Miracle-dz/UniFAR-FTUs huggingface dataset:
Sickness absence: Worker and establishment effects

Summary In this paper, we examine the determinants of absence due to illness for workers in Swedish establishments. The main question concerns to what extent absence due to illness can be related to worker effects and to what extent workers’ sick-reporting behavior can be linked to group effects associated with employing establishments. Our results indicate the existence of substantial establishment-level variation in sickness absence; a variation that cannot be explained by the standard worker and establishment characteristics used in the earlier literature.
Toward Allocentric Mixed-Reality Deictic Gesture

—Research has shown that robots that use physical deictic gestures such as pointing enable more effective and natural interaction. However, it is not yet clear whether these beneﬁts hold true for new forms of deictic gesture that become available in mixed-reality environments. In previous work, we presented a human-subject study suggesting that these beneﬁts may indeed translate in the case of allocentric mixed-reality gestures, in which target referents are picked out in users’ ﬁelds of view using annotations such as circles and arrows, especially when those gestures are paired with complex referring expressions. In this paper we provide additional evidence for this hypothesis through a second experiment that addresses potential confounds from our original experiment.
A Novel Position based Opportunistic Routing Protocol for Mobile Ad hoc Networks

: In Mobile Ad hoc Networks, since nodes move arbitrarily the network topology changes frequently and unpredictably. This paper address the problem of reliable data delivery in MANET, especially in a challenged environment with high mobility remains an issue. To deal with such issue, a novel position based opportunistic routing protocol (POR) is proposed to efficiently maintain uninterrupted communication. When a node sends or forwards a packet, the neighbor nodes that have overheard the transmission will serve as forwarding candidates, and forwards the packet if it is not relayed by the specific best forwarder. In this way, as long as one of the candidates succeeds in receiving and forwarding the packet, the data transmission will not be interrupted. To handle the communication voids, a virtual destination based void handling scheme is further proposed to enhance the robustness of POR. Simulations are done in NS2 to confirm the effectiveness and efficiency of POR in high mobility networks.
Intensify Standard Transformation of 802.11 Infrastructure

: Pour more efficiency in different conditions, equipment standards IEEE 802.11a / B / G wireless cards have their transmission rate dynamically. A lot of research and experimental work was conducted in the field of IP, the flow of essential survival mechanism of the execution of the two-Fi. We introduce an article CET on June first survey of existing mechanisms for regulating the speed advantages and disadvantages. We describe then proposal improves the performance of rate adaptation mechanisms. We have appointed two new mechanisms currently AARF and Clara. Clara is the culmination of the best features of OPN and RBAR mechanisms pour additional opportunities to promote the practice of fainting several routes channel detection and control signals. AARF moved pour systems with low latency and low complexity June 1. Although most similar figures from June to stationary and disappears wireless channels RBAR. Finally, we have the introduction of a new UN matching algorithm developed by spraying high river systems delay Who implemented and evaluated on the AR5212-Grund Device. The experimental results show a significant improvement in AR5212 Midwifi previously implemented in the algorithm.
165-171 Khairani-Bejo S.pmd

Flood is a potential driver in spreading waterborne diseases including leptospirosis, which is a zoonotic disease caused by pathogenic bacteria of the genus Leptospira. In the case of leptospirosis, cattle and goats can be incidental hosts and potential carriers of leptospirosis. Traditionally, serology such as microscopic agglutination test (MAT) and isolation of the organisms have been commonly used as the diagnostic approaches in diagnosing leptospirosis. However, nowadays, various molecular techniques have been developed for specific detection of Leptospira sp. such as, polymerase chain reaction (PCR), which is sensitive, specific and rapid in detecting the species. This study detected Leptospira sp. directly from the blood and urine of the animals such as, cattle, goats and sheep in Kelantan after a massive flood by using multiplex PCR (mPCR). From the results collected in the study, four blood samples (0.63%; 4/635) were found to be positive with Leptospira sp. and one urine sample (3.23%; 1/31) was detected as positive with Leptospira sp. The blood and urine samples that were detected to be positive with Leptospira sp. were collected from cattle and goats exposed to the flood. However, no Leptospira sp. was detected from the sheep in this study. Multiplex PCR (mPCR) was successfully used to detect the presence of Leptospira sp. in animals. Apart from that, it is also suggested that flood has a significant role in transmitting the disease to animals.
Injection Strategy for GCI Engine at Low Load

Gasoline Compression Ignition (GCI) engine showed the potential to match the efficiency of compression ignition (CI) engines with reduced emission of NOx and soot. The objective of this work is to understand the influence on gasoline combustion of different injector parameters such as umbrella angle, number of injection and injection timing when a 95 RON gasoline is direct injected in a commercial Diesel engine. Experiments were performed in a single-cylinder Diesel engine fueled with directinjected 95 RON gasoline at a constant fueling rate leading to a global fuel-air equivalence ratio of 0.3. Engine speed was set to 1500 rpm. Intake pressure was set to 1 bar in order to investigate typical low load operating conditions in which turbocharger can not supply extra pressure. Intake air was heated up in order to enable the autoignition of gasoline. Three different injectors with 156°, 120° and 90° umbrella angle were tested. For each injector, experiments were carried out with a single and a double injection strategy and over a wide range of injection timings. The behavior of the three injectors was compared in terms of engine performances and pollutant emission. Advanced injection timings were necessary to enable gasoline autoignition. Results showed that a wide umbrella angle can limit the advance in injection timing because of part of the fuel is directed towards the squish region and do not participate at the combustion event. A narrower injection angle allowed to better concentrate the fuel inside the piston head bowl: the autoignition of gasoline originated from fuelair mixture characterized by richer distribution of the local equivalence-ratio, improving combustion efficiency and reducing the formation of HC and CO while maintaining low emission of NOx. Moreover, results from experiments showed that because of spray-bowl geometrical interaction, abnormal NOx emission could be obtained in correspondence of certain early injection timings. Under these conditions, part of the exhaust NOx can be trapped in the hot residuals, reacting with gasoline fuel injected in the subsequent cycle leading to abnormal advance in combustion phasing. For both injectors, a double injection strategy was employed to introduce the sufficient fuel stratification necessary to reduce the heat release rate and the combustion noise. For low load operation of GCI engine and for a fixed bowl geometry the two injectors with 156° and 120° offered better performance in terms of indicated efficiency while maintaining acceptable level of noise. On the other side, a 90° injector showed the lower emission of NOx and HC.
Live Kidney Donation : Long -Term Health -Related Outcome

textabstractIn this thesis we have evaluated the long-term impact of living donation by reviewing the recent literature and analysing the current donor eligibility criteria. We have pointed out the limitations of recent studies on long-term outcome after live kidney donation that could have led to a potential overestimation of risk additional to donation. Key problems remain such as that donors are a pre-screened healthy selection of the general population, making it difficult to find an equal healthy unscreened comparison group. Studies should select the entire donor cohort to evaluate long-term outcomes, maintain similar selection criteria for donors and non-donors, match on baseline covariates to enhance comparability, and adjust for follow-up covariates to correct for lifestyle factors. We have evaluated the current donor eligibility acceptance criteria of our own center by performing a comparative follow-up cohort study in which donors were compared to matched non-donors and aforementioned adjustments have been implemented in the study design. The results demonstrated that one year after donation live kidney donors have a reduced renal function, remaining stable without any kidney-related morbidity or mortality to at least eight years of followup. However, the substantial decline in renal function may be further compromised when unforeseen circumstances affect renal function later in life. With the knowledge of this risk, albeit small, donors should be well-informed before donation and be offered lifelong follow-up thereafter. By monitoring the remnant renal function, donors at risk may be identified in early stage and adequate treatment may be offered. We consider this approach a prerequisite to legitimize a living kidney donor program.
Fast Online Adaptation in Robotics through Meta-Learning Embeddings of Simulated Priors

Meta-learning algorithms can accelerate the model-based reinforcement learning (MBRL) algorithms by finding an initial set of parameters for the dynamical model such that the model can be trained to match the actual dynamics of the system with only a few data-points. However, in the real world, a robot might encounter any situation starting from motor failures to finding itself in a rocky terrain where the dynamics of the robot can be significantly different from one another. In this paper, first, we show that when meta-training situations (the prior situations) have such diverse dynamics, using a single set of meta-trained parameters as a starting point still requires a large number of observations from the real system to learn a useful model of the dynamics. Second, we propose an algorithm called FAMLE that mitigates this limitation by meta-training several initial starting points (i.e., initial parameters) for training the model and allows robots to select the most suitable starting point to adapt the model to the current situation with only a few gradient steps. We compare FAMLE to MBRL, MBRL with a meta-trained model with MAML, and model-free policy search algorithm PPO for various simulated and real robotic tasks, and show that FAMLE allows robots to adapt to novel damages in significantly fewer time-steps than the baselines.
Nonlinear Power Method for Computing Eigenvectors of Proximal Operators and Neural Networks

Neural networks have revolutionized the field of data science, yielding remarkable solutions in a data-driven manner. For instance, in the field of mathematical imaging, they have surpassed traditional methods based on convex regularization. However, a fundamental theory supporting the practical applications is still in the early stages of development. We take a fresh look at neural networks and examine them via nonlinear eigenvalue analysis. The field of nonlinear spectral theory is still emerging, providing insights about nonlinear operators and systems. In this paper we view a neural network as a complex nonlinear operator and attempt to find its nonlinear eigenvectors. We first discuss the existence of such eigenvectors and analyze the kernel of ReLU networks. Then we study a nonlinear power method for generic nonlinear operators. For proximal operators associated to absolutely one-homogeneous convex regularization functionals, we can prove convergence of the method to an eigenvector of the proximal operator. This motivates us to apply a nonlinear method to networks which are trained to act similarly as a proximal operator. In order to take the non-homogeneity of neural networks into account we define a modified version of the power method. We perform extensive experiments on various shallow and deep neural networks designed for image denoising. For simple nets, we observe the influence of training data on the eigenvectors. For state-of-the-art denoising networks, we show that eigenvectors can be interpreted as (un)stable modes of the network, when contaminated with noise or other degradations.
The role of PET/CT in evaluation of bone marrow involvement in lymphoma patients at the initial staging

Objective: Bone marrow (BM) involvement is one of the most important prognostic factors in lymphoma patients. Therefore, it is important to determine the presence of BM involvement in lymphoma patients at the time of diagnosis. Bone marrow biopsy (BMB) is still accepted as the gold standard for evaluating the marrow but it is painful and invasive. In this retrospective study, we aimed to evaluate the role of positron emission tomography combined with computed tomography (PET/CT) in evaluating the BM involvement in lymphoma patients at the initial staging. Patients and Methods: The patients who were evaluated by PET/CT and bone marrow biopsy at time of diagnosis were enrolled in the study. Results: The overall sensitivity of PET/CT in demonstrating BM involvement was 65.8%, and the specificity was 89.4%. In the subgroup analysis of 176 Hodgkin lymphoma (HL) patients, the sensitivity and specificity of the PET/CT were 81% and 84% respectively. Negative predictive value was 98%. In 201 diffuse large B cell lymphoma (DLBCL) patients, the sensitivity and specificity of the test were 91.3% and 94.3%, respectively. Negative predictive value was 98.8%. Conclusion: PET/CT is an accurate and complementary modality with high specificity and sensitivity in detecting BM infiltration in HL and DLBCL patients.
Anomaly Detection in Beehives using Deep Recurrent Autoencoders

Precision beekeeping allows to monitor bees' living conditions by equipping beehives with sensors. The data recorded by these hives can be analyzed by machine learning models to learn behavioral patterns of or search for unusual events in bee colonies. One typical target is the early detection of bee swarming as apiarists want to avoid this due to economical reasons. Advanced methods should be able to detect any other unusual or abnormal behavior arising from illness of bees or from technical reasons, e.g. sensor failure. In this position paper we present an autoencoder, a deep learning model, which detects any type of anomaly in data independent of its origin. Our model is able to reveal the same swarms as a simple rule-based swarm detection algorithm but is also triggered by any other anomaly. We evaluated our model on real world data sets that were collected on different hives and with different sensor setups.
Karate Club: An API Oriented Open-Source Python Framework for Unsupervised Learning on Graphs

Graphs encode important structural properties of complex systems. Machine learning on graphs has therefore emerged as an important technique in research and applications. We present Karate Club - a Python framework combining more than 30 state-of-the-art graph mining algorithms. These unsupervised techniques make it easy to identify and represent common graph features. The primary goal of the package is to make community detection, node and whole graph embedding available to a wide audience of machine learning researchers and practitioners. Karate Club is designed with an emphasis on a consistent application interface, scalability, ease of use, sensible out of the box model behaviour, standardized dataset ingestion, and output generation. This paper discusses the design principles behind the framework with practical examples. We show Karate Club's efficiency in learning performance on a wide range of real world clustering problems and classification tasks along with supporting evidence of its competitive speed.
Multitask Emotion Recognition with Incomplete Labels

We train a unified model to perform three tasks: facial action unit detection, expression classification, and valence-arousal estimation. We address two main challenges of learning the three tasks. First, most existing datasets are highly imbalanced. Second, most existing datasets do not contain labels for all three tasks. To tackle the first challenge, we apply data balancing techniques to experimental datasets. To tackle the second challenge, we propose an algorithm for the multitask model to learn from missing (incomplete) labels. This algorithm has two steps. We first train a teacher model to perform all three tasks, where each instance is trained by the ground truth label of its corresponding task. Secondly, we refer to the outputs of the teacher model as the soft labels. We use the soft labels and the ground truth to train the student model. We find that most of the student models outperform their teacher model on all the three tasks. Finally, we use model ensembling to boost performance further on the three tasks. Our code is publicly available1.1https://github.com/wtomin/multitask-Emotion-Recognition-withIncomplete-Labels
Automating Representation Discovery with MAP-Elites

The way solutions are represented, or encoded, is usually the result of domain knowledge and experience. In this work, we combine MAP-Elites with Variational Autoencoders to learn a Data-Driven Encoding (DDE) that captures the essence of the highest-performing solutions while still able to encode a wide array of solutions. Our approach learns this data-driven encoding during optimization by balancing between exploiting the DDE to generalize the knowledge contained in the current archive of elites and exploring new representations that are not yet captured by the DDE. Learning representation during optimization allows the algorithm to solve high-dimensional problems, and provides a low-dimensional representation which can be then be re-used. We evaluate the DDE approach by evolving solutions for inverse kinematics of a planar arm (200 joint angles) and for gaits of a 6-legged robot in action space (a sequence of 60 positions for each of the 12 joints). We show that the DDE approach not only accelerates and improves optimization, but produces a powerful encoding that captures a bias for high performance while expressing a variety of solutions.
Performance Analysis of Message Dissemination Techniques in VANET using Fog Computing

Vehicular Ad-hoc Networks (VANET) is a derived subclass of Mobile Ad-hoc Networks (MANET) with vehicles as mobile nodes. VANET facilitate vehicles to share safety and non-safety information through messages. Safety information includes road accidents, natural hazards, roadblocks, etc. Non-safety information includes tolling information, traveler information, etc. The main goal behind sharing this information is to enhance road safety and reduce road accidents by alerting the driver about the unexpected hazards. However, routing of messages in VANET is challenging due to packet delays arising from high mobility of vehicles, frequently changing topology and high density of vehicles, leading to frequent route breakages and packet losses. This report summarizes the performance analysis of safety and non-safety message dissemination techniques in VANET based on the fog computing technique. Three main metrics to improve the performance of message dissemination are: 1) delay, 2) probability of message delivery, and 3) throughput. Analysis of such metrics plays an important role to improve the performance of existing message dissemination techniques. Simulations are usually conducted based on the metrics using ns-2 and Java discrete event simulator. The above three performance metrics and results published in literature help one to understand and increase the performance of various message dissemination techniques in a VANET environment.
Convex Hull Monte-Carlo Tree Search

This work investigates Monte-Carlo planning for agents in stochastic environments, with multiple objectives. We propose the Convex Hull Monte-Carlo Tree-Search (CHMCTS) framework, which builds upon Trial Based Heuristic Tree Search and Convex Hull Value Iteration (CHVI), as a solution to multi-objective planning in large environments. Moreover, we consider how to pose the problem of approximating multi-objective planning solutions as a contextual multi-armed bandits problem, giving a principled motivation for how to select actions from the view of contextual regret. This leads us to the use of Contextual Zooming for action selection, yielding Zooming CHMCTS. We evaluate our algorithm using the Generalised Deep Sea Treasure environment, demonstrating that Zooming CHMCTS can achieve a sublinear contextual regret and scales better than CHVI on a given computational budget.
Quantum sensing networks for the estimation of linear functions

The theoretical framework for networked quantum sensing has been developed to a great extent in the past few years, but there are still a number of open questions. Among these, a problem of great significance, both fundamentally and for constructing efficient sensing networks, is that of the role of inter-sensor correlations in the simultaneous estimation of multiple linear functions, where the latter are taken over a collection local parameters and can thus be seen as global properties. In this work we provide a solution to this when each node is a qubit and the state of the network is sensor-symmetric. First we derive a general expression linking the amount of inter-sensor correlations and the geometry of the vectors associated with the functions, such that the asymptotic error is optimal. Using this we show that if the vectors are clustered around two special subspaces, then the optimum is achieved when the correlation strength approaches its extreme values, while there is a monotonic transition between such extremes for any other geometry. Furthermore, we demonstrate that entanglement can be detrimental for estimating non-trivial global properties, and that sometimes it is in fact irrelevant. Finally, we perform a non-asymptotic analysis of these results using a Bayesian approach, finding that the amount of correlations needed to enhance the precision crucially depends on the number of measurement data. Our results will serve as a basis to investigate how to harness correlations in networks of quantum sensors operating both in and out of the asymptotic regime.
Topological Machine Learning for Mixed Numeric and Categorical Data

Topological data analysis is a relatively new branch of machine learning that excels in studying high-dimensional data, and is theoretically known to be robust against noise. Meanwhile, data objects with mixed numeric and categorical attributes are ubiquitous in real-world applications. However, topological methods are usually applied to point cloud data, and to the best of our knowledge there is no available framework for the classification of mixed data using topological methods. In this paper, we propose a novel topological machine learning method for mixed data classification. In the proposed method, we use theory from topological data analysis such as persistent homology, persistence diagrams and Wasserstein distance to study mixed data. The performance of the proposed method is demonstrated by experiments on a real-world heart disease dataset. Experimental results show that our topological method outperforms several state-of-the-art algorithms in the prediction of heart disease.