Miracle-dz/UniFAR-FTUs huggingface dataset:
Do interoception and attending to the upper limbs affect body ownership and body representation in the grasp illusion?

Passively grasping an unseen artificial finger induces ownership over this finger and an illusory coming together of one’s index fingers: a grasp illusion. Here we determine how interoceptive ability and attending to the upper limbs influence this illusion. Participants passively grasped an unseen artificial finger with their left index finger and thumb for 3 min while their right index finger, located 12 cm below, was lightly clamped. Experiment 1 (n = 30) investigated whether the strength of the grasp illusion (perceived index finger spacing and perceived ownership) is related to a person’s level of interoceptive accuracy (modified heartbeat counting task) and sensibility (Noticing subscale of the Multidimensional Assessment of Interoceptive Awareness). Experiment 2 (n = 30) investigated the effect of providing verbal or tactile cues to guide participants’ attention to their upper limbs. On their own, neither interoceptive accuracy and sensibility or verbal and tactile cueing had an effect on the grasp illusion. However, verbal cueing increased the strength of the grasp illusion in individuals with lower interoceptive ability. Across the observed range of interoceptive accuracy and sensibility, verbal cueing decreased perceived index spacing by 5.6 cm [1.91 to 9.38] (mean [95%CI]), and perceived ownership by ∼3 points on a 7-point Likert scale (slope -0.93 [-1.72 to -0.15]). Thus, attending to the upper limbs via verbal cues increases the strength of the grasp illusion in a way that is inversely proportional to a person’s level of interoceptive accuracy and sensibility.
Bayes factors for accelerated life testing models

In Bayesian accelerated life testing, the most used tool for model comparison is the deviance information criterion. An alternative and more formal approach is to use Bayes factors to compare models. However, Bayesian accelerated life testing models with more than one stressor often have mathematically intractable posterior distributions and Markov chain Monte Carlo methods are employed to obtain posterior samples to base inference on. The computation of the marginal likelihood is challenging when working with such complex models. In this paper, methods for approximating the marginal likelihood and the application thereof in the accelerated life testing paradigm are explored for dual-stress models.
Evaluating firefly extinction risk: Initial red list assessments for North America

Fireflies are a family of charismatic beetles known for their bioluminescent signals. Recent anecdotal reports suggest that firefly populations in North America may be in decline. However, prior to this work, no studies have undertaken a systematic compilation of geographic distribution, habitat specificity, and threats facing North American fireflies. To better understand their extinction risks, we conducted baseline assessments according to the categories and criteria of the International Union for Conservation of Nature (IUCN) Red List for 132 species from the United States and Canada (approximately 79% of described species in the region). We found at least 18 species (14%) are threatened with extinction (e.g. categorized as Critically Endangered, Endangered, or Vulnerable) due to various pressures, including habitat loss, light pollution, and climate change (sea level rise and drought). In addition, more than half of the species (53%) could not be evaluated against the assessment criteria due to insufficient data, highlighting the need for further study. Future research and conservation efforts should prioritize monitoring and protecting populations of at-risk species, preserving and restoring habitat, gathering data on population trends, and filling critical information gaps for data deficient species suspected to be at risk.
Near-optimal Quantum algorithms for multivariate mean estimation

We propose the first near-optimal quantum algorithm for estimating in Euclidean norm the mean of a vector-valued random variable with finite mean and covariance. Our result aims at extending the theory of multivariate sub-Gaussian estimators [Lugosi and Mendelson, 2019] to the quantum setting. Unlike classically, where any univariate estimator can be turned into a multivariate estimator with at most a logarithmic overhead in the dimension, no similar result can be proved in the quantum setting. Indeed, [Heinrich, 2004] ruled out the existence of a quantum advantage for the mean estimation problem when the sample complexity is smaller than the dimension. Our main result is to show that, outside this low-precision regime, there does exist a quantum estimator that outperforms any classical estimator. More precisely, we prove that the approximation error can be decreased by a factor of about the square root of the ratio between the dimension and the sample complexity. Our approach is substantially more involved than in the univariate setting, where most quantum estimators rely only on phase estimation. We exploit a variety of additional algorithmic techniques such as linear amplitude amplification, the Bernstein-Vazirani algorithm, and quantum singular value transformation. Our analysis is also deeply rooted in proving concentration inequalities for multivariate truncated statistics. Finally, we describe several applications of our algorithms, notably in measuring the expectation values of commuting observables and in the field of machine learning.
Spanners in randomly weighted graphs: Euclidean case

Given a connected graph G=(V,E) $G=(V,E)$ and a length function ℓ:E→R $\ell :E\to {\mathbb{R}}$ we let dv,w ${d}_{v,w}$ denote the shortest distance between vertex v $v$ and vertex w $w$ . A t $t$ ‐spanner is a subset E′⊆E $E^{\prime} \subseteq E$ such that if dv,w′ ${d}_{v,w}^{^{\prime} }$ denotes shortest distances in the subgraph G′=(V,E′) $G^{\prime} =(V,E^{\prime} )$ then dv,w′≤tdv,w ${d}_{v,w}^{^{\prime} }\le t{d}_{v,w}$ for all v,w∈V $v,w\in V$ . We study the size of spanners in the following scenario: we consider a random embedding Xp ${{\mathscr{X}}}_{p}$ of Gn,p ${G}_{n,p}$ into the unit square with Euclidean edge lengths. For ϵ>0 $\epsilon \gt 0$ constant, we prove the existence w.h. p. of (1+ϵ) $(1+\epsilon )$ ‐spanners for Xp ${{\mathscr{X}}}_{p}$ that have Oϵ(n) ${O}_{\epsilon }(n)$ edges. These spanners can be constructed in Oϵ(n2logn) ${O}_{\epsilon }({n}^{2}\mathrm{log}n)$ time. (We will use Oϵ ${O}_{\epsilon }$ to indicate that the hidden constant depends on ε $\varepsilon $ ). There are constraints on p $p$ preventing it going to zero too quickly.
Prevalence and Risk Factors for Resistant Hypertension: Cross-Sectional Study From a Tertiary Care Referral Hospital in South India

Background Around 10% patients with hypertension have resistant hypertension (RH). Older age, Black race, obesity, diabetes mellitus (DM) and chronic kidney disease (CKD) are the common risk factors for RH. The present study was done to find out the prevalence and factors associated with RH. Methods This cross-sectional study was done between December 2018 and February 2020. Adult patients registered with the hypertension clinic and on care for more than three months were included in the study. History was noted and blood pressure (BP) was measured using standard precautions. The patients were divided into two groups - resistant and non-resistant hypertension. Chi-square test was done to check the significance of the differences between the two groups. Binary logistic regression was done for the risk factors with a p-value < 0.2 in the Chi-square test. Results A total of 275 patients were included. The mean age was 56 ± 10 years and 61% were females. The mean duration of hypertension was 7 ± 5 years; 77% of patients were overweight or obese. A family history of hypertension was present in 30% and 18% had diabetes mellitus. History suggestive of secondary hypertension was present in 13%. BP was controlled (<130/80 mm of hg) in 145 (53%), uncontrolled in 130 (47%) and resistant hypertension was seen in 31 [(11%) 95% CI 8-16%] patients. Duration of hypertension, obesity, and elevated fasting blood sugar were significantly associated with RH. Conclusions RH was found in 11% of hypertensive patients. Longer duration of hypertension, obesity, and higher fasting blood glucose were associated with RH.
Theoretical linear convergence of deep unfolding network for block-sparse signal recovery

In this paper, we consider the recovery of the high-dimensional block-sparse signal from a compressed set of measurements, where the nonzero coefficients of the recovered signal occur in several blocks. Adopting the idea of deep unfolding, we explore the block-sparse structure and put forward a block-sparse reconstruction network named Ada-BlockLISTA, which performs gradient descent on every single block followed by a block-wise shrinkage. Furthermore, we prove the linear convergence rate of our proposed network, which also theoretically guarantees exact recovery for a potentially higher sparsity level based on the underlying block structure. Numerical results indicate that Ada-BlockLISTA yields better signal recovery performance than existing algorithms, which ignore the additional block structure in the signal model.
Optimal Decremental Connectivity in Non-Sparse Graphs

We present a dynamic algorithm for maintaining the connected and 2-edge-connected components in an undirected graph subject to edge deletions. The algorithm is Monte-Carlo randomized and processes any sequence of edge deletions in $O(m + n \operatorname{polylog} n)$ total time. Interspersed with the deletions, it can answer queries to whether any two given vertices currently belong to the same (2-edge-)connected component in constant time. Our result is based on a general Monte-Carlo randomized reduction from decremental $c$-edge-connectivity to a variant of fully-dynamic $c$-edge-connectivity on a sparse graph. While being Monte-Carlo, our reduction supports a certain final self-check that can be used in Las Vegas algorithms for static problems such as Unique Perfect Matching. For non-sparse graphs with $\Omega(n \operatorname{polylog} n)$ edges, our connectivity and $2$-edge-connectivity algorithms handle all deletions in optimal linear total time, using existing algorithms for the respective fully-dynamic problems. This improves upon an $O(m \log (n^2 / m) + n \operatorname{polylog} n)$-time algorithm of Thorup [J.Alg. 1999], which runs in linear time only for graphs with $\Omega(n^2)$ edges. Our constant amortized cost for edge deletions in decremental connectivity in non-sparse graphs should be contrasted with an $\Omega(\log n/\log\log n)$ worst-case time lower bound in the decremental setting [Alstrup, Thore Husfeldt, FOCS'98] as well as an $\Omega(\log n)$ amortized time lower-bound in the fully-dynamic setting [Patrascu and Demaine STOC'04].
The pregnancy experiences of amputee women: a qualitative exploration of online posts

ABSTRACT Background This paper seeks to explore the pregnancy experiences of amputee women. Relatively little is known about pregnancy for amputees, but the wider literature on disability and pregnancy shows that the disabled pregnant body is often viewed as ‘high risk’. The majority of amputations (90%) involve lower limb amputations; whilst our analytical interest was not limited to lower limb amputations, the sources we explored reflected the prevalence of lower limb amputees. Methods Using online blogs and first-person accounts from 6 different sources (3 authors of blogs, 2 authors of websites and 1 author of an information page on a general amputation website), we thematically analyse amputee women’s own narratives around their experiences of pregnancy Findings Four key themes were identified, which we describe as: The lack of information for the pregnant amputee; Managing risks; The embodied experience of suffering; and ‘It’s all worth it in the end’. Women amputees faced trade-offs between risks to self and the unborn child and reported physical discomforts due to pregnancy-related bodily changes impacting their prosthesis or residual limb. The challenges of pregnancy as an amputee were, however, all seen to be outweighed by the safe arrival of a healthy baby. Conclusions The lack of information on pregnancy for amputees may affect women’s resilience to the adaptation challenges they face. Research should further explore the experiences of amputee mothers to ensure adequate information is available to them and their caring professionals, and that the needs of women with more challenging experiences are also addressed.
Introduction to the Special LDLensRad Focus Issue

Recent epidemiological and experimental animal data, as well as reanalyses of data previously accumulated, indicate that the lens of the eye is more radiosensitive than was previously thought. This has resulted in a reduction of the occupational lens dose limit within the European Union countries, Japan and elsewhere. This Commentary introduces the work done by the LDLensRad Consortium contained within this Focus Issue, towards advancement of understanding of the mechanisms of low dose radiation cataract.
Numerical study of fluidic injection for noise reduction

We investigate the ability of fluidic injection to control the noise of a model gas-turbine nozzle exhaust. The work is conducted using a hybrid method of LES for the flow field and Lighthill's acoustic analogy for the acoustic field. The impact of injection on the flow development in terms of coherent structures and characteristic frequencies is evaluated. The main results show that a low momentum ratio fluidic injection can trigger a vortex pairing mechanism, resulting in energy transfer to a sub-harmonic of the jet preferred shedding frequency. This changes the development and structure of the flow. The corresponding acoustic sources are shown to be significantly damped. For large directivity angles, the spectral distribution and reduction of Sound Pressure Level is in good agreement with experiments.
Personalized Federated Learning through Local Memorization

Federated learning allows clients to collaboratively learn statistical models while keeping their data local. Federated learning was originally used to train a unique global model to be served to all clients, but this approach might be sub-optimal when clients' local data distributions are heterogeneous. In order to tackle this limitation, recent personalized federated learning methods train a separate model for each client while still leveraging the knowledge available at other clients. In this work, we exploit the ability of deep neural networks to extract high quality vectorial representations (embeddings) from non-tabular data, e.g., images and text, to propose a personalization mechanism based on local memorization. Personalization is obtained by interpolating a collectively trained global model with a local $k$-nearest neighbors (kNN) model based on the shared representation provided by the global model. We provide generalization bounds for the proposed approach in the case of binary classification, and we show on a suite of federated datasets that this approach achieves significantly higher accuracy and fairness than state-of-the-art methods.
The Quantum Eraser Paradox

The Delayed-Choice Quantum Eraser experiment is commonly interpreted as implying that in quantum mechanics a choice made at one time can influence an earlier event. We here suggest an extension of the experiment that results in a paradox when interpreted using a local realist interpretation combined with backward causation ("retrocausality"). We argue that resolving the paradox requires giving up the idea that, in quantum mechanics, a choice can influence the past, and that it instead requires a violation of Statistical Independence without retrocausality. We speculate what the outcome of the experiment would be.
Diet-induced changes in titer support a discrete response of Wolbachia-associated plastic recombination in Drosophila melanogaster

Abstract Plastic recombination in Drosophila melanogaster has been associated with a variety of extrinsic and intrinsic factors such as temperature, starvation, and parasite infection. The bacterial endosymbiont Wolbachia pipientis has also been associated with plastic recombination in D. melanogaster. Wolbachia infection is pervasive in arthropods and this infection induces a variety of phenotypes in its hosts, the strength of which can depend on bacterial titer. Here, we test the hypothesis that the magnitude of Wolbachia-associated plastic recombination in D. melanogaster depends on titer. To manipulate titer, we raised Wolbachia-infected and uninfected flies on diets that have previously been shown to increase or decrease Wolbachia titer relative to controls. We measured recombination in treated and control individuals using a standard backcrossing scheme with two X-linked visible markers. Our results recapitulate previous findings that Wolbachia infection is associated with increased recombination rate across the yellow-vermillion interval of the X chromosome. Our data show no significant effect of diet or diet by Wolbachia interactions on recombination, suggesting that diet-induced changes in Wolbachia titer have no effect on the magnitude of plastic recombination. These findings represent one of the first steps toward investigating Wolbachia-associated plastic recombination and demonstrate that the phenotype is a discrete response rather than a continuous one.
Coordination of Complex Product Development Processes

Product development processes of complex products are complex themselves and particularly difficult to plan and manage effectively. Although many organizations manage their product development processes by monitoring the status of documents that are created as deliverables, in fact the progress of the process is in large part based on the actual information flow which is required to develop the product and produce the documents. A vital element in making product development processes work well is the correct understanding of how information flows and how to facilitate its development. In this paper we describe an executable stochastic model of the product development process that incorporates the salient features of the interplay between the information development, exchange and progress of the technical work. Experiments with the model provide insight into the mechanisms that drive these complex processes.Copyright © 2010 by ASME
Point target detection using super-resolution reconstruction

Surveillance applications are primarily concerned with detection of targets. In electro-optical surveillance systems, missiles or other weapons coming towards you are observed as moving points. Typically, such moving targets need to be detected in a very short time. One of the problems is that the targets will have a low signal-to-noise ratio with respect to the background, and that the background can be severely cluttered like in an air-to-ground scenario. The first step in detection of point targets is to suppress the background. The novelty of this work is that a super-resolution reconstruction algorithm is used in the background suppression step. It is well-known that super-resolution reconstruction reduces the aliasing in the image. This anti-aliasing is used to model the specific aliasing contribution in the camera image, which results in a better estimate of the clutter in the background. Using super-resolution reconstruction also reduces the temporal noise, thus providing a better signal-to-noise ratio than the camera images. After the background suppression step common detection algorithms such as thresholding or track-before-detect can be used. Experimental results are given which show that the use of super-resolution reconstruction significantly increases the sensitivity of the point target detection. The detection of the point targets is increased by the noise reduction property of the super-resolution reconstruction algorithm. The background suppression is improved by the anti-aliasing.
Forecasting mergers and acquisitions failure based on partial-sigmoid neural network and feature selection

Traditional forecasting methods in mergers and acquisitions (M&A) data have two limitations that significantly reduce forecasting accuracy: (1) the imbalance of data, that is, the failure cases of M&A are far fewer than the successful cases (82%/18% of our sample), and (2) both the bidder and the target of the merger have numerous descriptive features, making it difficult to choose which ones to forecast. This study proposes a neural network using partial-sigmoid (i.e., partial-sigmoid neural network [PSNN]) as the activation function of the output layer and compares three feature selection methods, namely, chi-square (chi2) test, information gain and gradient boosting decision tree (GBDT). Experimental results prove that our PSNN (improved up to 0.37 precision, 0.49 recall, 0.41 G-Mean and 0.23 F1-measure) and feature selection (improved 1.83%-13.16% accuracy) method can effectively improve the adverse effects of the defects of the above two merger data on forecasting. Scholars who studied the forecast of merger failure have overlooked three important features: assets of the previous year, market value and capital expenditure. The chi2 test feature selection method is the best among the three feature selection methods.