Miracle-dz/UniFAR-FTUs huggingface dataset:
Cascading Classifiers for Named Entity Recognition in Clinical Notes

Clinical named entities convey great deal of knowledge in clinical notes. This paper investigates named entity recognition from clinical notes using machine learning approaches. We present a cascading system that uses a Conditional Random Fields model, a Support Vector Machine and a Maximum Entropy to reclassify the identified entities in order to reduce misclassification. Voting strategy was employed to determine the class of the recognised entities between the three classifiers. The experiments were conducted on a corpus of 311 manually annotated admission summaries form an Intensive Care Unit. The recognition of 10 types of clinical named entities using 10 fold cross-validation achieved an overall results of 83.3 F-score. The reclassifier effectively increased the performance over stand-alone CRF models by 3.35 F-score.
TRACLUS: Clustering de trayectorias

TRACLUS : Clustering de trayectorias arises from research in some clustering techniques, with the TRACLUS differences over other trajectory clustering algorithm. TRACLUS has been deployed using technologies and tools which are rising in data mining anda data science domain, applying a optimization after implementation, getting same results than the implementation without optimization but much lower computational time.
Divide-and-Conquer Posterior Sampling for Denoising Diffusion Priors

Interest in the use of Denoising Diffusion Models (DDM) as priors for solving inverse Bayesian problems has recently increased significantly. However, sampling from the resulting posterior distribution poses a challenge. To solve this problem, previous works have proposed approximations to bias the drift term of the diffusion. In this work, we take a different approach and utilize the specific structure of the DDM prior to define a set of intermediate and simpler posterior sampling problems, resulting in a lower approximation error compared to previous methods. We empirically demonstrate the reconstruction capability of our method for general linear inverse problems using synthetic examples and various image restoration tasks.
3D monitoring for plant growth parameters in field with a single camera by multi-view approach

In recent decades, some photogrammetric methods for 3D monitoring of plant growth and structure parameters have been studied by the automated feature extraction and matching. In this study on growth analysis of sweet potato (Ipomoea batatas L.) plants at different fertilizer conditions, we proposed a convenient solution of 3D reconstruction by a single camera photography system based on Structure from Motion (SfM) method. Also, we handled effectively the noise problem by minimizing re-projection errors. The results of 3D models demonstrated that the average percentage error was a constant about 4.8% for plant height or decreased from 13% to 8% (leaf area index, LAI=3.5) for leaf number and from 19% to 12% (LAI=3.5) for leaf area with increasing in LAI, although each percentage error fluctuated, especially at low LAI. In contrast, the average percentage error in 2D image processing was 20% to 45% for leaf number and 60% to 90% for leaf area, and the leaf height was immeasurable. Comparing with the errors of 3D results, the errors of 2D images were much larger because 2D imaging had some problems, such as not being robust against occlusion of plant organs, and the ambiguity between object size and distance from the camera. On the other hand, we examined a method to calibrate the estimates from 3D models using the regression model between the measured value and the value estimated from 3D model. The regression models showed the linear and good estimation for leaf height (R2 =0.97 and RMSE =0.71 cm), leaf number (R2 = 0.99 and RMSE =4.03) and leaf area (R2 = 0.98 and RMSE=0.12 m2), in spite of use of data across a wide range in fertilizer supply and growth stage of sweet potato plants. The results demonstrated that 3D imaging technique in the study has the potential to remotely monitor plant growth status and estimate growth and structure parameters at various environmental factors outdoors.
Dynamic changes in microbial community during anaerobic co-digestion

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Graph Partial Label Learning with Potential Cause Discovering

Graph Neural Networks (GNNs) have gained considerable attention for their potential in addressing challenges posed by complex graph-structured data in diverse domains. However, accurately annotating graph data for training is difficult due to the inherent complexity and interconnectedness of graphs. To tackle this issue, we propose a novel graph representation learning method that enables GNN models to effectively learn discriminative information even in the presence of noisy labels within the context of Partially Labeled Learning (PLL). PLL is a critical weakly supervised learning problem, where each training instance is associated with a set of candidate labels, including both the true label and additional noisy labels. Our approach leverages potential cause extraction to obtain graph data that exhibit a higher likelihood of possessing a causal relationship with the labels. By incorporating auxiliary training based on the extracted graph data, our model can effectively filter out the noise contained in the labels. We support the rationale behind our approach with a series of theoretical analyses. Moreover, we conduct extensive evaluations and ablation studies on multiple datasets, demonstrating the superiority of our proposed method.
A Deep Learning Method for Beat-Level Risk Analysis and Interpretation of Atrial Fibrillation Patients during Sinus Rhythm

Atrial Fibrillation (AF) is a common cardiac arrhythmia. Many AF patients experience complications such as stroke and other cardiovascular issues. Early detection of AF is crucial. Existing algorithms can only distinguish ``AF rhythm in AF patients'' from ``sinus rhythm in normal individuals'' . However, AF patients do not always exhibit AF rhythm, posing a challenge for diagnosis when the AF rhythm is absent. To address this, this paper proposes a novel artificial intelligence (AI) algorithm to distinguish ``sinus rhythm in AF patients'' and ``sinus rhythm in normal individuals'' in beat-level. We introduce beat-level risk interpreters, trend risk interpreters, addressing the interpretability issues of deep learning models and the difficulty in explaining AF risk trends. Additionally, the beat-level information fusion decision is presented to enhance model accuracy. The experimental results demonstrate that the average AUC for single beats used as testing data from CPSC 2021 dataset is 0.7314. By employing 150 beats for information fusion decision algorithm, the average AUC can reach 0.7591. Compared to previous segment-level algorithms, we utilized beats as input, reducing data dimensionality and making the model more lightweight, facilitating deployment on portable medical devices. Furthermore, we draw new and interesting findings through average beat analysis and subgroup analysis, considering varying risk levels.
A pure vision-based approach to topological SLAM

This paper describes a topological SLAM system using a purely vision-based approach. This robot utilizes a GPU-based omnidirectional catadioptric stereovision system to perceive and plan its path in the environment. Subsequently, the omnidirectional images generated are used to incrementally build a database of image signatures based on the standard 2D Haar Wavelet decomposition. In order to maintain a globally consistent topological map, a relaxation algorithm, which requires local metric information between nodes, is employed each time the appearance-based localization system revisits an existing node in the topological map. The relative transformation of the current position of the robot with respect to the actual position of the matched node is recovered by using a least squares estimation of the transformation parameters of two 3D point patterns generated by the stereovision system. In addition, local metric information is obtained by using the proposed visual odometry system which combines distance measurements calculated by using optical flow techniques which estimates the movement of a web camera relative to the ground being observed and bearing estimates from the omnidirectional catadioptric vision system. Experiments were conducted in a variety of environments ranging from indoor to outdoor environments which demonstrate the feasibility of this approach.
Resistive and ballistic phonon transport in $\beta$-Ga$_2$O$_3$

The anisotropic thermal conductivity and the phonon mean free path (mfp) in monoclinic $\beta$-Ga$_2$O$_3$ single crystals and homoepitaxial films of several $\mu$m were determined using the 3$\omega$-method in the temperature range from 10K-300 K. The measured effective thermal conductivity of both, single crystal and homoepitaxial films are in the order of 20 W/(mK) at room temperature, below 30 K it increases with a maximum of 1000 to 2000 W/(mK) and decreases with T$^3$ below 25 K. Analysis of the phonon mfp shows a dominance of phonon-phonon-Umklapp scattering above 80 K, below which the influence of point-defect scattering is observed. Below 30 K the phonon mfp increases until it is limited by the total $\beta$-Ga$_2$O$_3$ sample size. A crossover from resistive to ballistic phonon transport is observed below 20 K and boundary effects of the total sample size become dominant. This reveals that the homoepitaxial film-substrate interface is highly phonon-transparent. The resistive and ballistic phonon transport regimes in $\beta$-Ga$_2$O$_3$ are discussed corresponding to the models of Callaway and Majumdar, respectively.
Spiking Neural Networks for Fast-Moving Object Detection on Neuromorphic Hardware Devices Using an Event-Based Camera

Table tennis is a fast-paced and exhilarating sport that demands agility, precision, and fast reflexes. In recent years, robotic table tennis has become a popular research challenge for robot perception algorithms. Fast and accurate ball detection is crucial for enabling a robotic arm to rally the ball back successfully. Previous approaches have employed conventional frame-based cameras with Convolutional Neural Networks (CNNs) or traditional computer vision methods. In this paper, we propose a novel solution that combines an event-based camera with Spiking Neural Networks (SNNs) for ball detection. We use multiple state-of-the-art SNN frameworks and develop a SNN architecture for each of them, complying with their corresponding constraints. Additionally, we implement the SNN solution across multiple neuromorphic edge devices, conducting comparisons of their accuracies and run-times. This furnishes robotics researchers with a benchmark illustrating the capabilities achievable with each SNN framework and a corresponding neuromorphic edge device. Next to this comparison of SNN solutions for robots, we also show that an SNN on a neuromorphic edge device is able to run in real-time in a closed loop robotic system, a table tennis robot in our use case.
Not Just Change the Labels, Learn the Features: Watermarking Deep Neural Networks with Multi-View Data

With the increasing prevalence of Machine Learning as a Service (MLaaS) platforms, there is a growing focus on deep neural network (DNN) watermarking techniques. These methods are used to facilitate the verification of ownership for a target DNN model to protect intellectual property. One of the most widely employed watermarking techniques involves embedding a trigger set into the source model. Unfortunately, existing methodologies based on trigger sets are still susceptible to functionality-stealing attacks, potentially enabling adversaries to steal the functionality of the source model without a reliable means of verifying ownership. In this paper, we first introduce a novel perspective on trigger set-based watermarking methods from a feature learning perspective. Specifically, we demonstrate that by selecting data exhibiting multiple features, also referred to as $\textit{multi-view data}$, it becomes feasible to effectively defend functionality stealing attacks. Based on this perspective, we introduce a novel watermarking technique based on Multi-view dATa, called MAT, for efficiently embedding watermarks within DNNs. This approach involves constructing a trigger set with multi-view data and incorporating a simple feature-based regularization method for training the source model. We validate our method across various benchmarks and demonstrate its efficacy in defending against model extraction attacks, surpassing relevant baselines by a significant margin.
Inference of Monosynaptic Connections from Parallel Spike Trains: A Review

This article presents a mini-review about the progress in inferring monosynaptic connections from spike trains of multiple neurons over the past twenty years. First, we explain a variety of meanings of ``neuronal connectivity'' in different research areas of neuroscience, such as structural connectivity, monosynaptic connectivity, and functional connectivity. Among these, we focus on the methods used to infer the monosynaptic connectivity from spike data. We then summarize the inference methods based on two main approaches, i.e., correlation-based and model-based approaches. Finally, we describe available source codes for connectivity inference and future challenges. Although inference will never be perfect, the accuracy of identifying the monosynaptic connections has improved dramatically in recent years due to continuous efforts.
Use of active learning for earthquake damage mapping from UAV photogrammetric point clouds

ABSTRACT This article presents an effective classification method for earthquake damage mapping from unmanned aerial vehicles (UAV) photogrammetric point clouds. The classification method consists of three main components: (a) construction of a point feature descriptor regarding to spectral, textural, and geometrical features, (b) optimization of collecting informative training samples through an active learning (AL) method, and (c) fine-tuning the point-based classification results with contextual information. Besides using existing spectral and geometrical features, we design a textural feature based on fractal theory to construct a point feature descriptor through linear combination. A batch-model AL method called Margin Sampling and Multiclass Level Uncertainty (MS-MCLU) is proposed based on classification uncertainty using a Support Vector Machine classifier. We use a multi-label Markov random fields to fine-tune the point-based classification results with a pairwise model. The proposed method was tested using three sets of point clouds generated from UAV images over Mirabello, Lushan, and Wenchuan earthquake scenarios in 2012, Italy, and in 2013 and 2008, China, respectively. The proposed classification method was compared with that of two other feature descriptors, i.e. spectral combined with textural features (Spe_Tex) and geometrical features (Geo). The results show that classification accuracies were improved by using the proposed point feature descriptor. Results also show that the proposed MS-MCLU AL method evidently saved the cost of collecting informative training samples and produced higher classification accuracies than a random sampling strategy. Moreover, contextual information contributed to the improvement on the point-based classification results and was suggested to be considered in earthquake damage mapping applications.
Cellular interactions regulate stem cell differentiation in tri-culture

ABSTRACT Currently, the mechanism governing the regeneration of the soft tissue-to-bone interface, such as the transition between the anterior cruciate ligament (ACL) and bone, is not known. Focusing on the ACL-to-bone insertion, this study tests the novel hypothesis that interactions between cells from the ligament (fibroblasts) and bone (osteoblasts) initiate interface regeneration. Specifically, these heterotypic cell interactions direct the fibrochondrogenic differentiation of interface-relevant cell populations, defined here as ligament fibroblasts and bone marrow stromal cells (BMSC). The objective of this study is to examine the effects of heterotypic cellular interactions on BMSC or fibroblast growth and biosynthesis, as well as expression of fibrocartilage-relevant markers in tri-culture. The effects of cell–cell physical contact and paracrine interactions between fibroblasts and osteoblasts were also determined. It was found that, in tri-culture with fibroblasts and osteoblasts, BMSC exhibited greater fibrochondrogenic potential than ligament fibroblasts. The growth of BMSC decreased while proteoglycan production and TGF-β3 expression increased. Moreover, tri-culture regulated BMSC response via paracrine factors, and interestingly, fibroblast–osteoblast contact further promoted proteoglycan and TGF-β1 synthesis as well as induced SOX9 expression in BMSC. Collectively, the findings of this study suggest that fibroblast–osteoblast interactions play an important role in regulating the stem cell niche for fibrocartilage regeneration, and the mechanisms of these interactions are directed by paracrine factors and augmented with direct cell–cell contact.
Deep Neural Crossover

We present a novel multi-parent crossover operator in genetic algorithms (GAs) called ``Deep Neural Crossover'' (DNC). Unlike conventional GA crossover operators that rely on a random selection of parental genes, DNC leverages the capabilities of deep reinforcement learning (DRL) and an encoder-decoder architecture to select the genes. Specifically, we use DRL to learn a policy for selecting promising genes. The policy is stochastic, to maintain the stochastic nature of GAs, representing a distribution for selecting genes with a higher probability of improving fitness. Our architecture features a recurrent neural network (RNN) to encode the parental genomes into latent memory states, and a decoder RNN that utilizes an attention-based pointing mechanism to generate a distribution over the next selected gene in the offspring. To improve the training time, we present a pre-training approach, wherein the architecture is initially trained on a single problem within a specific domain and then applied to solving other problems of the same domain. We compare DNC to known operators from the literature over two benchmark domains -- bin packing and graph coloring. We compare with both two- and three-parent crossover, outperforming all baselines. DNC is domain-independent and can be easily applied to other problem domains.
Topology Deduction and Analysis of Voltage Balancers for DC Microgrid

A voltage balancer that transfers unipolar dc bus configuration to bipolar dc bus configuration has been widely employed in dc microgrid. Fortunately, unbalanced power flow between positive and negative dc buses can be eliminated through a well-designed voltage balancer. Based on the analysis of a Buck/Boost-type voltage balancer, a deduction method for a series of voltage balancers is proposed in this paper, and is further investigated by applying to other existing voltage balancers. Consequently, several new topologies are proposed for various purpose of improvement with respect to the characters of existing topologies. Furthermore, four new topologies, i.e., Cuk-type, Super-Sepic/Zeta-type, and interleaved Buck/Boost-type voltage balancers have been proposed, which are compared with existing ones in terms of key characters. Finally, the interleaved Buck/Boost topology has been selected as an example for verification and a laboratory prototype is therefore built. The experimental results show a promising effect of current sharing and a good dynamic characteristic of the proposed interleaved average current control strategy based on interleaved sampling while maintaining a balanced power flow between the two dc buses.
Expert Composer Policy: Scalable Skill Repertoire for Quadruped Robots

We propose the expert composer policy, a framework to reliably expand the skill repertoire of quadruped agents. The composer policy links pair of experts via transitions to a sampled target state, allowing experts to be composed sequentially. Each expert specializes in a single skill, such as a locomotion gait or a jumping motion. Instead of a hierarchical or mixture-of-experts architecture, we train a single composer policy in an independent process that is not conditioned on the other expert policies. By reusing the same composer policy, our approach enables adding new experts without affecting existing ones, enabling incremental repertoire expansion and preserving original motion quality. We measured the transition success rate of 72 transition pairs and achieved an average success rate of 99.99\%, which is over 10\% higher than the baseline random approach, and outperforms other state-of-the-art methods. Using domain randomization during training we ensure a successful transfer to the real world, where we achieve an average transition success rate of 97.22\% (N=360) in our experiments.
Rethinking Multi-view Representation Learning via Distilled Disentangling

Multi-view representation learning aims to derive robust representations that are both view-consistent and view-specific from diverse data sources. This paper presents an in-depth analysis of existing approaches in this domain, highlighting a commonly overlooked aspect: the redundancy between view-consistent and view-specific representations. To this end, we propose an innovative framework for multi-view representation learning, which incorporates a technique we term 'distilled disentangling'. Our method introduces the concept of masked cross-view prediction, enabling the extraction of compact, high-quality view-consistent representations from various sources without incurring extra computational overhead. Additionally, we develop a distilled disentangling module that efficiently filters out consistency-related information from multi-view representations, resulting in purer view-specific representations. This approach significantly reduces redundancy between view-consistent and view-specific representations, enhancing the overall efficiency of the learning process. Our empirical evaluations reveal that higher mask ratios substantially improve the quality of view-consistent representations. Moreover, we find that reducing the dimensionality of view-consistent representations relative to that of view-specific representations further refines the quality of the combined representations. Our code is accessible at: https://github.com/Guanzhou-Ke/MRDD.