Miracle-dz/UniFAR-FTUs huggingface dataset:
Variational Bayesian Phylogenies through Matrix Representation of Tree Space

In this paper, we study the distance matrix as a representation of a phylogeny by way of hierarchical clustering. By defining a multivariate normal distribution on (a subset of) the entries in a matrix, this allows us to represent a distribution over rooted time trees. Here, we demonstrate tree distributions can be represented accurately this way for a number of published tree distributions. Though such a representation does not map to unique trees, restriction to a subspace, in particular one we call a “cube”, makes the representation bijective at the cost of not being able to represent all possible trees. We introduce an algorithm “cubeVB” specifically for cubes and show through well calibrated simulation study that it is possible to recover parameters of interest like tree height and length. Although a cube cannot represent all of tree space, it is a great improvement over a single summary tree, and it opens up exciting new opportunities for scaling up Bayesian phylogenetic inference. We also demonstrate how to use a matrix representation of a tree distribution to get better summary trees than commonly used maximum clade credibility trees. An open source implementation of the cubeVB algorithm is available from https://github.com/rbouckaert/cubevb as the cubevb package for BEAST 2.
Origins and Evolution of Novel Bacteroides in Captive Apes

Bacterial strains evolve in response to the gut environment of their hosts, with genomic changes that influence their interactions with hosts as well as with other members of the gut community. Great apes in captivity have acquired strains of Bacteroides xylanisolvens, which are common within gut microbiome of humans but not typically found other apes, thereby enabling characterization of strain evolution following colonization. Here, we isolate, sequence and reconstruct the history of gene gain and loss events in numerous captive-ape-associated strains since their divergence from their closest human-associated strains. We show that multiple captive-ape-associated B. xylanisolvens lineages have independently acquired gene complexes that encode functions related to host mucin metabolism. Our results support the finding of high genome fluidity in Bacteroides, in that several strains, in moving from humans to captive apes, have rapidly gained large genomic regions that augment metabolic properties not previously present in their relatives. Significance statement Chronicling the changes that occur in bacterial genomes after a host-switch event is normally difficult due to age of most bacteria-host associations, which renders uncertainties about the bacterial ancestor (and ancestral genome) prior to colonization of the new host. However, the gut microbiomes of great apes in captivity contain bacterial strains that are unique to humans, allowing fine-scale assessment and reconstruction of the genomic changes that follow colonization. By sequencing and comparing closely related strains of Bacteroides that are restricted both to human and to captive great apes, we found that multiple bacterial lineages convergently acquired sets of genes involved in the metabolism of dietary polysaccharides. These results show that over relatively short timescales, the incorporation of strains into microbiomes involves large-scale genomic events that correspond to characteristics of the new host environment.
Prediction of fluvial erosion rate in Jamuna River, Bangladesh

ABSTRACT River bank erosion of alluvial rivers is one of the major challenges for river management. This paper deals with the prediction of the fluvial erosion rate along the left bank of Jamuna River in Bangladesh using the excess shear stress method. Surface water modelling systems i.e. SMS and SRH-2D, were used to estimate the hydrodynamic effects on the riverbank along the 8.21 km reach of the river. Riverbank along the selected reach of Jamuna River consists of two layers of soil type. The upper layer soil is sandy silt with a median size of D 50 = 0.028 mm and the bottom layer is silty sand with D 50 = 0.167 mm. Results of the present analysis show that shear stress along the riverbank attains its maximum and initiates the bank erosion, when the flow rate is about 45,000 m3/s. Based on the results obtained from model run and empirical analysis, riverbank materials have been categorized as ‘erodible’ to ‘very erodible’, as far as erodibility parameters are concerned. Critical shear stress of bank soil is found to be varied between 0.15 and 0.22 Pa using erodibility co-efficient ranges between 5.05 and 6.03 cm3/N s. From this study, the maximum bank erosion rates have been estimated as 51.95 m/year to 69.82 m/year. However, average erosion rates have been estimated which range between 38.65 and 40.57 m/y. It is hoped that the results obtained from the study will be helpful in determining the riverbank stability for the implementation of appropriate river protective measures along the riverbank of Jamuna River.
Beijing Declaration

The International Pledging Conference on Avian and Human Pandemic Influenza was convened in Beijing, China on 17-18 January 2006 under the co-sponsorship of the Government of the People’s Republic of China, the European Commission and the World Bank and in close coordination with the World Health Organization (WHO), Food and Agriculture Organization (FAO) and World Organization for Animal Health (OIE). The conference was attended by representatives from more than 100 countries around the world and representatives of international technical and financing agencies, organizations, the private sector and civil society.
Revising Weighted Knowledge Bases Using FH-Conditioning

Conditioning is an important task for updating and revising uncertain information when new information, often considered reliable, is added. This paper deals with the so-called Fagin and Halpern (FH-)conditioning within the framework of possibility theory. We discuss in particular the computation of FH-conditioning when it is applied to weighted knowledge bases. We also compare FH-conditioning with the two forms of standard possibilistic conditioning (min-based conditioning and product-based conditioning).
Ciclos económicos de las teorias de manchas solares al filtro de Hodrick Presscott: el caso Colombiano (Economic Cycles of the Theories of Solar Spots to Hodrick’s Presscott Filter: The Colombian Case)

Spanish Abstract: El documento realiza un estudio sobre la suavizacion de consumo, partiendo de los modelos de Ciclos Reales de Negocios, demostrando la bondad de esta metodologia frente a los modelos de corte Keynesiano; pudiendo asi llevar a cabo un modelo de tipo Data Panel que permite demostrar, que las familias de seis paises de America Latina, escogen trayectorias de consumo estables, de esta forma se puede probar que los planes de asistencia social focalizados en productividad son eficientes. English Abstract: The present document does a study on the smoothing consumption, from the models of Royal Business Cycles, demonstrating the quality of this methodology against the Keynesian models tendencies; being able to realize this way a model Data – Panel model that allows to indicate that the families of six countries of Latin America, choose stable paths of consumption, so it is possible to prove that the plans of social assistance focused in productivity are efficient.
Graph Spectral Perturbation for 3D Point Cloud Contrastive Learning

3D point cloud contrastive learning has attracted increasing attention due to its efficient learning ability. By distinguishing the similarity relationship between positive and negative samples in the feature space, it can learn effective point cloud feature representations without manual annotation. However, most point cloud contrastive learning methods construct contrastive samples by perturbing point clouds in data space or introducing multi-modality/format data, which may be difficult to control the intensity of the perturbation or introduce interference from different modalities/formats. To this end, in this paper, we propose a novel graph spectral perturbation based contrastive learning framework (GSPCon) for efficient and robust self-supervised 3D point cloud representation learning. It aims to perform perturbations in the graph spectral domain to construct contrastive samples of the point cloud. Specifically, we first naturally represent the point cloud as a k-nearest neighbors (KNN) graph, and adaptively transform the coordinates of the points into the graph spectral domain based on the graph Fourier transform (GFT). Then we implement data augmentation in the graph spectral domain by perturbing the spectral representations. Finally, the contrastive samples are generated by employing the inverse graph Fourier transform (IGFT) to transform the augmented spectral representations back to the point clouds. Experimental results show that our method achieves the state-of-the-art performance on various downstream tasks. Source code is available at https://github.com/yh-han/GSPCon.git.
Research on Recognition Algorithm Based on Polarization Characteristics of Coal and Gangue Surface: Polarization-based Recognition of Coal and Gangue

In this paper, an intelligent identification method of gangue based on polarization imaging technology is proposed. Firstly, a gangue image acquisition system was built in the laboratory, and 2000 groups of coal and gangue samples were collected, including polarization intensity images, polarization angle images and polarization degree images of coal and gangue; secondly, after preprocessing the polarization images to obtain the regions of interest of coal and gangue in the images, the grayscale characteristics of the polarized images, the grayscale features of the grayscale histogram features and the texture features of the polarized images were extracted respectively; finally, The Relief-SVM and the single hidden layer neural networks were used respectively to train the classification model. Experimental results show that the recognition rate of the images acquired by polarization cameras can reach 99.28% and 100% respectively. The accuracy rate is improved compared to the method of classifying ordinary black and white images of coal and gangue with non-polarization characteristics.
CGSMP: Controllable Generative Summarization via Multimodal Prompt

Natural Language Generation (NLG) has improved exponentially in recent years thanks to the development of a large language model (LLM), this advancement has resulted in more fluent and coherent Natural Language Generation, which has contributed to improved development in downstream tasks such as abstractive summarization. Despite the recent progress in LLM, hallucination has become a serious problem in NLG. Hallucination happens when language models generate nonsensical or unfaithful text, which will lead to severe problems with reliability and effectiveness. In this paper, we propose a novel approach called Controllable Generative Summarization via Multimodal Prompt (CGSMP), which uses entities extracted from content and images as multimodal prompt control signals, thereby reducing hallucination issues. Specifically, the proposed CGSMP consists of three main modules: (1) an image prefix module that obtains image representations; (2) a prompt encoder module that fusion entities and images as multimodal prompts; and (3) a pre-trained causal language model that fuses input and controllable prompt and serves as the backbone of the language model. Experimental results demonstrate that the proposed method significantly improves the quality of generated summaries compared to the state of the arts.
A Hardware-efficient Unified Motion Estimation for Video Coding

Motion estimation (ME) is one of the most critical tools in video coding and consumes the majority of the encoding complexity. Three types of ME are utilized in the latest video coding standards, namely integer, fractional, and affine MEs. They are implemented as three searches for the integer motion vector (IMV), fractional motion vector (FMV), and control point motion vectors (CPMVs). Many algorithms were proposed to reduce the complexity for them individually, but the overall overhead of three searches is still challenging for hardware implementations. Therefore, we propose a hardware-efficient Unified Motion Estimation (UME) to derive three types of MVs with only one search. An IME with sub-block refinement is performed to collect extra motion information while searching for the IMV. The FMV and CPMVs are then derived from the collected information using a mixed error surface and an overdetermined system. Compared to the default ME algorithms in VVC, the time cost for ME is reduced by 41.63% with a coding loss of only 0.87% under LDB configuration. For hardware implementations, the minimum required resources and corresponding latency are significantly reduced by 75.35% and 69.17%, respectively.
NeurSEG: A Segment Driven Deep Neural Model for Nested Named Entity Recognition

Named Entity Recognition (NER) is a fundamental problem in natural language processing (NLP). Apart from flat entities, nested entities are also commonly existed in real-life textual data. However, the current methods are not capable of handling nested structures for NER effectively. In this paper, we propose a novel segment driven modeling method (NeurSEG) for the nested NER problem, which can effectively extract entities from the nested structures in complex nesting scenarios. The proposed NeurSEG model first finds the nested label of each word in a sentence and determines the positional relationships between neighbouring words, and then extracts the entities and predicts the corresponding entity types. In addition, we also propose an augmented training method for further improving the performance. We have conducted extensive experiments based on both flat and nested NER benchmark datasets. The performance results have shown that our proposed NeurSEG model has achieved promising performance while retaining its runtime efficiency for the nested NER task. Moreover, the proposed model has also achieved very competitive results when compared with the existing models for the flat NER task, demonstrating its capability for tackling both nested and flat NER tasks.
Extraction of residential areas from remote sensing images based on log-polar transformation

The residential area panchromatic remote sensing image extraction method of based on fourier transformation and log-polar transformation was presented in the paper.First,the panchromatic image was translated by log-polar transform to convert the rotation to translation in different directions,then the fourier transform was used for the texture image of log-polar transform,and at last,the power of fourier transform was used to segment the texture as rotation invariant feature.Moreover,a mathematical morphology method was used for subsequent processing.Experimental results showed residential areas could be acquired from the remote sensing images by the method.
Emotionally Situated Text-to-Speech Synthesis in User-Agent Conversation

Conversational Text-to-speech Synthesis (TTS) aims to generate speech with proper style in the user-agent conversation scenario. Although previous works have explored modeling the context in the dialogue history to provide style information for the agent, there are still deficiencies in modeling the role-aware multi-modal context. Moreover, previous works ignore the emotional dependencies between the user and the agent, which includes: 1) agent understands emotional states of users, and 2) agent expresses proper emotion in the generated speech. In this work, we propose an Emotionally Situated Text-to-speech Synthesis (EmoSit-TTS) framework to understand users' semantics and subtle emotional states, and generate speech with proper speaking style and emotional expression in the user-agent conversation. Experiments on the DailyTalk dataset show the superiority of our proposed framework for the user-agent conversational TTS, especially in terms of emotion-aware expressiveness, which outperforms other state-of-the-art methods by 0.69 on MOS. Demos of our proposed framework are available at https://anonydemo.github. io.
OCSKB: An Object Component Sketch Knowledge Base for Fast 6D Pose Estimation

6D pose estimation from a single RGB image is a fundamental task in computer vision. In most methods of instance-level or category-level 6D pose estimation, accurate CAD models or point cloud models are indispensable part. It is not easy to quickly obtain the models of these everyday objects. To address this issue, we present a part-level object component sketch knowledge base which consists of 270 real-world object sketch models of 30 categories. Objects are disassembled into geometry components with spatial relationship according to their functions and structures, and convert them into three basic spatial structures: frustum, circular truncated cone, and sphere. We present a fast pipeline for sketch modeling with our tool. The average time for this method to build a simple model for everyday objects is about 2 minutes. Additionally, we leverage the geometric information and spatial relationships inherent in the multiple viewpoint projection maps of these sketch bases to develop a rapid inference framework for 6D pose estimation. The interpretable steps in our framework gradually retrieve and activate valid solutions in the discrete 6D pose space. Extensive experiments in real-world environments have demonstrated that our method can reliably and robustly estimate the 6D pose of objects, even without access to accurate CAD or point cloud models. Furthermore, our method achieves state-of-the-art performance, operating at a speed of 90 frames per second using parallel computing on GPU.
Efficient Multimedia Computing: Unleashing the Power of AutoML

As the field of multimedia computing has grown rapidly, so has the need for larger datasets[5] and increased modeling capacity. Navigating this complex landscape often necessitates the use of sophisticated tools and cloud architectures, which all need to be addressed before the actual research commences. Recently, AutoML, an innovation previously exclusive to tabular data, has expanded to encompass multimedia data. This development has the potential to greatly streamline the research process, allowing researchers to shift their focus from model construction to the core content of their problems. In doing so, AutoML not only optimizes resource utilization but also boosts the reproducibility of results. The aim of this tutorial is to acquaint the multimedia community with AutoML technologies, underscoring their advantages and their practical applications in the field.
Multimodal Physiological Signals Fusion for Online Emotion Recognition

Multimodal physiological-based emotion recognition is one of the most available but challenging studies due to complexity of emotions and individual differences in physiological signals. However, existing studies mainly combine multimodal data to fuse multimodal information in offline scenarios, ignoring data/modalities correlation among multimodal data and individual differences of non-stationary physiological signals in online scenarios. In this paper, we propose a novel Online Multimodal HyperGraph Learning (OMHGL) method to fuse multimodal information for emotion recognition based on time-series physiological signals. Our method consists of multimodal hypergraph fusion and online hypergraph learning. Specifically, the multimodal hypergraph fusion can fuse multimodal physiological signals to effectively obtain emotionally dependent information via leveraging multimodal information and higher-order correlations among multimodal data/modalities. The online hypergraph learning is designed to learn new information from online data by updating hypergraph projection. As a result, the proposed online emotion recognition model can be more effective for emotion recognition of target subjects when target data arrive in an online manner. Experimental results have demonstrated that the proposed method significantly outperforms the baselines and compared state-of-the-art methods in online emotion recognition tasks.
Generative Neutral Features-Disentangled Learning for Facial Expression Recognition

Facial expression recognition (FER) plays a critical role in human-computer interaction and affective computing. Traditional FER methods typically rely on comparing the difference between an examined facial expression and a neutral face of the same person to extract the motion of facial features and filter out expression-irrelevant information. With the extensive use of deep learning, the performance of FER has been further improved. However, existing deep learning-based methods rarely utilize neutral faces. To address this gap, we propose a novel deep learning-based FER method called Generative Neutral Features-Disentangled Learning (GNDL), which draws inspiration from the facial feature manifold. Our approach integrates a neutral feature generator (NFG) that generates neutral features in scenarios where the neutral face of the same subject is not available. The NFG uses fine-grained features from examined images as input and produces corresponding neutral features with the same identity. We train the NFG using a neutral feature reconstruction loss to ensure that the generative neutral features are consistent with the actual neutral features. We then disentangle the generative neutral features from the examined features to remove disturbance features and generate an expression deviation embedding for classification. Extensitive experimental results on three popular databases (CK+, Oulu-CASIA, and MMI) demonstrate that our proposed GNDL method outperforms state-of-the-art FER methods.
Research on Weld Detection Based on Weighted Feature Fusion Network

Weld seam grinding is a crucial process for welding cast products. Not only are the polished welds more attractive and durable, but they also have superior stress effects. However, the workplace is harsh and can significantly impact health. The first task for automated weld grinding is weld detection. This paper, a pyramid feature fusion network (YOLOv5-BiCA) learning model with embedded coordinate attention as a weighting condition that fully utilizes the shallow and deep network information. An online data augmentation strategy employing Mosaic+Mixup is proposed to address the problem of insufficient sample size. We use Focal Loss to enhance the classification function and increase the positive influence of positive samples on the loss function during the training process to address the problem of an imbalance between positive and negative sample. Our proposed YOLOv5-BiCA performs better in weld detection, mAP improves by 3.5%, and recall improves by 7.97%, according to experimental results based on homegrown datasets.
A Concept of User State Analysis for Evaluations of Interaction Design in Armored Vehicles

Crewmembers of armored vehicles are exposed to high-risk tasks in operational conditions. In modern armored vehicles multiple displays within the vehicle provide information necessary for maneuvering and tactical decision making. As hostile threats can cause stressful situations with high time pressure and potentially lethal outcomes of decision-making, it is important that the human-machine interface of armored vehicles is tailored to the specific impact factors of this work domain. In this respect, evaluations of the human-machine interface concept are necessary to ensure that the design concept meets the specific requirements of the identified use cases. We created a multimodal approach to facilitate evaluations of such complex safety critical human-machine interfaces and to support design decisions. The approach gathers manifold information on the crewmembers’ mental states and their interaction with the user interfaces. A concept is proposed for the use of user state analysis purpose fitted for armored vehicles. The concept highlights benefits of such an approach as an example of evaluations of military human-machine systems.