on overall accuracy values. The apatite-bearing units with high values of REEs were located in the northern and central parts of the mine. The results of the SFA, SGS and C-A fractal modeling show that this hybrid approach can be useful in determining anomalies with high accuracy.
Fast Trajectory Planning in Cartesian rather than Frenet Frame: A Precise Solution for Autonomous Driving in Complex Urban Scenarios

On-road trajectory planning is a direct reflection of an autonomous vehicle’s intelligence level when traveling on an urban road. The prevalent on-road trajectory planners include the spline-based, sample-and-search-based, and optimal-control-based methods. Path-velocity decomposition and Frenet frame have been widely adopted in the aforementioned methods, which, nonetheless, largely degrade the trajectory planning quality when the road curvature is large and/or the scenario is complex. This paper aims to plan precise and high-quality on-road trajectories, thus we choose to describe the concerned scheme as an optimal control problem, wherein the urban road scenario is described completely in the Cartesian frame rather than in the Frenet frame. The formulated optimal control problem should be numerically solved in real-time. To that end, a light-weighted iterative computation architecture is built. In each iteration, a tunnel construction strategy tractablely models the collision-avoidance constraints, and a constraint softening strategy helps to find an intermediate trajectory for constructing the tunnels in the next iteration. Efficacy of the proposed on-road trajectory planner is validated by simulations on a high-curvature urban road wherein the ego vehicle is surrounded by multiple social vehicles at various speeds.
Increasing the Reliability of Data Collection of Laser Line Triangulation Sensor by Proper Placement of the Sensor

In this paper, we investigated the effect of the incidence angle of a laser ray on the reflected laser intensity. A dataset on this dependence is presented for materials usually used in the industry, such as transparent and non-transparent plastics and aluminum alloys with different surface roughness. The measurements have been performed with a laser line triangulation sensor and a UR10e robot. The presented results are proposing where to place the sensor relative to the scanned object, thus increasing the reliability of the sensor data collection.
GreyReID: A Novel Two-stream Deep Framework with RGB-grey Information for Person Re-identification

In this article, we observe that most false positive images (i.e., different identities with query images) in the top ranking list usually have the similar color information with the query image in person re-identification (Re-ID). Meanwhile, when we use the greyscale images generated from RGB images to conduct the person Re-ID task, some hard query images can obtain better performance compared with using RGB images. Therefore, RGB and greyscale images seem to be complementary to each other for person Re-ID. In this article, we aim to utilize both RGB and greyscale images to improve the person Re-ID performance. To this end, we propose a novel two-stream deep neural network with RGB-grey information, which can effectively fuse RGB and greyscale feature representations to enhance the generalization ability of Re-ID. First, we convert RGB images to greyscale images in each training batch. Based on these RGB and greyscale images, we train the RGB and greyscale branches, respectively. Second, to build up connections between RGB and greyscale branches, we merge the RGB and greyscale branches into a new joint branch. Finally, we concatenate the features of all three branches as the final feature representation for Re-ID. Moreover, in the training process, we adopt the joint learning scheme to simultaneously train each branch by the independent loss function, which can enhance the generalization ability of each branch. Besides, a global loss function is utilized to further fine-tune the final concatenated feature. The extensive experiments on multiple benchmark datasets fully show that the proposed method can outperform the state-of-the-art person Re-ID methods. Furthermore, using greyscale images can indeed improve the person Re-ID performance in the proposed deep framework.
Spatiotemporal Optimization for Short-Term Solar Forecasting Based on Satellite Imagery

Solar forecasting is essential for optimizing the integration of solar photovoltaic energy into a power grid. This study presents solar forecasting models based on satellite imagery. The cloud motion vector (CMV) model is the most popular satellite-image-based solar forecasting model. However, it assumes constant cloud states, and its accuracy is, thus, influenced by changes in local weather characteristics. To overcome this limitation, satellite images are used to provide spatial data for a new spatiotemporal optimized model for solar forecasting. Four satellite-image-based solar forecasting models (a persistence model, CMV, and two proposed models that use clear-sky index change) are evaluated. The error distributions of the models and their spatial characteristics over the test area are analyzed. All models exhibited different performances according to the forecast horizon and location. Spatiotemporal optimization of the best model is then conducted using best-model maps, and our results show that the skill score of the optimized model is 21% better than the previous CMV model. It is, thus, considered to be appropriate for use in short-term forecasting over large areas. The results of this study are expected to promote the use of spatial data in solar forecasting models, which could improve their accuracy and provide various insights for the planning and operation of photovoltaic plants.
Domestic Robots and the Dream of Automation: Understanding Human Interaction and Intervention

Domestic robots such as vacuum cleaners or lawnmowers are becoming popular consumer products in private homes, but while current HCI research on domestic robots has highlighted for example personalisation, long-term effects, or design guidelines, little attention has been paid to automation. To address this, we conducted a qualitative study with 24 participants in private households using interviews, contextual technology tours, and robot deployment. Through thematic analysis we identified three themes related to 1) work routines and automation, 2) domestic robot automation and the physical environment, as well as 3) interaction and breakdown intervention. We present an empirical understanding of how task automation using domestic robots can be implemented in the home. Lastly, we discuss our findings in relation to existing literature and highlight three opportunities for improved task automation using domestic robots for future research.
Fast classification rates without standard margin assumptions

Abstract We consider the classical problem of learning rates for classes with finite Vapnik–Chervonenkis (VC) dimension. It is well known that fast learning rates up to $O\left (\frac{d}{n}\right )$ are achievable by the empirical risk minimization (ERM) algorithm if low noise or margin assumptions are satisfied. These usually require the optimal Bayes classifier to be in the class, and it has been shown that when this is not the case, the fast rates cannot be achieved even in the noise free case. In this paper, we further investigate the question of the fast rates in the agnostic setting, when the Bayes classifier is not in the class and the noise in the labeling is allowed. First, we consider classification with a reject option, namely Chow’s reject option model, and show that by slightly lowering the impact of hard instances, a learning rate of order $O\left (\frac{d}{n}\log \frac{n}{d}\right )$ is always achievable in the agnostic setting by a specific learning algorithm. Similar results were only known under special versions of margin assumptions. As an auxiliary result, we show that under the Bernstein assumption the performance of the proposed algorithm is never worse than the performance of ERM even if some of the labels are predicted at random. Based on those results, we derive the necessary and sufficient conditions for classification (without a reject option) with fast rates in the agnostic setting achievable by improper learners. This simultaneously extends the work by Massart &amp; Nédélec (2006, Ann. Stat., 34, 2326–2366), which studied this question in the case where the Bayesian optimal rule belongs to the class, and the work by Ben-David and Urner (2014, COLT, pp. 527–542 ), which allows the misspecification but is limited to the no noise setting. Our result also provides the first general setup in statistical learning theory in which an improper learning algorithm may significantly improve the learning rate for non-convex losses.
Lesion Extraction of Endometriotic images using Open Computer Vision

The Endometriosis is caused by the endometriotic tissue that streaks the uterus from outside. A total of 6% of women under child bearing age is affected by the endometriosis disorder. The presence of the tissue can be identified though MRI, Transvaginal Ultra Sound Scan (TVUS), Laparoscopic images and pathological slides. Image based applications are processed using Image Pre-processing, Image Segmentation, Image Enhancement and Feature Extraction techniques. The Open Computer Vision (Open CV) is used to extract the features from Laparoscopic Endometriosis Images (LPI). This paper proposed a method to identify the endometriotic tissue by using the LPI images. The proposed method uses several image processing techniques of OpenCV includes Adaptive Threshold, Contour Mask to extract the lesion area from the Laparoscopic images. The designed algorithm has been experimented and the results yielded 53.5 % mean value intensity in identifying the lesion area.
The show must go on! Strategies for making and makerspaces during pandemic

The COVID-19 pandemic is challenging for Fab Labs and makerspaces where the use of digital fabrication machines and working with physical materials in collaboration with others are at the heart of the activities. We have been actively promoting children's technology education both by training local teachers and by working with children themselves. The restrictions have resulted in limiting the number of participants or moving to online working, or even closing the workspaces and cancelling the events. To continue our work, we needed to explore new solutions for the situation. We have provided online training for teachers, experimented with working in family groups and fully online, while access to the digital fabrication machines and children's engagement in online activities were the largest challenges we encountered. We report in this paper our experiences with different solutions as well as challenges we have faced, both as regards technology education of children and collecting research data related to that.