Miracle-dz/UniFAR-FTUs huggingface dataset:
Industrial Production Process Improvement by a Process Engine Visual Analytics Dashboard

Digitalization reshapes production in a sense that production processes are required to be more flexible and more interconnected to produce products in smaller lot sizes. This makes the process improvement much more challenging, as traditional approaches, which are based on the learning curve, are difficult to apply. Data-driven technologies promise help in learning faster by making use of the massive data volumes collected in production environments. Visual analytics approaches are particularly promising in this regard as they aim to enable engineers with their rich domain knowledge to identify opportunities for process improvements. Based on the assumption that process improvement should be connected with the process engine managing the process execution, we propose a visual analytics dashboard which integrates process models. Based on a case study in the smart factory of Vienna, we conducted two pair analytics sessions. The first results seem promising, whereas domain experts articulate their wish for improvements and future work.
The Use Of Post-Intervention Data From Waitlist Controls To Improve Estimation Of Treatment Effect In Longitudinal Randomized Controlled Trials

In medicine and public health research, the randomized delayed-intervention controlled trial (RDICT), also known as a wait-listed or stepped wedge design, is commonly used to study overt, slow-acting treatments in comparison to a control condition over time. Ten RDICT designs are specified as generalizations of the motivating example, a longitudinal psychology study of a psychoeducational intervention for children with bipolar disorder. These designs vary according to number of observation occasions, time between observations, and length of delay before the control group receives treatment. Two estimators of fixed effects in separate linear mixed effects (LME) models, θ̂2 and θ̂1, are proposed to measure treatment effect based on data from an RDICT design. The LME models have a piecewise linear mean structure, allowing phases for treatment, placebo, and leveling-off effects. The treatment effect is traditionally conceptualized as the difference in slopes between the immediate treatment (IT) and pre-intervention control groups, which we call θ1. Alternately, in an RDICT design, the treatment effect can be the change in slope post-intervention in the delayed-treatment (DT) control group, called θ0. The full model, which allows these treatment effects to differ, produces the standard estimator, θ̂1. A reduced model, nested within the full one, forces the inter and intra treatment effects to be identical and produces the novel estimator, θ̂2. ii A simulation study was conducted to observe the relative efficiency of θ̂2 to θ̂1 as it varies over the 10 RDICT designs and 8 scenarios, which differ in size of treatment effect, intraclass correlation, and sample allocation to DT group. The best-performing and recommended RDICT design, called H2.5 with a DT:IT allocation ratio of 2:1, achieved a relative efficiency of 1.3 when the group-specific treatment effects are identical. The H2.5 design has the longest overall calendar duration of the 10 designs considered and is an extension of the design used in the motivating example study of childhood mood disorders.
Coronavirus and oil price crash: A note

Coronavirus (COVID-19) creates fear and uncertainty, hitting the global economy and amplifying the financial markets volatility. The oil price reaction to COVID-19 was gradually accommodated until March 09, 2020, when, 49 days after the release of the first coronavirus monitoring report by the World Health Organization (WHO), Saudi Arabia floods the market with oil. As a result, international prices drop with more than 20% in one single day. Against this background, the purpose of this paper is to investigate the impact of COVID-19 numbers on crude oil prices, while controlling for the impact of financial volatility and the United States (US) economic policy uncertainty. Our ARDL estimation shows that the COVID-19 daily reported cases of new infections have a marginal negative impact on the crude oil prices in the long run. Nevertheless, by amplifying the financial markets volatility, COVID-19 also has an indirect effect on the recent dynamics of crude oil prices.
Evoked Potentials In Clinical Medicine 3rd Edition

evoked potentials in clinical medicine 3rd edition is available in our book collection an online access to it is set as public so you can download it instantly. Our digital library spans in multiple locations, allowing you to get the most less latency time to download any of our books like this one. Kindly say, the evoked potentials in clinical medicine 3rd edition is universally compatible with any devices to read.
Lifting the Eisenhart-Duval Lift to a Minimal Brane

The motion of a dynamical system on an $n$-dimensional configuration space may be regarded as the lightlike shadow of null geodsics moving in an $(n+2)$ dimensional spacetime known as its Einsenhart-Duval lift. In this paper it is shown that if the configuration space is $n$-dimensional Euclidean space, and in the absence of magnetic type forces, the Eisenhart-Duval lift may be regarded as an $(n+1)$-brane moving in a flat $(n+4)$ -dimensional space with two times. If the Eisenhart-Duval lift is Ricci flat, then the $(n+1)$-brane moves in such a way as to extremise its spacetime volume. A striking example is provided by the motion of $N$ point particles moving in three-dimensional Euclidean space under the influence of their mutual gravitational attraction. Embeddings with curved configuration space metrics and velocity dependent forces are also be constructed. Some of the issues arising from the two times are addressed.
A Note on Early Epidemiological Analysis of Coronavirus Disease 2019 Outbreak using Crowdsourced Data

Crowdsourcing data can prove of paramount importance in monitoring and controlling the spread of infectious diseases. The recent paper by Sun, Chen and Viboud (2020) is important because it contributes to the understanding of the epidemiology and of the spreading of Covid-19 in a period when most of the epidemic characteristics are still unknown. However, the use of crowdsourcing data raises a number of problems from the statistical point of view which run the risk of invalidating the results and of biasing estimation and hypothesis testing. While the work by Sun, Chen and Viboud (2020) has to be commended, given the importance of the topic for worldwide health security, in this paper we deem important to remark the presence of the possible sources of statistical biases and to point out possible solutions to them
Functional outcome of arthroscopic Bankart repair for anterior shoulder instability based on suture anchor configuration

Background: The objective of the study was to evaluate the functional outcome of arthroscopic Bankart repair for anterior shoulder instability to assess whether the number and position of suture anchors plays a role in determining the functional outcome. Methods: This was a prospective study on 32 patients operated with arthroscopic Bankart repair between December 2017 to April 2019. Pre-op and regular follow-up scores were measured at 1 month, 3 months and 6 months post-op using Rowe score and American Shoulder and Elbow Surgeons score to assess functional outcome. Results: Mean age of the study group was 24.5±6.9 years. Functional outcome as determined by Rowe score and ASES score at 6 months follow-up were found to be 90.5±7.2 and 85.9±14.1 respectively when compared to the pre-op scores of 23.2±8.2 and 47.9±5.7 respectively; all of which showed highly significant functional improvement with highly significant reduction in visual analog scale (VAS) pain score. Patients had no recurrent dislocations with mean external rotation limitation of 5 o . 25 (78.1%) patients had two suture anchors inserted and 7 (21.9%) patients had multiple (>2) anchors; and when analysis was done, there was no statistically significant difference between number of suture anchors used with respect to the functional scores. Conclusions: We conclude that arthroscopic Bankart repair is a useful and successful procedure. Patient identification and selection remains the key in determining the success of repair. Meticulous surgical technique and correct positioning of suture anchors may help in reducing the number of anchors without compromising on the final functional outcome, thereby reducing the economic burden on patients.
Learning to Generalize Across Long-Horizon Tasks from Human Demonstrations

Imitation learning is an effective and safe technique to train robot policies in the real world because it does not depend on an expensive random exploration process. However, due to the lack of exploration, learning policies that generalize beyond the demonstrated behaviors is still an open challenge. We present a novel imitation learning framework to enable robots to 1) learn complex real world manipulation tasks efficiently from a small number of human demonstrations, and 2) synthesize new behaviors not contained in the collected demonstrations. Our key insight is that multi-task domains often present a latent structure, where demonstrated trajectories for different tasks intersect at common regions of the state space. We present Generalization Through Imitation (GTI), a two-stage offline imitation learning algorithm that exploits this intersecting structure to train goal-directed policies that generalize to unseen start and goal state combinations. In the first stage of GTI, we train a stochastic policy that leverages trajectory intersections to have the capacity to compose behaviors from different demonstration trajectories together. In the second stage of GTI, we collect a small set of rollouts from the unconditioned stochastic policy of the first stage, and train a goal-directed agent to generalize to novel start and goal configurations. We validate GTI in both simulated domains and a challenging long-horizon robotic manipulation domain in the real world. Additional results and videos are available at this https URL .
Long-term Prediction of Vehicle Behavior using Short-term Uncertainty-aware Trajectories and High-definition Maps

Motion prediction of surrounding vehicles is one of the most important tasks handled by a self-driving vehicle, and represents a critical step in the autonomous system necessary to ensure safety for all the involved traffic actors. Recently a number of researchers from both academic and industrial communities have focused on this important problem, proposing ideas ranging from engineered, rule-based methods to learned approaches, shown to perform well at different prediction horizons. In particular, while for longer-term trajectories the engineered methods outperform the competing approaches, the learned methods have proven to be the best choice at short-term horizons. In this work we describe how to overcome the discrepancy between these two research directions, and propose a method that combines the disparate approaches under a single unifying framework. The resulting algorithm fuses learned, uncertainty-aware trajectories with lane-based paths in a principled manner, resulting in improved prediction accuracy at both shorter- and longer-term horizons. Experiments on real-world, large-scale data strongly suggest benefits of the proposed unified method, which outperformed the existing state-of-the-art. Moreover, following offline evaluation the proposed method was successfully tested onboard a self-driving vehicle.
Multifrequency Observations of the Candidate Neutrino-emitting Blazar BZB J0955+3551

The recent spatial and temporal coincidence of the blazar TXS 0506+056 with the IceCube-detected neutrino event IC-170922A has opened up a realm of multimessenger astronomy with blazar jets as a plausible site of cosmic-ray acceleration. After TXS 0506+056, a second blazar, BZB J0955+3551, was recently found to be spatially coincident with the IceCube-detected neutrino event IC-200107A and undergoing its brightest X-ray flare measured so far. Here we present the results of our multifrequency campaign to study this peculiar event that includes observations with the NuSTAR, Swift, Neutron star Interior Composition Explorer (NICER), and 10.4 m Gran Telescopio Canarias (GTC). The optical spectroscopic observation from GTC secured its redshift as and the central black hole mass as . Both NuSTAR and NICER data reveal a rapid flux variability, albeit at low significance ( ). We explore the origin of the target photon field needed for the photopion production using analytical calculations and considering the observed optical-to-X-ray flux level. We conclude that seed photons may originate from outside the jet, similar to that reported for TXS 0506+056, although a scenario invoking a comoving target photon field (e.g., electron synchrotron) cannot be ruled out. The electromagnetic output from the neutrino-producing photohadronic processes are likely to make only a subdominant contribution to the observed spectral energy distribution, suggesting that the X-ray flaring event may not be directly connected with IC-200107A.
Application of Deep Q-Network in Portfolio Management

Machine Learning algorithms and Neural Networks are widely applied to many different areas such as stock market prediction, facial recognition and automatic machine translation. This paper introduces a novel strategy based on the classic Deep Reinforcement Learning algorithm, Deep QNetwork, for stock market portfolio management. It is a type of deep neural network which is optimized by Q Learning. To adapt the Deep Q-Network for stock market production, we first discretize the action space so that portfolio management becomes a problem that Deep Q-Network can solve. Following this, we combine the Convolutional Neural Network and dueling Q-Net to enhance the recognition ability of the algorithm. We choose five low-relevant American stocks to test our model. It is found that the Deep Q-Network based strategy outperforms the ten other traditional strategies. The profit of Deep Q-Network algorithm is 30% more than the profit of other strategies. Moreover, the Sharpe ratio and Max Drawdown demonstrates that the risk of policy associated with Deep Q-Network is the lowest.
Graph Convolutional Topic Model for Data Streams

Learning hidden topics in data streams has been paid a great deal of attention by researchers with a lot of proposed methods, but exploiting prior knowledge in general and a knowledge graph in particular has not been taken into adequate consideration in these methods. Prior knowledge that is derived from human knowledge (e.g. Wordnet) or a pre-trained model (e.g.Word2vec) is very valuable and useful to help topic models work better, especially on short texts. However, previous work often ignores this resource, or it can only utilize prior knowledge of a vector form in a simple way. In this paper, we propose a novel graph convolutional topic model (GCTM) which integrates graph convolutional networks (GCN) into a topic model and a learning method which learns the networks and the topic model simultaneously for data streams. In each minibatch, our method not only can exploit an external knowledge graph but also can balance between the external and old knowledge to perform well on new data. We conduct extensive experiments to evaluate our method with both human graph knowledge(Wordnet) and a graph built from pre-trained word embeddings (Word2vec). The experimental results show that our method achieves significantly better performances than the state-of-the-art baselines in terms of probabilistic predictive measure and topic coherence. In particular, our method can work well when dealing with short texts as well as concept drift. The implementation of GCTM is available at this https URL.
Structural, magnetic, and insulator-to-metal transitions under pressure in the 
GaV4S8
 Mott insulator: A rich phase diagram up to 14.7 GPa

In addition to its promising potential for applications, $\mathrm{Ga}{\mathrm{V}}_{4}{\mathrm{S}}_{8}$ shows very interesting physical properties with temperature and magnetic field. These properties can be tuned by applying hydrostatic pressure in order to reveal and understand the physics of these materials. Not only pressure induces an insulator-to-metal transition in $\mathrm{Ga}{\mathrm{V}}_{4}{\mathrm{S}}_{8}$ but it also has an interesting effect on the structural and magnetic transitions. Using a combination of AC calorimetry, capacitance, and resistivity measurements under pressure, we determine the evolution of the structural and magnetic transitions with pressure and thus establish the $T\text{\ensuremath{-}}P$ phase diagram of $\mathrm{Ga}{\mathrm{V}}_{4}{\mathrm{S}}_{8}$. To detect the insulator-to-metal transition (IMT), we use optical conductivity and DC resistivity measurements and we follow the evolution of the Mott gap under pressure. The structural transition temperature increases with pressure and a second transition appears above 6 GPa indicating a possible new phase with a very small gap. Pressure has a surprisingly very weak effect on the ferromagnetic transition that persists even very close to the IMT that occurs at around 14 GPa, implying that the metallic state may also be magnetic.
Confinement-mediated phase behavior of hydrocarbon fluids: Insights from Monte Carlo simulations.

The phase behavior of hydrocarbon fluids confined in porous media has been reported to deviate significantly from that in the bulk environment due to the existence of sub-10nm pores. Though experiments and simulations have measured the bubble/dew points and sorption isotherms of hydrocarbons confined in both natural and synthetic nanopores, the confinement effects in terms of the strength of fluid-pore interactions tuned by surface wettability and chemistry have received comparably less discussion. More importantly, the underlying physics of confinement-induced phenomena remain obfuscated. In this work, we studied the phase behavior and capillary condensation of n-hexane to understand the effects of confinement at the molecular level. To systematically investigate the pore effects, we constructed two types of wall confinements; one is a structureless virtual wall described by the Steele potential and the other one is an all-atom amorphous silica structure with surface modified by hydroxyl groups. Our numerical results demonstrated the importance of fluid-pore interaction, pore size, and pore morphology effects in mediating the pressure-volume-temperature (PVT) properties of hydrocarbons. The most remarkable finding of this work was that the saturation pressure predicted from the van der Waals-type adsorption isothermal loop could be elevated or suppressed relative to the bulk phase, as illustrated in the graphical abstract. As the surface energy (i.e., fluid-pore interaction) decreased, the isothermal vapor pressure increased, indicating a greater preference for the fluid to exist in the vapor state. Sufficient reduction of the fluid-pore interactions could even elevate the vapor pressure above that of the bulk fluid.
Communication Efficient Sparsification for Large Scale Machine Learning

The increasing scale of distributed learning problems necessitates the development of compression techniques for reducing the information exchange between compute nodes. The level of accuracy in existing compression techniques is typically chosen before training, meaning that they are unlikely to adapt well to the problems that they are solving without extensive hyper-parameter tuning. In this paper, we propose dynamic tuning rules that adapt to the communicated gradients at each iteration. In particular, our rules optimize the communication efficiency at each iteration by maximizing the improvement in the objective function that is achieved per communicated bit. Our theoretical results and experiments indicate that the automatic tuning strategies significantly increase communication efficiency on several state-of-the-art compression schemes.
LiDAR guided Small obstacle Segmentation

Detecting small obstacles on the road is critical for autonomous driving. In this paper, we present a method to reliably detect such obstacles through a multi-modal framework of sparse LiDAR(VLP-16) and Monocular vision. LiDAR is employed to provide additional context in the form of confidence maps to monocular segmentation networks. We show significant performance gains when the context is fed as an additional input to monocular semantic segmentation frameworks. We further present a new semantic segmentation dataset to the community, comprising of over 3000 image frames with corresponding LiDAR observations. The images come with pixel-wise annotations of three classes off-road, road, and small obstacle. We stress that precise calibration between LiDAR and camera is crucial for this task and thus propose a novel Hausdorff distance based calibration refinement method over extrinsic parameters. As a first benchmark over this dataset, we report our results with 73 % instance detection up to a distance of 50 meters on challenging scenarios. Qualitatively by showcasing accurate segmentation of obstacles less than 15 cms at 50m depth and quantitatively through favourable comparisons vis a vis prior art, we vindicate the method’s efficacy. Our project and dataset is hosted at https://small-obstacle-dataset.github. io/
Constructing the theory at the boundary, its dynamics and degrees of freedom

Given a theory in a region of space-time with boundaries, defined by a Lagrangian, we propose a method to find the corresponding theory at the boundary (either space-like or time-like), that allows us to identify the degrees of freedom at the boundary without any gauge fixing nor further assumptions a priori. We start by finding the corresponding action principle at the boundary. From this, we can find the corresponding equations of motion, the symmetries and degrees of freedom at the boundary. Then we characterise the degrees of freedom at the boundary. We exemplify this method by analysing the broadly studied 3-dimensional Abelian Chern-Simons theory. We found, through the Hamiltonian framework, that the boundary theory has one degree of freedom that can be characterised by the chiral boson: the Quantum Hall edge modes. We also find a correspondence between a gauge fixing in the bulk with a boundary condition and discuss whether this is enough to have a well-posed action principle and the possible scenarios.
On The Simulated Annealing In $\mathbf{R}^d$

Using a localization procedure and the result of Holley-Kusuoka-Stroock [7] in the torus, we widely weaken the usual growth assumptions concerning the success of the continuous-time simulated annealing in $\mathbf{R}^d$. Our only assumption is the existence of an invariant probability measure for a sufficiently low temperature. We also prove, in an appendix, a non-explosion criterion for a class of time-inhomogeneous diffusions.
Invariant Causal Prediction for Block MDPs

Generalization across environments is critical to the successful application of reinforcement learning algorithms to real-world challenges. In this paper, we consider the problem of learning abstractions that generalize in block MDPs, families of environments with a shared latent state space and dynamics structure over that latent space, but varying observations. We leverage tools from causal inference to propose a method of invariant prediction to learn model-irrelevance state abstractions (MISA) that generalize to novel observations in the multi-environment setting. We prove that for certain classes of environments, this approach outputs with high probability a state abstraction corresponding to the causal feature set with respect to the return. We further provide more general bounds on model error and generalization error in the multi-environment setting, in the process showing a connection between causal variable selection and the state abstraction framework for MDPs. We give empirical evidence that our methods work in both linear and nonlinear settings, attaining improved generalization over single- and multi-task baselines.