Miracle-dz/UniFAR-FTUs huggingface dataset:
CHARTING CULTURAL VARIATION IN CHIMPANZEES

Cultural variation among chimpanzee communities or unit-groups at nine long-term study sites was charted through a systematic, collaborative procedure in which the directors of the sites first agreed a candidate list of 65 behaviour patterns (here fully defined), then classified each pattern in relation to its local frequency of occurrence. Thirty-nine of the candidate behaviour patterns were discriminated as cultural variants, sufficiently frequent at one or more sites to be consistent with social transmission, yet absent at one or more others where environmental explanations were rejected. Each community exhibited a unique and substantial profile of such variants, far exceeding cultural variation reported before for any other non-human species. Evaluation of these pan-African distributions against three models for the diffusion of traditions identified multiple cases consistent with cultural evolution involving differentiation in form, function and targets of behaviour patterns.
Rethinking Semi-Supervised Learning in VAEs

We present an alternative approach to semi-supervision in variational autoencoders(VAEs) that incorporates labels through auxiliary variables rather than directly through the latent variables. Prior work has generally conflated the meaning of labels, i.e. the associated characteristics of interest, with the actual label values themselves-learning latent variables that directly correspond to the label values. We argue that to learn meaningful representations, semi-supervision should instead try to capture these richer characteristics and that the construction of latent variables as label values is not just unnecessary, but actively harmful. To this end, we develop a novel VAE model, the reparameterized VAE (ReVAE), which "reparameterizes" supervision through auxiliary variables and a concomitant variational objective. Through judicious structuring of mappings between latent and auxiliary variables, we show that the ReVAE can effectively learn meaningful representations of data. In particular, we demonstrate that the ReVAE is able to match, and even improve on the classification accuracy of previous approaches, but more importantly, it also allows for more effective and more general interventions to be performed. We include a demo of ReVAE at this https URL.
Exergames for telerehabilitation

Recent advancements in technology have improved the connectivity between humans enhancing the transfer of information. Leveraging these technological marvels in the healthcare industry has led to the development of telehealth allowing patients and clinicians to receive and administer treatment remotely. Telerehabilitation is a subset of telehealth that facilitates remote rehabilitation treatment for patients. Providing rehabilitative services to the aging baby boomer population requires tech-savvy solutions to augment the therapists and clinicians for effective remote monitoring and tele-medicine. Hence, this thesis develops easy-to-use exergames for low-cost mechatronic devices targeting rehabilitation of post-stroke patients. Specifically, it demonstrates wearable inertial sensors for exergames consisting of an animated virtual coach for providing patients with instructions for performing range of motion exercises. Next, a gaming environment is developed for task-specific rehabilitation such as eating. Finally, exergames are developed for rehabilitation of pincer grasping. In addition to gamified interfaces providing an engaging rehabilitation experience to the user, the data acquired from the mechatronic devices facilitate data-driven telerehabilitation.
Distinct Classes of Central GLUT2-Dependent Sensors Control Counterregulation and Feeding

The pancreatic β-cell paradigm for glucose sensing has been proposed to apply to brain glucose sensors controlling counterregulation to hypoglycemia and feeding behavior. Over recent years, we tested this model in mice by first showing that inactivation of the GLUT2 gene suppressed glucose sensing and correctly regulated insulin secretion by pancreatic β-cells. Then, we restored the function of the β-cell in GLUT2-null mice by transgenic expression of a glucose transporter under the control of the rat insulin promoter. Using these rescued mice, we showed that GLUT2-dependent sensors are present in several anatomical sites, including the hepatoportal vein and the central nervous system. When these extrapancreatic glucose sensors are inactivated, the mice display loss of first-phase insulin secretion and hyperglucagonemia in the fed state, and they eat more than control mice—defects characteristic of developing obesity/diabetes. By gene complementation experiments, we further showed that glucose sensors controlling glucagon secretion require GLUT2 expression in glial cells. However, transgenic expression of GLUT2 in astrocytes or neurons failed to restore the normal control of feeding, indicating that different classes of glucose sensors control the response to hypoglycemia and food intake.
A Block Coordinate Descent Optimizer for Classification Problems Exploiting Convexity

Second-order optimizers hold intriguing potential for deep learning, but suffer from increased cost and sensitivity to the non-convexity of the loss surface as compared to gradient-based approaches. We introduce a coordinate descent method to train deep neural networks for classification tasks that exploits global convexity of the cross-entropy loss in the weights of the linear layer. Our hybrid Newton/Gradient Descent (NGD) method is consistent with the interpretation of hidden layers as providing an adaptive basis and the linear layer as providing an optimal fit of the basis to data. By alternating between a second-order method to find globally optimal parameters for the linear layer and gradient descent to train the hidden layers, we ensure an optimal fit of the adaptive basis to data throughout training. The size of the Hessian in the second-order step scales only with the number weights in the linear layer and not the depth and width of the hidden layers; furthermore, the approach is applicable to arbitrary hidden layer architecture. Previous work applying this adaptive basis perspective to regression problems demonstrated significant improvements in accuracy at reduced training cost, and this work can be viewed as an extension of this approach to classification problems. We first prove that the resulting Hessian matrix is symmetric semi-definite, and that the Newton step realizes a global minimizer. By studying classification of manufactured two-dimensional point cloud data, we demonstrate both an improvement in validation error and a striking qualitative difference in the basis functions encoded in the hidden layer when trained using NGD. Application to image classification benchmarks for both dense and convolutional architectures reveals improved training accuracy, suggesting possible gains of second-order methods over gradient descent.
Monte Carlo Simulation Techniques

Monte Carlo simulations are widely used in many areas including particle accelerators. In this lecture, after a short introduction and reviewing of some statistical backgrounds, we will discuss methods such as direct inversion, rejection method, and Markov chain Monte Carlo to sample a probability distribution function, and methods for variance reduction to evaluate numerical integrals using the Monte Carlo simulation. We will also briefly introduce the quasi-Monte Carlo sampling at the end of this lecture.
Ultra Low-latency, Low-area Inference Accelerators using Heterogeneous Deep Quantization with QKeras and hls4ml

In this paper, we introduce the QKeras library, an extension of the Keras library allowing for the creation of heterogeneously quantized versions of deep neural network models, through drop-in replacement of Keras layers. These models are trained quantization-aware, where the user can trade off model area or energy consumption by accuracy. We demonstrate how the reduction of numerical precision, through quantization-aware training, significantly reduces resource consumption while retaining high accuracy when implemented on FPGA hardware. Together with the hls4ml library, this allows for a fully automated deployment of quantized Keras models on chip, crucial for ultra low-latency inference. As a benchmark problem, we consider a classification task for the triggering of events in proton-proton collisions at the CERN Large Hadron Collider, where a latency of ${\mathcal O}(1)~\mu$s is required.
3D Pipe Network Reconstruction Based on Structure from Motion with Incremental Conic Shape Detection and Cylindrical Constraint

Pipe inspection is a critical task for many industries and infrastructure of a city. The 3D information of a pipe can be used for revealing the deformation of the pipe surface and position of the camera during the inspection. In this paper, we propose a 3D pipe reconstruction system using sequential images captured by a monocular endoscopic camera. Our work extends a state-of-the-art incremental Structure-from-Motion (SfM) method to incorporate prior constraints given by the target shape into bundle adjustment (BA). Using this constraint, we can minimize the scale-drift that is the general problem in SfM. Moreover, our method can reconstruct a pipe network composed of multiple parts including straight pipes, elbows, and tees. In the experiments, we show that the proposed system enables more accurate and robust pipe mapping from a monocular camera in comparison with existing state-of-the-art methods.
Gene Transfer of Multiple Flowers and Pubescent Leaf from Capsicum chinense into Capsicum annuum Backgrounds

The inheritance of multiple flowers and leaf pubescence resulting from the crosses between accessions from pepper species Capsicum annuum L. and C. chinense Jacq. was examined. Hand cross- and self-pollinations were made in a glass greenhouse. Only eight normal F1 plants were obtained from crosses between the two species when C. annuum L. was the female parent. F2 and backcross generations obtained from the F, and the two parents were grown in the field. Two field studies indicated that multiple flowers and leaf pubescence were controlled by dominant genes. A three-gene model leading to an F2 segregation ratio of 45:9:10 and a two-gene model leading to an F 2 segregation ratio of 13:3 were suggested for the inheritance of multiple flowers and leaf pubescence, respectively. Epistasis was evoked in the interpretation of the data. No linkage was found between the two characters. The inconsistencies between F2 and backcross data might be due to selective elimination of genes from one or the other parent in an Interspecific hybridization. Segregation ratios from intraspecific crosses for leaf pubescence supported a two-gene model and gave an F 2 ratio of 13 pubescent leaf : 3 glabrous leaf progeny. Although pepper plants have often been studied for their ag- ricultural value, the inheritance of certain morphological traits has not been we&established (Lippert et al., 1966). Because of the interest in transferring useful genes from wild-type peppers into widely grown cultivars, it is desirable to have genetic in- formation on important morphological traits. C. annum L. , the most widely grown pepper species on a world basis, sets a single flower at each node, whereas C. chi-
Interannual Variability in Ostreopsis Ovata Bloom Dynamic along Genoa Coast (North-Western Mediterranean): A Preliminary Modeling Approach

Abstract The tropical genus Ostreopsis has been recorded along Italian coasts of the Mediterranean Sea since the ‘90s, but large bloom events have been reported only in recent years. In order to describe O. ovata bloom dynamics and provide a better understanding of environmental variables involved in triggering blooms, we collected a time series of data in Genoa (North Western Mediterranean) from 2006 to 2010. Cell abundances in the water column and epiphyte on the macroalgae were assessed during the summer months. Water and meteorological variables were concurrently collected. We elaborated a meaningful explanatory model, performing multiple correlations between bloom magnitude (maximum cell concentration) and length (extent of the bloom event) and water/meteorological features. Such a model highlights a significant role of water temperature, barometric pressure and wind speed in affecting bloom dynamics. It represents a good base for managers in the attempt of forecasting O. ovata blooms and, specifically, toxic events, in an ecological, economic and sanitary perspective.
Housing Market Prediction Problem using Different Machine Learning Algorithms: A Case Study

Developing an accurate prediction model for housing prices is always needed for socio-economic development and well-being of citizens. In this paper, a diverse set of machine learning algorithms such as XGBoost, CatBoost, Random Forest, Lasso, Voting Regressor, and others, are being employed to predict the housing prices using public available datasets. The housing datasets of 62,723 records from January 2015 to November 2019 are obtained from Florida Volusia County Property Appraiser website. The records are publicly available and include the real estate or economic database, maps, and other associated information. The database is usually updated weekly according to the State of Florida regulations. Then, the housing price prediction models using machine learning techniques are developed and their regression model performances are compared. Finally, an improved housing price prediction model for assisting the housing market is proposed. Particularly, a house seller or buyer, or a real estate broker can get insight in making better-informed decisions considering the housing price prediction. The empirical results illustrate that based on prediction model performance, Coefficient of Determination (R2), Mean Square Error (MSE), Mean Absolute Error (MAE), and computational time, the XGBoost algorithm performs superior to the other models to predict the housing price.
Guarantees for Hierarchical Clustering by the Sublevel Set method

Meila (2018) introduces an optimization based method called the Sublevel Set method, to guarantee that a clustering is nearly optimal and "approximately correct" without relying on any assumptions about the distribution that generated the data. This paper extends the Sublevel Set method to the cost-based hierarchical clustering paradigm proposed by Dasgupta (2016).
Biocidal spectrum of a rice field cyanobacterium Nostoc sp.

The antimicrobial efficacy of hexane, dichloromethane and ethyl acetate extracts of a rice-field cyanobacterium, Nostoc sp. , were evaluated against cyanobacteria and phytopathogenic fungi. The maximum production of biocidal compounds was observed in cultures grown for 20 days under optimized conditions (phosphate =1.4 mg/l; light intensity ≈ 3000-4000 lux). Fractionation of the extracts showed six spots on silica-gel-coated plates. Hexane extract was the most potent biocide, showing marked (35-42%) reduction in the growth of fungi, but no negative influences on seed germination or seedling growth of wheat, rice and mung bean, emphasizing its suitability for use in agriculture. Keywords: Antifungal; extract; inhibition zone; metabolite; Pythium debaryanum; Rhizoctonia solani
Impacts of demographic variation in spawning characteristics on reference points for fishery management

Parametric relationships of recruitment to an index of parental stock size assume the latter is proportionally related to spawning potential, irrespective of the demographic composition of adults. Recent empirical information, however, suggests that reproduction by older and experienced females is more successful than by young and inexperienced females. New models are developed incorporating the proportion of each age group spawning for the first, second, etc., time (from information contained in the maturity ogive) and differences in the survival of eggs and larvae in relation to the demography of spawners (based on experimental results). A series of spawning metrics [spawning-stock biomass (SSB), egg production, hatched egg production, viable larval production] and associated recruitment-based fishing mortality reference points (Fmed ,F crash, and the F that allows at least one lifetime spawning per recruit) are contrasted for the Georges Bank Atlantic cod (Gadus morhua) stock. The time-series of spawning intensity is significantly altered when hatched eggs or viable larvae are used as the metric, reflecting the contribution of first- and second-time spawners in some years, and the increasing trend in F. The effect of reduced average maternal size in relation to egg viability was a more important factor contributing to discrepancies with SSB than was spawning experience in relation to hatching success. Percent maximum spawning potential (%MSP) per recruit curves in relation to F are steeper (i.e. result in lower values of %MSP for a given F) for hatched eggs and viable larvae than for SSB or egg production per recruit. Lifetime expected numbers of spawnings per recruit are significantly reduced when the effects of spawning experience on egg hatching success are included. And finally, although point estimates of Fmed and Fcrash are similar using SSB and viable larval production, the median Fcrash estimated from 5000 bootstrap realizations of the Beverton-Holt stock recruitment curve for viable larvae is much lower than that from SSB, with narrower confidence bounds. Our results suggest that traditional approaches to F-based reference points using SSB systematically overestimate the resiliency of stocks to fishing. If age-at-spawning is at least partially heritable, then intense fishing on younger ages may exert high levels of selection for early maturity with negative impacts on net reproductive effort and trait diversity.
SatImNet: Structured and Harmonised Training Data for Enhanced Satellite Imagery Classification

Automatic supervised classification with complex modelling such as deep neural networks requires the availability of representative training data sets. While there exists a plethora of data sets that can be used for this purpose, they are usually very heterogeneous and not interoperable. In this context, the present work has a twofold objective: (i) to describe procedures of open-source training data management, integration, and data retrieval, and (ii) to demonstrate the practical use of varying source training data for remote sensing image classification. For the former, we propose SatImNet, a collection of open training data, structured and harmonized according to specific rules. For the latter, two modelling approaches based on convolutional neural networks have been designed and configured to deal with satellite image classification and segmentation.
A short-review of brown ring disease, a vibriosis affecting clams, Ruditapes philippinarum and Ruditapes decussatus

Brown ring disease (BRD) in Ruditapes philippinarum and Ruditapes decussatus is a shell disease caused by Vibrio tapetis. This disease has begun in 1987 in clams beds in Brittany (France) and then has spread along the European Atlantic coast. Since about fifteen years, research on BRD has progress a lot and the purpose of this review is to give a short description of BRD in clams in retrospect and in addition of recent and pertinent results. Diagnosis including isolation, biochemical, serological and molecular characterization has been developed to identify and detect the pathogen. Therefore, Vibrio tapetis-like strains has been detected in other host specie, in cultured fishes during mortalities, such as the wrasse, Symphodus melops in Norway and the halibut, Hippoglossus hippoglossus in Scotland. Mechanisms of host-pathogen interactions modulated by environmental factors were studied using in vivo bacterial challenge and in vitro bio-assays. According to these studies, adherence and cytotoxic factors have been yet identified as virulence factors; identification and characterization of virulence genes are in progress. Field and experimental studies confirmed the significant effect of temperature on the development of BRD and on clam defense related activities. A significant increase in temperature over 21 ◦ C may have a preventive effect on the development of the disease.
Approximations of geometrically ergodic reversible markov chains

Abstract A common tool in the practice of Markov chain Monte Carlo (MCMC) is to use approximating transition kernels to speed up computation when the desired kernel is slow to evaluate or is intractable. A limited set of quantitative tools exists to assess the relative accuracy and efficiency of such approximations. We derive a set of tools for such analysis based on the Hilbert space generated by the stationary distribution we intend to sample, $L_2(\pi)$. Our results apply to approximations of reversible chains which are geometrically ergodic, as is typically the case for applications to MCMC. The focus of our work is on determining whether the approximating kernel will preserve the geometric ergodicity of the exact chain, and whether the approximating stationary distribution will be close to the original stationary distribution. For reversible chains, our results extend the results of Johndrow et al. (2015) from the uniformly ergodic case to the geometrically ergodic case, under some additional regularity conditions. We then apply our results to a number of approximate MCMC algorithms.
The Recurrent Neural Tangent Kernel

The study of deep networks (DNs) in the infinite-width limit, via the so-called Neural Tangent Kernel (NTK) approach, has provided new insights into the dynamics of learning, generalization, and the impact of initialization. One key DN architecture remains to be kernelized, namely, the Recurrent Neural Network (RNN). In this paper we introduce and study the Recurrent Neural Tangent Kernel (RNTK), which sheds new insights into the behavior of overparametrized RNNs, including how different time steps are weighted by the RNTK to form the output under different initialization parameters and nonlinearity choices, and how inputs of different lengths are treated. We demonstrate via a number of experiments that the RNTK offers significant performance gains over other kernels, including standard NTKs across a range of different data sets. A unique benefit of the RNTK is that it is agnostic to the length of the input, in stark contrast to other kernels.
Cell-Free Massive MIMO with Nonorthogonal Pilots for Internet of Things

We consider Internet of Things (IoT) organized on the principles of cell-free massive MIMO. Since the number of things is very large, orthogonal pilots cannot be assigned to all of them even if the things are stationary. This results in an unavoidable pilot contamination problem, worsened by the fact that, for IoT, since the things are operating at very low transmit power. To mitigate this problem and achieve a high throughput, we use cell-free systems with optimal linear minimum mean squared error (LMMSE) channel estimation, while traditionally simple suboptimal estimators have been used in such systems. We further derive the analytical uplink and downlink signal-to-interference-plus-noise ratio (SINR) expressions for this scenario, which depends only on large scale fading coefficients. This allows us to design new power control algorithms that require only infrequent transmit power adaptation. Simulation results show a 40% improvement in uplink and downlink throughputs and 95% in energy efficiency over existing cell-free wireless systems and at least a three-fold uplink improvement over known IoT systems based on small-cell systems.