Reviewerly/exHarmony huggingface dataset:
Doodle to Object: Practical Zero-Shot Sketch-Based 3D Shape Retrieval

Zero-shot (ZS) sketch-based three-dimensional (3D) shape retrieval (SBSR) is challenging due to the abstraction of sketches, cross-domain discrepancies between two-dimensional sketches and 3D shapes, and ZS-driven semantic knowledge transference from seen to unseen categories. Extant SBSR datasets suffer from lack of data, and no current SBSR methods consider ZS scenarios. In this paper, we contribute a new Doodle2Object (D2O) dataset consisting of 8,992 3D shapes and over 7M sketches spanning 50 categories. Then, we propose a novel prototype contrastive learning (PCL) method that effectively extracts features from different domains and adapts them to unseen categories. Specifically, our PCL method combines the ideas of contrastive and cluster-based prototype learning, and several randomly selected prototypes of different classes are assigned to each sample. By comparing these prototypes, a given sample can be moved closer to the same semantic class of samples while moving away from negative ones. Extensive experiments on two common SBSR benchmarks and our D2O dataset demonstrate the efficacy of the proposed PCL method for ZS-SBSR. Resource is available at https://github.com/yigohw/doodle2object.
3D Human Pose Lifting with Grid Convolution

Existing lifting networks for regressing 3D human poses from 2D single-view poses are typically constructed with linear layers based on graph-structured representation learning. In sharp contrast to them, this paper presents Grid Convolution (GridConv), mimicking the wisdom of regular convolution operations in image space. GridConv is based on a novel Semantic Grid Transformation (SGT) which leverages a binary assignment matrix to map the irregular graph-structured human pose onto a regular weave-like grid pose representation joint by joint, enabling layer-wise feature learning with GridConv operations. We provide two ways to implement SGT, including handcrafted and learnable designs. Surprisingly, both designs turn out to achieve promising results and the learnable one is better, demonstrating the great potential of this new lifting representation learning formulation. To improve the ability of GridConv to encode contextual cues, we introduce an attention module over the convolutional kernel, making grid convolution operations input-dependent, spatial-aware and grid-specific. We show that our fully convolutional grid lifting network outperforms state-of-the-art methods with noticeable margins under (1) conventional evaluation on Human3.6M and (2) cross-evaluation on MPI-INF-3DHP. Code is available at https://github.com/OSVAI/GridConv.
Progressive Neighborhood Aggregation for Semantic Segmentation Refinement

Multi-scale features from backbone networks have been widely applied to recover object details in segmentation tasks. Generally, the multi-level features are fused in a certain manner for further pixel-level dense prediction. Whereas, the spatial structure information is not fully explored, that is similar nearby pixels can be used to complement each other. In this paper, we investigate a progressive neighborhood aggregation (PNA) framework to refine the semantic segmentation prediction, resulting in an end-to-end solution that can perform the coarse prediction and refinement in a unified network. Specifically, we first present a neighborhood aggregation module, the neighborhood similarity matrices for each pixel are estimated on multi-scale features, which are further used to progressively aggregate the high-level feature for recovering the spatial structure. In addition, to further integrate the high-resolution details into the aggregated feature, we apply a self-aggregation module on the low-level features to emphasize important semantic information for complementing losing spatial details. Extensive experiments on five segmentation datasets, including Pascal VOC 2012, CityScapes, COCO-Stuff 10k, DeepGlobe, and Trans10k, demonstrate that the proposed framework can be cascaded into existing segmentation models providing consistent improvements. In particular, our method achieves new state-of-the-art performances on two challenging datasets, DeepGlobe and Trans10k. The code is available at https://github.com/liutinglt/PNA.
StereoDistill: Pick the Cream from LiDAR for Distilling Stereo-Based 3D Object Detection

In this paper, we propose a cross-modal distillation method named StereoDistill to narrow the gap between the stereo and LiDAR-based approaches via distilling the stereo detectors from the superior LiDAR model at the response level, which is usually overlooked in 3D object detection distillation. The key designs of StereoDistill are: the X-component Guided Distillation~(XGD) for regression and the Cross-anchor Logit Distillation~(CLD) for classification. In XGD, instead of empirically adopting a threshold to select the high-quality teacher predictions as soft targets, we decompose the predicted 3D box into sub-components and retain the corresponding part for distillation if the teacher component pilot is consistent with ground truth to largely boost the number of positive predictions and alleviate the mimicking difficulty of the student model. For CLD, we aggregate the probability distribution of all anchors at the same position to encourage the highest probability anchor rather than individually distill the distribution at the anchor level. Finally, our StereoDistill achieves state-of-the-art results for stereo-based 3D detection on the KITTI test benchmark and extensive experiments on KITTI and Argoverse Dataset validate the effectiveness.
Learnable Path in Neural Controlled Differential Equations

Neural controlled differential equations (NCDEs), which are continuous analogues to recurrent neural networks (RNNs), are a specialized model in (irregular) time-series processing. In comparison with similar models, e.g., neural ordinary differential equations (NODEs), the key distinctive characteristics of NCDEs are i) the adoption of the continuous path created by an interpolation algorithm from each raw discrete time-series sample and ii) the adoption of the Riemann--Stieltjes integral. It is the continuous path which makes NCDEs be analogues to continuous RNNs. However, NCDEs use existing interpolation algorithms to create the path, which is unclear whether they can create an optimal path. To this end, we present a method to generate another latent path (rather than relying on existing interpolation algorithms), which is identical to learning an appropriate interpolation method. We design an encoder-decoder module based on NCDEs and NODEs, and a special training method for it. Our method shows the best performance in both time-series classification and forecasting.
Diversified and Realistic 3D Augmentation via Iterative Construction, Random Placement, and HPR Occlusion

In autonomous driving, data augmentation is commonly used for improving 3D object detection. The most basic methods include insertion of copied objects and rotation and scaling of the entire training frame. Numerous variants have been developed as well. The existing methods, however, are considerably limited when compared to the variety of the real world possibilities. In this work, we develop a diversified and realistic augmentation method that can flexibly construct a whole-body object, freely locate and rotate the object, and apply self-occlusion and external-occlusion accordingly. To improve the diversity of the whole-body object construction, we develop an iterative method that stochastically combines multiple objects observed from the real world into a single object. Unlike the existing augmentation methods, the constructed objects can be randomly located and rotated in the training frame because proper occlusions can be reflected to the whole-body objects in the final step. Finally, proper self-occlusion at each local object level and external-occlusion at the global frame level are applied using the Hidden Point Removal (HPR) algorithm that is computationally efficient. HPR is also used for adaptively controlling the point density of each object according to the object's distance from the LiDAR. Experiment results show that the proposed DR.CPO algorithm is data-efficient and model-agnostic without incurring any computational overhead. Also, DR.CPO can improve mAP performance by 2.08% when compared to the best 3D detection result known for KITTI dataset.
SEnsor Alignment for Multivariate Time-Series Unsupervised Domain Adaptation

Unsupervised Domain Adaptation (UDA) methods can reduce label dependency by mitigating the feature discrepancy between labeled samples in a source domain and unlabeled samples in a similar yet shifted target domain. Though achieving good performance, these methods are inapplicable for Multivariate Time-Series (MTS) data. MTS data are collected from multiple sensors, each of which follows various distributions. However, most UDA methods solely focus on aligning global features but cannot consider the distinct distributions of each sensor. To cope with such concerns, a practical domain adaptation scenario is formulated as Multivariate Time-Series Unsupervised Domain Adaptation (MTS-UDA). In this paper, we propose SEnsor Alignment (SEA) for MTS-UDA to reduce the domain discrepancy at both the local and global sensor levels. At the local sensor level, we design the endo-feature alignment to align sensor features and their correlations across domains, whose information represents the features of each sensor and the interactions between sensors. Further, to reduce domain discrepancy at the global sensor level, we design the exo-feature alignment to enforce restrictions on the global sensor features. Meanwhile, MTS also incorporates the essential spatial-temporal dependencies information between sensors, which cannot be transferred by existing UDA methods. Therefore, we model the spatial-temporal information of MTS with a multi-branch self-attention mechanism for simple and effective transfer across domains. Empirical results demonstrate the state-of-the-art performance of our proposed SEA on two public MTS datasets for MTS-UDA. The code is available at https://github.com/Frank-Wang-oss/SEA
CDTA: A Cross-Domain Transfer-Based Attack with Contrastive Learning

Despite the excellent performance, deep neural networks (DNNs) have been shown to be vulnerable to adversarial examples. Besides, these examples are often transferable among different models. In other words, the same adversarial example can fool multiple models with different architectures at the same time. Based on this property, many black-box transfer-based attack techniques have been developed. However, current transfer-based attacks generally focus on the cross-architecture setting, where the attacker has access to the training data of the target model, which is not guaranteed in realistic situations. In this paper, we design a Cross-Domain Transfer-Based Attack (CDTA), which works in the cross-domain scenario. In this setting, attackers have no information about the target model, such as its architecture and training data. Specifically, we propose a contrastive spectral training method to train a feature extractor on a source domain (e.g., ImageNet) and use it to craft adversarial examples on target domains (e.g., Oxford 102 Flower). Our method corrupts the semantic information of the benign image by scrambling the outputs of both the intermediate feature layers and the final layer of the feature extractor. We evaluate CDTA with 16 target deep models on four datasets with widely varying styles. The results confirm that, in terms of the attack success rate, our approach can consistently outperform the state-of-the-art baselines by an average of 11.45% across all target models. Our code is available at https://github.com/LiulietLee/CDTA.
Super-efficient Echocardiography Video Segmentation via Proxy- and Kernel-Based Semi-supervised Learning

Automatic segmentation of left ventricular endocardium in echocardiography videos is critical for assessing various cardiac functions and improving the diagnosis of cardiac diseases. It is yet a challenging task due to heavy speckle noise, significant shape variability of cardiac structure, and limited labeled data. Particularly, the real-time demand in clinical practice makes this task even harder. In this paper, we propose a novel proxy- and kernel-based semi-supervised segmentation network (PKEcho-Net) to comprehensively address these challenges. We first propose a multi-scale region proxy (MRP) mechanism to model the region-wise contexts, in which a learnable region proxy with an arbitrary shape is developed in each layer of the encoder, allowing the network to identify homogeneous semantics and hence alleviate the influence of speckle noise on segmentation. To sufficiently and efficiently exploit temporal consistency, different from traditional methods which only utilize the temporal contexts of two neighboring frames via feature warping or self-attention mechanism, we formulate the semi-supervised segmentation with a group of learnable kernels, which can naturally and uniformly encode the appearances of left ventricular endocardium, as well as extracting the inter-frame contexts across the whole video to resist the fast shape variability of cardiac structures. Extensive experiments have been conducted on two famous public echocardiography video datasets, EchoNet-Dynamic and CAMUS. Our model achieves the best performance-efficiency trade-off when compared with other state-of-the-art approaches, attaining comparative accuracy with a much faster speed. The code is available at https://github.com/JingyinLin/PKEcho-Net.
Photonic Hopfield neural network for the Ising problem

The Ising problem, a vital combinatorial optimization problem in various fields, is hard to solve by traditional Von Neumann computing architecture on a large scale. Thus, lots of application-specific physical architectures are reported, including quantum-based, electronics-based, and optical-based platforms. A Hopfield neural network combined with a simulated annealing algorithm is considered one of the effective approaches but is still limited by large resource consumption. Here, we propose to accelerate the Hopfield network on a photonic integrated circuit composed of the arrays of Mach–Zehnder interferometer. Our proposed Photonic Hopfield Neural Network (PHNN), utilizing the massively parallel operations and integrated circuit with ultrafast iteration rate, converges to a stable ground state solution with high probability. The average success probabilities for the MaxCut problem with a problem size of 100 and the Spin-glass problem with a problem size of 60 can both reach more than 80%. Moreover, our proposed architecture is inherently robust to the noise induced by the imperfect characteristics of components on chip.
ALSA: Adaptive Low-light Correction and Self-Attention Module for Vehicle Re-Identification

Multi-Camera Vehicle Re-identification and Tracking (MCVRT) is a challenging task that involves identifying and tracking vehicles across multiple camera views in a surveillance network. Multi-Target Multi-Camera Tracking (MTMCT) and vehicle Re-Identification (Re-ID) are two major technologies applied to MCVRT tasks. Variations in aspect ratio, occlusion, orientation, and lighting conditions make vehicle re-identification and multi-camera tracking challenging. While some existing methods address these problems, it remains a significant challenge in the field. Additionally, most Re-ID datasets only include images captured in well-lit environments, and the impact of dark images on the performance of existing models needs to be explored further. This paper presents a new approach to address the challenge of low-light images in vehicle re-identification and achieves state-of-the-art results on public datasets. Our approach is based on two key components: (i) an Adaptive Low-light correction and Self-Attention module (ALSA) for image pre-processing in Vehicle Re-ID networks, and (ii) a new loss function called Log Triplet Loss (LT-Loss). We evaluated the presented approach through computer simulations on the VeRi-776 dataset, and the results showed that our model achieved a Rank@1 accuracy of 98.99%, and also outperformed commonly used models on dark images. Our study highlights the importance of considering lighting conditions in vehicle re-identification and provides a new approach to address this challenge.
Modern Alphaset (APNB) Flexible Formulations-Enhanced Performance

APNB (alkaline phenolic no bake), widely known as Alphaset is one of the major sand binder systems used in foundries to make molds and cores without application of heat or gas.This is a two part system comprising of a phenol-formaldehyde resin in alkaline medium as binder and range of esters of dibasic acids and/or polyhydric alcohols as hardeners.Resin performance varies depending upon formulations.Major variables in formulations are mole ratio of phenol: formaldehyde, total alkali content, ratio of two alkalis (NaOH & KOH) and molecular weight of polymers i.e. chain length.In present work, one mole ratio of phenol & formaldehyde has been chosen to prepare 8 resins with following details.
The Effect of Storytelling Methods and Self-Confidence Children's Expressive Language Skills

This study aims to determine the effect of the storytelling method and self-confidence on the expressive language skills of early childhood. The approach used is quantitative through an experimental method with treatment by level 2x2 design. This research was conducted on children in group B Kindergarten in Tanah Datar Regency. The sampling technique used is simple random sampling (simple random sampling). The number of samples in this study was 24 children (12 in the experimental class and 12 in the control class. Data collection in this study used a self-made checklist. The data analysis technique in this study used two-way ANOVA to see the main effects and interactions then used the Tukey test). To see a simple effect, the results of this study are as follows: 1) The expressive language ability of children who are given the storytelling method is higher than the children who are given the show and tell treatment. 2) There is an interaction between the storytelling method and self-confidence, in general, it has a significant effect on children's expressive language skills. 3) Children who have high self-confidence, by being treated with the storytelling method have higher expressive language skills than using the show and tell storytelling method). 4) Children who have low self-confidence using the show and tell storytelling method have higher expressive language skills than those who are treated using the storytelling method
Principles of the ecosystem approach: Possibilities for modeling educational ecosystems

Introduction. The article reveals the problem of applying the ecosystem approach for the transformation of education in the face of current global socio-economic and technological challenges. The aim of the article is to identify and summarize the principles of the ecosystem approach to education, to justify the possibilities of their application for modeling the national education system. Materials and Methods. The methodological basis of the study includes systematic approach, socio-environmental approach, comparative analysis and thematic content analysis. In order to achieve the purpose of the research, an analytical review has been conducted in a comparative context: more than 60 full-text sources revealing the features of the ecosystem approach have been studied using the method of qualitative thematic content analysis. The comparative analysis made it possible to identify and clarify different points of view on the ecosystem approach and the prospects for its implementation in modern education. At first, the article examines the operating conditions of ecosystems. The authors have identified and described their key principles in business and entrepreneurship. In the second phase, the logical and methodological method of extrapolation has allowed the authors to implement these principles to education. A conceptual architecture of the educational ecosystem has been designed based on modeling method. Results. The authors have identified the key principles of social ecosystems functioning in business and entrepreneurship, substantiated the possibility of their application to the education sector. The authors have outlined and revealed six fundamental ecosystem principles in their entirety and systematic coherence: customer focus, configuration among participants’ relations, regulation and management, environmental influence, digital technologies and online platforms, symbiosis and co-evolution of participants. These principles have formed the basis for the conceptual architecture of the educational ecosystem. This meaningful mental model explicates inner meanings of ecosystem connections in education. It represents key features, new nature and configuration of relationships among dissimilar participants and stakeholders, such as formal and non-formal education, the state, the innovative business and the non-profit sector. Based on the analytical review and the comparative analysis of international and Russian sources, the authors have substantiated theoretical and practical potential, advantages and limitations of the ecosystem approach to education, as well as designed the model of the educational ecosystem. Conclusions. In conclusion, the authors summarize the identified fundamental principles of educational ecosystems; justify the possibilities of their application for the organizational, technological and content transformation of the national education system.
Metric Learning in Histopathological Image Classification: Opening the Black Box

The application of machine learning techniques to histopathology images enables advances in the field, providing valuable tools that can speed up and facilitate the diagnosis process. The classification of these images is a relevant aid for physicians who have to process a large number of images in long and repetitive tasks. This work proposes the adoption of metric learning that, beyond the task of classifying images, can provide additional information able to support the decision of the classification system. In particular, triplet networks have been employed to create a representation in the embedding space that gathers together images of the same class while tending to separate images with different labels. The obtained representation shows an evident separation of the classes with the possibility of evaluating the similarity and the dissimilarity among input images according to distance criteria. The model has been tested on the BreakHis dataset, a reference and largely used dataset that collects breast cancer images with eight pathology labels and four magnification levels. Our proposed classification model achieves relevant performance on the patient level, with the advantage of providing interpretable information for the obtained results, which represent a specific feature missed by the all the recent methodologies proposed for the same purpose.