Miracle-dz/UniFAR-FTUs huggingface dataset:
Antioxidant activity of eight plants consumed by great apes in Côte d'Ivoire

Oxidative stress is an aggravating factor involved in a number of pathologies. The source and mobilization of antioxidant compounds are a challenge for the public health sector and new approaches are needed to assess and identify the main sources of antioxidants. Monkeys and great apes are considered to tolerate the Simian immunodeficiency virus (SIV) infection and other diseases. The current study aimed at screening wild chimpanzee’s diet to select plants with high antioxidant potential as supplement for improving health status of people under oxidative stress. Bio-cultural approach based on chimpanzee’s diet or auto-medication and human traditional medicine was used for selection of eight species, Ficus elasticoides, Ficus lyrata, Ficus umbelleta, Ficus thonningii, Ficus mucuso, Xylopia quintasii,Sherbournia calycina and Myrianthus libericus. Further, antioxidant activity of extracts (methanolic and dichloromethane) from these plants was assessed by 2, 2-diphenyl-1-picrylhydrazyl (DPPH) and ferric reducing antioxidant power (FRAP) methods. Methanolic extract of leaves from F. elasticoides showed the highest radical scavenging activity with 96.69% of DPPH inhibition, followed by extracts of F. lyrata(94.53%), X. quintasii (94.36%) and F. mucuso (94.33%). The IC50 values of extracts were respectively 7.8, 9.3, 8.3 and 8.7 µg/ml and close to those of ascorbic acid (8.00 µg/ml) and gallic acid (8.20 µg/ml). The ferric reducing power of F. lyrata (185.01 µM) was the strongest. Active species contain monoterpenoid, secoiridoides and polyphenols. Further investigation on the use of such plants in the traditional medicine will contribute to generate an added value at the interface of human and animal nutrition to provide nutraceuticals for immunocompromised people. Key words: Cote d’Ivoire, great apes diet, oxidative stress, antioxidant activity.
Multi-dimensional Aggregation Recommendation Algorithm Based on Average Prediction

Traditional collaborative filtering recommendation algorithm uses single dimensional data to calculate the similarity between users or items, ignoring the user’s preference, thus affect the recommendation accuracy. To this end, an averaging forecasting based multi-dimensional aggregation recommendation algorithm was proposed in this paper, which constructs the relationship aggregation function by user’s total score and dimension scores firstly, then apply the aggregation function to the initial multi-dimensional score that calculated by the modified averaging forecasting algorithm. The experiment result shows that compared with the previous collaborative filtering based recommendation algorithm, it has higher recommendation accuracy.
Drug dependence, mental impairment and education

This study aimed to evaluate the mental conditions of cocaine-dependent individuals and school commitment/attachment. We evaluated 50 patients referred to the psychiatry emergency room due to mental disorders from chemical dependence. After clinical diagnosis, clinical interview, Hospital Anxiety and Depression Scale, Hamilton Scale for Depression and Brief Psychiatric Rating Scale were applied. The Spearman and Mann-Whitney nonparametric tests, as well as the tStudent test were utilized for statistical analysis. The accepted significance value was 0.05. All subjects had used cocaine or crack and other substances. Only 13 (26%) did not drop out of school (group 1). Regarding the other 37 (74%), irregular class attendance , successive failures and definitive school drop out rates (group 2) were verified. These subjects presented an early substance use when compared with those which did not drop out of school ( p=0.0001). Patients with an early substance use presented higher school dropout rates than those with a later initiation to substance
Reconciling Utility and Membership Privacy via Knowledge Distillation

Large capacity machine learning models are prone to membership inference attacks in which an adversary aims to infer whether a particular data sample is a member of the target model's training dataset. Such membership inferences can lead to serious privacy violations as machine learning models are often trained using privacy-sensitive data such as medical records and controversial user opinions. Recently, defenses against membership inference attacks are developed, in particular, based on differential privacy and adversarial regularization; unfortunately, such defenses highly impact the classification accuracy of the underlying machine learning models. In this work, we present a new defense against membership inference attacks that preserves the utility of the target machine learning models significantly better than prior defenses. Our defense, called distillation for membership privacy (DMP), leverages knowledge distillation to train machine learning models with membership privacy. We analyze the key requirements for membership privacy and provide a novel criterion to select data used for knowledge transfer, in order to improve membership privacy of the final models. DMP works effectively against the attackers with either a whitebox or blackbox access to the target model. We evaluate DMP's performance through extensive experiments on different deep neural networks and using various benchmark datasets. We show that DMP provides a significantly better tradeoff between inference resistance and classification performance than state-of-the-art membership inference defenses. For instance, a DMP-trained DenseNet provides a classification accuracy of 65.3% for a 54.4% blackbox membership inference attack accuracy, while an adversarially regularized DenseNet provides a classification accuracy of only 53.7% for a (much worse) 68.7% blackbox membership inference attack accuracy.
Finding the Needle in the Haystack with Convolutions: on the benefits of architectural bias

Despite the phenomenal success of deep neural networks in a broad range of learning tasks, there is a lack of theory to understand the way they work. In particular, Convolutional Neural Networks (CNNs) are known to perform much better than Fully-Connected Networks (FCNs) on spatially structured data: the architectural structure of CNNs benefits from prior knowledge on the features of the data, for instance their translation invariance. The aim of this work is to understand this fact through the lens of dynamics in the loss landscape. We introduce a method that maps a CNN to its equivalent FCN (denoted as eFCN). Such an embedding enables the comparison of CNN and FCN training dynamics directly in the FCN space. We use this method to test a new training protocol, which consists in training a CNN, embedding it to FCN space at a certain ``relax time'', then resuming the training in FCN space. We observe that for all relax times, the deviation from the CNN subspace is small, and the final performance reached by the eFCN is higher than that reachable by a standard FCN of same architecture. More surprisingly, for some intermediate relax times, the eFCN outperforms the CNN it stemmed, by combining the prior information of the CNN and the expressivity of the FCN in a complementary way. The practical interest of our protocol is limited by the very large size of the highly sparse eFCN. However, it offers interesting insights into the persistence of architectural bias under stochastic gradient dynamics. It shows the existence of some rare basins in the FCN loss landscape associated with very good generalization. These can only be accessed thanks to the CNN prior, which helps navigate the landscape during the early stages of optimization.
Intelligent Anomaly Detection and Mitigation in Data Centers

Data centers play a key role in today's Internet. Cloud applications are mainly hosted on multi-tenant warehouse-scale data centers. Anomalies pose a serious threat to data centers' operations. If not controlled properly, a simple anomaly can spread throughout the data center, resulting in a cascading failure. Amazon AWS had been affected by such incidents recently. Although some solutions are proposed to detect anomalies and prevent cascading failures, they mainly rely on application-specific metrics and case-based diagnosis to detect the anomalies. Given the variety of applications on a multi-tenant data center, proposed solutions are not capable of detecting anomalies in a timely manner. In this paper we design an application-agnostic anomaly detection scheme. More specifically, our design uses a highly distributed data mining scheme over network-level traffic metrics to detect anomalies. Once anomalies are detected, simple actions are taken to mitigate the damage. This ensures that errors are confined and prevents cascading failures before administrators intervene.
Large Scale 3D Modelling via Sparse Volumes

Spatial 3D reconstruction received enormous interest in the last years. However, the goal to store, to process and to visualize the acquired data is still very challenging. Discrete voxel based representation techniques became state of the art in todays research approaches. These allow summary of redundant measurements and fast coordinate based access to the data leading to efficient volume computations. Unfortunately, representing the 3D space with a dense voxel grid requires huge amount of storage. Representing a volume of 100 x 100 x 100m3 with resolution of 1cm with a dense grid of 32-bit floating point values, results in a 3:8 TB storage requirement. This motivated many state of the art approaches to apply octrees to build sparse 3D volumes, where only the occupied voxels are stored. This however, increases the data access complexity from O(1) to O(d) with d as the depth of the octree, growing logarithmically when the volume or the resolution of the model is increasing. In this work we propose to combine octrees with hash tables leading to sparse voxel representation well suited for efficient storage and fast data access common in 3D modelling computations. The hash table is used to access grid cells, which further contain an octree in itself. Since the internal octrees are constructed of much smaller depth e.g. di = 1, this dramatically decreases the access time complexity to O(di). For a standard octree with depth d = 16 , this leads to a speed-up of factor 16. An additional advantage of the hash table approach is that the volume size is not limited and is suited for modelling huge environments.
Blocking facial mimicry selectively alters early stages of facial expression processing

Simulation models of facial expressions suggest that posterior visual areas and brain areas underpinning sensorimotor simulations might interact to improve facial expression processing. According to these models, facial mimicry may contribute to the visual perceptual processing of facial expressions by influencing early stages of face processing, thus playing a crucial role in understanding the observed emotion. The aim of the present study was to assess whether and how early sensorimotor simulation influences face structural encoding/processing. A secondary aim was to investigate whether there is a relationship between alexithymic traits and sensorimotor simulation as a mechanism for fine facial expression discrimination. In order to examine the time-course of face processing, we monitored P1 and N170 components of the event-related potentials (ERP) in participants performing a fine discrimination task of facial expressions. An animal discrimination task was implemented as a control condition. In half of the experiment, participants could freely use their facial mimicry whereas, in the other half, they had their facial mimicry blocked by a gel. Our results revealed that the P1 ERP component was not modulated by the mimicry manipulation while the N170 amplitude was larger in the blocked mimicry condition when compared to the free mimicry condition selectively for the face stimuli. Interestingly, this modulation interacted with the alexithymic traits, with a reduction of the N170 amplitude modulation as a function of the facial mimicry manipulation for participants with the highest levels of alexithymic traits. These results demonstrate that sensorimotor simulation influences visual processing of facial expressions at early stages and that participants with higher alexithymic traits tend to underutilize the sensorimotor simulation system.
RECAL: Reuse of Established CNN classifer Apropos unsupervised Learning paradigm

Recently, clustering with deep network framework has attracted attention of several researchers in the computer vision community. Deep framework gains extensive attention due to its efficiency and scalability towards large-scale and high-dimensional data. In this paper, we transform supervised CNN classifier architecture into an unsupervised clustering model, called RECAL, which jointly learns discriminative embedding subspace and cluster labels. RECAL is made up of feature extraction layers which are convolutional, followed by unsupervised classifier layers which is fully connected. A multinomial logistic regression function (softmax) stacked on top of classifier layers. We train this network using stochastic gradient descent (SGD) optimizer. However, the successful implementation of our model is revolved around the design of loss function. Our loss function uses the heuristics that true partitioning entails lower entropy given that the class distribution is not heavily skewed. This is a trade-off between the situations of "skewed distribution" and "low-entropy". To handle this, we have proposed classification entropy and class entropy which are the two components of our loss function. In this approach, size of the mini-batch should be kept high. Experimental results indicate the consistent and competitive behavior of our model for clustering well-known digit, multi-viewed object and face datasets. Morever, we use this model to generate unsupervised patch segmentation for multi-spectral LISS-IV images. We observe that it is able to distinguish built-up area, wet land, vegetation and waterbody from the underlying scene.
Inner-Imaging Networks: Put Lenses Into Convolutional Structure

Despite the tremendous success in computer vision, deep convolutional networks suffer from serious computation costs and redundancies. Although previous works address that by enhancing the diversities of filters, they have not considered the complementarity and the completeness of the internal convolutional structure. To respond to this problem, we propose a novel inner-imaging (InI) architecture, which allows relationships between channels to meet the above requirement. Specifically, we organize the channel signal points in groups using convolutional kernels to model both the intragroup and intergroup relationships simultaneously. A convolutional filter is a powerful tool for modeling spatial relations and organizing grouped signals, so the proposed methods map the channel signals onto a pseudoimage, like putting a lens into the internal convolution structure. Consequently, not only is the diversity of channels increased but also the complementarity and completeness can be explicitly enhanced. The proposed architecture is lightweight and easy to be implement. It provides an efficient self-organization strategy for convolutional networks to improve their performance. Extensive experiments are conducted on multiple benchmark datasets, including CIFAR, SVHN, and ImageNet. Experimental results verify the effectiveness of the InI mechanism with the most popular convolutional networks as the backbones.
Open Domain Event Extraction Using Neural Latent Variable Models

We consider open domain event extraction, the task of extracting unconstraint types of events from news clusters. A novel latent variable neural model is constructed, which is scalable to very large corpus. A dataset is collected and manually annotated, with task-specific evaluation metrics being designed. Results show that the proposed unsupervised model gives better performance compared to the state-of-the-art method for event schema induction.
Learning in Cournot Games with Limited Information Feedback

In this work, we study the interaction of strategic players in continuous action Cournot games with limited information feedback. Cournot game is the essential market model for many socio-economic systems where players learn and compete without the full knowledge of the system or each other. In this setting, it becomes important to understand the dynamics and limiting behavior of the players. We consider concave Cournot games and two widely used learning strategies in the form of no-regret algorithms and policy gradient. We prove that if the players all adopt one of the two algorithms, their joint action converges to the unique Nash equilibrium of the game. Notably, we show that myopic algorithms such as policy gradient can achieve exponential convergence rate, while no-regret algorithms only converge (sub)linearly. Together, our work presents significantly sharper convergence results and shows how exploiting the structure of the game can lead to much faster convergence rates.
Euler Equations for the Estimation of Dynamic Discrete Choice Structural Models

We derive marginal conditions of optimality (i.e., Euler equations) for a general class of Dynamic Discrete Choice (DDC) structural models. These conditions can be used to estimate structural parameters in these models without having to solve for or approximate value functions. This result extends to discrete choice models the GMM-Euler equation approach proposed by Hansen and Singleton (1982) for the estimation of dynamic continuous decision models. We first show that DDC models can be represented as models of continuous choice where the decision variable is a vector of choice probabilities. We then prove that the marginal conditions of optimality and the envelope conditions required to construct Euler equations are also satisfied in DDC models. The GMM estimation of these Euler equations avoids the curse of dimensionality associated to the computation of value functions and the explicit integration over the space of state variables. We present an empirical application and compare estimates using the GMM-Euler equations method with those from maximum likelihood and two-step methods.
Local Human Capital Externalities or Sorting? Evidence From a Displaced Workers Sample

In this paper we want to assess if the positive association between individual earnings and local average education is due to human capital externalities or just reflects some ommitted factors. Using a sample of displaced workers, we take into account firm characteristics as well as individual fixed e.ects. We find that, on average educational externalities are quite small, however it seems that these externalities are not captured equally by di.erent types of individuals. More educated workers benefit from a highly-educated environment relative to workers with lower levels of education (about 3% more) and these gains are not all acquired immediately, but through time.
A Switching Regression Model to Assess the Policy Consequences of Direct Legislation

Most theoretical models predict that institutions allowing for direct legislation should lead, on average, to policies more closely re∞ecting the wishes of the voters. While some agreement exists at the theoretical level about the expected policy consequences of direct legislation, empirical evidence has been scant so far. In this paper I discuss the reasons for this scantiness of empirical evidence, namely the intricacies of the adequate empirical model to test the theoretical proposition, and suggest possible solutions to this problem. Re-analyzing a dataset with which some authors have found no evidence in support of the theoretical claim, I show that with a better adapted empirical model we flnd results in synch with our theoretical expectations. Thus, policies in states that allow for direct legislation re∞ect on average more closely the voters’ wishes. Using Monte-Carlo simulations I also demonstrate the properties of the proposed estimator and suggest that it could be used in other contexts, like when assessing the responsiveness of legislators.
University of Groningen alpha II-spectrin and beta II-spectrin do not affect TGF beta 1-induced myofibroblast differentiation Piersma,

Mechanosensing of fibroblasts plays a key role in the development of fibrosis. So far, no effective treatments are available to treat this devastating disorder. Spectrins regulate cell morphology and are potential mechanosensors in a variety of non-erythroid cells, but little is known about the role of spectrins in fibroblasts. We investigate whether αIIand βII-spectrin are required for the phenotypic properties of adult human dermal (myo)fibroblasts. Knockdown of αIIor βII-spectrin in fibroblasts did not affect cell adhesion, cell size and YAP nuclear/cytosolic localization. We further investigated whether αIIand βII-spectrin play a role in the phenotypical switch from fibroblasts to myofibroblasts under the influence of the pro-fibrotic cytokine TGFβ1. Knockdown of spectrins did not affect myofibroblast formation, nor did we observe changes in the organization of αSMA stress fibers. Focal adhesion assembly was unaffected by spectrin deficiency, as was collagen type I mRNA expression and protein deposition. Wound closure was unaffected as well, showing that important functional properties of myofibroblasts are unchanged without αIIor βII-spectrin. In fact, fibroblasts stimulated with TGFβ1 demonstrated significantly lower endogenous mRNA levels of αIIand βII-spectrin. Taken together, despite the diverse roles of spectrins in a variety of other cells,αIIandβII-spectrin do not regulate cell adhesion, cell size andYAP localization in human dermal fibroblasts and are not required for the dermal myofibroblast phenotypical switch.
Chemical pressure and hidden one-dimensional behavior in rare earth tri-telluride

We report on the first optical measurements of the rare-earth tri-telluride charge-density-wave systems. Our data, collected over an extremely broad spectral range, allow us to observe both the Drude component and the single-particle peak, ascribed to the contributions due to the free charge carriers and to the charge-density-wave gap excitation, respectively. The data analysis displays a diminishing impact of the charge-density-wave condensate on the electronic properties with decreasing lattice constant across the rare-earth series. We propose a possible mechanism describing this behavior and we suggest the presence of a one-dimensional character in these two-dimensional compounds. We also envisage that interactions and umklapp processes might play a relevant role in the formation of the charge-density-wave state in these compounds.
Factor Affecting Geometry of TiO2 Nanotube Arrays (TNAs) in Aqueous and Organic Electrolyte

TiO 2 nanotube arrays (TNA) have attracted scientific interest due to the combination of functional material properties with controllable nanostructure. Superior properties of TNA, including vectorial pathway of e (cid:1) transport, minimized e (cid:1) recombination, and high specific surface area render them as the most promising candidate for environment remediation, energy conversion and biocompatibilityapplications. The superior properties and efficacyof the TNA in various applications influenced by structural characteristics such as pore size, length and wall thickness. Therefore in this chapter the effect of various electrochemical parameters such as applied voltage, anodization time, electrolyte composition on the forma- tion of controlled dimension of TNA in aqueous and organic electrolytes are reviewed.