Miracle-dz/UniFAR-FTUs huggingface dataset:
Weakly- and Semisupervised Probabilistic Segmentation and Quantification of Reverberation Artifacts

Objective and Impact Statement. We propose a weakly- and semisupervised, probabilistic needle-and-reverberation-artifact segmentation algorithm to separate the desired tissue-based pixel values from the superimposed artifacts. Our method models the intensity decay of artifact intensities and is designed to minimize the human labeling error. Introduction. Ultrasound image quality has continually been improving. However, when needles or other metallic objects are operating inside the tissue, the resulting reverberation artifacts can severely corrupt the surrounding image quality. Such effects are challenging for existing computer vision algorithms for medical image analysis. Needle reverberation artifacts can be hard to identify at times and affect various pixel values to different degrees. The boundaries of such artifacts are ambiguous, leading to disagreement among human experts labeling the artifacts. Methods. Our learning-based framework consists of three parts. The first part is a probabilistic segmentation network to generate the soft labels based on the human labels. These soft labels are input into the second part which is the transform function, where the training labels for the third part are generated. The third part outputs the final masks which quantifies the reverberation artifacts. Results. We demonstrate the applicability of the approach and compare it against other segmentation algorithms. Our method is capable of both differentiating between the reverberations from artifact-free patches and modeling the intensity fall-off in the artifacts. Conclusion. Our method matches state-of-the-art artifact segmentation performance and sets a new standard in estimating the per-pixel contributions of artifact vs underlying anatomy, especially in the immediately adjacent regions between reverberation lines. Our algorithm is also able to improve the performance of downstream image analysis algorithms.
Motility-induced clustering and meso-scale turbulence in active polar fluids

Meso-scale turbulence was originally observed experimentally in various suspensions of swimming bacteria, as well as in the collective motion of active colloids. The corresponding large scale dynamical patterns were reproduced in a simple model of a polar fluid, assuming a constant density of active particles. Recent, more detailed studies in a variety of experimental realizations of active polar fluids revealed additional interesting aspects, such as anomalous velocity statistics and clustering phenomena. Those phenomena cannot be explained by currently available models for active polar fluids. Herein, we extend the continuum model suggested by Dunkel et al to include density variations and a local feedback between the local density and self-propulsion speed of the active polar particles. If the velocity decreases strong enough with the density, a linear stability analysis of the resulting model shows that, in addition to the short-wavelength instability of the original model, a long-wavelength instability occurs. This is typically observed for high densities of polar active particles and is analogous to the well-known phenomenon of motility-induced phase separation (MIPS) in scalar active matter. We determine a simple phase diagram indicating the linear instabilities and perform systematic numerical simulations for the various regions in the corresponding parameter space. The interplay between the well understood short-range instability (leading to meso-scale turbulence) and the long-range instability (associated with MIPS) leads to interesting dynamics and novel phenomena concerning nucleation and coarsening processes. Our simulation results display a rich variety of novel patterns, including phase separation into domains with dynamically changing irregularly shaped boundaries. Anomalous velocity statistics are observed in all phases where the system segregates into regions of high and low densities. This offers a simple explanation for their occurrence in recent experiments with bacterial suspensions.
Physics of ULIRGs with MUSE and ALMA: The PUMA project

Context. Ultraluminous infrared galaxies (ULIRGs) are characterised by extreme starburst (SB) and active galactic nucleus (AGN) activity, and are therefore ideal laboratories for studying the outflow phenomena and their feedback effects. We have recently started a project called Physics of ULIRGs with MUSE and ALMA (PUMA), which is a survey of 25 nearby (z < 0.165) ULIRGs observed with the integral field spectrograph MUSE and the interferometer ALMA. This sample includes systems with both AGN and SB nuclear activity in the pre- and post-coalescence phases of major mergers. Aims. The main goals of the project are (i) to study the prevalence of (ionised, neutral, and molecular) outflows as a function of the galaxy properties, (ii) to constrain the driving mechanisms of the outflows (e.g. distinguish between SB and AGN winds), and (iii) to identify and characterise feedback effects on the host galaxy. In this first paper, we present details on the sample selection, MUSE observations, and data reduction, and derive first high-level data products. Methods. MUSE data cubes were analysed to study the dynamical status of each of the 21 ULIRGs observed so far, taking the stellar kinematics and the morphological properties inferred from MUSE narrow-band images into account. We also located the ULIRG nuclei, taking advantage of near-infrared (HST) and millimeter (ALMA) data, and studied their optical spectra to infer (i) the ionisation state through standard optical line ratio diagnostics, and (ii) outflows in both atomic ionised ([O III], Hα) and neutral (Na ID) gas. Results. We show that the morphological and stellar kinematic classifications are consistent: post-coalescence systems are more likely associated with ordered motions, while interacting (binary) systems are dominated by non-ordered and streaming motions. We also find broad and asymmetric [O III] and Na ID profiles in almost all nuclear spectra, with line widths in the range [300 − 2000] km s−1, possibly associated with AGN- and SB-driven winds. This result reinforces previous findings that indicated that outflows are ubiquitous during the pre- and post-coalescence phases of major mergers.
Selectable Heaps and Optimal Lazy Search Trees

We show the $O(\log n)$ time extract minimum function of efficient priority queues can be generalized to the extraction of the $k$ smallest elements in $O(k \log(n/k))$ time, where we define $\log(x)$ as $\max(\log_2(x), 1)$. We first show heap-ordered tree selection (Kaplan et al., SOSA '19) can be applied on the heap-ordered trees of the classic Fibonacci heap to support the extraction in $O(k \log(n/k))$ amortized time. We then show selection is possible in a priority queue with optimal worst-case guarantees by applying heap-ordered tree selection on Brodal queues (SODA '96), supporting the operation in $O(k \log(n/k))$ worst-case time. Via a reduction from the multiple selection problem, $\Omega(k \log(n/k))$ time is necessary if insertion is supported in $o(\log n)$ time. We then apply the result to lazy search trees (Sandlund & Wild, FOCS '20), creating a new interval data structure based on selectable heaps. This gives optimal $O(B+n)$ time lazy search tree performance, lowering insertion complexity into a gap $\Delta_i$ to $O(\log(n/|\Delta_i|))$ time. An $O(1)$ time merge operation is also made possible when used as a priority queue, among other situations. If Brodal queues are used, runtimes of the lazy search tree can be made worst-case in the general case of two-sided gaps. The presented data structure makes fundamental use of soft heaps (Chazelle, J. ACM '00), biased search trees, and efficient priority queues, approaching the theoretically-best data structure for ordered data.
Unequal Representations: Analyzing Intersectional Biases in Word Embeddings Using Representational Similarity Analysis

We present a new approach for detecting human-like social biases in word embeddings using representational similarity analysis. Specifically, we probe contextualized and non-contextualized embeddings for evidence of intersectional biases against Black women. We show that these embeddings represent Black women as simultaneously less feminine than White women, and less Black than Black men. This finding aligns with intersectionality theory, which argues that multiple identity categories (such as race or sex) layer on top of each other in order to create unique modes of discrimination that are not shared by any individual category.
A Convenient Infinite Dimensional Framework for Generative Adversarial Learning

In recent years, generative adversarial networks (GANs) have demonstrated impressive experimental results while there are only a few works that foster statistical learning theory for GANs. In this work, we propose an infinite dimensional theoretical framework for generative adversarial learning. Assuming the class of uniformly bounded $k$-times $\alpha$-Holder differentiable and uniformly positive densities, we show that the Rosenblatt transformation induces an optimal generator, which is realizable in the hypothesis space of $\alpha$-Holder differentiable generators. With a consistent definition of the hypothesis space of discriminators, we further show that in our framework the Jensen-Shannon divergence between the distribution induced by the generator from the adversarial learning procedure and the data generating distribution converges to zero. Under sufficiently strict regularity assumptions on the density of the data generating process, we also provide rates of convergence based on concentration and chaining.
Usefulness of sRtot and Rint in bronchodilator testing in the diagnosis of asthma in children

Introduction The usefulness of total specific resistance (sRtot) and interrupter resistance (Rint) as a bronchodilator reversibility test (BT) alternative to forced expiratory volume in 1 s (FEV1) in asthma diagnosis in children has not been established. Aim To compare different applied definitions of airflow obstruction in children measuring response to the bronchodilator by spirometry, plethysmography and the interrupter technique in asthmatic children. Material and methods It was a prospective, real-life, non-interventional study. Children, aged 6–18 with newly diagnosed asthma, able to perform lung function tests were included into the study. Subjects underwent a history taking, physical examination, reversibility test in spirometry, plethysmography, and the interrupter technique. A standard cut-off of 12% from the initial value for reversibility in FEV1 was employed. Improvement in the pre-bronchodilator sRtot and Rint ≥ 25% and ≥ 35% was assessed after administration of salbutamol (400 µg) as well as allergen sensitization were measured. Results We included 135 children diagnosed with asthma into the analysis. All investigated parameters changed statistically significantly due to the bronchodilator administration in the examined patients. The FEV1 was not as useful in diagnostics of asthma as the sRtot and Rint, taking into consideration the fait accompli that all the study participants had aptly been diagnosed as having asthma (p < 0.001). The differences between the sRtot and Rint were not statistically significant (p = 0.215). Conclusions Our results suggest that sRtot and Rint may be useful parameters in BT in clinical practice in the asthma diagnostic process in children.
Radon and thoron exhalation rate, emanation factor and radioactivity risks of building materials of the Iberian Peninsula

Radon (222Rn) and thoron (220Rn) are radioactive gases emanating from geological materials. Inhalation of these gases is closely related to an increase in the probability of lung cancer if the levels are high. The majority of studies focus on radon, and the thoron is normally ignored because of its short half-life (55.6 s). However, thoron decay products can also cause a significant increase in dose. In buildings with high radon levels, the main mechanism for entry of radon is pressure-driven flow of soil gas through cracks in the floor. Both radon and thoron can also be released from building materials to the indoor atmosphere. In this work, we study the radon and thoron exhalation and emanation properties of an extended variety of common building materials manufactured in the Iberian Peninsula (Portugal and Spain) but exported and used in all countries of the world. Radon and thoron emission from samples collected in the closed chamber was measured by an active method that uses a continuous radon/thoron monitor. The correlations between exhalation rates of these gases and their parent nuclide exhalation (radium/thorium) concentrations were examined. Finally, indoor radon and thoron and the annual effective dose were calculated from radon/thoron concentrations in the closed chamber. Zircon is the material with the highest concentration values of 226Ra and 232Th and the exhalation and emanation rates. Also in the case of zircon and some granites, the annual effective dose was higher than the annual exposure limit for the general public of 1 mSv y−1, recommended by the European regulations.
COVID-19 Clinical Trials Registered Worldwide for Drug Intervention: An Overview and Characteristic Analysis

Objective This study aims to comprehensively evaluate the characteristics of clinical drug trials to facilitate the collection of evidence for COVID-19 drug treatments. Methods A retrospective analysis of 910 trials retrieved on August 7, 2020. Results A total of 910 registered clinical trials with at least one drug intervention were evaluated. The number of registrations (32.4%, 295) from the United States accounted for nearly one-third of the total and far exceeded that of other countries individually. Furthermore, the peak number of trials were registered in April (34.3%, 312). Over half of the trials (51.2%, 466) are in the recruitment phase, and only 4.2% (38) of the trials have been completed. The median (interquartile range) estimated enrollment is 127 (59, 365). In 39% (355) of trials, the estimated enrollment is less than 100 participants. A total of 94.5% (790) of the trials use randomization in the allocation, 82.7% (753) use a parallel intervention mode, and 52.2% (475) use masking. A total of 287 drug names have been standardized and mapped. “Hydroxychloroquine” is the leading drug among the registered trials (7.47%, 68). Among the main countries contributing to investigations on “hydroxychloroquine”, the United States ranks first with 36.76% (25) of the trials. Conclusion The designs of COVID-19 clinical drug trials have greatly improved in terms of the implementation of randomization and, particularly, blinding methods. In terms of drug reuse, the number of drug types has greatly increased, and hundreds of drugs have been used for efficacy screening. The emergence of large-sample registration trials is expected to address the uncertainty regarding the current clinical efficacy of some drugs.
When Machine Learning Meets Privacy

The newly emerged machine learning (e.g., deep learning) methods have become a strong driving force to revolutionize a wide range of industries, such as smart healthcare, financial technology, and surveillance systems. Meanwhile, privacy has emerged as a big concern in this machine learning-based artificial intelligence era. It is important to note that the problem of privacy preservation in the context of machine learning is quite different from that in traditional data privacy protection, as machine learning can act as both friend and foe. Currently, the work on the preservation of privacy and machine learning are still in an infancy stage, as most existing solutions only focus on privacy problems during the machine learning process. Therefore, a comprehensive study on the privacy preservation problems and machine learning is required. This article surveys the state of the art in privacy issues and solutions for machine learning. The survey covers three categories of interactions between privacy and machine learning: (i) private machine learning, (ii) machine learning-aided privacy protection, and (iii) machine learning-based privacy attack and corresponding protection schemes. The current research progress in each category is reviewed and the key challenges are identified. Finally, based on our in-depth analysis of the area of privacy and machine learning, we point out future research directions in this field.
Silencing circSLC19A1 Inhibits Prostate Cancer Cell Proliferation, Migration and Invasion Through Regulating miR-326/MAPK1 Axis

Background Emerging evidence indicates that circular RNAs (circRNAs), which form as covalently closed loops, play a regulatory role in various types of cancer, including prostate cancer (PCa). CircSLC19A1, one kind of circRNA, was subjected to the study and its role in PCa was explored. Methods Expressions of circSLC19A1, miR-326 and MAPK1 in PCa tissues and cells were assessed by qRT-PCR. CircSLC19A1 was identified by RNase R treatment. The binding relations between circSLC19A1 and miR-326 and between miR-326 and MAPK1 were predicted by RegRNA2.0 or Targetscan7.2 and further confirmed by dual-luciferase reporter assay. Pearson correlation analysis of the correlation among circSLC19A1, miR-326 and MAPK1 was performed. CCK-8, cell colony formation, wound healing and Transwell assays were used to assess PCa cell viability, proliferation, migration and invasion, respectively. Results CircSLC19A1 expression was up-regulated in PCa tissue and cell cytoplasm. Silencing circSLC19A1 inhibited PCa cell viability, proliferation, migration, invasion and miR-326 expression. MiR-326 inhibitor promoted the luciferase activities of circSLC19A1 and MAPK1, increased MAPK1 expression and facilitated PCa cell progression. MiR-326 expression was down-regulated in PCa tissue and there was a negative correlation between miR-326 and circSLC19A1 expressions. MAPK1 expression was up-regulated in PCa tissue. There was a negative correlation between MAPK1 and miR-326 expressions as well as a positive correlation between MAPK1 and circSLC19A1 expressions. Silencing MAPK1 promoted the viability, proliferation, migration, and invasion of PCa cells co-transfected with siRNA-circSLC19A1a and miR-326 inhibitor. Conclusion CircSLC19A1 silencing inhibited PCa cell proliferation, migration and invasion through regulating miR-326/MAPK1 axis.
Metabolic Effects of Cortisone Acetate vs Hydrocortisone in Patients With Secondary Adrenal Insufficiency

Abstract Context Pharmacokinetic properties of cortisone acetate (CA) and hydrocortisone (HC) differ because CA needs to be converted into cortisol to become active. Objective This work analyzed the metabolic consequences of switching CA to an equivalent daily dose of HC in patients with secondary adrenal insufficiency (SAI). Design This was a post hoc analysis from a prospective study including individuals with hypopituitarism receiving growth hormone replacement. Data were collected before and after a switch from CA to an equivalent dose of HC (switch group). Two control groups were included: patients continuing CA replacement (CA control group) and adrenal-sufficient hypopituitary patients (AS control group). Results The analysis included 229 patients: 105, 31, and 93 in the switch, CA control, and AS control groups, respectively. After the change from CA to HC, increases in mean body weight (1.2 kg; P < .05), waist circumference (2.9 cm; P < .001), body fat measured by dual-energy x-ray absorptiometry (1.3 kg; P < .001), and glycated hemoglobin (0.3%; P < .05) were recorded in the switch group. The increase in mean waist circumference was greater than in the AS control group (0.9 cm; P < .05). Mean body fat increased in the switch group but not in the CA control group (–0.7 kg; P < .05). Conclusions A switch from CA to an equivalent dose of HC was associated with a worsened metabolic profile, suggesting that HC has a more powerful metabolic action than CA based on the assumption that 20 mg HC equals 25 mg CA.
Synthesis and characterization of tellurium based glasses for far infrared sensing and thermoelectric applications

Les verres de tellures sont des materiaux recemment remis au gout du jour pour des applications en optiques. Certaines compositions permettent en effet de transmettre la lumiere loin dans l’infrarouge au-dela de 20 µm, mais leur tendance naturelle a recristalliser rend difficile la fabrication d’objet pour la photonique telle que des fibres optiques. Des verres du systeme Te-Ge-Se ont ete developpes dans le cadre du projet Darwin de l’Agence Spatiale Europeenne permettant de detecter la bande d’absorption du CO2 a 15 µm. La premiere fibre optique monomode a ete obtenue a partir d’une nouvelle methode de fabrication de preforme par moulage. Les verres de tellure du systeme Te-Ge-AgI sont les seuls a ne pas presenter de pic de cristallisation en analyse thermique. Leur stabilite a ete mise a profit pour developper des fibres optiques avec un niveau tres bas de pertes optiques, de l’ordre de 3 dB∙m-1, ce qui constitue un record. Ces fibres ont ete utilisees pour mettre en œuvre des experiences de spectroscopie par ondes evanescentes permettant d’acceder a une gamme de longueurs d’onde encore jamais atteinte de 2 a 16 µm. Ce gain sera de premiere importance pour la mise en service de ces fibres en biologie ou medecine.Par ailleurs, les verres de tellure sont les verres presentant les conductivites electroniques les plus elevees jamais mesurees. Il s’agit donc de materiaux potentiellement interessants pour la thermoelectricite. Certaines compositions du systeme (Te/Se)-(As/Sb/Bi)-(Cu/Ag) ont ete synthetisees et caracterisees. Des materiaux composites obtenus par broyage et compression de poudres de verre et de Bi0.5Sb1. 5Te3 cristallise ont ete prepares. Ces derniers presentent un ZT = 0.365 a 413 K, ce qui est encourageant pour l’avenir.
The effective field theory and perturbative analysis for log-density fields

A logarithm transformation over the matter overdensity field δ brings information from the bispectrum and higher-order n-point functions to the power spectrum. We calculate the power spectrum for the log-transformed field A at one, two and three loops using perturbation theory (PT). We compare the results to simulated data and give evidence that the PT series is asymptotic already on large scales, where the k modes no longer decouple. This motivates us to build an alternative perturbative series for the log-transformed field that is not constructed on top of perturbations of δ but directly over the equations of motion for A itself. This new approach converges faster and better reproduces the large scales at low z. We then show that the large-scale behaviour for the log-transformed field power spectrum can be captured by a small number of free parameters. Finally, we add the counter-terms expected within the effective field theory framework and show that the theoretical model, together with the IR-resummation procedure, agrees with the measured spectrum with percent precision until k ≃ 0.38 Mpc−1h at z=0. It indicates that the non-linear transformation indeed linearizes the density field and, in principle, allows us to access information contained on smaller scales.