Miracle-dz/UniFAR-FTUs huggingface dataset:
Tensor Networks for Multi-Modal Non-Euclidean Data

Modern data sources are typically of large scale and multi-modal natures, and acquired on irregular domains, which poses serious challenges to traditional deep learning models. These issues are partially mitigated by either extending existing deep learning algorithms to irregular domains through graphs, or by employing tensor methods to alleviate the computational bottlenecks imposed by the Curse of Dimensionality. To simultaneously resolve both these issues, we introduce a novel Multi-Graph Tensor Network (MGTN) framework, which leverages on the desirable properties of graphs, tensors and neural networks in a physically meaningful and compact manner. This equips MGTNs with the ability to exploit local information in irregular data sources at a drastically reduced parameter complexity, and over a range of learning paradigms such as regression, classification and reinforcement learning. The benefits of the MGTN framework, especially its ability to avoid overfitting through the inherent low-rank regularization properties of tensor networks, are demonstrated through its superior performance against competing models in the individual tensor, graph, and neural network domains.
Agregação e estoque de carbono em argissolo submetido a diferentes práticas de manejo agrícola

A estrutura fisica e uma propriedade essencial para que o solo cumpra adequadamente suas funcoes e tenha qualidade. Com o objetivo de avaliar a contribuicao de praticas de manejo na recuperacao da agregacao de um solo fisicamente degradado, estudou-se a distribuicao de agregados estaveis em agua e particulas simples em classes de diâmetro (9,51-4,76; 4,76-2,00; 2,00-0,25; 0,25-0,053; < 0,053 mm) e o estoque de C na camada superficial de 0-7,5 cm de um Argissolo Vermelho distrofico tipico da Depressao Central do Rio Grande do Sul sob sistemas de manejo. Em parcelas experimentais com 17 anos, avaliaram-se os sistemas: solo mantido sem plantas e sem revolvimento (Descoberto); plantio direto de Lablab purpureus e milho (Zea mays) (Lablab/m); plantio direto de Cajanus cajan e milho (Guandu/m); e pastagem perene de Digitaria decumbens (Pangola). Em parcelas experimentais com 15 anos, avaliaram-se os sistemas: preparo convencional e plantio direto de Avena strigosa e milho (pc a/m e pd a/m, respectivamente) e Avena strigosa + Vicia sativa e milho + Vigna unguiculata (pc av/mc e pd av/mc, respectivamente). Avaliaram-se, tambem, area de lavoura de graos em preparo convencional conduzida por 30 anos (Lavoura) e area de campo nativo (CN), representando a condicao degradada e o ecossistema nativo deste solo, respectivamente. As avaliacoes foram realizadas em amostras de solo indeformadas coletadas em duas epocas (setembro de 1999 e setembro de 2000), compondo seis repeticoes por sistema de manejo. Os dados foram analisados por meio da analise de variância, e o teste de Tukey (5 %) foi utilizado para verificar as diferencas entre os sistemas de manejo. O CN apresentou 76,4 % de solo agregado, distribuidos em 63,8 % de macroagregados e 23,6 % de particulas simples, com estoque de C de 20,0 Mg ha-1 na profundidade de 0-7,5 cm. O uso agricola com revolvimento intenso do solo e baixa adicao de residuos (sistema Lavoura) reduziu a proporcao de solo agregado para 49,9 % e aumentou a de particulas simples para 50,1 %. O estoque de C reduziu para 11,8 Mg ha-1 na profundidade de 0-7,5 cm. A partir dessa condicao, o sistema sem revolvimento de solo, com maior diversidade de especies de plantas e grande adicao de residuos (pd av/mc) elevou, em 15 anos, a proporcao de solo agregado para 70,7 %, e a dos macroagregados, para 53,5 %. A pastagem perene (Pangola) elevou a proporcao de solo agregado para 74,1 % e a dos macroagregados para 61,8 %, igualando-se ao CN. Os sistemas pd av/mc, Guandu/m e Lablab/m recuperaram os estoques de C no nivel do CN. No entanto, a Pangola, que apresentou os maiores indices de agregacao do solo, teve o estoque de C inferior ao desses sistemas, enfatizando a acao positiva do sistema radicular denso na recuperacao da agregacao do solo.
Co-Imitation Learning without Expert Demonstration

Imitation learning is a primary approach to improve the efficiency of reinforcement learning by exploiting the expert demonstrations. However, in many real scenarios, obtaining expert demonstrations could be extremely expensive or even impossible. To overcome this challenge, in this paper, we propose a novel learning framework called Co-Imitation Learning (CoIL) to exploit the past good experiences of the agents themselves without expert demonstration. Specifically, we train two different agents via letting each of them alternately explore the environment and exploit the peer agent's experience. While the experiences could be valuable or misleading, we propose to estimate the potential utility of each piece of experience with the expected gain of the value function. Thus the agents can selectively imitate from each other by emphasizing the more useful experiences while filtering out noisy ones. Experimental results on various tasks show significant superiority of the proposed Co-Imitation Learning framework, validating that the agents can benefit from each other without external supervision.
An equivalent nonlinear optimization model with triangular low-rank factorization for semidefinite programs

ABSTRACT In this paper, we propose a new nonlinear optimization model to solve semidefinite optimization problems (SDPs), providing some properties related to local optimal solutions. The proposed model is based on another nonlinear optimization model given by [S. Burer and R. Monteiro, A nonlinear programming algorithm for solving semidefinite programs via low-rank factorization, Math. Program. Ser. B 95 (2003), pp. 329–357], but it has several nice properties not seen in the existing one. Firstly, the decision variable of the proposed model is a triangular low-rank matrix. Secondly, the existence of a strict local optimum of the proposed model is guaranteed under some conditions, whereas the existing model has no strict local optimum. In other words, it is difficult to construct solution methods equipped with fast convergence using the existing model. We also present some numerical results, showing that the use of the proposed model allows to deliver highly accurate solutions.
Shape-Constrained Symbolic Regression—Improving Extrapolation with Prior Knowledge

Abstract We investigate the addition of constraints on the function image and its derivatives for the incorporation of prior knowledge in symbolic regression. The approach is called shape-constrained symbolic regression and allows us to enforce, for example, monotonicity of the function over selected inputs. The aim is to find models which conform to expected behavior and which have improved extrapolation capabilities. We demonstrate the feasibility of the idea and propose and compare two evolutionary algorithms for shape-constrained symbolic regression: (i) an extension of tree-based genetic programming which discards infeasible solutions in the selection step, and (ii) a two-population evolutionary algorithm that separates the feasible from the infeasible solutions. In both algorithms we use interval arithmetic to approximate bounds for models and their partial derivatives. The algorithms are tested on a set of 19 synthetic and four real-world regression problems. Both algorithms are able to identify models which conform to shape constraints which is not the case for the unmodified symbolic regression algorithms. However, the predictive accuracy of models with constraints is worse on the training set and the test set. Shape-constrained polynomial regression produces the best results for the test set but also significantly larger models.
Hidden-nodes in coexisting LAA & Wi-Fi: a measurement study of real deployments

LTE-Licensed Assisted Access (LAA) networks are beginning to be deployed widely in major metropolitan areas in the US in the unlicensed 5 GHz bands, which have existing dense deployments of Wi-Fi. This provides a real-world opportunity to study the problems due to hidden-node scenarios between LAA and Wi-Fi. The hidden node problem has been well studied in the context of overlapping Wi-Fi APs. However, when Wi-Fi coexists with LAA, the hidden node problem is exacerbated since LAA cannot use the well known Request-to-Send (RTS)/Clearto-Send (CTS) mechanism to resolve contentions, resulting in throughput degradation for Wi-Fi. In this paper we describe detailed measurements and conclusions from experiments on the campus of the University of Chicago which presents a perfect hidden node scenario where Wi-Fi access points (APs) controlled by us and a LAA base-station (BS) deployed by AT&T are hidden from each other, but the clients are not. We performed careful experiments in three different regions of the coexistence area: (i) clients midway between LAA & Wi-Fi; (ii) clients close to the Wi-Fi AP; and (iii) clients close to the LAA BS. Our results show that in a situation where LAA uses an aggregate of three unlicensed channels (60 MHz bandwidth) which overlap with a 80 MHz Wi-Fi transmission, the Wi-Fi throughput at client devices suffers considerably. Overall, Wi-Fi performance is impacted by the hidden node problem more severely than LAA. In the best outdoor conditions, the throughput of LAA and Wi-Fi is reduced by 35% and 97% respectively when coexisting with each other as compared when the other system is not present. Furthermore, we conclude that when both LAA and Wi-Fi use multiple 20 MHz channels, and there are multiple Wi-Fi APs coexisting with LAA on the same set of channels, the choice of Wi-Fi primary channels can have a significant impact on LAA throughput.
Characterization of angularly resolved EUV emission from 2-µm-wavelength laser-driven Sn plasmas using preformed liquid disk targets

The emission properties of tin plasmas, produced by the irradiation of preformed liquid tin targets by several-ns-long 2 µm-wavelength laser pulses, are studied in the extreme ultraviolet (EUV) regime. In a two-pulse scheme, a pre-pulse laser is first used to deform tin microdroplets into thin, extended disks before the main (2 µm) pulse creates the EUV-emitting plasma. Irradiating 30- to 300 µm-diameter targets with 2 µm laser pulses, we find that the efficiency in creating EUV light around 13.5 nm follows the fraction of laser light that overlaps with the target. Next, the effects of a change in 2 µm drive laser intensity (0.6–1.8 × 1011 W cm−2) and pulse duration (3.7–7.4 ns) are studied. It is found that the angular dependence of the emission of light within a 2% bandwidth around 13.5 nm and within the backward 2π hemisphere around the incoming laser beam is almost independent of intensity and duration of the 2 µm drive laser. With increasing target diameter, the emission in this 2% bandwidth becomes increasingly anisotropic, with a greater fraction of light being emitted into the hemisphere of the incoming laser beam. For direct comparison, a similar set of experiments is performed with a 1 µm-wavelength drive laser. Emission spectra, recorded in a 5.5–25.5 nm wavelength range, show significant self-absorption of light around 13.5 nm in the 1 µm case, while in the 2 µm case only an opacity-related broadening of the spectral feature at 13.5 nm is observed. This work demonstrates the enhanced capabilities and performance of 2 µm-driven plasmas produced from disk targets when compared to 1 µm-driven plasmas, providing strong motivation for the use of 2 µm lasers as drive lasers in future high-power sources of EUV light.
Learning On Heterogeneous Graphs Using High-Order Relations

A heterogeneous graph consists of different vertices and edges types. Learning on heterogeneous graphs typically employs meta-paths to deal with the heterogeneity by reducing the graph to a homogeneous network, guide random walks or capture semantics. These methods are however sensitive to the choice of meta-paths, with suboptimal paths leading to poor performance. In this paper, we propose an approach for learning on heterogeneous graphs without using meta-paths. Specifically, we decompose a heterogeneous graph into different homogeneous relation-type graphs, which are then combined to create higher-order relation-type representations. These representations preserve the heterogeneity of edges and retain their edge directions while capturing the interaction of different vertex types multiple hops apart. This is then complemented with attention mechanisms to distinguish the importance of the relation-type based neighbors and the relation-types themselves. Experiments demonstrate that our model generally outperforms other state-of-the-art baselines in the vertex classification task on three commonly studied heterogeneous graph datasets.
Going Deeper Into Face Detection: A Survey

Face detection is a crucial first step in many facial recognition and face analysis systems. Early approaches for face detection were mainly based on classifiers built on top of hand-crafted features extracted from local image regions, such as Haar Cascades and Histogram of Oriented Gradients. However, these approaches were not powerful enough to achieve a high accuracy on images of from uncontrolled environments. With the breakthrough work in image classification using deep neural networks in 2012, there has been a huge paradigm shift in face detection. Inspired by the rapid progress of deep learning in computer vision, many deep learning based frameworks have been proposed for face detection over the past few years, achieving significant improvements in accuracy. In this work, we provide a detailed overview of some of the most representative deep learning based face detection methods by grouping them into a few major categories, and present their core architectural designs and accuracies on popular benchmarks. We also describe some of the most popular face detection datasets. Finally, we discuss some current challenges in the field, and suggest potential future research directions.
Large-Scale Approximate k-NN Graph Construction on GPU

k-nearest neighbor graph is a key data structure in many disciplines such as manifold learning, machine learning and information retrieval, etc. NN-Descent was proposed as an effective solution for the graph construction problem. However, it cannot be directly transplanted to GPU due to the intensive memory accesses required in the approach. In this paper, NN-Descent has been redesigned to adapt to the GPU architecture. In particular, the number of memory accesses has been reduced significantly. The redesign fully exploits the parallelism of the GPU hardware. In the meantime, the genericness as well as the simplicity of NN-Descent are well-preserved. In addition, a simple but effective k-NN graph merge approach is presented. It allows two graphs to be merged efficiently on GPUs. More importantly, it makes the construction of high-quality k-NN graphs for out-of-GPU-memory datasets tractable. The results show that our approach is 100-250x faster than single-thread NN-Descent and is 2.5-5x faster than existing GPU-based approaches.
Video Classification with FineCoarse Networks

A rich representation of the information in video data can be realized by means of frequency analysis. Fine motion details from the boundaries of moving regions are characterized by high frequencies in the spatio-temporal domain. Meanwhile, lower frequencies are encoded with coarse information containing substantial redundancy, which causes low efﬁciency for those video models that take as input raw RGB frames. In this work, we propose a Motion Band-pass Module (MBPM) for separating the ﬁne-grained information from coarse information in raw video data. By representing the coarse information with low resolution, we can increase the efﬁciency of video data processing. By embedding the MBPM into a two-pathway CNN architecture, we deﬁne a FineCoarse network. The efﬁciency of the FineCoarse network is determined by avoiding the redundancy in the feature space processed by the two pathways: one operates on downsampled features of low-resolution data, while the other operates on the ﬁne-grained motion information captured by the MBPM. The proposed FineCoarse network outperforms many recent video processing models on Kinetics400, UCF101 and HMDB51. Furthermore, our approach achieves the state-of-the-art with 57.0% top-1 accuracy on Something-Something V1.
Connectionism, Complexity, and Living Systems: a comparison of Artificial and Biological Neural Networks

While Artificial Neural Networks (ANNs) have yielded impressive results in the realm of simulated intelligent behavior, it is important to remember that they are but sparse approximations of Biological Neural Networks (BNNs). We go beyond comparison of ANNs and BNNs to introduce principles from BNNs that might guide the further development of ANNs as embodied neural models. These principles include representational complexity, complex network structure/energetics, and robust function. We then consider these principles in ways that might be implemented in the future development of ANNs. In conclusion, we consider the utility of this comparison, particularly in terms of building more robust and dynamic ANNs. This even includes constructing a morphology and sensory apparatus to create an embodied ANN, which when complemented with the organizational and functional advantages of BNNs unlocks the adaptive potential of lifelike networks.
Transitive Learning: Exploring the Transitivity of Degradations for Blind Super-Resolution

Being extremely dependent on the iterative estimation and correction of data or models, the existing blind super-resolution (SR) methods are generally time-consuming and less effective. To address it, this paper proposes a transitive learning method for blind SR using an end-to-end network without any additional iterations in inference. To begin with, we analyze and demonstrate the transitivity of degradations, including the widely used additive and convolutive degradations. We then propose a novel Transi-tive Learning method for blind Super-Resolution on transitive degradations (TLSR), by adaptively inferring a transitive transformation function to solve the unknown degradations without any iterative operations in inference. Specifically, the end-to-end TLSR network consists of a degree of transitivity (DoT) estimation network, a homogeneous feature extraction network, and a transitive learning module. Quantitative and qualitative evaluations on blind SR tasks demonstrate that the proposed TLSR achieves superior performance and consumes less time against the state-of-the-art blind SR methods. The code is available at github.com/YuanfeiHuang/TLSR.
TFPose: Direct Human Pose Estimation with Transformers

We propose a human pose estimation framework that solves the task in the regression-based fashion. Unlike previous regression-based methods, which often fall behind those state-of-the-art methods, we formulate the pose estimation task into a sequence prediction problem that can effectively be solved by transformers. Our framework is simple and direct, bypassing the drawbacks of the heatmap-based pose estimation. Moreover, with the attention mechanism in transformers, our proposed framework is able to adaptively attend to the features most relevant to the target keypoints, which largely overcomes the feature misalignment issue of previous regression-based methods and considerably improves the performance. Importantly, our framework can inherently take advantages of the structured relationship between keypoints. Experiments on the MS-COCO and MPII datasets demonstrate that our method can significantly improve the state-of-the-art of regression-based pose estimation and perform comparably with the best heatmap-based pose estimation methods.
Translate to Adapt: RGB-D Scene Recognition across Domains

—Scene classiﬁcation is one of the basic problems in computer vision research with extensive applications in robotics. When available, depth images provide helpful geometric cues that complement the RGB texture information and help to identify more discriminative scene image features. Depth sensing technology developed fast in the last years and a great variety of 3D cameras have been introduced, each with different acquisition properties. However, when targeting big data collections, often multi-modal images are gathered disregarding their original nature. In this work we put under the spotlight the existence of a possibly severe domain shift issue within multi-modality scene recognition datasets. We design an experimental testbed to study this problem and present a method based on self-supervised inter-modality translation able to adapt across different camera domains. Our extensive experimental analysis conﬁrms the effectiveness of the proposed approach.
Product semantics translation from brain activity via adversarial learning

A small change of design semantics may affect a user's satisfaction with a product. To modify a design semantic of a given product from personalised brain activity via adversarial learning, in this work, we propose a deep generative transformation model to modify product semantics from the brain signal. We attempt to accomplish such synthesis: 1) synthesising the product image with new features corresponding to EEG signal; 2) maintaining the other image features that irrelevant to EEG signal. We leverage the idea of StarGAN and the model is designed to synthesise products with preferred design semantics (colour&shape) via adversarial learning from brain activity, and is applied with a case study to generate shoes with different design semantics from recorded EEG signals. To verify our proposed cognitive transformation model, a case study has been presented. The results work as a proof-of-concept that our framework has the potential to synthesis product semantic from brain activity.
Contextual Scene Augmentation and Synthesis via GSACNet

Indoor scene augmentation has become an emerging topic in the field of computer vision and graphics with applications in augmented and virtual reality. However, current state-of-the-art systems using deep neural networks require large datasets for training. In this paper we introduce GSACNet, a contextual scene augmentation system that can be trained with limited scene priors. GSACNet utilizes a novel parametric data augmentation method combined with a Graph Attention and Siamese network architecture followed by an Autoencoder network to facilitate training with small datasets. We show the effectiveness of our proposed system by conducting ablation and comparative studies with alternative systems on the Matterport3D dataset. Our results indicate that our scene augmentation outperforms prior art in scene synthesis with limited scene priors available.
From Synthetic to Real: Unsupervised Domain Adaptation for Animal Pose Estimation

Animal pose estimation is an important field that has received increasing attention in the recent years. The main challenge for this task is the lack of labeled data. Existing works circumvent this problem with pseudo labels generated from data of other easily accessible domains such as synthetic data. However, these pseudo labels are noisy even with consistency check or confidence-based filtering due to the domain shift in the data. To solve this problem, we design a multi-scale domain adaptation module (MDAM) to reduce the domain gap between the synthetic and real data. We further introduce an online coarse-to-fine pseudo label updating strategy. Specifically, we propose a self-distillation module in an inner coarse-update loop and a mean-teacher in an outer fine-update loop to generate new pseudo labels that gradually replace the old ones. Consequently, our model is able to learn from the old pseudo labels at the early stage, and gradually switch to the new pseudo labels to prevent overfitting in the later stage. We evaluate our approach on the TigDog and VisDA 2019 datasets, where we outperform existing approaches by a large margin. We also demonstrate the generalization ability of our model by testing extensively on both unseen domains and unseen animal categories. Our code is available at the project website1.