Miracle-dz/UniFAR-FTUs huggingface dataset:
Cochlear implant electrode array tip-foldover detection by electrode voltage telemetry

Objectives With the introduction of more flexible and thinner electrodes, such as Cochlear’s Slim Modiolar Electrode, there is a higher risk of electrode insertion problems, in particular the tip foldover. Timely intraoperative detection of the problem would allow for direct intraoperative correction. This paper describes a non-radiological method for intraoperative tip foldover detection that is applicable in all surgical centers and can quickly deliver accurate results. Methods Postoperative radiographs of 118 CI-recipients implanted with Nucleus devices were retrospectively analyzed on the presence of a tip foldover. Electrode Voltage Telemetry (EVT), also called Electric Field Imaging, was performed by means of Cochlear’s EVT software tool, which is now integrated into Custom Sound–EP as the Trans-Impedance-Matrix measurement option. Tip foldover detection was automated by using the linear Hough transform for extracting straight-line patterns in the Trans-Impedance Matrix’s heatmap. Results The six cases of electrode tip foldover were accurately identified by the EVT measurements, including two cases with folding location very close to the electrode tip (contact 20). Conclusion Electrode Voltage Telemetry measures the Trans-Impedance Matrix, which can accurately detect tip foldovers of the cochlear implant electrodes within 1 min. This method can be reliably applied in all patients with normal cochlear anatomy and is able to intraoperatively detect foldovers localized even very close to the electrode tip. Application of the linear Hough transform allows for automatic detection of electrode tip foldovers that shows excellent agreement with visual evaluation of the radiological images and the transimpedance matrix’s heatmap.
Academic resilience from school entry to third grade: Child, parenting, and school factors associated with closing competency gaps

There is substantial evidence confirming that children who begin school with strong developmental skills tend to maintain positive academic trajectories across the elementary school years. Much less is known about children who begin school with poorer developmental competencies yet go on to achieve academically on par with, or above, their initially more competent peers, demonstrating academic resilience. This study used a large population dataset, the Longitudinal Study of Australian Children (n = 2,118) to identify children who demonstrated academic resilience between school entry and third grade, and the child, parenting, and school characteristics associated with this resilience. Findings show that two in five children who were initially identified as academically vulnerable on a school entry measure of language and cognitive skills were classified as academically resilient by Grade 3. In multivariate analysis, higher attentional regulation and receptive vocabulary skills were key factors associated with academic resilience in reading and numeracy, along with paternal consistency (for reading resilience) and fewer sleep problems (for numeracy resilience). Bivariate relations (ANOVAs) showed that resilient children, when compared to children who remained vulnerable, also showed fewer peer problems, fewer behavioral sleep problems, higher levels of parenting consistency and lower levels of parenting anger by mothers and fathers, higher levels of parental engagement in children’s school, and higher levels of teacher self-efficacy. Supporting resilient pathways for children who are identified as vulnerable at school entry should include a particular focus on vocabulary development and attentional regulation, along with social skills and peer relationships, school-based parental engagement, and positive parenting support.
Risk of Non-cutaneous Cancers in Individuals with Basal Cell Carcinoma: A Population-based Cohort Study

Evidence of the association between a personal history of basal cell carcinoma and the risk of non-cutaneous malignancies is conflicting. The aim of this study was to retrospectively clarify the risk of non-cutaneous cancers in individuals with basal cell carcinoma using nationwide Finnish registry data for 96,304 patients and 394,503 randomly selected population controls. In this study, individuals with basal cell carcinoma have an increased risk of other cancers (odds ratio (OR) 1.38; 95% confidence interval (95% CI) 1.36–1.40). The risk was most prominent for lip cancer (OR 5.29; 95% CI 4.50–6.21), mycosis fungoides (OR 3.13; 95% CI 2.31–4.23) and soft tissue cancers (OR 2.77; 95% CI 2.43–3.16). In age-adjusted model, men had higher risk of cancers overall compared with women (p < 0.05). In conclusion, the study found increased overall cancer risk among patients with basal cell carcinoma compared with randomly selected population controls.
Reduced muscle strength in patients with long-COVID-19 syndrome is mediated by limb muscle mass

Understanding the impact of COVID-19 on muscle strength may help to elucidate the organ systems that contribute to acute and chronic COVID-19 sequelae. We questioned whether patients with postdischarge symptoms after COVID-19 had compromised muscle strength compared with a control group, and if this potential relationship was mediated by the lower appendicular lean mass index (ALMI). A total of 99 patients with long-COVID-19 and 97 control participants were screened. Maximal grip strength was assessed with a TKK 5101 digital dynamometer, and leg extension 1RM was measured using EGYM Smart Strength machines. Body composition (fat mass percentage, lean mass, visceral fat and appendicular lean mass index) was determined using a whole-body dual-energy X-ray densitometer. Results showed that grip strength and leg extension strength were significantly higher in controls than in COVID-19 survivors (mean [SD], 32.82 [10.01] vs. 26.94 [10.33] kg; difference, 5.87 kg; P < 0.001) and (mean [SD], 93.98 [33.73] vs. 71.59 [33.70] kg; difference, 22.38 kg; P < 0.001), respectively). The relationship between long-COVID syndrome and grip/leg strength levels was partly mediated by ALMI, which explained 52% of the association for grip strength and 39% for leg extension. Our findings provide novel insights into the mechanisms underlying the relationship between long-COVID syndrome and grip/leg strength levels, supporting the negative effects of long-COVID syndrome on muscle function.
The New Mitogenome of Erpornis zantholeuca (Aves: Passeriformes): Sequence, Structure, and Phylogenetic Analyses

White-bellied Erpornis (Erpornis zantholeuca) is a group of birds in the order Passeriformes, but its taxonomic status remains controversial. To understand the phylogenetic position of E. zantholeuca and phylogenetic relations within this family, we sequenced the complete mitochondrial genome of E. zantholeuca, which was 16,902 bp in length, containing 13 protein-coding genes (PCGs), 22 transfer RNA (tRNA) genes, 2 ribosomal RNA (rRNA) genes, and a control region. The nucleotide composition of the whole genome was 30.10% A, 30.48% C, 15.14% G, and 24.28% T and showed an elevated AT content (54.38%). All genes were encoded on the H-strand, with the exceptions of 8 tRNAs (trnQ, trnA, trnN, trnC, trnY, trnS2(UCN), trnP, trnE) and 1 PCG (Mt-ND6). Most PCGs used standard ATN as start codons, and TAN as stop codons. All tRNAs were predicted to form the typical cloverleaf secondary structures. The gene order of E. zantholeuca was consistent with that of Gallus gallus, which was considered to be a plesiomorphic or typical avian gene order. Phylogenetic relationships based on bayesian inference and maximum likelihood methods showed that E. zantholeuca was well supported as the sister group of (Vireo altiloquus + Vireo olivaceus). In addition, Pteruthius melanotis was sister to the other members of Vireonidae.
Effect of Dyadic Haptic Collaboration on Ankle Motor Learning and Task Performance

Optimizing skill acquisition during novel motor tasks and regaining lost motor functions have been the interest of many researchers over the past few decades. One approach shown to accelerate motor learning involves haptically coupling two individuals through robotic interfaces. Studies have shown that an individual’s solo performance during upper-limb tracking tasks may improve after haptically-coupled training with a partner. In this study, our goal was to investigate whether these findings can be translated to lower-limb motor tasks, more specifically, during an ankle position tracking task. Using one-degree-of-freedom ankle movements, pairs of participants (i.e., dyads) tracked target trajectories independently. Participants alternated between tracking trials with and without haptic coupling, achieved by rendering a virtual spring between two ankle rehabilitation robots. In our analysis, we compared changes in task performance across trials while training with and without haptic coupling. The tracking performance of both individuals (i.e., dyadic task performance) improved during haptic coupling, which was likely due to averaging of random errors of the dyadic pair during tracking. However, we found that dyadic haptic coupling did not lead to faster individual learning for the tracking task. These results suggest that haptic coupling between unimpaired individuals may not be an effective method of training ankle movements during a simple, one-degree-of-freedom task.
Pulse shape analysis of scintillation signals from pure and xenon-doped liquid argon for radioactive background identification

The response of a 19 kg active mass liquid argon scintillation detector to gamma, alpha and neutron radiation and its characteristic scintillation photon time distribution has been studied in the framework of the Gerda double beta decay research and development program. The achieved photo-electron yield of 1.24 pe/keV was stable during the two years of operation. The detector exhibits excellent energy resolution of 8.7/17.2 keV (σ) for gamma energies of 60/239 keV. Radon was loaded into the liquid argon to study alpha energy quenching and decay time correlation of its progenies. A robust pulse shape analysis method was used to identify and discriminate amongst the different radiation types. 60 keV gamma signals could be discriminated against neutron recoils of the same visible energy with a miss-identification probability of < 5 10−4 limited by statistics and ambient backgrounds. Xenon doping of liquid argon increased the photo-electron yield and improved the spectroscopic performance of the detector leading to an energy resolution of 7.2/15.4 keV (σ) for 60/239 keV. The discrimination power improved slightly with the addition of xenon up to concentrations of 300 ppm. Applications for background identification and discrimination in double beta decay search with 76Ge crystals, as well as for Dark Matter search with liquid argon are discussed.
On the origin of photoluminescence in indium oxide octahedron structures

A sixfold decrease in photoluminescence signal intensity at 590nm with increase in deposition time from 3to12h has been observed in single crystalline indium oxide octahedron structures grown by vapor-phase evaporation method. Electron paramagnetic resonance and energy dispersive x-ray analysis confirm that the concentration of oxygen vacancies increases with deposition time. These results are contrary to the previous reports where oxygen vacancies were shown to be responsible for photoluminescence in indium oxide structures. Our results indicate that indium interstitials and their associated complex defects other than oxygen vacancies are responsible for the photoluminescence in In2O3 microstructures.
3D Neural Field Generation Using Triplane Diffusion

Diffusion models have emerged as the state-of-the-art for image generation, among other tasks. Here, we present an efficient diffusion-based model for 3D-aware generation of neural fields. Our approach pre-processes training data, such as ShapeNet meshes, by converting them to continuous occupancy fields and factoring them into a set of axis-aligned triplane feature representations. Thus, our 3D training scenes are all represented by 2D feature planes, and we can directly train existing 2D diffusion models on these representations to generate 3D neural fields with high quality and diversity, outperforming alternative approaches to 3D-aware generation. Our approach requires essential modifications to existing triplane factorization pipelines to make the resulting features easy to learn for the diffusion model. We demonstrate state-of-the-art results on 3D generation on several object classes from ShapeNet.
Effective Emission Heights of Various OH Lines From X‐Shooter and SABER Observations of a Passing Quasi‐2‐Day Wave

Chemiluminescent radiation of the vibrationally and rotationally excited hydroxyl (OH) radical, which dominates the nighttime near‐infrared emission of the Earth's atmosphere in wide wavelength regions, is an important tracer of the chemical and dynamical state of the mesopause region between 80 and 100 km. As radiative lifetimes and rate coefficients for collision‐related transitions depend on the OH energy level, line‐dependent emission profiles are expected. However, except for some height differences for whole bands mostly revealed by satellite‐based measurements, there is a lack of data for individual lines. We succeeded in deriving effective emission heights for 298 OH lines thanks to the joint observation of a strong quasi‐2‐day wave (Q2DW) in eight nights in 2017 with the medium‐resolution spectrograph X‐shooter at the Very Large Telescope at Cerro Paranal in Chile and the limb‐sounding SABER radiometer on the TIMED satellite. Our fitting procedure revealed the most convincing results for a single wave with a period of about 44 hr and a vertical wavelength of about 32 km. The line‐dependent as well as altitude‐resolved phases of the Q2DW then resulted in effective heights which differ by up to 8 km and tend to increase with increasing vibrational and rotation excitation. The measured dependence of emission heights and wave amplitudes (which were strongest after midnight) on the line parameters implies the presence of a cold thermalized and a hot non‐thermalized population for each vibrational level.
An MPI-based parallel genetic algorithm for multiple geographical feature label placement based on the hybrid of fixed-sliding models

Multiple geographical feature label placement (MGFLP) has been a fundamental problem in geographic information visualization for decades. The nature of label positioning is proven an NP-hard problem, where the complexity of such a problem is directly influenced by the volume of input datasets. Advances in computer technology and robust approaches have addressed the problem of labeling. However, what is less considered in recent studies is the computational complexity of MGFLP, which significantly decreases the adoptability of those recently introduced approaches. In this study, an MPI parallel genetic algorithm is proposed for MGFLP based on a hybrid of fixed position model and sliding model to label fixed-types of geographical features. To evaluate the quality of label placement, a quality function is defined based on four quality metrics, label-feature conflict, label-label conflict, label ambiguity factor, and label position priority for points and polygons. Experimental results reveal that the proposed algorithm significantly reduced the overall score of the quality function and the computational time of label placement compared to the previous studies. The algorithm achieves a result in less than one minute with 6 label-feature conflicts, while Parallel-MS (Lessani et al., 2021) obtains the result in more than 20 minutes with 12 label-feature conflicts for the same dataset.
A Unifying Theory of Distance from Calibration

We study the fundamental question of how to define and measure the distance from calibration for probabilistic predictors. While the notion of perfect calibration is well-understood, there is no consensus on how to quantify the distance from perfect calibration. Numerous calibration measures have been proposed in the literature, but it is unclear how they compare to each other, and many popular measures such as Expected Calibration Error (ECE) fail to satisfy basic properties like continuity. We present a rigorous framework for analyzing calibration measures, inspired by the literature on property testing. We propose a ground-truth notion of distance from calibration: the ℓ1 distance to the nearest perfectly calibrated predictor. We define a consistent calibration measure as one that is polynomially related to this distance. Applying our framework, we identify three calibration measures that are consistent and can be estimated efficiently: smooth calibration, interval calibration, and Laplace kernel calibration. The former two give quadratic approximations to the ground truth distance, which we show is information-theoretically optimal in a natural model for measuring calibration which we term the prediction-only access model. Our work thus establishes fundamental lower and upper bounds on measuring the distance to calibration, and also provides theoretical justification for preferring certain metrics (like Laplace kernel calibration) in practice.
Verification of the generalized reduced-order particle-in-cell scheme in a radial–azimuthal E × B plasma configuration

In this article, we present an in-depth verification of the generalized electrostatic reduced-order particle-in-cell (PIC) scheme in a cross electric and magnetic field configuration representative of a radial–azimuthal section of a Hall thruster. The setup of the simulations follows a well-established benchmark case. The main purpose of this effort is to demonstrate that our novel PIC scheme can reliably resolve the complex two-dimensional dynamics and interactions of the plasma instabilities in the radial–azimuthal coordinates of a Hall thruster at a fraction of the computational cost compared to full-2D PIC codes. To this end, we first present the benchmarking of our newly developed full-2D PIC code. Next, we provide an overview of the reduced-order PIC scheme and the resulting “quasi-2D” code, specifying that the degree of order reduction in the quasi-2D PIC is defined in terms of the number of “regions” along the simulation’s directions used to divide the computational domain. We compare the predictions of the quasi-2D simulation in various approximation degrees of the 2D problem against our full-2D simulation results. We show that, with an increase in the number of regions in the Q2D simulations, the quasi-2D results converge to the 2D ones. Nonetheless, we also highlight that a quasi-2D simulation that provides a factor of 5 reduction in the computational cost resolves the underlying physical processes in an almost indistinguishable manner with respect to the full-2D simulation and incurs an L2-norm error of only about 2% in the ion number density and below 1% in the electron temperature.
On Regret-optimal Cooperative Nonstochastic Multi-armed Bandits

We consider the nonstochastic multi-agent multi-armed bandit problem with agents collaborating via a communication network with delays. We show a lower bound for individual regret of all agents. We show that with suitable regularizers and communication protocols, a collaborative multi-agent \emph{follow-the-regularized-leader} (FTRL) algorithm has an individual regret upper bound that matches the lower bound up to a constant factor when the number of arms is large enough relative to degrees of agents in the communication graph. We also show that an FTRL algorithm with a suitable regularizer is regret optimal with respect to the scaling with the edge-delay parameter. We present numerical experiments validating our theoretical results and demonstrate cases when our algorithms outperform previously proposed algorithms.
Operationalizing Legislative Bodies: A Methodological and Empirical Perspective

This manuscript extensively reviews applications, extensions, and models derived from the Bayesian ideal point estimator. We primarily focus our attention on studies conducted in the United States as well as Latin America. First, we provide a detailed description of the Bayesian ideal point estimator. Next, we propose a new taxonomy to synthesize and frame technical developments and applications associated with the estimator in the context of North American and Latin American governing bodies. The literature available in Latin America allows us to conclude that few legislatures in the region have been analyzed using the methodology under discussion. Also, we highlight those parliaments of Latin America embedded in democratic presidential systems as novel scenarios for operationalizing the electoral behavior of legislative bodies through nominal voting data. Our findings show some alternatives for future research. Finally, to fix ideas and illustrate the capabilities of the Bayesian ideal point estimator, we present an application involving the Colombian House of Representatives 2010-2014.
Behavior of Gordian graphs at infinity

The present paper refers to the knot theory and is devoted to the study of global properties of Gordian graphs of various local moves. In 2005, Gambaudo and Ghys raised the question of the behavior at infinity of the crossing change Gordian graph. They proposed studying its"ends", that is, unbounded connected components of complements of bounded subsets. We provide a complete description of the behavior at infinity for local moves from three well-known infinite families, namely, rational moves, $C(n)$-moves, and $H(n)$-moves (note that each of the first two families contains the crossing change). Also, in 2005, March\'e gave a different perspective on the behavior of Gordian graphs at infinity, proposing to consider complements of finite subsets. We describe the behavior at infinity in this sense for all local moves with the infinite neighborhood of the unknot in the corresponding Gordian graph.
Average Path Length: Sparsification of Nonlinearties Creates Surprisingly Shallow Networks

We perform an empirical study of the behaviour of deep networks when pushing its activation functions to become fully linear in some of its feature channels through a sparsity prior on the overall number of nonlinear units in the network. To measure the ”depth” of the resulting partially linearized network, we compute the average number of active nonlinearities encountered along a path in the network graph. In experiments on CNNs with sparsiﬁed PReLUs on typical image classiﬁcation tasks, we make several observations: Under sparsity pressure, the remaining nonlinear units organize into distinct structures, forming core-networks of near constant effective depth and width, which in turn depend on task difﬁculty. We consistently observe a slow decay of performance with depth until the onset of a rapid collapse in accuracy, allowing for surprisingly shallow networks at moderate losses in accuracy that outperform base-line networks of similar depth, even after increasing width to a comparable number of parameters. In terms of training, we observe a nonlinear advantage: Reducing nonlinearity after training leads to a better performance than before, in line with previous ﬁndings in linearized training, but with a gap depending on task difﬁculty that vanishes for easy problems.
Subharmonic transitions and group formation in a wind wave field

Laboratory experiments in which waves from a mechanical wave generator were injected at the upwind end of a fetch-limited wave field. The injected wave groups appear to grow at the expense of the ambient wind wave field, and the wind profiles in the boundary layer show large fluctuations when waves are injected. The effects observed appear to demonstrate the sensitivity of nonlinear modulational dynamics of surface waves to weak disturbances.