Miracle-dz/UniFAR-FTUs huggingface dataset:
Multistate modeling and structure selection for multitype recurrent events and terminal event data

In cardiovascular disease studies, a large number of risk factors are measured but it often remains unknown whether all of them are relevant variables and whether the impact of these variables is changing with time or remains constant. In addition, more than one kind of cardiovascular disease events can be observed in the same patient and events of different types are possibly correlated. It is expected that different kinds of events are associated with different covariates and the forms of covariate effects also vary between event types. To tackle these problems, we proposed a multistate modeling framework for the joint analysis of multitype recurrent events and terminal event. Model structure selection is performed to identify covariates with time‐varying coefficients, time‐independent coefficients, and null effects. This helps in understanding the disease process as it can detect relevant covariates and identify the temporal dynamics of the covariate effects. It also provides a more parsimonious model to achieve better risk prediction. The performance of the proposed model and selection method is evaluated in numerical studies and illustrated on a real dataset from the Atherosclerosis Risk in Communities study.
Magnetic field arising from current dipoles randomly distributed in a homogeneous spherical volume conductor

Certain types of magnetic noise arising from physical as well as biological sources can be explained by the model of current dipoles randomly distributed in a volume conductor. The volume conductor investigated in the present study is the homogeneous sphere, which is the model commonly used in analyses of the magnetic field of the human brain (magnetoencephalogram). Uniform distributions of random dipoles in three different source spaces are considered: a spherical surface, a spherical shell, and a sphere. The main emphasis is put on the radial component of the magnetic field. It is shown that the expectation value for the product of magnetic fields simultaneously measured at two locations, the covariance, depends on only two parameters of the measurement locations: the angular distance and the product of the radii. The formulas derived for the covariance can be expressed in terms of elliptic integrals of the first and the second kind, so that a very efficient numerical calculation is possible. For the special case of the variance (two identical measurement locations) these formulas reduce to expressions composed of elementary functions. Numerical examples show that the noise produced by random dipoles in a sphere is similar to the noise generated by random dipoles on a spherical surface having a slightly smaller radius.
Distinct chemical resistance-inducing stimuli induce common transcriptional, metabolic and nematode community signatures in rice root and rhizosphere.

Induced resistance (IR), a phenotypic state induced by an exogenous stimulus and characterized by enhanced resistance to future (a)biotic challenge, is an important component of plant immunity. Numerous IR-inducing stimuli have been described in various plant species, but relatively little is known about 'core' systemic responses shared by these distinct IR stimuli and the effects of IR on plant-associated microbiota. In this study, we foliarly applied four distinct IR stimuli (β-aminobutyric acid, acibenzolar-S-methyl, dehydroascorbic acid and piperonylic acid) capable of inducing systemic IR in rice (Oryza sativa) against the root-knot nematode Meloidogyne graminicola and evaluated their effect on the root transcriptome, exudome and root-associated nematode communities. Our results reveal shared transcriptional responses -notably induction of jasmonic acid and phenylpropanoid metabolism - and shared alterations to the exudome that include increased amino acid, benzoate and fatty acid exudation. In rice plants grown in soil from a rice field, IR stimuli significantly affected the composition of rhizosphere nematode communities three days after treatment, but by 14 days after treatment these changes had largely reverted . Notably, IR stimuli did not reduce nematode diversity, which suggests that IR might offer a sustainable option for managing plant-parasitic nematodes.
Quality of life and associated factors among the youth with substance use in Northwest Ethiopia: Using structural equation modeling

Background Substance use leads to serious clinical conditions with the potential to cause major health and emotional impairments in individuals. Individuals with substance use typically report significantly poorer QoL than the general population and as low as those with other serious psychiatric disorders. It has a high impact on morbidity, mortality, and productivity, it also compromises the general safety and performance of the users, i.e., affects the quality of life. Therefore, this study aimed to assess quality of life and identify the potential predictors among youths who use substances. Methods A multicenter cross-sectional study design was applied to assess quality of life and associated factors among substance use youths in the central Gondar zone from January 1 to March 30/ 2021. A total of 373 substance use youths were included in the study. The data were collected using face-to-face interview by structured questionnaires, and entered to Epi-data version 4.6 and exported to STATA version 16, and AMOS software for further statistical analysis. To identify factors associated with health-related quality of life, structural equation modeling was used, and it also used to estimate the relationships among exogenous, mediating, and endogenous variables. Results Substance used youths had a moderate overall health-related quality of life (mean score = 50.21 and 14.32 standard deviation, p-value < 0.,0001), and poor health-related quality of life in the environmental health domain (mean score of 45.76 with standard deviation of 17.60). Age (β = 0.06, p<0.001), sex (β = 0.30, p<0.001), psychotic symptoms (β = -0.12, p<0.001), employment status (β = 0.06, p = 0.008,), loss of family (β = 0.35, p<0.001), and social support (β = 0.27, p<0.001) were variables significantly associated with health-related quality of life. Conclusion According to the findings of this study, substance abuse during adolescence is associated with lower health-related quality of life and a higher report of psychopathological symptoms. Given this finding, mental health and health promotion professionals should learn about and emphasize the impact of substance use on youth quality of life.
A Midbrain Inspired Recurrent Neural Network Model for Robust Change Detection

We present a biologically inspired recurrent neural network (RNN) that efficiently detects changes in natural images. The model features sparse, topographic connectivity (st-RNN), closely modeled on the circuit architecture of a “midbrain attention network. ” We deployed the st-RNN in a challenging change blindness task, in which changes must be detected in a discontinuous sequence of images. Compared with a conventional RNN, the st-RNN learned 9x faster and achieved state-of-the-art performance with 15x fewer connections. An analysis of low-dimensional dynamics revealed putative circuit mechanisms, including a critical role for a global inhibitory (GI) motif, for successful change detection. The model reproduced key experimental phenomena, including midbrain neurons' sensitivity to dynamic stimuli, neural signatures of stimulus competition, as well as hallmark behavioral effects of midbrain microstimulation. Finally, the model accurately predicted human gaze fixations in a change blindness experiment, surpassing state-of-the-art saliency-based methods. The st-RNN provides a novel deep learning model for linking neural computations underlying change detection with psychophysical mechanisms. SIGNIFICANCE STATEMENT For adaptive survival, our brains must be able to accurately and rapidly detect changing aspects of our visual world. We present a novel deep learning model, a sparse, topographic recurrent neural network (st-RNN), that mimics the neuroanatomy of an evolutionarily conserved “midbrain attention network. ” The st-RNN achieved robust change detection in challenging change blindness tasks, outperforming conventional RNN architectures. The model also reproduced hallmark experimental phenomena, both neural and behavioral, reported in seminal midbrain studies. Lastly, the st-RNN outperformed state-of-the-art models at predicting human gaze fixations in a laboratory change blindness experiment. Our deep learning model may provide important clues about key mechanisms by which the brain efficiently detects changes.
Effects of Breach Area and Length to Exchange Flow Rates Under the Lova Condition in a Fusion Reactor

As one of thermofluid safety studies in ITER, buoyancy-driven exchange flow behavior through breaches of the vacuum vessel was investigated quantitatively using a preliminary LOVA (Loss Of VAcuum event) apparatus which simulated the Tokamak vacuum vessel of a fusion reactor with a small-scaled model. Helium gas and air were used as the working fluids. Experimental parameters were breach position, breach number, breach length, breach diameter, breach combination and the wall temperature of the VV. The present study showed that the relationship between the exchange rate and time depended on the magnitude of the potential energy from the ground level to the breach position and the wall temperature of the vacuum vessel. The exchange rate decreased as the breach length increased and the breach diameter decreased. 4 refs., 14 figs.
Gesture2Path: Imitation Learning for Gesture-aware Navigation

As robots increasingly enter human-centered environments, they must not only be able to navigate safely around humans, but also adhere to complex social norms. Humans often rely on non-verbal communication through gestures and facial expressions when navigating around other people, especially in densely occupied spaces. Consequently, robots also need to be able to interpret gestures as part of solving social navigation tasks. To this end, we present Gesture2Path, a novel social navigation approach that combines image-based imitation learning with model-predictive control. Gestures are interpreted based on a neural network that operates on streams of images, while we use a state-of-the-art model predictive control algorithm to solve point-to-point navigation tasks. We deploy our method on real robots and showcase the effectiveness of our approach for the four gestures-navigation scenarios: left/right, follow me, and make a circle. Our experiments indicate that our method is able to successfully interpret complex human gestures and to use them as a signal to generate socially compliant trajectories for navigation tasks. We validated our method based on in-situ ratings of participants interacting with the robots.
Frame Size Optimization Using a Machine Learning Approach in WLAN Downlink MU-MIMO Channel

Machine Learning (ML) is an innovative solution that can autonomously extract patterns and predict trends based on environmental measurements and performance indicators as input to provide self-driven intelligent network systems that can configure and optimize themselves. Under the effects of heterogeneous traffic demand among users and varying channel conditions in WLAN downlink MU-MIMO channels, achieving the maximum system throughput performance is challenging. In addressing these issues, the existing studies have proposed different approaches. However, most of these approaches did not consider a machine-learning based optimization solution. The main contribution of this paper is to propose a machine-learning based adaptive approach that can optimize system frame size that would maximize the system throughput of WLAN in the downlink MUMIMO channel. In this approach, the Access Point (AP) performs the maximum system throughput measurement and collects the “frame size-system throughput patterns” which contains knowledge about the effects of traffic condition, channel condition, and number of stations (STAs). Based on these patterns, our approach uses neural networks to correctly model the system throughput as a function of the system frame size. After training the neural network, we obtain the gradient information to adjust the system frame size. The performance of the proposed ML approach is evaluated over the FIFO aggregation algorithm under the effects of heterogenous traffic patterns for VoIP and Video traffic applications, channel conditions, and number of STAs.
Superstatistics in Econophysics

We consider an ideal closed stock market, in which 100 traders have economic activities. The assets of the traders change through buying and selling stocks. We simulate the assets under conservation of both total currency and total number of stocks. If the traders are identical, then the assets are distributed as a stationary Gaussian. When variety among the traders makes winners and losers, the asset distribution displays power law scaling such as the Pareto law. We discuss this power law scaling from the point of view of superstatistics. It is given as a superposition of scaled distributions for each hierarchical level. The various traders have the same growth rate distribution to keep the scaling.
B{\'e}nard convection in a slowly rotating penny shaped cylinder subject to constant heat flux boundary conditions

We consider axisymmetric Boussinesq convection in a shallow cylinder radius, L, and depth, H (<<L), which rotates with angular velocity $\Omega$ about its axis of symmetry aligned to the vertical. Constant heat flux boundary conditions, top and bottom, are adopted, for which the onset of instability occurs on a long horizontal length scale provided that $\Omega$ is sufficiently small. We investigate the nonlinear development by well-established two-scale asymptotic expansion methods. Comparisons of the results with the direct numerical simulations (DNS) of the primitive governing equations are good at sufficiently large Prandtl number, $\sigma$. As $\sigma$ is reduced, the finite amplitude range of applicability of the asymptotics reduces in concert. Though the large meridional convective cell, predicted by the DNS, is approximated adequately by the asymptotics, the azimuthal flow fails almost catastrophically, because of significant angular momentum transport at small $\sigma$, exacerbated by the cylindrical geometry. To appraise the situation, we propose hybrid methods that build on the meridional streamfunction $\psi$ derived from the asymptotics. With $\psi$ given, we solve the now linear azimuthal equation of motion for the azimuthal velocity v by DNS. Our ''hybrid'' methods enable us to explain features of the flow at large Rayleigh number, found previously by Oruba, Davidson \&Dormy (J. Fluid Mech.,vol. 812, 2017, pp. 890-904).
S2TD: a Separation Logic Verifier that Supports Reasoning of the Absence and Presence of Bugs

Heap-manipulating programs are known to be challenging to reason about. We present a novel veriﬁer for heap-manipulating programs called S2TD, which encodes programs systematically in the form of Constrained Horn Clauses (CHC) using a novel extension of separation logic (SL) with recursive predicates and dangling predicates. S2TD actively explores cyclic proofs to address the path-explosion problem. S2TD differentiates itself from existing CHC-based veriﬁers by focusing on heap-manipulating programs and by employing cyclic proof to efﬁciently verify or falsify them with counterexamples. Compared with existing SL-based veriﬁers, S2TD precisely speciﬁes the heaps over de-allocated pointers to avoid false positives in reasoning about the presence of bugs. S2TD has been evaluated using a comprehensive set of benchmark programs from the SV-COMP repository. The results show that S2TD is more effective than state-of-art program veriﬁers and is more efﬁcient than most of them.
De-speckling of Optical Coherence Tomography Images Using Anscombe Transform and a Noisier2noise Model

Optical Coherence Tomography (OCT) image denoising is a fundamental problem as OCT images suﬀer from multiplicative speckle noise, resulting in poor visibility of retinal layers. The traditional denoising methods consider speciﬁc statistical properties of the noise, which are not always known. Furthermore, recent deep learning-based denoising methods require paired noisy and clean images, which are often diﬃ-cult to obtain, especially medical images. Noise2Noise family architectures are generally proposed to overcome this issue by learning without noisy-clean image pairs. However, for that, multiple noisy observations from a single image are typically needed. Also, sometimes the experi-ments are demonstrated by simulating noises on clean synthetic images, which is not a realistic scenario. This work shows how a single real-world noisy observation of each image can be used to train a denoising network. Along with a theoretical understanding, our algorithm is exper-imentally validated using a publicly available OCT image dataset. Our approach incorporates Anscombe transform to convert the multiplicative noise model to additive Gaussian noise to make it suitable for OCT images. The quantitative results show that this method can outperform several other methods where a single noisy observation of an image is needed for denoising. The code and implementation of this paper will be available publicly upon acceptance of this paper.
CofeNet: Context and Former-Label Enhanced Net for Complicated Quotation Extraction

Quotation extraction aims to extract quotations from written text. There are three components in a quotation: source refers to the holder of the quotation, cue is the trigger word(s), and content is the main body. Existing solutions for quotation extraction mainly utilize rule-based approaches and sequence labeling models. While rule-based approaches often lead to low recalls, sequence labeling models cannot well handle quotations with complicated structures. In this paper, we propose the Context and Former-Label Enhanced Net () for quotation extraction. is able to extract complicated quotations with components of variable lengths and complicated structures. On two public datasets (and ) and one proprietary dataset (), we show that our achieves state-of-the-art performance on complicated quotation extraction.
Active Particle Filter Networks: Efficient Active Localization in Continuous Action Spaces and Large Maps

Accurate localization is a critical requirement for most robotic tasks. The main body of existing work is focused on passive localization in which the motions of the robot are assumed given, abstracting from their influence on sampling informative observations. While recent work has shown the benefits of learning motions to disambiguate the robot's poses, these methods are restricted to granular discrete actions and directly depend on the size of the global map. We propose Active Particle Filter Networks (APFN), an approach that only relies on local information for both the likelihood evaluation as well as the decision making. To do so, we couple differentiable particle filters with a reinforcement learning agent that attends to the most relevant parts of the map. The resulting approach inherits the computational benefits of particle filters and can directly act in continuous action spaces while remaining fully differentiable and thereby end-to-end optimizable as well as agnostic to the input modality. We demonstrate the benefits of our approach with extensive experiments in photorealistic indoor environments built from real-world 3D scanned apartments. Videos and code are available at http://apfn.cs. uni-freiburg.de.
Exploring Human-robot Interaction by Simulating Robots

As collaborative robots enter industrial shop floors, logistics, and manufacturing, rapid and flexible evaluation of human-machine interaction has become more important. The availability of consumer headsets for virtual and augmented realities has lowered the barrier of entry for virtual environments. In this paper, we explore the different aspects of using such environments for simulating robots in user studies and present the first findings from our own research work. Finally, we recommend directions for applying and using simulation in human-robot interaction.
Language Varieties of Italy: Technology Challenges and Opportunities

Abstract Italy is characterized by a one-of-a-kind linguistic diversity landscape in Europe, which implicitly encodes local knowledge, cultural traditions, artistic expressions, and history of its speakers. However, most local languages and dialects in Italy are at risk of disappearing within a few generations. The NLP community has recently begun to engage with endangered languages, including those of Italy. Yet, most efforts assume that these varieties are under-resourced language monoliths with an established written form and homogeneous functions and needs, and thus highly interchangeable with each other and with high-resource, standardized languages. In this paper, we introduce the linguistic context of Italy and challenge the default machine-centric assumptions of NLP for Italy’s language varieties. We advocate for a shift in the paradigm from machine-centric to speaker-centric NLP, and provide recommendations and opportunities for work that prioritizes languages and their speakers over technological advances. To facilitate the process, we finally propose building a local community towards responsible, participatory efforts aimed at supporting vitality of languages and dialects of Italy.
QuestSim: Human Motion Tracking from Sparse Sensors with Simulated Avatars

Real-time tracking of human body motion is crucial for interactive and immersive experiences in AR/VR. However, very limited sensor data about the body is available from standalone wearable devices such as HMDs (Head Mounted Devices) or AR glasses. In this work, we present a reinforcement learning framework that takes in sparse signals from an HMD and two controllers, and simulates plausible and physically valid full body motions. Using high quality full body motion as dense supervision during training, a simple policy network can learn to output appropriate torques for the character to balance, walk, and jog, while closely following the input signals. Our results demonstrate surprisingly similar leg motions to ground truth without any observations of the lower body, even when the input is only the 6D transformations of the HMD. We also show that a single policy can be robust to diverse locomotion styles, different body sizes, and novel environments.
On the Semi-bounded Solution of Cauchy Type Singular Integral Equations of the First Kind

This paper presents an efficient approximate method to obtain a numerical solution, which is bounded at the end point x=-1, for Cauchy type singular integral equations of the first kind on the interval [-1,1]. The solution is derived by approximating the unknown density function using the weighted Chebyshev polynomials of the third kind, and then computing the Cauchy singular integral which is obtained analytically. The known force function is interpolated using the Chebyshev polynomials of the fourth kind. The exactness of this approximate method is shown for characteristic equation when the force function is a cubic. The particular result also shows the exactness of this method.