Miracle-dz/UniFAR-FTUs huggingface dataset:
High power lower hybrid current drive experiments in the Tore Supra tokamak

A review of the LH current drive experiments carried out on the Tore Supra tokamak is presented. The issues for an effective application of the LH wave at high power in reactor relevant conditions are highlighted. A promising performance has been obtained with the new launcher that is designed to couple up to 4 MW during 1000 s at a power density of 25 MW m-2. The heat load on the guard limiter of the antenna and the fast electron acceleration in the near electric field of the grill mouth remain at a low level, while the mean reflection coefficient never exceeds 10%. The powerful diagnosis capabilities of hard X ray (HXR) fast electron bremsstrahlung tomography has led to significant progress in the understanding of LH wave dynamics. The role of the fastest electrons driven by the LH wave is clearly identified. From HXR measurements, an increase of LH current drive efficiency with plasma current is predicted and confirmed by a direct determination at zero loop voltage. Lower hybrid power absorption is observed to be off-axis under almost all plasma conditions, and its radial width clearly depends on the antenna phasing conditions. A correlation between the HXR profiles and the onset of an improved core confinement is identified in fully non-inductive discharges. This regime ascribed to some vanishing of the magnetic shear is found to be transient and usually ends when the minimum of the safety factor becomes very close to 2, leading to a large MHD activity. Experimental observations and numerical simulations suggest that LH power is absorbed after a few transits across the plasma. However, besides toroidal mode coupling, additional mechanisms probably contribute to a spectral broadening of the LH wave.
On Complexity Bounds for the Maximal Admissible Set of Linear Time-Invariant Systems

Given a dynamical system with constrained outputs, the maximal admissible set (MAS) is defined as the set of all initial conditions such that the output constraints are satisfied for all time. It has been previously shown that for discrete-time, linear, time-invariant, stable, observable systems with polytopic constraints, this set is a polytope described by a finite number of inequalities (i.e., has finite complexity). However, it is not possible to know the number of inequalities apriori from problem data. To address this gap, this contribution presents two computationally efficient methods to obtain upper bounds on the complexity of the MAS. The first method is algebraic and is based on matrix power series, while the second is geometric and is based on Lyapunov analysis. The two methods are rigorously introduced, a detailed numerical comparison between the two is provided, and an extension to systems with constant inputs is presented. Knowledge of such upper bounds can speed up the computation of MAS, and can be beneficial for defining the memory and computational requirements for storing and processing the MAS, as well as the control algorithms that leverage the MAS.
Using Thermal Imaging to Measure Hand Hygiene Quality

OBJECTIVES Hand hygiene has long been promoted as the most effective way to prevent the transmission of infection. However, due to the low compliance and quality of hand hygiene reported in previous studies, constant monitoring of healthcare workers' hand hygiene compliance and quality is crucial. In this study, we investigate the feasibility of using a thermal camera together with an RGB camera to detect hand coverage of alcohol-based formulation, thereby monitoring handrub quality. METHODS We recruited 32 participants for this study. Participants were required to perform four types of handrubs to produce different hand coverage of alcohol-based formulation. After each task, participants' hands were photographed under a thermal camera and an RGB camera, while an ultraviolet (UV) test was used to provide the ground truth of hand coverage of alcohol-based formulation. Then, a U-Net was used to segment areas covered with alcohol-based formulations from thermal images, and the system performance was evaluated by comparing coverage differences between thermal images and UV images regarding accuracy and Dice coefficient. RESULTS The system yields promising results in terms of accuracy (93.5%) and Dice coefficient (87.1%) when observations take place 10 seconds after performing handrub. In addition, we also examine the system performance change over a 60-second observation period, and the accuracy and Dice coefficient still remain at about 92.4% and 85.7% when observation happens at the 60-second time point. CONCLUSIONS Given these encouraging results, thermal imaging shows its potential feasibility in providing accurate, constant, and systematic hand hygiene quality monitoring.
SurgT: Soft-Tissue Tracking for Robotic Surgery, Benchmark and Challenge

This paper introduces the SurgT MICCAI 2022 challenge and its ﬁrst results. There were two purposes for the creation of this challenge: (1) the establishment of the ﬁrst standardised benchmark for the research community to assess soft-tissue trackers; and (2) to encourage the development of unsupervised deep learning methods, given the lack of annotated data in surgery. A dataset of 157 stereo endoscopic videos from 20 clinical cases, along with stereo camera calibration parameters, are provided. The participants were tasked with the development of algorithms to track a bounding box on each stereo endoscopic video. At the end of the challenge, the developed methods were assessed on a previously hidden test subset. This assessment uses benchmarking metrics that were purposely developed for this challenge and are now available online. The teams were ranked according to their Expected Average Overlap (EAO) score, which is a weighted average of Intersection over Union (IoU) scores. The top team achieved an EAO score of 0.583 in the test subset. Tracking soft-tissue using unsupervised algorithms was found to be achievable. The dataset and benchmarking tool have been successfully created and made publicly available online. This challenge is expected to contribute to the development of autonomous robotic surgery, and other digital surgical technologies
Enhancing Human-Robot Collaboration Transportation through Obstacle-Aware Vibrotactile Feedback

Transporting large and heavy objects can benefit from Human-Robot Collaboration (HRC), increasing the contribution of robots to our daily tasks and reducing the risk of injuries to the human operator. This approach usually posits the human collaborator as the leader, while the robot has the follower role. Hence, it is essential for the leader to be aware of the environmental situation. However, when transporting a large object, the operator's situational awareness can be compromised as the object may occlude different parts of the environment. This paper proposes a novel haptic-based environmental awareness module for a collaborative transportation framework that informs the human operator about surrounding obstacles. The robot uses two LIDARs to detect the obstacles in the surroundings. The warning module alerts the operator through a haptic belt with four vibrotactile devices that provide feedback about the location and proximity of the obstacles. By enhancing the operator's awareness of the surroundings, the proposed module improves the safety of the human-robot team in co-carrying scenarios by preventing collisions. Experiments with two non-expert subjects in two different situations are conducted. The results show that the human partner can successfully lead the co-transportation system in an unknown environment with hidden obstacles thanks to the haptic feedback.
Quantum gravity and the holographic principle

In this thesis we study two different approaches to holography, and comment on the possible relation between them. The first approach is an analysis of the high-energy regime of quantum gravity in the eikonal approximation, where the theory reduces to a topological field theory. This is the regime where particles interact at high energies but with small momentum transfer. We do this for the cases of asymptotically dS and AdS geometries and find that in both cases the theory is topological. We discuss the relation of our solutions in AdS to those of Horowitz and Itzhaki. We also consider quantum gravity away from the extreme eikonal limit and explain the sense in which the covariance of the theory is equivalent to taking into account transfer of momentum. The second approach we pursue is the AdS/CFT correspondence. We provide a holographic reconstruction of the bulk space-time metric and of bulk fields on this space-time, out of conformal field theory data. Knowing which sources are turned on is sufficient in order to obtain an asymptotic expansion of the bulk metric and of bulk fields near the boundary to high enough order so that all infrared divergences of the on-shell action are obtained. We provide explicit formulae for the holographic stress-energy tensors associated with an arbitrary asymptotically AdS geometry. We also study warped compactifications, where our d-dimensional world is regarded as a slice of a (d+1)-dimensional space-time, and analyse in detail the question as to where the d-dimensional observer can find the information about the extra dimension.
Modelling the control of antimicrobial resistance induced by mutations and horizontal gene transfer

Antimicrobial resistance (AMR) is associated with more signiﬁcant mortality, extended hospitalization and higher costs. AMR can aﬀect people at any stage of life and put pressure on health services, veterinary medicine and agriculture. This makes it one of the world’s most urgent public health problems. Hence, there is a demand to create mathematical and statistical models capable of predicting how the spread of AMR will behave and apply controls to visualize what can be done to eliminate resistant bacteria or at least lower the amount. In this study, we formulate a simple deterministic mathematical model using ordinary diﬀerential equations (ODEs) where sensitive, and resistant bacteria coexist in the environment, and plasmids and transposons are a function of resistant bacteria. We show that the system is well-posed, and we analyze the existence and stability of the equilibrium points. Later, we propose an optimal control problem where prophylaxis is the primary control strategy. Theoretical results are illustrated with numerical experiments in each stage of this study. To this end, we use data from the European Antimicrobial Resistance Surveillance Network (EARS-Net). We prove numerically that the density of sensitive and resistant bacteria is directly related to geographic location and the type of antibiotic supplied.
Isolated Main Galaxy Pairs from the SDSS Data Release 4

From the Main galaxy data of the SDSS Data Release 4 (SDSS4), we have iden- tified close galaxy pairs at neighbourhood radius R = 100kpc by three-dimensional cluster analysis. Using the criterion that an "isolated galaxy pair" must be separated from its "nearest neighbor"by more than 500kpc, we constructed an isolated galaxy pair sample of 1158 pairs. We also constructed a random pair sample by randomly selecting 1158 galaxy pairs from the Main galaxy sample, which has the same redshift distribution as the isolated galaxy pair sample, and in which the two components of any pair have the same redshifts. Comparative studies of luminosity and size between the members of the galaxy pairs are performed. We find and further confirm there is no tendency for paired galaxies to have similar luminosities or sizes. From the isolated pair sample we also selected a subsample with the magnitude limit of the primary raised by 2 magnitudes,so as to include pairs in which the secondary is 2 mag- nitudes fainter than the primary. This subsample contains 82 pairs. A random pair sample is similarly constructed.
DOTcvpSB: all problems in one package

1.1 Drug displacement problem . 41.2 Phase resetting of a calcium oscillator problem . 51.2. 1 The scenario with free transition times and one control variable . 81.2. 2 The scenario with free transition times and two control variables . 8
The Born Rule -- Axiom or Result?

The Born rule is part of the collapse axiom in the standard version of quantum theory, as presented by standard textbooks on the subject. We show here that its signature quadratic dependence follows from a single additional physical assumption beyond the other axioms - namely, that the probability of a particular measurement outcome (the state $\phi_k$, say) is independent of the choice of observable to be measured, so long as one of its eigenstates corresponds to that outcome. We call this assumption ``observable independence. '' As a consequence, the Born rule cannot be completely eliminated from the list of axioms, but it can, in principle, be reduced to a more physical statement. Our presentation is suitable for advanced undergraduates or graduate students who have taken a standard course in quantum theory. It does not depend on any particular interpretation of the theory.
Amazon Last-Mile Delivery Trajectory Prediction Using Hierarchical TSP with Customized Cost Matrix

In response to the Amazon Last-Mile Routing Challenge, Team Permission Denied proposes a hierarchical Travelling Salesman Problem (TSP) optimization with a customized cost matrix. The higher level TSP solves for the zone sequence while the lower level TSP solves the intra-zonal stop sequence. The cost matrix is modified to account for routing patterns beyond the shortest travel time. Lastly, some post-processing is done to edit the sequence to match commonly observed routing patterns, such as when travel times are similar, drivers usually start with stops with more packages than those with fewer packages. The model is tested on 1223 routes that are randomly selected out of the training set and the score is 0.0381. On the 13 routes in the given model apply set, the score was 0.0375.
Deep-water internal solitaty waves

An experimental investigation of mode-2 (’lump-Like’) Solitary waves propagaling on a thin interface between two deep layers of different densities is presented. Small-and large-amplitude waves behaved differently: small waves carried energy and momentum, whereas sufficiently large waves also carried mass. Weakly nonlinear theory anticipated the result for amplitudes a/h [les ] 0.5 but did not provide even a qualitative description of the large-amplitude waves. In particular, the prediction that for waves to maintain permanent form their wavelength must decrease with increasing amplitude failed; instead the wavelength of large waves was observed to increase with increasing amplitude. Furthermore, whilst the waves were expected to emerge from interactions along their precollision trajectories, the large waves actually suffered a backward shift.
Mechanical feedback in size regulation

The mechanism with which cells measure the dimension of the organ in which they are embedded, and slow down their growth when the ﬁnal size is reached, is a long standing problem of developmental biology. The role of mechanics in this feedback is thought to be important. Morphoelasticity is a standard continuum framework for modeling growing elastic tissues. However, in this theory, in the absence of additional variables, the feedback between growth and mechanical stress leads to either a collapse or unbounded growth of the tissue, but usually prohibits reaching a ﬁnite asymptotic size (‘size control’). In this article, we modify this classical setting to include an energetic cost associated with growth, leading to the physical eﬀect of size control. The present model simultaneously provides a qualitatively correct residual stress proﬁle and has a naturally emerging necrotic core, all of which have previously been established experimentally in multicellular spheroids. This is achieved through a local feedback mechanism derived from a thermodynamical framework. The model delivers testable predictions for experimental systems, and could be a step towards the understanding of the role of mechanics in the multifaceted question of how growing organs attain their ﬁnal size.
Variability of ultrahigh-range-resolution radar profiles and some implications for target recognition

Radar-based noncooperative target recognition (NCTR) can be attempted based on various types of radar signatures. One natural choice for such a signature domain is that of ultra-high range resolution (UHRR) profiles. These profiles provide a one-dimensional `map' of the target scatterers in the range dimension (with respect to the radar line of sight). Research in this area has shown that this signature domain seems to hold much promise, but that there are concomitant challenges that arise in connection with its exploitation. Radar waveform development, algorithm selection and development, data generation, and algorithm training and testing are all areas that can present acute challenges. This paper focuses on the issues related to the extreme variability that one may expect in UHRR signatures of fixed-wing targets. The degree of variability is quantified and it is shown that this extreme variability contributes to challenges related to algorithm development, training, and testing.
mixdistreg: An R Package for Fitting Mixture of Experts Distributional Regression with Adaptive First-order Methods

This paper presents a high-level description of the R software package mixdistreg to fit mixture of experts distributional regression models. The proposed framework is implemented in R using the deepregression software template, which is based on TensorFlow and follows the neural structured additive learning principle. The software comprises various approaches as special cases, including mixture density networks and mixture regression approaches. Various code examples are given to demonstrate the package's functionality.
Root Laplacian Eigenmaps with their application in spectral embedding

The root laplacian operator or the square root of Laplacian which can be obtained in complete Riemannian manifolds in the Gromov sense has an analog in graph theory as a square root of graph-Laplacian. Some potential applications have been shown in geometric deep learning (spectral clustering) and graph signal processing.
Improved Policy Evaluation for Randomized Trials of Algorithmic Resource Allocation

We consider the task of evaluating policies of algorithmic resource allocation through randomized controlled trials (RCTs). Such policies are tasked with optimizing the utilization of limited intervention resources, with the goal of maximizing the benefits derived. Evaluation of such allocation policies through RCTs proves difficult, notwithstanding the scale of the trial, because the individuals' outcomes are inextricably interlinked through resource constraints controlling the policy decisions. Our key contribution is to present a new estimator leveraging our proposed novel concept, that involves retrospective reshuffling of participants across experimental arms at the end of an RCT. We identify conditions under which such reassignments are permissible and can be leveraged to construct counterfactual trials, whose outcomes can be accurately ascertained, for free. We prove theoretically that such an estimator is more accurate than common estimators based on sample means -- we show that it returns an unbiased estimate and simultaneously reduces variance. We demonstrate the value of our approach through empirical experiments on synthetic, semi-synthetic as well as real case study data and show improved estimation accuracy across the board.
Codes for Correcting t Limited-Magnitude Sticky Deletions

Codes for correcting sticky insertions/deletions and limited-magnitude errors have attracted significant attention due to their applications of flash memories, racetrack memories, and DNA data storage systems. In this paper, we first consider the error type of t sticky deletions with ℓ-limited-magnitude and propose a non-systematic code for correcting this type of error with redundancy 2t(1 − 1/p) • log(n + 1) + O(1), where p is the smallest prime larger than ℓ + 1. Next, we present a systematic code construction with an efficient encoding and decoding algorithm with redundancy $\frac{{\left\lceil {2t(1 - 1/p)} \right\rceil \cdot \left\lceil {\log p} \right\rceil }}{{\log p}}\log (n + 1) + O(\log \log n)$, where p is the smallest prime larger than ℓ + 1.
Hybrid path-lifting algorithm and Equivalence of Stability results for MRP-based control strategies

The modified Rodrigues parameters (MRP) consist of two numerically different triplets that, by switching between them, yield a minimal globally non-singular attitude description with advantageous properties. The MRP space results from the Alexandroff compactification of the three-dimensional Euclidean space and is a double cover of $\mathrm{SO(3)}$. By capitalizing on instrumental properties of the covering map, this paper proposes a novel hybrid dynamic path-lifting mechanism to unambiguously and robustly extract the MRP from the attitude space. This hybrid solution allows applying an MRP-based feedback controller to the attitude dynamics in the base space while preserving its asymptotic and exponential stability properties. Furthermore, by profiting from the distinct characteristics of the MRP, the resulting interconnection is impervious to the unwinding phenomenon. The design and validation of an MRP-based controller exemplify the application of the proposed algorithm alongside the novel results for equivalence of stability between spaces. The solution renders the attitude space tracking dynamics robustly globally exponentially stable, demonstrating the potential of this novel methodology.
Harmonic Power-Flow Study of Hybrid AC/DC Grids with Converter-Interfaced Distributed Energy Resources

As the share of Converter-Interfaced Distributed Energy Resources (CIDERs) in power distribution systems increases, hybrid AC/DC distribution systems are drawing more interest. Indeed, CIDERs usually rely on DC power and hybrid AC/DC grids with few Network-Interfacing Converters (NICs) are a promising solution to decrease installation costs and conversion losses compared to a pure AC grid. However, interactions between the AC and DC subsystem of a hybrid AC/DC grid can lead to undesirable amplification and propagation of harmonics. The authors of this paper have recently proposed a Harmonic PowerFlow (HPF) method that accurately represents the AC power flows including the coupling between different harmonics. The HPF framework is formulated through the mismatch equations of the nodal equations between the grid and the resource models and solved by a Newton Raphson algorithm. This paper extends the HPF method to model hybrid AC/DC grids interfaced via NICs, s.t. harmonics originating from, or propagating through, DC subsystems can be modelled. To this end, the model of the NICs and the resulting coupling between the AC and DC subsystems is included in the mismatch equations and the Jacobian matrix of the algorithm. The updated HPF method is applied to a typical hybrid AC/DC grid, and its accuracy is validated through detailed time-domain simulations with Simulink.
HyperSLICE: HyperBand optimized spiral for low‐latency interactive cardiac examination

Interactive cardiac MRI is used for fast scan planning and MR‐guided interventions. However, the requirement for real‐time acquisition and near‐real‐time visualization constrains the achievable spatio‐temporal resolution. This study aims to improve interactive imaging resolution through optimization of undersampled spiral sampling and leveraging of deep learning for low‐latency reconstruction (deep artifact suppression).