Miracle-dz/UniFAR-FTUs huggingface dataset:
RAT iLQR: A Risk Auto-Tuning Controller to Optimally Account for Stochastic Model Mismatch

Successful robotic operation in stochastic environments relies on accurate characterization of the underlying probability distributions, yet this is often imperfect due to limited knowledge. This work presents a control algorithm that is capable of handling such distributional mismatches. Specifically, we propose a novel nonlinear MPC for distributionally robust control, which plans locally optimal feedback policies against a worst-case distribution within a given KL divergence bound from a Gaussian distribution. Leveraging mathematical equivalence between distributionally robust control and risk-sensitive optimal control, our framework also provides an algorithm to dynamically adjust the risk-sensitivity level online for risk-sensitive control. The benefits of the distributional robustness as well as the automatic risk-sensitivity adjustment are demonstrated in a dynamic collision avoidance scenario where the predictive distribution of human motion is erroneous.
Latent Vector Recovery of Audio GANs

Advanced Generative Adversarial Networks (GANs) are remarkable in generating intelligible audio from a random latent vector. In this paper, we examine the task of recovering the latent vector of both synthesized and real audio. Previous works recovered latent vectors of given audio through an auto-encoder inspired technique that trains an encoder network either in parallel with the GAN or after the generator is trained. With our approach, we train a deep residual neural network architecture to project audio synthesized by WaveGAN into the corresponding latent space with near identical reconstruction performance. To accommodate for the lack of an original latent vector for real audio, we optimize the residual network on the perceptual loss between the real audio samples and the reconstructed audio of the predicted latent vectors. In the case of synthesized audio, the Mean Squared Error (MSE) between the ground truth and recovered latent vector is minimized as well. We further investigated the audio reconstruction performance when several gradient optimization steps are applied to the predicted latent vector. Through our deep neural network based method of training on real and synthesized audio, we are able to predict a latent vector that corresponds to a reasonable reconstruction of real audio. Even though we evaluated our method on WaveGAN, our proposed method is universal and can be applied to any other GANs.
Mixed-hybrid and mixed-discontinuous Galerkin methods for linear dynamical elastic–viscoelastic composite structures

Abstract We introduce and analyze a stress-based formulation for Zener’s model in linear viscoelasticity. The method is aimed to tackle efficiently heterogeneous materials that admit purely elastic and viscoelastic parts in their composition.We write the mixed variational formulation of the problem in terms of a class of tensorial wave equation and obtain an energy estimate that guaranties the well-posedness of the problem through a standard Galerkin procedure. We propose and analyze mixed continuous and discontinuous Galerkin space discretizations of the problem and derive optimal error bounds for each semidiscrete solution in the corresponding energy norm. Finally, we discuss full discretization strategies for both Galerkin methods.
Generating Diverse Translation from Model Distribution with Dropout

Despite the improvement of translation quality, neural machine translation (NMT) often suffers from the lack of diversity in its generation. In this paper, we propose to generate diverse translations by deriving a large number of possible models with Bayesian modelling and sampling models from them for inference. The possible models are obtained by applying concrete dropout to the NMT model and each of them has specific confidence for its prediction, which corresponds to a posterior model distribution under specific training data in the principle of Bayesian modeling. With variational inference, the posterior model distribution can be approximated with a variational distribution, from which the final models for inference are sampled. We conducted experiments on Chinese-English and English-German translation tasks and the results shows that our method makes a better trade-off between diversity and accuracy.
Projection-free Online Learning over Strongly Convex Sets

To efficiently solve online problems with complicated constraints, projection-free algorithms including online frank-wolfe (OFW) and its variants have received significant interest recently. However, in the general case, existing efficient projection-free algorithms only achieved the regret bound of O(T^{3/4}), which is worse than the regret of projection-based algorithms, where T is the number of decision rounds. In this paper, we study the special case of online learning over strongly convex sets, for which we first prove that OFW can enjoy a better regret bound of O(T^{2/3}) for general convex losses. The key idea is to refine the decaying step-size in the original OFW by a simple line search rule. Furthermore, for strongly convex losses, we propose a strongly convex variant of OFW by redefining the surrogate loss function in OFW. We show that it achieves a regret bound of O(T^{2/3}) over general convex sets and a better regret bound of O(T^{1/2}) over strongly convex sets.
Active Visual Analytics: Assisted Data Discovery in Interactive Visualizations via Active Search

We propose the Active Visual Analytics technique (ActiveVA), an augmentation of interactive visualizations with active search to aid data foraging and information extraction. We accomplish this by integrating an active search algorithm into the visual interface, which leads users to explore promising data points on a visualization and updates suggestions upon observing user feedback. Using a fictitious epidemic dataset published by the VAST community, we conduct two evaluations. First, we present simulation results that demonstrate the effectiveness of active search in data discovery. Second, we show how a human-computer partnership based on ActiveVA can result in more meaningful interactions during interactive visual exploration and discovery with a crowd-sourced user study. Finally, we outline open research questions regarding human factors in active search and the implications of this line of research in real-world scenarios such as visual drug discovery and intelligence analysis.
Robust Keypoint Detection and Pose Estimation of Robot Manipulators with Self-Occlusions via Sim-to-Real Transfer

Keypoint detection is an essential building block for many robotic applications like motion capture and pose estimation. Historically, keypoints are detected using uniquely engineered markers such as checkerboards, fiducials, or markers. More recently, deep learning methods have been explored as they have the ability to detect user-defined keypoints in a marker-less manner. However, deep neural network (DNN) detectors can have an uneven performance for different manually selected keypoints along the kinematic chain. An example of this can be found on symmetric robotic tools where DNN detectors cannot solve the correspondence problem correctly. In this work, we propose a new and autonomous way to define the keypoint locations that overcomes these challenges. The approach involves finding the optimal set of keypoints on robotic manipulators for robust visual detection. Using a robotic simulator as a medium, our algorithm utilizes synthetic data for DNN training, and the proposed algorithm is used to optimize the selection of keypoints through an iterative approach. The results show that when using the optimized keypoints, the detection performance of the DNNs improved so significantly that they can even be detected in cases of self-occlusion. We further use the optimized keypoints for real robotic applications by using domain randomization to bridge the reality gap between the simulator and the physical world. The physical world experiments show how the proposed method can be applied to the wide-breadth of robotic applications that require visual feedback, such as camera-to-robot calibration, robotic tool tracking, and whole-arm pose estimation.
On cohomogeneity one Hermitian non-K\"ahler manifolds

In this note, we begin the study of Hermitian manifolds with a compact Lie group action by holomorphic isometries with principal orbits of codimension one. In particular, we focus on a special class of these manifolds constructed by following B\'erard-Bergery: we compute the Chern-Ricci curvatures and we characterize the special Hermitian metrics, such as balanced, K\"ahler-like, pluriclosed, locally conformally K\"ahler, Vaisman, Gauduchon.
Elasticlave: An Efficient Memory Model for Enclaves

Trusted-execution environments (TEE), like Intel SGX, isolate user-space applications into secure enclaves without trusting the OS. Thus, TEEs reduce the trusted computing base, but add one to two orders of magnitude slow-down. The performance cost stems from a strict memory model, which we call the spatial isolation model, where enclaves cannot share memory regions with each other. In this work, we present Elasticlave---a new TEE memory model that allows enclaves to selectively and temporarily share memory with other enclaves and the OS. Elasticlave eliminates the need for expensive data copy operations, while offering the same level of application-desired security as possible with the spatial model. We prototype Elasticlave design on an RTL-designed cycle-level RISC-V core and observe 1 to 2 orders of magnitude performance improvements over the spatial model implemented with the same processor configuration. Elasticlave has a small TCB. We find that its performance characteristics and hardware area footprint scale well with the number of shared memory regions it is configured to support.
Downregulation of the DST Transcription Factor Using Artificial microRNA to Increase Yield, Salt and Drought Tolerance in Rice

Abiotic stresses like salinity and drought directly affect plant growth and water availability, resulting in lower yield in rice. So, a combination of stress tolerance along with enhanced grain yield is a major focus of rice breeding. It was reported earlier that loss in function of the drought and salt tolerance (DST) gene results in increase in grain production through downregulating Gn1a/OsCKX2 expression. Moreover, dst mutants also showed enhanced drought and salt tolerance in rice by regulating genes involved in ROS homeostasis. In the present study, we proceeded to test these reports by downregulating DST using artificial microRNA technology in the commercial but salt sensitive, high-yielding, BRRIdhan 28 (BR28). This cultivar was transformed with DST_artificial microRNA (DST_amiRNA) driven by the constitutive CaMV35S promoter using tissue culture independent Agrobacterium mediated in planta transformation. DST_amiRNA transgenic plants were confirmed by artificial microRNA specific PCR. Transformed plants at T0 generation showed vigorous growth with significantly longer panicle length and higher primary branching resulting in higher yield, compared to the wild type (WT) BR28. Semi-quantitative RT PCR confirmed the decrease in DST expression in the BR28 transgenic plants compared to WT. T1 transgenic plants also showed improvement in a number of physiological parameters and greater growth compared to WT after 14 days of 120 mM salt (NaCl) stress at seedling stage. Therefore, DST downregulated transgenic plants showed both higher stress tolerance as well as better yields. Furthermore, stable inheritance of the improved phenotype of the DST_amiRNA transgenics will be tested in advanced generations.
The roles of transcription factors in Nucleotide excision repair in yeast

xi CHAPTER 1 LITERATURE REVIEW NUCLEOTIDE EXCISION REPAIR: A CONSERVED, VERSATILE AND COMPLICATED DNA REPAIR MECHANISM FROM PROKARYOTIC CELLS TO EUKARYOTIC CELLS. 1 1.
Functional consequences of mutations in GRIN2A and GRIN2B associated with mental disorders

GRIN2A and GRIN2B encode the GluN2A and GluN2B subunits of the NMDA receptor, a subtype of ionotropic glutamate receptor that displays voltage-dependent block by Mg2+ and a high permeability to Ca2+. These receptors play important roles in synaptogenesis, synaptic transmission and synaptic plasticity, as well as contributing to neuronal loss and dysfunction in several neurological disorders. Recently, individuals with a range of childhood onset epilepsies, intellectual disability and other neurodevelopmental abnormalities have been found to carry heterozygous gene-disrupting or protein-altering point mutations in GRIN2A and GRIN2B. This thesis addresses the hypothesis that these point mutations cause key functional disturbances to NMDA receptor properties that contribute to neurodevelopmental disorders. To test this hypothesis, a group of related mutations were selected for functional assessment in heterologous systems: four missense mutations affecting residues in or near the subunit pore regions, all of which are associated with epilepsy and intellectual disability. To model the impact of gene disrupting mutations in GRIN2A, a preliminary analysis of the functional consequences of GluN2A haploinsufficiency in a genetically modified rat was also performed. Three of the four missense mutations were found to be associated with profound alterations in fundamental NMDA receptor properties: compared to wild type, GluN2AN615K was found to reduce Mg2+ block, GluN2BN615I and GluN2BV618G to cause potentiation by Mg2+, and GluN2AN615K and GluN2BN615I showed reduced conductance. GluN2AR586K was not found to influence the parameters assessed. When GluN2AN615K was expressed alongside wild type subunits in the same NMDA receptor, it was found to have a dominant negative effect. Finally, I established successful gene targeting in a new rat Grin2A knock-out model, and observed that heterozygous neurons had lower GluN2A protein expression and current density, making a good model to study human epilepsies associated with loss of a GRIN2A allele. This thesis provides evidence that three missense mutations in GRIN2A and GRIN2B affect physiologically important properties of the NMDA receptor, and that GluN2A haploinsufficiency influences important neural properties in vitro. This is consistent with these mutations causing disease and highlights these and related mutations as potential therapeutic targets in the future.
Sexual selection, social selection and individual quality : underlying mechanisms and ultimate consequences of ornamentation in a monomorphic species, the King penguin (Aptenodytes patagonicus)

Darwin’s seminal theory of evolution by means of natural selection, first published 157 years ago, has been in constant refinement ever since. The production and maintenance of extravagant ornaments is widely suggested to evolve by conspecific preference providing information on individual intrinsic quality in sexual contexts or on individual social quality in non-reproductive contexts. The king penguin is a monomorphic bird species and an outstanding model to study ornament evolution. My results show that those ornaments are partly condition-dependent, and reliable traits that may be used to assess the quality of a potential sexual partner, implying that their evolution and maintenance is partly determined by sexual selection. On the other hand, some traits remained condition-independent in their production, suggesting that the cost associated with their expression was deferred over time and the evolution of those ornaments likely shaped by non-sexual social selection.
Performance of Serum Quad Test in Screening for Fetal Down Syndrome in a Large-Scale Unselected Population in a Developing Country

Objective: To assess the effectiveness of Quad test in the detection of Down syndrome (DS) in routine practice among a large-scale population and to compare the effectiveness of Quad test based on the Western reference model (WM) and that based on Thai reference model (TM). Methods: Quad test was performed on 42,769 pregnancies at 14–21 weeks. The fetal risk of DS derived from Quad test was automatically computed based on WM and used in evaluating the effectiveness. Also, the fetal risk was calculated based on the TM. Results: Of 39,740 women with complete follow-ups including 74 fetuses with DS, with WM, the detection and false positive rates were 81.1% and 7.2%, respectively, whereas the detection and false positive rates with TM were 87.8%, and 6.8%, respectively. According to ROC curves, the performance of Quad test based on TM was slightly but significantly better than that based on WM (AUC of 0.959 vs. 0.940, p = 0.001). Conclusion: Quad test is highly effective in service settings and suitable for developing countries and the effectiveness is even higher when based on ethnicity-specific reference model.
Impact of acute and chronic restraint stress on behaviour and neuronal morphology in genetically modified mouse models

The German Mouse Clinic has established a platform for investigating complex gene x environment interactions. This study presents the establishment of a highly reproducible acute and a chronic stress challenge. The acute stress challenge measures non-invasively, through a behavioural read out, alterations in stress-reactivity and corticosterone response in mice. Subsequently mutant mouse lines can be chronically challenged and different endpoints relevant for etiopathology can be analyzed.
Multiscale simulation of peripheral neural signaling

Bioelectronic Medicines that modulate the activity patterns on peripheral nerves have promise as a new way of treating diverse medical conditions from epilepsy to rheumatism. Progress in the field builds upon time consuming and expensive experiments in living organisms to evaluate spontaneous activity patterns, stimulation efficiency, and organ responses. To reduce experimentation load and allow for a faster, more detailed analysis of both recording from and stimulation of peripheral nerves, adaptable computational models incorporating insights won in experiments will be of great help. We present a peripheral nerve simulator that combines biophysical axon models and numerically solved and idealized extracellular space models in one environment. Two different scales of abstraction were merged. On the one hand we modeled the extracellular space in a finite element solver as a three dimensional resistive continuum governed by the electro-quasistatic approximation of the Maxwell equations. Potential distributions were precomputed for different media (homogeneous, nerve in saline, nerve in cuff). Axons, on the other hand, were modeled at a higher level of abstraction as one dimensional chains of compartments; each consisting of lumped linear elements and, for some, channels with non-linear dynamics. Unmyelinated fibres were based on the Hodgkin-Huxley model; for myelinated fibers, we instead adapted the model proposed by McIntyre et al. in 2002 to smaller diameters. To obtain realistic axon shapes, an iterative algorithm positioned fibers along the nerve with variable tortuosity, with tortuosity values fit to imaged trajectories. We validated our model with data from the stimulated rat vagus nerve. Simulation results predicted that tortuosity leads to differentiation in recorded signal shapes, with unmyelinated axons being the most affected. Tortuosity was further shown to increase the stimulation threshold. The model we developed can easily be adapted to different nerves, and may be of use for Bioelectronic Medicine research in the future.
Insights to the genetic etiology of a lifestyle-related disease with differential levels of severity using a hierarchy of common genetic variants

Genome evolution in disparate species is synopsized to highlight the role of common genetic variants during their adaptation, and an evolutionary perspective is provided to infer lifestyle-related disease progression in humans. Cardiovascular disease (CVD) is a multi-factorial disease, where maladaptation due to a sedentary lifestyle and faulty diet can influence its prognosis, but the genetic basis for its differential severity remains unknown. As a healthy diet and lifestyle may restrict its prognosis, we hypothesize that a hierarchy of common genetic variants may differentially modulate that severity in a subpopulation. We suggest that the loss-of-function paradigm due to a genetic variant may give rise to a broad spectrum of CVD severity in conjunction with other variants. Here, we have inferred that CAD severity may be a consequence of the plasticity of common variants exclusive among patients with disparate disease severity. Most importantly, we have used a small and outbred subpopulation to demonstrate that common genetic variants can be exploited to trace this unique facet of CAD etiology. Moreover, we corroborate our hypothesis by reporting that a hierarchical plasticity of the LDLR gene, which has been implicated in a differential response to lipid metabolism, is associated with differential CVD severity.
The effect of vesicular-arbuscular mycorrhizal inoculation on the growth and root colonization of ten strawberry cultivars

Ten strawberry cultivars, four early maturing, three late maturing and three “special” cultivars, were inoculated with six strains ofvesicuiar-arbuscular mycorrhizal (VAM) fungi in a pot experiment. Growth effects and colonization of the VAM fungi were studied. Three strains. Glomus macrocarpum V3, G. mosseae Rothamsted and G. sp. V4, were highly efficient, causing significant growth increases in most cultivars. ’Jonsok’ showed the highest mycorrhizal dependency index, 648, and ’Ostara’ the lowest, 269, for the mean response ofall six fungi. The fungal strains which increased shoot growth the most also increased the runner plant formation the most. Early cultivars showed higher colonization percentages than late maturing cultivars. Sporulation of the introduced VAM fungi was on average more abundant in early and special cultivars than in late cultivars. Root colonization and strawberry shoot dry weight correlated significantly in most cultivars, but the correlation between colonization and runner formation was generally poor.
MOLECULAR IDENTIFICATION OF PRE-EXISTING IMMUNITYIN HUMAN AGAINST H9N2 INFLUENZA VIRUSES USING HLA-A*0201 BINDING PEPTIDES

Background and Aims: The contribution genetic and antigenic diversity of H9N2 influenza viruses in evading from immune responses, cytotoxic T lymphocytes (CTL) epitopes in hemagglutinin (HA) protein restricted by HLA binding peptides was identified. Materials and Methods: Phylogenetic analyses were carried out for all of full length HA and deduced amino acid sequences of H9N2 viruses available in GenBank from their emergence to now. The impact of amino acid substitutions on emergence of antigenic variants was evaluated by using in silico prediction of the HLA-A*0201 binding peptides within HA protein. Potentially changes in structure and antigenic function of HA protein were quantified. Results: The viruses phylogenetically clustered in Middle East and China. HA protein of the Middle East viruses represented a single sublineage, G1-like, while China isolates grouped to various sublineages and genotypes. Despite Middle Eastern viruses, more variations found in CTL epitopes of China isolates correlated to phylogenic analysis. Quantify and scoring of HA epitopes revealed that structure and antigenic function of the protein was not changed within H9N2 viruses during the previous decades indicating viral recognition by host-specific CTL response was not affected. Conclusion: Using an evolutionary and immunological approach, we showed that substantial levels of immunogenic peptide conservation for H9N2 HA protein was presented by HLAA*0201. The potentially pre-existing immunity to H9N2 viruses in human is important for determining the outcome of influenza infection and developing vaccine with a T cell–based component against the public health threat.