Miracle-dz/UniFAR-FTUs huggingface dataset:
GIANT ICHTHYOSAURS OF THE TRIASSIC—A NEW SPECIES OF SHONISAURUS FROM THE PARDONET FORMATION (NORIAN: LATE TRIASSIC) OF BRITISH COLUMBIA

Abstract The ichthyosaur Shonisaurus sikanniensis sp. nov. is described from the Pardonet Formation (Norian, Late Triassic) of northeastern British Columbia. The type specimen is considerably larger than Shonisaurus popularis, with an estimated length of 21 m. It also differs from S. popularis in having a fan-shaped scapula, and relatively longer vertebral centra. Studies of this new species, along with re-examination of S. popularis, suggests that Shonisaurus was not as deep-bodied as previously reconstructed. Teeth are set in sockets, but are known only in a few, small individuals. This implies that teeth were present only in juveniles, and that adults were edentulous. The new species extends the geographical and stratigraphical range of Shonisaurus to the middle Norian of British Columbia.
A Capture to Registration Framework for Realistic Image Super-Resolution in the Industry Environment

The acquisition and processing of visual data in industrial environments are of paramount importance. High-resolution (HR) images offer superior clarity and richer textural detail compared to low-resolution (LR) images. On the one hand, owing to the incorporation of richer information, HR images demonstrate substantially enhanced performance compared to LR images in downstream applications, such as anomaly detection. On the other hand, they provide valuable insights to designers and quality inspectors who require a detailed understanding of the images. Currently, the majority of research on super-resolution focuses on natural scenes such as cities and fields, however, the development of datasets for industrial scenes is still in its infancy. To address the image distortion in building realistic LR-HR image pairs in the industry environment, we design a capture to registration framework. It consists of the standard imaging system, physical calibration of the imaging system, as well as the rigid to elastic registration of the LR-HR image pairs. Thus, we build the first realistic industrial sence super-resolution dataset (IndSR), comprises of 50 sets of calibrated images with three scale factors and five typical defects. To benchmark IndSR, we employ quantitative, qualitative, and task-oriented studies to evaluate the representative super-resolution and anomaly detection methods. Besides, we systematically investigate and discuss the performances and results of the existing SISR methods to advance research in the field of super-resolution in industry environment. The IndSR dataset can be available from https://byw4ng.github. io/IndSR/.
Multi-Modal Generative AI with Foundation Models

Generating photorealistic and controllable visual contents has been a long-pursuing goal of artificial intelligence (AI), with extensive real-world applications. It is also at the core of embodied intelligence. In this talk, I will discuss our work in AI-driven visual context generation of humans [1, 2], objects [3] and scenes [4], with an emphasis on combing the power of neural rendering with large multimodal foundation models [5]. Our generative AI framework has shown its effectiveness and generalizability on a wide range of tasks.
MultiMediate 2023: Engagement Level Detection using Audio and Video Features

Real-time engagement estimation holds significant potential across various research areas, particularly in the realm of human-computer interaction. It empowers artificial agents to dynamically adjust their responses based on user engagement levels, fostering more intuitive and immersive interactions. Despite the strides in automating real-time engagement estimation, the task remains challenging in real-world settings, especially when handling multi-modal human social signals. Capitalizing on human body and audio signals, this paper explores the appropriate feature representations of different modalities and effective modelling of dual conversations. This results in a novel and efficient multi-modal engagement detection model.We thoroughly evaluated our method in the MultiMediate'23 grand challenge. It performs consistently, with a notable improvement over the baseline model. Specifically, while the baseline achieves a concordance correlation coefficient (CCC) of 0.59, our approach yields a CCC of 0.70, suggesting its promising efficacy in real-life engagement detection.
Cross-layer design of coded multicast for wireless random access networks

Joint optimization of network coding and Aloha-based medium access control (MAC) for multi-hop wireless networks is considered. The multicast throughput is maximized subject to flow conservation and MAC achievable rate constraints to obtain the optimal transmission probabilities. The resultant optimization problem is inherently non-convex and hence difficult to solve even in a centralized manner. A successive convex approximation technique is employed to obtain a Karush-Kuhn-Tucker solution. A separable problem structure is obtained and the dual decomposition technique is adopted to develop a distributed solution. The algorithm is thus applicable to large networks, and amenable to online implementation. Numerical tests verify performance and complexity advantages of the proposed approach over existing designs.
Scene Graph Masked Variational Autoencoders for 3D Scene Generation

Generating realistic 3D indoor scenes requires a deep understanding of objects and their spatial relationships. However, existing methods often fail to generate realistic 3D scenes due to the limited understanding of object relationships. To tackle this problem, we propose a Scene Graph Masked Variational Auto-Encoder (SG-MVAE) framework that fully captures the relationships between objects to generate more realistic 3D scenes. Specifically, we first introduce a relationship completion module that adaptively learns the missing relationships between objects in the scene graph. To accurately predict the missing relationships, we employ multi-group attention to capture the correlations between the objects with missing relationships and other objects in the scene. After obtaining the complete scene relationships, we mask the relationships between objects and use a decoder to reconstruct the scene. The reconstruction process enhances the model's understanding of relationships, generating more realistic scenes. Extensive experiments on benchmark datasets show that our model outperforms state-of-the-art methods.
Long-Tailed Recognition of Food Crop Disease Images Based on Deep Neural Networks: Long-Tailed Recognition of Food Crop Disease Images

Food crops are very important to people's production and life, and intelligent recognition of food crop disease images is of great significance. However, in the natural environment, the health and disease samples of food crops often have a long-tailed distribution. Long-tailed data is a difficult point for image classification, and insufficient training of tail data often occurs. In this paper, based on transfer learning, the Bilateral-Branch Network is used as the framework to solve the long-tailed recognition problem. Meanwhile, three re-sampling strategies are designed for the Bilateral-Branch Network: balanced re-sampling, smooth reverse re-sampling and reverse re-sampling. After that, we conduct comparative experiments with different model structures and different re-sampling strategies. Finally, we get the conclusion that the Bilateral-Branch Network can effectively solve the long-tailed recognition problem. Among several re-sampling strategies, reverse re-sampling gives the best results. The best result of our method on the long-tailed dataset of food crop disease images is 94.3%, which is 0.7 percentage points higher than that of the traditional single-branch network model under the same conditions.
Partial Annotation-based Video Moment Retrieval via Iterative Learning

Given a descriptive language query, Video Moment Retrieval (VMR) aims to seek the corresponding semantic-consistent moment clip in the video, which is represented as a pair of the start and end timestamps. Although current methods have achieved satisfying performance, training these models heavily relies on the fully-annotated VMR datasets. Nonetheless, precise video temporal annotations are extremely labor-intensive and ambiguous due to the diverse preferences of different annotators. Although there are several works trying to explore weakly supervised VMR tasks with scattered annotated frames as labels, there is still much room to improve in terms of accuracy. Therefore, we design a new setting of VMR where users can easily point to small segments of non-controversy video moments and our proposed method can automatically fill in the remaining parts based on the video and query semantics. To support this, we propose a new framework named Video Moment Retrieval via Iterative Learning (VMRIL). It treats the partial temporal region as the seed, then expands the pseudo label by iterative training. In order to restrict the expansion with reasonable boundaries, we utilize a pretrained video action localization model to provide coarse guidance of potential video segments. Compared with other VMR methods, our VMRIL achieves a trade-off between satisfying performance and annotation efficiency. Experimental results show that our proposed method can achieve the SOTA performance in the weakly supervised VMR setting, and are even comparable with some fully-supervised VMR methods but with much less annotation cost.
The Driving Force of Environmental Change During the Last 40 Years in the Heihe River Basin

Based on the data of hydrology,socio-economy during the last 40 years in Heihe River Basin,this research estimated the impact of anthropogenic activities on surface runoff through trend analysis,and measured the relationship between factors of anthropogenic activities and surface runoff by correlation analysis and principal component analysis.The results show:from the midstream to downstream,the difference product curves of hydrological stations are rising at first,then continually going down.When it is much nearer to the downstream,the decreasing of runoff is much more obvious,but the runoff in upstream is increasing in the late 40 years which shows that difference product curves of hydrological stations are mainly due to anthropogenic activities,which leading to the destroying of eco-environment of the downstream.
An AI approach for tracking target with severe Doppler ambiguity in HFSWR: AI tracking target with Doppler ambiguity

When high frequency surface wave radar (HFSWR) detects high-speed moving targets, Doppler ambiguity issue often occurs. At this time, the detection and tracking of targets becomes a challenging problem. How to find the ambiguous target in the clutter accurately and implement effective tracking is the key to improve capabilities of HFSWR. Using deep learning for tracking could solve the problem of Doppler Ambiguity, and also has important theoretical value for echo processing of near and long-range simultaneous detection for HFSWR.
Optimal resource allocation for MIMO ad hoc cognitive radio networks

Maximization of the weighted sum-rate of secondary users (SUs) possibly equipped with multi-antenna transmitters and receivers is considered in the context of cognitive radio (CR) networks with coexisting primary user(s) (PU). Total interference power received at the primary receiver is constrained to maintain reliable communication for the PU. An interference channel configuration is considered for ad hoc networking, where the receivers treat the interference from undesired transmitters as noise. Without the CR constraint, a distributed algorithm is developed to obtain (at least) a locally optimal solution. With the CR constraint, a semi-distributed algorithm is also introduced along with an alternative centralized algorithm based on geometric programming and network duality.
Paleoecological implications inferred from stable isotopic signatures

Stable isotopic signatures measured in bone collagen provide with data related to the species diet type. In this paper we compare δ 13 C and δ 15 N outcomes in Ursus spelaeus ROS. -HEIN. bone remains from Linares site and Cova Eiros site (Galicia, NW of the Iberian Peninsula). Some data on fossil Ursus arctos L. and Pleistocene Cervus elaphus L. from Galician caves are also presented, as a first approach to distinguish paleodiets of different species inferred from their isotopic signatures. Once all data have been analyzed with proper statistical tools and since this work was planned in order to reduce variation in stable isotopic signals caused by metabolic causes, we may assume that the observed differences between both studied groups are exclusively due to environmental factors and show a migration of the cave bear population in the Serra do Courel mountains from higher to lower altitudes because of the transition from warm climatic conditions to colder ones.
DANet: Multi-scale UAV Target Detection with Dynamic Feature Perception and Scale-aware Knowledge Distillation

Multi-scale infrared unmanned aerial vehicle (UAV) targets (IRUTs) detection under dynamic scenarios remains a challenging task due to weak target features, varying shapes and poses, and complex background interference. Current detection methods find it difficult to address the above issues accurately and efficiently. In this paper, we design a dynamic attentive network (DANet) incorporating a scale-adaptive feature enhancement mechanism (SaFEM) and an attention-guided cross-weighting feature aggregator (ACFA). The SaFEM adaptively adjusts the network's receptive fields at hierarchical network levels leveraging separable deformable convolution (SDC), which enhances the network's multi-scale IRUT awareness. The ACFA, modulated by two crossing attention mechanisms, strengthens structural and semantic properties on neighboring levels for the accurate representation of multi-scale IRUT features from different levels. A plug-and-play anti-distractor contrastive regularization (ADCR) is also imposed on our DANet, which enforces similarity on features of targets and distractors from a new uncompressed feature projector (UFP) to increase the network's anti-distractor ability in complex backgrounds. To further increase the multi-scale UAV detection performance of DANet while maintaining its efficiency superiority, we propose a novel scale-specific knowledge distiller (SSKD) based on a divide-and-conquer strategy. For the "divide'' stage, we intendedly construct three task-oriented teachers to learn tailored knowledge for small-, medium-, and large-scale IRUTs. For the "conquer'' stage, we propose a novel element-wise attentive distillation module (EADM), where we employ a pixel-wise attention mechanism to highlight teacher and student IRUT features, and incorporate IRUT-associated prior knowledge for the collaborative transfer of refined multi-scale IRUT features to our DANet. Extensive experiments on real infrared UAV datasets demonstrate that our DANet is able to detect multi-scale UAVs with a satisfactory balance between accuracy and efficiency.
Embeddings in Natural Language Processing

Embeddings have been one of the most important topics of interest in NLP for the past decade. Representing knowledge through a low-dimensional vector which is easily integrable in modern machine learning models has played a central role in the development of the field. Embedding techniques initially focused on words but the attention soon started to shift to other forms. This tutorial will provide a high-level synthesis of the main embedding techniques in NLP, in the broad sense. We will start by conventional word embeddings (e.g., Word2Vec and GloVe) and then move to other types of embeddings, such as sense-specific and graph alternatives. We will finalize with an overview of the trending contextualized representations (e.g., ELMo and BERT) and explain their potential and impact in NLP.
Development of Migratory Birds Population Monitoring System Using Digital Single Reflex Camera

Traditionally, biologists trace migratory birds by using their naked eye and binocular for counting. The counters will feel tired and easily lost their concentration after waiting and counting the migratory birds for a long period of time. Other than that, when there is a large number of migratory birds fly together, it is very difficult to count them. In this case, the accuracy of the ground count is unreliable. In order to increase the accuracy, we developed a monitoring system for counting migratory birds. Since Digital Single Lens Reflex (DSLR) camera price become more and more affordable, we proposed to use DSLR as a remote sensor to capture images. The images were processed based on the image processing technique to obtain the amount of migratory birds in the image. This software was tested in Kenting National Park during autumn migratory season 2010. The computed populations were compared with the manually counted numbers. The results produce a high degree of accuracy as indicated by the high coefficient of determination (R2) of 0.979. The results show that it can be effective improved the accuracy of ground count.
Event-guided Frame Interpolation and Dynamic Range Expansion of Single Rolling Shutter Image

In the presence of abrupt motion, the pushbroom scanning mechanism of a rolling shutter (RS) camera tends to bring undesirable distortion, which is recently shown to be beneficial for high-speed frame interpolation. Although promising results have been reported by using multiple consecutive RS frames, to interpolate intermediate distortion-free frames from a single RS image is still an open question, due to the existence of multiple motions that can account for the recorded distortion. Another limitation of RS cameras in complex dynamic scenarios lies in the dynamic range, since traditional ways of multiple exposure for high dynamic range (HDR) imaging will fail due to alignment issues. To deal with these two challenges simultaneously, we propose to use an event camera for assistance, which has much faster temporal response and wider dynamic range. Since there does not exist learning data for this brand new imaging setup, we first build a quad-axis imaging system to capture a realistic dataset called REG-HDR, with pairs of fully aligned RS image and its associated events, as well as their corresponding high-speed HDR GS images. We also propose a flow-based network for frame interpolation, compounded with an attention-based fusion network for dynamic range expansion. Experimental results have verified the effectiveness of our proposed algorithm and the superiority of using realistic data for this challenging dural-purpose enhancement task.
Towards Flexible and Universal: A Novel Endpoint-based Framework for Vessel Structural Information Extraction

In computer-assisted intravascular interventional surgery, extracting detailed information of target vessels from X-ray angiographic images can be meaningful in improving safety and effectiveness. However, large amounts of effort have been dedicated to segmenting the whole blood vessels from the background while ignoring the internal structure, which is limited in clinical application. In this paper, we propose a flexible and universal endpoint-based framework for vessel structural information extraction. The framework first localizes all the endpoints of target vessel segments through a Coarse-to-Fine Keypoint Detection Network (CFKD-Net), in which the designed Multi-branch Feature Aggregation (MFA) module captures both in-patch and cross-patch information to help recognize the points of interest based on global structure. A novel MaskMSELoss is also proposed to disambiguate those irrelevant responses. Then a designed VEssel Segmentation and Analysis (VESA) algorithm will generate the segmentation mask and morphological analysis for each vessel segment simply based on the endpoints. It can also be flexibly applied to analyze variant blood vessels which are not pre-defined before. Extensive experiments on two different coronary artery datasets consistently demonstrate that this framework can achieve state-of-the-art detection performance and successfully extract and analyze target vessel segments. Since the framework shows excellent performance on the coronary arteries with severe deformation and strong noise, it is highly promising for analyzing other vascular images.
Semi-Supervised Panoptic Narrative Grounding

Despite considerable progress, the advancement of Panoptic Narrative Grounding (PNG) remains hindered by costly annotations. In this paper, we introduce a novel Semi-Supervised Panoptic Narrative Grounding (SS-PNG) learning scheme, capitalizing on a smaller set of labeled image-text pairs and a larger set of unlabeled pairs to achieve competitive performance. Unlike visual segmentation tasks, PNG involves one pixel belonging to multiple open-ended nouns. As a result, existing multi-class based semi-supervised segmentation frameworks cannot be directly applied to this task. To address this challenge, we first develop a novel SS-PNG Network (SS-PNG-NW) tailored to the SS-PNG setting. We thoroughly investigate strategies such as Burn-In and data augmentation to determine the optimal generic configuration for the SS-PNG-NW. Additionally, to tackle the issue of imbalanced pseudo-label quality, we propose a Quality-Based Loss Adjustment (QLA) approach to adjust the semi-supervised objective, resulting in an enhanced SS-PNG-NW+. Employing our proposed QLA, we improve BCE Loss and Dice loss at pixel and mask levels, respectively. We conduct extensive experiments on PNG datasets, with our SS-PNG-NW+ demonstrating promising results comparable to fully-supervised models across all data ratios. Remarkably, our SS-PNG-NW+ outperforms fully-supervised models with only 30% and 50% supervision data, exceeding their performance by 0.8% and 1.1% respectively. This highlights the effectiveness of our proposed SS-PNG-NW+ in overcoming the challenges posed by limited annotations and enhancing the applicability of PNG tasks. The source code is available at https://github.com/nini0919/SSPNG.
A theoretical discussion of ecological and environmental water requirements of river course

For solving the problems occurred in the process of surface water utilization,the the-ories of ecological and environmental water requirements of river course are studied in this pa-per.The following conclusions are mainly drawn:(1)Based on the analyses of the old concept s of ecological and environmental water requirements,definitions of ecological water requirement and environmental requirement are given. (2)The ecological water requirements and environ-mental water requirement of river course are expounded especially theoretically.Firstly,the con-cepts related to the research of ecological and environmental water requirements of river course have been reviewed.Secondly,the mechanism and composition of ecological and environmental water requirements of river course are discussed according to the balance of river water quantity.Thirdly,according to the impacting intensity of human being on surface water,the exploitation and utilization of water resources are divided into four stages,and the characteristic of river e-cosystem of each stage is described.The impacts of river flow reduction on the whole river e-cosystem are analyzed.Finally,the concepts of ecological water requirement and environmental water requirement of river course are given on the basis of the above analyses.The research of this article can provide some theoretical foundation and basis for the study of ecological and en-vironmental water requirements of river course.