Miracle-dz/UniFAR-FTUs huggingface dataset:
Time trade-off study to establish utility decrements in individuals with a spinal cord injury who perform intermittent catheterization

Abstract Aims To generate utility decrements for three attributes associated with catheterization for individuals with a spinal cord injury (SCI): the process of catheterization, the physical impact of urinary tract infections (UTIs) and worry associated with hospitalization. Materials and methods Health state vignettes comprising various levels of the three attributes were developed. Two cohorts of respondents, corresponding to people with SCIs and a sample broadly representative of the UK population, were presented with nine vignettes (three vignettes for the mild, moderate and severe health states in addition to a random set of six vignettes). It was assumed no or a nominal decrement was associated with the mild health state. Utility decrements were derived from analysing the data obtained from the online time trade-off (TTO). A proportion of the SCI cohort (n = 57) also completed the EQ-5D-5L questionnaire. Results Utility decrements were generated using statistical models for the general population (n = 358), the SCI population (n = 48) and the two populations combined (merged model, n = 406). Results from the two cohorts showed minimal differences. For the merged model, SCI status was not statistically significant. All interaction terms, excluding SCI and the severe level of the physical attribute, were not statistically significant. Compared to the mild level, the greatest utility decrement calculated was the severe level of the emotional (worry) attribute (0.09, p < .001) for the SCI population. A significant decrement of 0.02 (p < .001) was calculated for the moderate level of the emotional attribute for all models. The mean utility score for those with SCI having completed the EQ-5D-5L was 0.371. Limitations Modest sample size of respondents from the SCI population (n = 48). Conclusions Worry associated with hospitalization had the greatest impact on patients’ health-related quality of life (HRQoL). The catheterization process, such as the lubrication and repositioning of the catheter, also impacted on patients’ HRQoL.
Surveillance of avian malaria and related haemoparasites in common terns (Sterna hirundo) on the Atlantic coast of South America

Abstract Abstract Haemosporidia (Apicomplexa, Haemosporida) are protozoa that infect vertebrate blood cells and are transmitted by vectors. Among vertebrates, birds possess the greatest diversity of haemosporidia, historically placed in 3 genera: Haemoproteus, Leucocytozoon and Plasmodium, the causative agent of avian malaria. In South America, existing data on haemosporidia are spatially and temporally dispersed, so increased surveillance is needed to improve the determination and diagnosis of these parasites. During the non-breeding season in 2020 and 2021, 60 common terns (Sterna hirundo) were captured and bled as part of ongoing research on the population health of migratory birds on the Argentinian Atlantic coast. Blood samples and blood smears were obtained. Fifty-eight samples were screened for Plasmodium, Haemoproteus and Leucocytozoon, as well as for Babesia parasites by nested polymerase chain reaction and by microscopic examination of smears. Two positive samples for Plasmodium were found. The cytochrome b lineages detected in the present study are found for the first time, and are close to Plasmodium lineages found in other bird orders. The low prevalence (3.6%) of haemoparasites found in this research was similar to those reported for previous studies on seabirds, including Charadriiformes. Our findings provide new information about the distribution and prevalence of haemosporidian parasites from charadriiforms in the southernmost part of South America, which remains understudied.
Perceived Government Transparency and COVID-19 Conspiracy Beliefs: The Mediating Role of Conspiracy Mentality

As transparency is believed to be a key factor linked to trust in the government, we explore the link between the perceived lack of transparency and COVID-19 conspiracy beliefs. Two studies were conducted (N1 = 264 and N2 = 113) using both correlational (Study 1) and experimental (Study 2) designs. The results show a positive relationship between the perception of a lack of transparencies in the context of pandemic policies (Study 1), general lack of transparency in the decision-making process (Study 2), and belief in conspiracy theories about the emergence of the COVID-19 virus and vaccines’ related fake news. This effect was mediated by a general conspiracy mentality. That is, people who evaluated policies as non-transparent presented a higher conspiracy mentality, and this, in turn, was related to belief in specific COVID-19 conspiracy theories.
COVID-19 Vaccine Acceptance Among Korean American Immigrants in the United States: A Qualitative Study

Introduction: Immigrant populations in the United States are at increased risk of reduced COVID-19 vaccine acceptance. Currently, no qualitative research focuses on COVID-19 vaccine acceptance among Korean American immigrants (KAIs). This phenomenological study aims to uncover needs, beliefs, and practices that may affect COVID-19 vaccine acceptance within this immigrant group. Method: Twelve study participants answered 10 semi-structured interview questions. Inclusion criteria for participants are as follows: (a) above 18 years, (b) has immigrated from Korea, and (c) can understand and speak English. Interview data were analyzed using Colaizzi’s data analysis method. Results: Eight themes emerged from the study. Themes included apprehension and indifference, disruption of normalcy, patterns of acceptance, duty to protect, fear of contagion, perceived self-efficacy, relief and safety, and acceptance of new normal. Discussion: Findings from this study can inform health care professionals of cultural factors related to COVID-19 vaccine acceptance and health promotion behaviors among the KAIs.
Windings of Brownian motion and random walks in the plane

We are interested in the almost sure asymptotic behavior of the windings of planar Brownian motion. Both the usual lim sup and Chung's lim inf versions of the law of the iterated logarithm are presented for the so-called big and small winding angles. Our method is based on some very accurate estimates of the winding clock. The corresponding problem for a spherically symmetric random walk in R 2 is also studied. A strong approximation using the Brownian big winding process is established. Similar results are obtained.
MDST: multi-domain sparse-view CT reconstruction based on convolution and swin transformer

Objective.Sparse-view computed tomography (SVCT), which can reduce the radiation doses administered to patients and hasten data acquisition, has become an area of particular interest to researchers. Most existing deep learning-based image reconstruction methods are based on convolutional neural networks (CNNs). Due to the locality of convolution and continuous sampling operations, existing approaches cannot fully model global context feature dependencies, which makes the CNN-based approaches less efficient in modeling the computed tomography (CT) images with various structural information. Approach. To overcome the above challenges, this paper develops a novel multi-domain optimization network based on convolution and swin transformer (MDST). MDST uses swin transformer block as the main building block in both projection (residual) domain and image (residual) domain sub-networks, which models global and local features of the projections and reconstructed images. MDST consists of two modules for initial reconstruction and residual-assisted reconstruction, respectively. The sparse sinogram is first expanded in the initial reconstruction module with a projection domain sub-network. Then, the sparse-view artifacts are effectively suppressed by an image domain sub-network. Finally, the residual assisted reconstruction module to correct the inconsistency of the initial reconstruction, further preserving image details. Main results. Extensive experiments on CT lymph node datasets and real walnut datasets show that MDST can effectively alleviate the loss of fine details caused by information attenuation and improve the reconstruction quality of medical images. Significance. MDST network is robust and can effectively reconstruct images with different noise level projections. Different from the current prevalent CNN-based networks, MDST uses transformer as the main backbone, which proves the potential of transformer in SVCT reconstruction.
High efficiency Yb:YAG thin disk laser at room and cryogenic temperatures

Yb:YAG thin-disk laser performance at room and cryogenic (80K) temperatures is presented. The Yb:YAG gain media, which is Indium soldered to specialized CuW mounting caps, is cooled using either a pressurized R134A refrigerant system or by a two-phase liquid nitrogen spray boiler. At cryogenic temperatures spontaneous emission measurements reveal sharper transition lines and a decrease in the fluorescence lifetime. Lasing reflects that a true four-level laser. Interchangeable mounting caps allow the same Yb:YAG media to be switched between the two systems. This allows direct comparison of lasing, amplified spontaneous emission, and temperature performance at 15 °C and at -200 oC.
Contrastive Explanation and Causal Triangulation

Alan Garfinkel (1981) and Bas van Fraassen (1980), among others, have proposed a contrastive theory of explanation, according to which the proper form of an explanatory why-question is not simply "Why P? " but "Why P rather than Q? ". Dennis Temple (1988) has argued in this journal that the contrastive explanandum "P rather than Q" is equivalent to the conjunction, "P and not-Q". I show that the contrast is not equivalent to the conjunction, nor to other plausible noncontrastive candidates. I then consider David Lewis's (1986) proposal for the way contrasts determine an explanatory cause, which does not require recasting the contrastive explanandum. Lewis's proposal is found to be unacceptable, but it suggests an improvement that shows contrastive explanations to employ a mechanism of "causal triangulation", similar to Mill's method of difference.
Raman scattering from Ge nanostructures grown on Si substrates: Power and limitations

The analysis of Raman spectra obtained for different germanium nanostructures grown on silicon substrates is presented. Comparison of these spectra with a Raman spectrum of a silicon wafer reveals a one-to-one correspondence of features located around 229, 300, and 435 cm−1. It is argued that the peaks observed at these frequencies and often ascribed to Ge nanostructures are, in fact, coming from the Si substrate. The erroneous ascription of the peaks makes the corresponding conclusions incorrect.
Detecting long-range dependence for time-varying linear models

We consider the problem of testing for long-range dependence in time-varying coefficient regression models, where the covariates and errors are locally stationary, allowing complex temporal dynamics and heteroscedasticity. We develop KPSS, R/S, V/S, and K/S-type statistics based on the nonparametric residuals. Under the null hypothesis, the local alternatives as well as the fixed alternatives, we derive the limiting distributions of the test statistics. As the four types of test statistics could degenerate when the time-varying mean, variance, long-run variance of errors, covariates, and the intercept lie in certain hyperplanes, we show the bootstrap-assisted tests are consistent under both degenerate and non-degenerate scenarios. In particular, in the presence of covariates the exact local asymptotic power of the bootstrap-assisted tests can enjoy the same order as that of the classical KPSS test of long memory for strictly stationary series. The asymptotic theory is built on a new Gaussian approximation technique for locally stationary long-memory processes with short-memory covariates, which is of independent interest. The effectiveness of our tests is demonstrated by extensive simulation studies and real data analysis.
Fast algorithms for Vizing's theorem on bounded degree graphs

Vizing's theorem states that every graph $G$ of maximum degree $\Delta$ can be properly edge-colored using $\Delta + 1$ colors. The fastest currently known $(\Delta+1)$-edge-coloring algorithm for general graphs is due to Sinnamon and runs in time $O(m\sqrt{n})$, where $n :=|V(G)|$ and $m :=|E(G)|$. In this paper we investigate the case when $\Delta$ is constant, i.e., $\Delta = O(1)$. In this regime, the running time of Sinnamon's algorithm is $O(n^{3/2})$, which can be improved to $O(n \log n)$, as shown by Gabow, Nishizeki, Kariv, Leven, and Terada. Here we give an algorithm whose running time is only $O(n)$, which is obviously best possible. We also develop new algorithms for $(\Delta+1)$-edge-coloring in the $\mathsf{LOCAL}$ model of distributed computation. Namely, we design a deterministic $\mathsf{LOCAL}$ algorithm with running time $\tilde{O}(\log^5 n)$ and a randomized $\mathsf{LOCAL}$ algorithm with running time $O(\log ^2 n)$. All these results are new already for $\Delta = 4$. Although our focus is on the constant $\Delta$ regime, our results remain interesting for $\Delta$ up to $\log^{o(1)} n$. The key new ingredient in our algorithms is a novel application of the entropy compression method.
Asymptotic behaviour of large-scale solutions of Hitchin's equations in higher rank

Let $X$ be a compact Riemann surface. Let $(E,\theta)$ be a stable Higgs bundle of degree $0$ on $X$. Let $h_{\det(E)}$ denote a flat metric of the determinant bundle $\det(E)$. For any $t>0$, there exists a unique harmonic metric $h_t$ of $(E,\theta)$ such that $\det(h_t)=h_{\det(E)}$. We prove that if the Higgs bundle is induced by a line bundle on the normalization of the spectral curve, then the sequence $h_t$ is convergent to the naturally defined decoupled harmonic metric at the speed of the exponential order. We also obtain a uniform convergence for such a family of Higgs bundles.
Estimating the Oblateness of Dark Matter Halos Using Neutral Hydrogen Velocity Dispersion

We derive the oblateness parameter q of the dark matter halo of a sample of gas-rich, face-on disk galaxies. We have assumed that the halos are triaxial in shape but their axes in the disk plane (a and b) are equal, so that q = c/a measures the halo flattening. We have used the H i velocity dispersion, derived from the stacked H i emission lines and the disk surface density, determined from the H i flux distribution, to determine the disk potential and the halo shape at the R 25 and 1.5R 25 radii. We have applied our model to 20 nearby galaxies, of which six are large disk galaxies with M(stellar) > 1010, eight have moderate stellar masses, and six are low-surface-brightness dwarf galaxies. Our most important result is that gas-rich galaxies that have M(gas)/M(baryons) > 0.5 have oblate halos (q < 0.55), whereas stellar-dominated galaxies have a range of q values from 0.21 ± 0.07 in NGC4190 to 1.27 ± 0.61 in NGC5194. Our results also suggest a positive correlation between the stellar mass and the halo oblateness q, which indicates that galaxies with massive stellar disks have a higher probability of having halos that are spherical or slightly prolate, whereas low-mass galaxies have oblate halos (q < 0.55).
Symmetries of the CAR algebra

A *-automorphism of period two of the UHF C*-algebra of type 2' is constructed for which the fixed-point algebra is not approximately finite dimensional. The construction also yields a diagonal maximal commutative C*-subalgebra whose spectrum is not totally disconnected. Variations and consequences of the construction are discussed.
Infrared Ejecta and Cold Dust in the Young Supernova Remnant N132D

We present Spitzer, Wide-field Infrared Survey Explorer (WISE), and Herschel observations of the young supernova remnant (SNR) N132D in the Large Magellanic Cloud, including 3–40 μm Spitzer Infrared Spectrograph (IRS) mapping, 12 μm WISE and 70, 100, 160, 250, 350, and 500 μm Herschel images. The high-velocity lines of [Ne ii] at 12.8 μm, [Ne iii] at 15.5 μm, and [O iv] 26 μm reveal infrared ejecta concentrated in a central ring and coincide with the optical and X-ray ejecta. Herschel images reveal far-IR emission coinciding with the central ejecta, which suggests that the IR emission is freshly formed, cold dust in the SN ejecta. The infrared spectra are remarkably similar to those of another young SNR of 1E0102 with Ne and O lines. Shock modeling of the Ne ejecta emission suggests a gas temperature of 300–600 K and densities in the range 1000–2 × 104 cm−3 in the postshock photoionized region. The IR continuum from the ejecta shows an 18 μm peak dust feature. We performed spectral fitting to the IRS dust continuum and Herschel photometry. The dust mass associated with the central ejecta is 1.25 ± 0.65 M ⊙, while the 18 μm dust feature requires forsterite grains. The dust mass of the central ejecta region in N132D is higher than those of other young SNRs, which is likely associated with its higher progenitor mass. We discuss the dust productivity in the ejecta of N132D and infer its plausible implications for dust in the early universe.
Recent Advances of Deep Robotic Affordance Learning: A Reinforcement Learning Perspective

As a popular concept proposed in the field of psychology, affordance has been regarded as one of the important abilities that enable humans to understand and interact with the environment. Briefly, it captures the possibilities and effects of the actions of an agent applied to a specific object or, more generally, a part of the environment. This article provides a short review of the recent developments of deep robotic affordance learning (DRAL), which aims to develop data-driven methods that use the concept of affordance to aid in robotic tasks. We first classify these papers from a reinforcement learning (RL) perspective and draw connections between RL and affordances. The technical details of each category are discussed and their limitations are identified. We further summarize them and identify future challenges from the aspects of observations, actions, affordance representation, data-collection, and real-world deployment. A final remark is given at the end to propose a promising future direction of the RL-based affordance definition to include the predictions of arbitrary action consequences.
Longitudinal Assessment of Reference Quality on Wikipedia

Wikipedia plays a crucial role in the integrity of the Web. This work analyzes the reliability of this global encyclopedia through the lens of its references. We operationalize the notion of reference quality by defining reference need (RN), i.e., the percentage of sentences missing a citation, and reference risk (RR), i.e., the proportion of non-authoritative references. We release Citation Detective, a tool for automatically calculating the RN score, and discover that the RN score has dropped by 20 percent point in the last decade, with more than half of verifiable statements now accompanying references. The RR score has remained below 1% over the years as a result of the efforts of the community to eliminate unreliable references. We propose pairing novice and experienced editors on the same Wikipedia article as a strategy to enhance reference quality. Our quasi-experiment indicates that such a co-editing experience can result in a lasting advantage in identifying unreliable sources in future edits. As Wikipedia is frequently used as the ground truth for numerous Web applications, our findings and suggestions on its reliability can have a far-reaching impact. We discuss the possibility of other Web services adopting Wiki-style user collaboration to eliminate unreliable content.
Generative Model-Based Attack on Learnable Image Encryption for Privacy-Preserving Deep Learning

In this paper, we propose a novel generative model-based attack on learnable image encryption methods proposed for privacy-preserving deep learning. Various learnable encryption methods have been studied to protect the sensitive visual information of plain images, and some of them have been investigated to be robust enough against all existing attacks. However, previous attacks on image encryption focus only on traditional cryptanalytic attacks or reverse translation models, so these attacks cannot recover any visual information if a block-scrambling encryption step, which effectively destroys global information, is applied. Accordingly, in this paper, generative models are explored to evaluate whether such models can restore sensitive visual information from encrypted images for the first time. We first point out that encrypted images have some similarity with plain images in the embedding space. By taking advantage of leaked information from encrypted images, we propose a guided generative model as an attack on learnable image encryption to recover personally identifiable visual information. We implement the proposed attack in two ways by utilizing two state-of-the-art generative models: a StyleGAN-based model and latent diffusion-based one. Experiments were carried out on the CelebA-HQ and ImageNet datasets. Results show that images reconstructed by the proposed method have perceptual similarities to plain images.
Non-aligned supervision for Real Image Dehazing

Removing haze from real-world images is challenging due to unpredictable weather conditions, resulting in the misalignment of hazy and clear image pairs. In this paper, we propose an innovative dehazing framework that operates under non-aligned supervision. This framework is grounded in the atmospheric scattering model, and consists of three interconnected networks: dehazing, airlight, and transmission networks. In particular, we explore a non-alignment scenario that a clear reference image, unaligned with the input hazy image, is utilized to supervise the dehazing network. To implement this, we present a multi-scale reference loss that compares the feature representations between the referred image and the dehazed output. Our scenario makes it easier to collect hazy/clear image pairs in real-world environments, even under conditions of misalignment and shift views. To showcase the effectiveness of our scenario, we have collected a new hazy dataset including 415 image pairs captured by mobile Phone in both rural and urban areas, called"Phone-Hazy". Furthermore, we introduce a self-attention network based on mean and variance for modeling real infinite airlight, using the dark channel prior as positional guidance. Additionally, a channel attention network is employed to estimate the three-channel transmission. Experimental results demonstrate the superior performance of our framework over existing state-of-the-art techniques in the real-world image dehazing task. Phone-Hazy and code will be available at https://fanjunkai1.github. io/projectpage/NSDNet/index.html.
Out-of-distribution Detection with Implicit Outlier Transformation

Outlier exposure (OE) is powerful in out-of-distribution (OOD) detection, enhancing detection capability via model fine-tuning with surrogate OOD data. However, surrogate data typically deviate from test OOD data. Thus, the performance of OE, when facing unseen OOD data, can be weakened. To address this issue, we propose a novel OE-based approach that makes the model perform well for unseen OOD situations, even for unseen OOD cases. It leads to a min-max learning scheme -- searching to synthesize OOD data that leads to worst judgments and learning from such OOD data for uniform performance in OOD detection. In our realization, these worst OOD data are synthesized by transforming original surrogate ones. Specifically, the associated transform functions are learned implicitly based on our novel insight that model perturbation leads to data transformation. Our methodology offers an efficient way of synthesizing OOD data, which can further benefit the detection model, besides the surrogate OOD data. We conduct extensive experiments under various OOD detection setups, demonstrating the effectiveness of our method against its advanced counterparts.