Reviewerly/exHarmony huggingface dataset:
MVSPlenOctree: Fast and Generic Reconstruction of Radiance Fields in PlenOctree from Multi-view Stereo

We present MVSPlenOctree, a novel approach that can efficiently reconstruct radiance fields for view synthesis. Unlike previous scene-specific radiance fields reconstruction methods, we present a generic pipeline that can efficiently reconstruct 360-degree-renderable radiance fields via multi-view stereo (MVS) inference from tens of sparse-spread out images. Our approach leverages variance-based statistic features for MVS inference, and combines this with image based rendering and volume rendering for radiance field reconstruction. We first train a MVS Machine for reasoning scene's density and appearance. Then, based on the spatial hierarchy of the PlenOctree and coarse-to-fine dense sampling mechanism, we design a robust and efficient sampling strategy for PlenOctree reconstruction, which handles occlusion robustly. A 360-degree-renderable radiance fields can be reconstructed in PlenOctree from MVS Machine in an efficient single forward pass. We trained our method on real-world DTU, LLFF datasets, and synthetic datasets. We validate its generalizability by evaluating on the test set of DTU dataset which are unseen in training. In summary, our radiance field reconstruction method is both efficient and generic, a coarse 360-degree-renderable radiance field can be reconstructed in seconds and a dense one within minutes. Please visit the project page for more details: https://derry-xing.github.io/projects/MVSPlenOctree.
Cross-Modality Domain Adaptation for Freespace Detection: A Simple yet Effective Baseline

As one of the fundamental functions of autonomous driving system, freespace detection aims at classifying each pixel of the image captured by the camera as drivable or non-drivable. Current works of freespace detection heavily rely on large amount of densely labeled training data for accuracy and robustness, which is time-consuming and laborious to collect and annotate. To the best of our knowledge, we are the first work to explore unsupervised domain adaptation for freespace detection to alleviate the data limitation problem with synthetic data. We develop a cross-modality domain adaptation framework which exploits both RGB images and surface normal maps generated from depth images. A Collaborative Cross Guidance (CCG) module is proposed to leverage the context information of one modality to guide the other modality in a cross manner, thus realizing inter-modality intra-domain complement. To better bridge the domain gap between source domain (synthetic data) and target domain (real-world data), we also propose a Selective Feature Alignment (SFA) module which only aligns the features of consistent foreground area between the two domains, thus realizing inter-domain intra-modality adaptation. Extensive experiments are conducted by adapting three different synthetic datasets to one real-world dataset for freespace detection respectively. Our method performs closely to fully supervised freespace detection methods (93.08% v.s. 97.50% F1 score) and outperforms other general unsupervised domain adaptation methods for semantic segmentation with large margins, which shows the promising potential of domain adaptation for freespace detection.
Semi-supervised Crowd Counting via Density Agency

In this paper, we propose a new agency-guided semi-supervised counting approach. First, we build a learnable auxiliary structure, namely the density agency to bring the recognized foreground regional features close to corresponding density sub-classes (agents) and push away background ones. Second, we propose a density-guided contrastive learning loss to consolidate the backbone feature extractor. Third, we build a regression head by using a transformer structure to refine the foreground features further. Finally, an efficient noise depression loss is provided to minimize the negative influence of annotation noises. Extensive experiments on four challenging crowd counting datasets demonstrate that our method achieves superior performance to the state-of-the-art semi-supervised counting methods by a large margin. The code is available at https://github.com/LoraLinH/Semi-supervised-Crowd-Counting-via-Density-Agency.
An interactive and collaborative augmented reality environment for civil engineering education: steel reinforcement bars teaching as an example

Purpose At present, teaching methods based on 2D drawings are still commonly used for educating students on the location of steel reinforcement bars in concrete. However, traditional teaching methods have limitations as students can find it difficult to understand 2D drawings. This study aims to develop an interactive and collaborative augmented reality environment (ICARE) using augmented reality (AR) technology to improve students' engagement in learning. Design/methodology/approach This study develops an ICARE prototype, which is organized into two stages: (1) The augmented teaching environment comprising of models and interactive components; (2) The AR collaborative application which uses Photon Unity Networking (PUN) plugin and Azure spatial anchors cloud service. The AR-based teaching environment runs with Universal Windows Platform (UWP) to enable development in the HoloLens 2 through Microsoft Visual Studio. Findings An experimental study was conducted, where 60 students were divided into three groups employing Drawings-based, building information modeling (BIM)-based and AR-based methods for teaching. After the test, the three groups of students were requested to complete a questionnaire. According to the analysis of the experimental results, the ICARE can improve students' comprehension, memory of learned materials and their ability to read and understand steel reinforcement drawings improving the quality of teaching, especially interactivity and engagement. Originality/value As illustrated in the experiments, the developed ICARE has outstanding performance over conventional approaches in civil engineering courses that can improve students' comprehension and memory of knowledge and their ability to read and understand steel bar drawings. This study provides empirical evidence that AR is a promising technology that can be integrated with traditional classroom instruction and can improve students' comprehension and memory of knowledge and their ability to read and understand steel bar drawings.
Redundancy criteria for linguistic fuzzy rules

The elimination of redundant rules in a given system is considered to be an important step towards its simplicity, clarity, and comprehensibility. In this paper, we return to the problem of redundancies in systems of linguistic fuzzy IF-THEN rules based on the perception-based logical deduction inference method. We generalize the existing criteria for detecting redundant rules in these systems to general ones, and elaborate a new algorithm for an automatic search and deletion of redundant linguistic fuzzy rules. The criteria are provided in a theoretically formulated form with full proofs. Furthermore, we present the final algorithm and demonstrative examples that show the positive impact of the new algorithm.
Virtual reality for immersive multi-user firefighter-training scenarios

Virtual Reality (VR) applications are tools used to provide comprehensive training scenarios that are difficult or impossible to represent in physical configurations. This includes team training for emergency services, e.g. in firefighting. To achieve effective virtual training, creating a high level of immersion is essential. In that respect, motion capture systems offer the possibility to create highly immersive multi-user training experiences including full-body avatars. This work presents a preliminary prototype that enables the extinguishing of a fire on a container ship as a VR training scenario. The prototype provides a full-body and multi-user VR experience, based on the synthesis of position data provided by the motion capture system and orientation data from the VR headsets. As a consequence, the prototype allows an initial evaluation of results. The results confirm the value of using VR to train procedures that are difficult to train in the real world. Furthermore, the results show that motion capture based VR technologies are particularly useful for firefighting training, in which participants can collaborate in otherwise difficult-to-access environments. However, the work also illustrates that increasing the immersion of such training remains a challenge.
Whisper: Profile-Guided Branch Misprediction Elimination for Data Center Applications

Modern data center applications experience frequent branch mispredictions– degrading performance, increasing cost, and reducing energy efficiency in data centers. Even the state-of the-art branch predictor, TAGE-SC-L, suffers from an average branch Mispredictions Per Kilo Instructions (branch-MPKI) of 3.0 (0.5-7.2) for these applications since their large code footprints exhaust TAGE-SC-L’s intended capacity. In this work, we propose Whisper, a novel profile-guided mechanism to avoid branch mispredictions. Whisper investigates the in-production profile of data center applications to identify precise program contexts that lead to branch mispredictions. Corresponding prediction hints are then inserted into code to strategically avoid those mispredictions during program execution. Whisper presents three novel profile-guided techniques: (1) hashed history correlation which efficiently encodes hard-to-predict correlations in branch history using lightweight Boolean formulas, (2) randomized formula testing which selects a locally-optimal Boolean formula from a randomly selected subset of possible formulas to predict a branch, and (3) the extension of Read-Once Monotone Boolean Formulas with Implication and Converse Non-Implication to improve the branch history coverage of these formulas with minimal overhead. We evaluate Whisper on 12 widely-used data center applications and demonstrate that Whisper enables traditional branch predictors to achieve a speedup close to that of an ideal branch predictor. Specifically, Whisper achieves an average speedup of 2.8% (0.4%-4.6%) by reducing 16.8% (1.7%-32.4%) of branch mispredictions over TAGE-SC-L and outperforms the state-of the-art profile-guided branch prediction mechanisms by 7.9% on average.
Breast Cancer Classification of Histopathological Images using Deep Convolutional Neural Networks

Histopathology refers to the diagnosis of tissue diseases and involves the thorough examination of tissues and cells under a microscope. Tissues are collected by biopsy and viewed under the microscope after being properly processed. Modern medical image processing techniques involve the collection of histopathological images taken under a microscope and their analysis using different algorithms and techniques. Deep Learning is widely used in the field of medical imaging as it does not require any specialized prior knowledge in the problem domain. The dataset used for our experiments comprises of histopathological scans derived from the PatchCamelyon dataset. Various Convolutional Neural Network architectures were implemented, where their hyperparameters were fine tuned and the classification results are presented. The deep learning neural networks are accessed for their worth in terms of accuracy, loss, AUC, precision, recall and time required.
CMA-CLIP: Cross-Modality Attention Clip for Text-Image Classification

Multi-modal learning with both text and images benefits multiple applications, such as attribute extraction for e-commerce products. In this paper, we propose Cross-Modality Attention Contrastive Language-Image Pre-training (CMA-CLIP), a new multi-modal architecture to jointly learn the fine-grained inter-modality relationship. It fuses CLIP with a sequence-wise attention module and a modality-wise attention module. The network uses CLIP to bridge the inter-modality gap at the global level, and uses the sequence-wise attention module to capture the fine-grained alignment between text and images. Besides, it leverages a modality-wise attention module to learn the relevance of each modality to downstream tasks, making the network robust against irrelevant modalities. CMA-CLIP outperforms the state-of-the-art method on Fashion-Gen by 5.5% in accuracy, achieves competitive performance on Food101 and performance on par with the state-of-the-art method on MM-IMDb. We also demonstrate CMA-CLIP’s robustness against irrelevant modalities on an Amazon dataset for the task of product attribute extraction.
Yolo-SG: Salience-Guided Detection Of Small Objects In Medical Images

Object detection, a crucial component of medical image analysis, provides physicians with an interpretable auxiliary diagnostic basis. Although existing object detection models have had great success with natural images, the growing resolution of medical images makes the problem especially challenging because of the increased expectations to exploit the image details and discover small targets in images. For instance, lesions are occasionally diminutive relative to high-resolution medical images. To address this problem, we present YOLO-SG, a salience-guided (SG) deep learning model that improves small object detection by attending to detailed regions via a generated salience map. YOLO-SG performs two rounds of detection: coarse detection and salience-guided detection. In the first round of coarse detection, YOLO-SG detects objects using a deep convolutional detection model and proposes a salience map utilizing the context surrounding objects to guide the subsequent round of detection. In the second round, YOLO-SG extracts salient regions from the original input image based on the generated salience map and combines local detail with global context information to improve the object detection performance. The experimental results demonstrate that YOLO-SG outperforms the state-of-the-art models, especially when detecting small objects.
Deep feature selection using local search embedded social ski-driver optimization algorithm for breast cancer detection in mammograms

Abstract Breast cancer has become a common malignancy in women. However, early detection and identification of this disease can save many lives. As computer-aided detection helps radiologists in detecting abnormalities efficiently, researchers across the world are striving to develop reliable models to deal with. One of the common approaches to identifying breast cancer is through breast mammograms. However, the identification of malignant breasts from mass lesions is a challenging research problem. In the current work, we propose a method for the classification of breast mass using mammograms which consists of two main stages. At first, we extract deep features from the input mammograms using the well-known VGG16 model while incorporating an attention mechanism into this model. Next, we apply a meta-heuristic called Social Ski-Driver (SSD) algorithm embedded with Adaptive Beta Hill Climbing based local search to obtain an optimal features subset. The optimal features subset is fed to the K-nearest neighbors (KNN) classifier for the classification. The proposed model is demonstrated to be very useful for identifying and differentiating malignant and healthy breasts successfully. For experimentation, we evaluate our model on the digital database for screening mammography (DDSM) database and achieve 96.07% accuracy using only 25% of features extracted by the attention-aided VGG16 model. The Python code of our research work is publicly available at: https://github.com/Ppayel/BreastLocalSearchSSD .
Securing Reset Operations in NISQ Quantum Computers

A secure reset operation could be an enabling technology that allows sharing of a quantum computer among different users, or among different quantum programs of the same user. Today, the dominant method to erase the qubit state is a full system wipe, which effectively resets all the qubits at the same time. In today's superconducting qubit machines from IBM, for example, a full system wipe takes up to 1000 μs, and it fully erases all information in the system. However, with a full system wipe there is no means for only a few qubits to be cleared and assigned to a new user or program; everything has to be erased at the same time. A secure reset operation could allow resetting only a subset of qubits, and it could be built upon existing (insecure) reset operation available from superconducting qubit machines from IBM, for example. The (insecure) reset operation is available today, which can be used to reset the state of a qubit in a time on the order of 10 μs down to 1 μs. The reset operation is thus much faster than a full system wipe. However, as this work demonstrates, it is possible to leak some information across the (insecure) reset operation as it does not perfectly reset the qubit state between two users or programs who may be sequentially scheduled on the same qubit. Further, crosstalk-like effects are observed where reset behavior of one qubit can be inferred from an adjacent qubit. This work analyzes the existing (insecure) reset operation in order to understand how a secure reset operation could be built upon it. This work then describes the design, implementation, and evaluation of the proposed secure reset operation which can reset qubits without leaking information, and retains a factor of 300 speedup over a full system~wipe.
Grid-Based Clustering Using Boundary Detection

Clustering can be divided into five categories: partitioning, hierarchical, model-based, density-based, and grid-based algorithms. Among them, grid-based clustering is highly efficient in handling spatial data. However, the traditional grid-based clustering algorithms still face many problems: (1) Parameter tuning: density thresholds are difficult to adjust; (2) Data challenge: clusters with overlapping regions and varying densities are not well handled. We propose a new grid-based clustering algorithm named GCBD that can solve the above problems. Firstly, the density estimation of nodes is defined using the standard grid structure. Secondly, GCBD uses an iterative boundary detection strategy to distinguish core nodes from boundary nodes. Finally, two clustering strategies are combined to group core nodes and assign boundary nodes. Experiments on 18 datasets demonstrate that the proposed algorithm outperforms 6 grid-based competitors.
Multi-Strategy Learning Boosted Colony Predation Algorithm for Photovoltaic Model Parameter Identification

Modeling solar systems necessitates the effective identification of unknown and variable photovoltaic parameters. To efficiently convert solar energy into electricity, these parameters must be precise. The research introduces the multi-strategy learning boosted colony predation algorithm (MLCPA) for optimizing photovoltaic parameters and boosting the efficiency of solar power conversion. In MLCPA, opposition-based learning can be used to investigate each individual’s opposing position, thereby accelerating convergence and preserving population diversity. Level-based learning categorizes individuals according to their fitness levels and treats them differently, allowing for a more optimal balance between variation and intensity during optimization. On a variety of benchmark functions, the MLCPA’s performance is compared to the performance of the best algorithms currently in use. On a variety of benchmark functions, the MLCPA’s performance is compared to that of existing methods. MLCPA is then used to estimate the parameters of the single, double, and photovoltaic modules. Last but not least, the stability of the proposed MLCPA algorithm is evaluated on the datasheets of many manufacturers at varying temperatures and irradiance levels. Statistics have demonstrated that the MLCPA is more precise and dependable in predicting photovoltaic mode critical parameters, making it a viable tool for solar system parameter identification issues.
Tuning gradient boosting for imbalanced bioassay modelling with custom loss functions

Abstract While in the last years there has been a dramatic increase in the number of available bioassay datasets, many of them suffer from extremely imbalanced distribution between active and inactive compounds. Thus, there is an urgent need for novel approaches to tackle class imbalance in drug discovery. Inspired by recent advances in computer vision, we investigated a panel of alternative loss functions for imbalanced classification in the context of Gradient Boosting and benchmarked them on six datasets from public and proprietary sources, for a total of 42 tasks and 2 million compounds. Our findings show that with these modifications, we achieve statistically significant improvements over the conventional cross-entropy loss function on five out of six datasets. Furthermore, by employing these bespoke loss functions we are able to push Gradient Boosting to match or outperform a wide variety of previously reported classifiers and neural networks. We also investigate the impact of changing the loss function on training time and find that it increases convergence speed up to 8 times faster. As such, these results show that tuning the loss function for Gradient Boosting is a straightforward and computationally efficient method to achieve state-of-the-art performance on imbalanced bioassay datasets without compromising on interpretability and scalability. Graphical Abstract
Probability and Certainty in the Performance of Evolutionary and Swarm Optimization Algorithms

Reporting the empirical results of swarm and evolutionary computation algorithms is a challenging task with many possible difficulties. These difficulties stem from the stochastic nature of such algorithms, as well as their inability to guarantee an optimal solution in polynomial time. This research deals with measuring the performance of stochastic optimization algorithms, as well as the confidence intervals of the empirically obtained statistics. Traditionally, the arithmetic mean is used for measuring average performance, but we propose quantiles for measuring average, peak and bad-case performance, and give their interpretations in a relevant context for measuring the performance of the metaheuristics. In order to investigate the differences between arithmetic mean and quantiles, and to confirm possible benefits, we conducted experiments with 7 stochastic algorithms and 20 unconstrained continuous variable optimization problems. The experiments showed that median was a better measure of average performance than arithmetic mean, based on the observed solution quality. Out of 20 problem instances, a discrepancy between the arithmetic mean and median happened in 6 instances, out of which 5 were resolved in favor of median and 1 instance remained unresolved as a near tie. The arithmetic mean was completely inadequate for measuring average performance based on the observed number of function evaluations, while the 0.5 quantile (median) was suitable for that task. The quantiles also showed to be adequate for assessing peak performance and bad-case performance. In this paper, we also proposed a bootstrap method to calculate the confidence intervals of the probability of the empirically obtained quantiles. Considering the many advantages of using quantiles, including the ability to calculate probabilities of success in the case of multiple executions of the algorithm and the practically useful method of calculating confidence intervals, we recommend quantiles as the standard measure of peak, average and bad-case performance of stochastic optimization algorithms.