Miracle-dz/UniFAR-FTUs huggingface dataset:
A Spatio-Temporal Model and Inference Tools for Longitudinal Count Data on Multicolor Cell Growth

Abstract Multicolor cell spatio-temporal image data have become important to investigate organ development and regeneration, malignant growth or immune responses by tracking different cell types both in vivo and in vitro. Statistical modeling of image data from common longitudinal cell experiments poses significant challenges due to the presence of complex spatio-temporal interactions between different cell types and difficulties related to measurement of single cell trajectories. Current analysis methods focus mainly on univariate cases, often not considering the spatio-temporal effects affecting cell growth between different cell populations. In this paper, we propose a conditional spatial autoregressive model to describe multivariate count cell data on the lattice, and develop inference tools. The proposed methodology is computationally tractable and enables researchers to estimate a complete statistical model of multicolor cell growth. Our methodology is applied on real experimental data where we investigate how interactions between cancer cells and fibroblasts affect their growth, which are normally present in the tumor microenvironment. We also compare the performance of our methodology to the multivariate conditional autoregressive (MCAR) model in both simulations and real data applications.
Un cas d'hypercalcémie secondaire à une hyperparathyrïdie primaire canine traité chirurgicalement.

5u le monde vétérinaire | #159| avril 2016 Un chien croisé Beagle mâle entier de neuf ans est présenté pour fasciculations musculaires évoluant depuis six mois et polyurie-polydipsie évoluant depuis trois mois. Un bilan sanguin révèle une hypercalcémie totale et ionisée sans hyperphosphatémie. Une origine néoplasique, rénale et un Addison sont écartés, et des examens d’imagerie (dont échographie cervicale ventrale) et dosage de la parathormone permettent de diagnostiquer une hyperparathyroïdie primaire. Une parathyroïdectomie droite est réalisée. Le suivi de la calcémie post-opératoire confirme la résolution de l’hypercalcémie. Une légère hypocalcémie ionisée transitoire est apparue, sans signes cliniques. Aucun traitement n’est alors mis en place. L›analyse histopathologique conclut à un adénome parathyroïdien. Une résolution de la polyurie-polydipsie est rapportée un mois après la chirurgie et le calcium ionisé reste dans les normes.
Unsupervised Domain Adaptation Using Exemplar-SVMs with Adaptation Regularization

Domain adaptation has recently attracted attention for visual recognition. It assumes that source and target domain data are drawn from the same feature space but different margin distributions and its motivation is to utilize the source domain instances to assist in training a robust classifier for target domain tasks. Previous studies always focus on reducing the distribution mismatch across domains. However, in many real-world applications, there also exist problems of sample selection bias among instances in a domain; this would reduce the generalization performance of learners. To address this issue, we propose a novel model named Domain Adaptation Exemplar Support Vector Machines (DAESVMs) based on exemplar support vector machines (exemplar-SVMs). Our approach aims to address the problems of sample selection bias and domain adaptation simultaneously. Comparing with usual domain adaptation problems, we go a step further in slacking the assumption of i.i. d. First, we formulate the DAESVMs training classifiers with reducing Maximum Mean Discrepancy (MMD) among domains by mapping data into a latent space and preserving properties of original data, and then, we integrate classifiers to make a prediction for target domain instances. Our experiments were conducted on Office and Caltech10 datasets and verify the effectiveness of the model we proposed.
Semantic Structure-based Unsupervised Deep Hashing

Hashing is becoming increasingly popular for approximate nearest neighbor searching in massive databases due to its storage and search efficiency. Recent supervised hashing methods, which usually construct semantic similarity matrices to guide hash code learning using label information, have shown promising results. However, it is relatively difficult to capture and utilize the semantic relationships between points in unsupervised settings. To address this problem, we propose a novel unsupervised deep framework called Semantic Structure-based unsupervised Deep Hashing (SSDH). We first empirically study the deep feature statistics, and find that the distribution of the cosine distance for point pairs can be estimated by two half Gaussian distributions. Based on this observation, we construct the semantic structure by considering points with distances obviously smaller than the others as semantically similar and points with distances obviously larger than the others as semantically dissimilar. We then design a deep architecture and a pair-wise loss function to preserve this semantic structure in Hamming space. Extensive experiments show that SSDH significantly outperforms current state-of-the-art methods.
Intégration Optimale des Véhicules Électriques Hybrides Rechargeables dans un Réseau Résidentiel

RESUME –Ce papier présente une approche linéaire pour calculer les tensions de chaque nœud sur un réseau résidentiel, basé sur la charge instantanée de chaque maison en présence des voitures électriques hybrides rechargeables (Plug-in Hybrid Electric Vehicles - PHEV). Sur la base de ce fonctionnement, le papier propose une formulation de programmation linéaire détaillée du problème de la charge des PHEVs, et ce, tout en offrant un service de support de tension au réseau. Plusieurs cas d'évaluation sont inclus afin de tester la performance de l'approche à maintenir des tensions dans les limites de sécurité et fournir des séquences optimales de consommation / injection. Un autre cas est inclus afin de prouver la propension des véhicules électriques hybrides rechargeables à résoudre les problèmes de tension existants sur le réseau résidentiel. ABSTRACT – This paper provides a linear approach to compute the voltages at any node on a residential test grid based on the house instantaneous load and the presence of charging Plug-In Hybrid Electric Vehicles (PHEV) on the grid (and the corresponding instantaneous consumption or injection). Based on this linear operation, the paper provides a detailed linear programming formulation of the problem of charging the PHEVs while providing a voltage support service to the grid. Multiple evaluation cases are included in order to test the ability of the approach to maintain voltages within safety limits and provide optimal consumption/injection policies. An additional case is included to prove the potential of the PHEVs to solve existing voltage technical issues on a residential grid.
Sandwich construction for keyed sponges: independence between capacity and construction queries

In this study, the authors study the pseudo-random function (PRF) security of keyed sponges. ‘Capacity’ is a parameter of a keyed sponge that usually defines a dominant term in the PRF-security bound. The previous works have improved the capacity term in the PRF-security bound of the ‘prefix’ keyed sponge, where a secret key is prepended to an input message, and then the resultant value is inputted into the sponge function. A tight bound for the capacity term was given by Naito and Yasuda (FSE 2016): ( q Q + q 2 ) / 2 c for the capacity c, the number of construction queries q and the number of primitive queries Q. Thus, the following question naturally arises: Can they construct a keyed sponge with beyond the ( q 2 + q Q ) / 2 c -bound security? In this study, they consider the ‘sandwich’ keyed sponge, where a secret key is both prepended and appended to an input message, and then the resultant value is inputted into the sponge function. They prove that the capacity term becomes r Q / 2 c for the rate r, which is usually r ≪ q and r ≪ Q . That is, the dependence between the capacity and construction queries can be removed by the sandwich construction.
Sequential Emboli Detection From Ultrasound Outpatient Data

This paper addresses the detection of emboli from signals acquired with a new miniaturized and portable transcranial Doppler ultrasound device. The use of this device enables outpatient monitoring but increases the number of artifacts. These artifacts usually come from the patient voice and motion and can be superimposed to emboli. For this reason and because of the scarcity of emboli compared to artifacts, reliably detect emboli is a challenging task. As an example, the 11809 s of signal used in this study contained 0.06<inline-formula><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> of embolic events and 10.14<inline-formula><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> of artifacts. Herein, we propose an automatic and sequential approach. The method is based on sequential determination of high intensity transient signals. We also define efficient features to describe emboli in the time frequency representation. On our database, the number of artifacts detected as emboli is divided by more than 10 compared to the other algorithms reported in the literature.
Topological Properties of the Structural Brain Network in Autism via ϵ-Neighbor Method

Objective: Topological characteristics of the brain can be analyzed using structural brain networks constructed by diffusion tensor imaging (DTI). When a brain network is constructed by the existing parcellation method, the structure of the network changes depending on the scale of parcellation and arbitrary thresholding. To overcome these issues, we propose to construct brain networks using the improved $\varepsilon $-neighbor construction, which is a parcellation free network construction technique. Methods: We acquired DTI from 14 control subjects and 15 subjects with autism. We examined the differences in topological properties of the brain networks constructed using the proposed method and the existing parcellation between the two groups. Results: As the number of nodes increased, the connectedness of the network decreased in the parcellation method. However, for brain networks constructed using the proposed method, connectedness remained at a high level even with an increase in the number of nodes. We found significant differences in several topological properties of brain networks constructed using the proposed method, whereas topological properties were not significantly different for the parcellation method. Conclusion: The brain networks constructed using the proposed method are considered as more realistic than a parcellation method with respect to the stability of connectedness. We found that subjects with autism showed the abnormal characteristics in the brain networks. These results demonstrate that the proposed method may provide new insights to analysis in the structural brain network. Significance: We proposed the novel brain network construction method to overcome the shortcoming in the existing parcellation method.
Camera-Assisted Video Saliency Prediction and Its Applications

Video saliency prediction is an indispensable yet challenging technique which can facilitate various applications, such as video surveillance, autonomous driving, and realistic rendering. Based on the popularity of embedded cameras, we in this paper predict region-level saliency from videos by leveraging human gaze locations recorded using a camera, (e.g., those equipped on an iMAC and laptop PC). Our proposed camera-assisted mechanism improves saliency prediction by discovering human attended regions inside a video clip. It is orthogonal to the current saliency models, i.e., any existing video/image saliency model can be boosted by our mechanism. First of all, the spatial-and temporal-level visual features are exploited collaboratively for calculating an initial saliency map. We notice that the current saliency models are not sufficiently adaptable to the variations in lighting, different view angles, and complicated backgrounds. Therefore, assisted by a camera tracking human gaze movements, a non-negative matrix factorization algorithm is designed to accurately localize the semantically/visually salient video regions perceived by humans. Finally, the learned human gaze locations as well as the initial saliency map are integrated to optimize video saliency calculation. Empirical results thoroughly demonstrated that: 1) our approach achieves the state-of-the-art video saliency prediction accuracy by outperforming 11 mainstream algorithms considerably and 2) our method can conveniently and successfully enhance video retargeting, quality estimation, and summarization.
Deep Convolutional Identifier for Dynamic Modeling and Adaptive Control of Unmanned Helicopter

Helicopters are complex high-order and time-varying nonlinear systems, strongly coupling with aerodynamic forces, engine dynamics, and other phenomena. Therefore, it is a great challenge to investigate system identification for dynamic modeling and adaptive control for helicopters. In this paper, we address the system identification problem as dynamic regression and propose to represent the uncertainties and the hidden states in the system dynamic model with a deep convolutional neural network. Particularly, the parameters of the network are directly learned from the real flight data of aerobatic helicopter. Since the deep convolutional model has a good performance for describing the dynamic behavior of the hidden states and uncertainties in the flight process, the proposed identifier manifests strong robustness and high accuracy, even for untrained aerobatic maneuvers. The effectiveness of the proposed method is verified by various experiments with the real-world flight data from the Stanford Autonomous Helicopter Project. Consequently, an adaptive flight control scheme including a deep convolutional identifier and a backstepping-based controller is presented. The stability of the flight control scheme is rigorously proved by the Lyapunov theory. It reveals that the tracking errors for both the position and attitude of unmanned helicopter asymptotic converge to a small neighborhood of the origin.
Identifying Core Objects for Trace Summarization by Analyzing Reference Relations and Dynamic Properties

SUMMARY Behaviors of an object-oriented system can be visualized as reverse-engineered sequence diagrams from execution traces. This approach is a valuable tool for program comprehension tasks. However, owing to the massiveness of information contained in an execution trace, a reverse-engineered sequence diagram is often a ﬄ icted by a scalability issue. To address this issue, many trace summarization techniques have been proposed. Most of the previous techniques focused on reducing the vertical size of the diagram. To cope with the scalability issue, decreasing the horizontal size of the diagram is also very important. Nonetheless, few studies have addressed this point; thus, there is a lot of needs for further development of horizontal summarization techniques. We present in this paper a method for identifying core objects for trace summarization by analyzing reference relations and dynamic properties. Visualizing only interactions related to core objects, we can obtain a horizontally compactiﬁed reverse-engineered sequence diagram that contains system’s key behaviors. To identify core objects, ﬁrst, we detect and eliminate temporary objects that are trivial for a system by analyzing reference relations and lifetimes of objects. Then, estimating the importance of each non-trivial object based on their dynamic properties, we identify highly important ones (i.e., core objects). We implemented our technique in our tool and evaluated it by using traces from various open-source software systems. The results showed that our technique was much more e ﬀ ective in terms of the horizontal reduction of a reverse-engineered sequence diagram, compared with the state-of-the-art trace summarization technique. The horizontal compression ratio of our technique was 134.6 on average, whereas that of the state-of-the-art technique was 11.5. The runtime overhead imposed by our technique was 167.6% on average. This overhead is relatively small compared with recent scalable dynamic analysis techniques, which shows the practicality of our technique. Overall, our technique can achieve a signiﬁcant reduction of the horizontal size of a reverse-engineered sequence diagram with a small overhead and is expected to be a valuable tool for program comprehension.
$p$ -Laplacian Regularization for Scene Recognition

The explosive growth of multimedia data on the Internet makes it essential to develop innovative machine learning algorithms for practical applications especially where only a small number of labeled samples are available. Manifold regularized semi-supervised learning (MRSSL) thus received intensive attention recently because it successfully exploits the local structure of data distribution including both labeled and unlabeled samples to leverage the generalization ability of a learning model. Although there are many representative works in MRSSL, including Laplacian regularization (LapR) and Hessian regularization, how to explore and exploit the local geometry of data manifold is still a challenging problem. In this paper, we introduce a fully efficient approximation algorithm of graph <inline-formula> <tex-math notation="LaTeX">${p}$ </tex-math></inline-formula>-Laplacian, which significantly saving the computing cost. And then we propose <inline-formula> <tex-math notation="LaTeX">${p}$ </tex-math></inline-formula>-LapR (pLapR) to preserve the local geometry. Specifically, <inline-formula> <tex-math notation="LaTeX">${p}$ </tex-math></inline-formula>-Laplacian is a natural generalization of the standard graph Laplacian and provides convincing theoretical evidence to better preserve the local structure. We apply pLapR to support vector machines and kernel least squares and conduct the implementations for scene recognition. Extensive experiments on the Scene 67 dataset, Scene 15 dataset, and UC-Merced dataset validate the effectiveness of pLapR in comparison to the conventional manifold regularization methods.
Integrating Space, Time, and Orientation in Spiking Neural Networks: A Case Study on Multimodal Brain Data Modeling

Recent progress in a noninvasive brain data sampling technology has facilitated simultaneous sampling of multiple modalities of brain data, such as functional magnetic resonance imaging, electroencephalography, diffusion tensor imaging, and so on. In spite of the potential benefits from integrating predictive modeling of multiple modality brain data, this area of research remains mostly unexplored due to a lack of methodological advancements. The difficulty in fusing multiple modalities of brain data within a single model lies in the heterogeneous temporal and spatial characteristics of the data sources. Recent advances in spiking neural network systems, however, provide the flexibility to incorporate multidimensional information within the model. This paper proposes a novel, unsupervised learning algorithm for fusing temporal, spatial, and orientation information in a spiking neural network architecture that could potentially be used to understand and perform predictive modeling using multimodal data. The proposed algorithm is evaluated both qualitatively and quantitatively using synthetically generated data to characterize its behavior and its ability to utilize spatial, temporal, and orientation information within the model. This leads to improved pattern recognition capabilities and performance along with robust interpretability of the brain data. Furthermore, a case study is presented, which aims to build a computational model that discriminates between people with schizophrenia who respond or do not respond to monotherapy with the antipsychotic clozapine.
An Entropy-Regularized Framework for Detecting Copy Number Variants

The detection of DNA copy number variants (CNVs) is essential for the diagnosis and prognosis of multiple diseases including cancer. Array-based comparative genomic hybridization (aCGH) is a technique to find these aberrations. The available methods for CNV discovery are often predicated on several critical assumptions based on which various regularizations are employed. However, most of the resulting problems are not differentiable and finding their optimums needs massive computations. This paper addresses a new entropic regularization, which is significantly fast and robust against various types of noises. The proposed problem takes advantage of the quadratic Renyi's entropy estimation which is not convex, but the half-quadratic programming gives an efficient solution with guaranteed convergence. We further theoretically prove that minimizing Renyi's entropy estimation would induce the sparsity and smoothness, two salient and desired features for recovered aCGH profiles. Extensive experimental results on simulated and real datasets illustrate the robustness and speed of the proposed method in comparison to the state-of-the-art algorithms.
Distributed Algorithms for Searching Generalized Nash Equilibrium of Noncooperative Games

In this paper, the distributed Nash equilibrium (NE) searching problem is investigated, where the feasible action sets are constrained by nonlinear inequalities and linear equations. Different from most of the existing investigations on distributed NE searching problems, we consider the case where both cost functions and feasible action sets depend on actions of all players, and each player can only have access to the information of its neighbors. To address this problem, a continuous-time distributed gradient-based projected algorithm is proposed, where a leader-following consensus algorithm is employed for each player to estimate actions of others. Under mild assumptions on cost functions and graphs, it is shown that players’ actions asymptotically converge to a generalized NE. Simulation examples are presented to demonstrate the effectiveness of the theoretical results.