Miracle-dz/UniFAR-FTUs huggingface dataset:
Multimodal Spatial Attention Module for Targeting Multimodal PET-CT Lung Tumor Segmentation

Multimodal positron emission tomography-computed tomography (PET-CT) is used routinely in the assessment of cancer. PET-CT combines the high sensitivity for tumor detection of PET and anatomical information from CT. Tumor segmentation is a critical element of PET-CT but at present, the performance of existing automated methods for this challenging task is low. Segmentation tends to be done manually by different imaging experts, which is labor-intensive and prone to errors and inconsistency. Previous automated segmentation methods largely focused on fusing information that is extracted separately from the PET and CT modalities, with the underlying assumption that each modality contains complementary information. However, these methods do not fully exploit the high PET tumor sensitivity that can guide the segmentation. We introduce a deep learning-based framework in multimodal PET-CT segmentation with a multimodal spatial attention module (MSAM). The MSAM automatically learns to emphasize regions (spatial areas) related to tumors and suppress normal regions with physiologic high-uptake from the PET input. The resulting spatial attention maps are subsequently employed to target a convolutional neural network (CNN) backbone for segmentation of areas with higher tumor likelihood from the CT image. Our experimental results on two clinical PET-CT datasets of non-small cell lung cancer (NSCLC) and soft tissue sarcoma (STS) validate the effectiveness of our framework in these different cancer types. We show that our MSAM, with a conventional U-Net backbone, surpasses the state-of-the-art lung tumor segmentation approach by a margin of 7.6% in Dice similarity coefficient (DSC).
Ultrafast x-ray imaging of pulsed plasmas in water

Pulsed plasmas in liquids exhibit complex interaction between three phases of matter (liquids, gas, plasmas) and are currently used in a wide range of applications across several fields, however significant knowledge gaps in our understanding of plasma initiation in liquids hinder additional application and control; this area of research currently lacks a comprehensive predictive model. To aid progress in this area experimentally, here we present the first-known ultrafast (50 ps) X-ray images of pulsed plasma initiation processes in water (+25 kV, 10 ns, 5 mJ), courtesy of the X-ray imaging techniques available at Argonne National Laboratory's Advanced Photon Source (APS), with supporting nanosecond optical imaging and a computational X-ray diffraction model. These results clearly resolve narrow (~10 micron) low-density plasma channels during initiation timescales typically obscured by optical emission (<100 ns), a well-known and difficult problem to plasma experiments without access to state-of-the-art X-ray sources such as the APS synchrotron. Images presented in this work speak to several of the prevailing plasma initiation hypotheses, supporting electrostriction and bubble deformation as dominant initiation phenomena. We also demonstrate the plasma setup used in this work as a cheap ($<$US\$100k), compact, and repeatable benchmark imaging target (29.1 km/s, 1 TW/cm$^2$) useful for the development of next-generation ultrafast imaging of high-energy-density physics (HEDP), as well as easier integration of HEDP research into synchrotron-enabled facilities.
A Probabilistic Approach to Growth Networks

Single-class closed queueing networks, consisting of infinite-server and single-server queues with exponential service times and probabilistic routing, admit product-from solutions. Such solutions, although seemingly tractable, are difficult to characterize explicitly for practically relevant problems due to the exponential combinatorial complexity of its normalization constant (partition function). In “A Probabilistic Approach to Growth Networks,” Jelenković, Kondev, Mohapatra, and Momčilović develop a novel methodology, based on a probabilistic representation of product-form solutions and large-deviations concentration inequalities, which identifies distinct operating regimes and yields explicit expressions for the marginal distributions of queue lengths. From a methodological perspective, a fundamental feature of the proposed approach is that it provides exact results for order-one probabilities, even though the analysis involves large-deviations rate functions, which characterize only vanishing probabilities on a logarithmic scale.
Fantastic Embeddings and How to Align Them: Zero-Shot Inference in a Multi-Shop Scenario

This paper addresses the challenge of leveraging multiple embedding spaces for multi-shop personalization, proving that zero-shot inference is possible by transferring shopping intent from one website to another without manual intervention. We detail a machine learning pipeline to train and optimize embeddings within shops first, and support the quantitative findings with additional qualitative insights. We then turn to the harder task of using learned embeddings across shops: if products from different shops live in the same vector space, user intent - as represented by regions in this space - can then be transferred in a zero-shot fashion across websites. We propose and benchmark unsupervised and supervised methods to "travel" between embedding spaces, each with its own assumptions on data quantity and quality. We show that zero-shot personalization is indeed possible at scale by testing the shared embedding space with two downstream tasks, event prediction and type-ahead suggestions. Finally, we curate a cross-shop anonymized embeddings dataset to foster an inclusive discussion of this important business scenario.
Accurate 2D soft segmentation of medical image via SoftGAN network

Accurate 2D lung nodules segmentation from medical Computed Tomography (CT) images is crucial in medical applications. Most current approaches cannot achieve precise segmentation results that preserving both rich edge details description and smooth transition representations between image regions due to the tininess, complexities, and irregularities of lung nodule shapes. To address this issue, we propose a novel Cascaded Generative Adversarial Network (CasGAN) to cope with CT images super-resolution and segmentation tasks, in which the semantic soft segmentation form on precise lesion representation is introduced for the first time according to our knowledge, and lesion edges can be retained accurately after our segmentation that can promote rapid acquisition of high-quality large-scale annotation data based on RECIST weak supervision information. Extensive experiments validate that our CasGAN outperforms the state-of-the-art methods greatly in segmentation quality, which is also robust on the application of medical images beyond lung nodules. Besides, we provide a challenging lung nodules soft segmentation dataset of medical CT images for further studies.
Powerful ionized gas outflows in the interacting radio galaxy 4C+29.30

We investigate the ionized gas excitation and kinematics in the inner $4.3\, \times \, 6.2$ kpc2 of the merger radio galaxy 4C+29.30. Using optical integral field spectroscopy with the Gemini North Telescope, we present flux distributions, line-ratio maps, peak velocities and velocity dispersion maps as well as channel maps with a spatial resolution of $\approx\! 955\,$ pc. We observe high blueshifts of up to $\sim\! -650\,$$\rm km\, s^{-1}$ in a region ∼1 arcsec south of the nucleus (the southern knot – SK), which also presents high velocity dispersions ($\sim\! 250\,$$\rm km\, s^{-1}$), which we attribute to an outflow. A possible redshifted counterpart is observed north from the nucleus (the northern knot – NK). We propose that these regions correspond to a bipolar outflow possibly due to the interaction of the radio jet with the ambient gas. We estimate a total ionized gas mass outflow rate of $\dot{M}_{\mathrm{ out}} = 25.4 ^{+11.5 }_{ -7.5}\,$ M⊙ yr−1with a kinetic power of $\dot{E} = 8.1 ^{+10.7 }_{ -4.0} \times 10^{42}\,$ erg s−1, which represents $5.8 ^{+7.6 }_{ -2.9} {{\ \rm per\ cent}}$ of the active galactic nucleus (AGN) bolometric luminosity. These values are higher than usually observed in nearby active galaxies with the same bolometric luminosities and could imply a significant impact of the outflows in the evolution of the host galaxy. The excitation is higher in the NK – that correlates with extended X-ray emission, indicating the presence of hotter gas – than in the SK, supporting a scenario in which an obscuring dust lane is blocking part of the AGN radiation to reach the southern region of the galaxy.
An Uncertainty-Aware Transfer Learning-Based Framework for COVID-19 Diagnosis

The early and reliable detection of COVID-19 infected patients is essential to prevent and limit its outbreak. The PCR tests for COVID-19 detection are not available in many countries, and also, there are genuine concerns about their reliability and performance. Motivated by these shortcomings, this article proposes a deep uncertainty-aware transfer learning framework for COVID-19 detection using medical images. Four popular convolutional neural networks (CNNs), including VGG16, ResNet50, DenseNet121, and InceptionResNetV2, are first applied to extract deep features from chest X-ray and computed tomography (CT) images. Extracted features are then processed by different machine learning and statistical modeling techniques to identify COVID-19 cases. We also calculate and report the epistemic uncertainty of classification results to identify regions where the trained models are not confident about their decisions (out of distribution problem). Comprehensive simulation results for X-ray and CT image data sets indicate that linear support vector machine and neural network models achieve the best results as measured by accuracy, sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve (AUC). Also, it is found that predictive uncertainty estimates are much higher for CT images compared to X-ray images.
Learning Video Representations from Textual Web Supervision

Videos found on the Internet are paired with pieces of text, such as titles and descriptions. This text typically describes the most important content in the video, such as the objects in the scene and the actions being performed. Based on this observation, we propose to use such text as a method for learning video representations. To accomplish this, we propose a data collection process and use it to collect 70M video clips shared publicly on the Internet, and we then train a model to pair each video with its associated text. We fine-tune the model on several down-stream action recognition tasks, including Kinetics, HMDB-51, and UCF-101. We find that this approach is an effective method of pretraining video representations. Specifically, it leads to improvements over from-scratch training on all benchmarks, outperforms many methods for self-supervised and webly-supervised video representation learning, and achieves an improvement of 2.2% accuracy on HMDB-51.
A Volatile-poor Formation of LHS 3844b Based on Its Lack of Significant Atmosphere

Exoplanet discoveries have reached into the realm of terrestrial planets that are becoming the subject of atmospheric studies. One such discovery is LHS 3844b, a 1.3 Earth radius planet in a 0.46 day orbit around an M4.5-5 dwarf star. Follow-up observations indicate that the planet is largely devoid of substantial atmosphere. This lack of significant atmosphere places astrophysical and geophysical constraints on LHS 3844b, primarily the degree of volatile outgassing and the rate of atmosphere erosion. We estimate the age of the host star as 7.8 ± 1.6 Gyr and find evidence of an active past comparable to that of Proxima Centauri. We use geodynamical models of volcanic outgassing and atmospheric erosion to show that the apparent lack of atmosphere is consistent with a volatile-poor mantle for LHS 3844b. We show the core is unlikely to host enough C to produce a sufficiently volatile-poor mantle, unless the bulk planet is volatile-poor relative to Earth. While we cannot rule out a giant impact stripping LHS 3844b’s atmosphere, we show that this mechanism would require significant mantle stripping, potentially leaving LHS 3844b as an Fe-rich “super-Mercury. ” Atmospheric erosion by smaller impacts is possible, but only if the planet has already begun degassing and is bombarded by 103 impactors of radius 500–1000 km traveling at escape velocity. We discuss formation and migration scenarios that could account for a volatile-poor origin, including the potential for an unobserved massive companion planet. A relatively volatile-poor composition of LHS 3844b suggests that the planet formed interior to the system snow line.
Volatility Model Calibration With Convolutional Neural Networks

We use a supervised deep convolution neural network to replicate the calibration of the Heston model to equity volatility surfaces. For this purpose we treat the implied volatility surface together with some auxiliary data, namely the strikes and moneyness of the corresponding options and the equity forwards, as a 3-dimensional input tensor for the neural network, in analogy to a colour channel image representation like the RGB. To extract the main features of the input data we are using inception layers with (1;1), (1;3) and (2;1) dimensional kernels. The specific choice is motivated by the no-arbitrage conditions on the call price surface. In terms of a local surface modelling the (1;3) filters with different weights can model the position, slope and curvature in the moneyness direction while the (2;1) filters can model Position and slope in the maturity direction. The neural network has been implemented using the open source library tensorflow.
Safety and Efficacy of Sofosbuvir with Ribavirin® in Hepatitis C, Genotype 3 Patients with Cirrhosis: A Real-world Experience

Introduction Hepatitis C virus (HCV) is the leading cause of cirrhosis. The advent of Directly Acting Antivirals (DAAs) like Sofosbuvir (SOF) has dramatized the treatment and is the cornerstone for the treatment of HCV. Most trials have been conducted in HCV genotype 1 (GT-1) and data for Interferon-free regimen in genotype 3 (GT-3) is limited especially in cirrhotic patients. Aim To evaluate the safety and efficacy of SOF plus Ribavirin® (RIB) in patients with compensated and decompensated cirrhosis. Methods This was a quasi-experimental study in HCV patients with compensated and decompensated cirrhosis. Each group (compensated and decompensated) was further subdivided into the treatment-naïve and treatment-experienced groups. Efficacy was assessed by end treatment response (ETR) and sustained viral response (SVR) in the treatment-naïve and experienced groups. Adverse events were recorded on designed proforma on serial follow-up visits. Results The study consisted of 110 consecutive patients. Among 110 patients, 51 had compensated cirrhosis and 59 had decompensated cirrhosis. The mean age was 53.8 ± 11 years. Males were n=56 (50.9%) and females were n=54 (49.1%). All the patients in Child-Turcotte-Pugh class A were in the compensated group. CTP B class was found to be 10.5% and 89.5% in the compensated and decompensated groups, respectively, whereas all the patients in CTP class C were in the decompensated group. In the compensated cirrhosis group, ETR was achieved in 36 (87.8%) treatment-naïve and 8 (88.9%) experienced patients. In decompensated cirrhosis, treatment-naïve and experienced patients achieved ETR in 28 (82.4%) and 18 (85.7%) patients, respectively. Whereas in compensated cirrhosis treatment-naïve and experienced patients, SVR was achieved in 25 (83.3%) and five (71.4%), respectively. In decompensated cirrhosis, 21 (77.8%) treatment-naïve and 12 (75%) experienced patients achieved SVR. The most common adverse events experienced by the patients were fatigue followed by myalgia, nausea, and diarrhea. The new onset of complications found due to cirrhosis were ascites, followed by hepatoma, upper gastrointestinal bleed, portosystemic encephalopathy, acute on chronic liver failure, and death. Conclusion Sofosbuvir in combination with Ribavirin® is safe but suboptimal in treatment outcomes, particularly in treatment-experienced patients with decompensated cirrhosis than in treatment-naive patients with compensated cirrhosis due to HCV GT-3.
Accurate and scalable multi-element graph neural network force field and molecular dynamics with direct force architecture

Recently, machine learning (ML) has been used to address the computational cost that has been limiting ab initio molecular dynamics (AIMD). Here, we present GNNFF, a graph neural network framework to directly predict atomic forces from automatically extracted features of the local atomic environment that are translationally-invariant, but rotationally-covariant to the coordinate of the atoms. We demonstrate that GNNFF not only achieves high performance in terms of force prediction accuracy and computational speed on various materials systems, but also accurately predicts the forces of a large MD system after being trained on forces obtained from a smaller system. Finally, we use our framework to perform an MD simulation of Li7P3S11, a superionic conductor, and show that resulting Li diffusion coefficient is within 14% of that obtained directly from AIMD. The high performance exhibited by GNNFF can be easily generalized to study atomistic level dynamics of other material systems.
The Uchuu simulations: Data Release 1 and dark matter halo concentrations

We introduce the Uchuu suite of large high-resolution cosmological $N$-body simulations. The largest simulation, named Uchuu, consists of 2.1 trillion ($12800^3$) dark matter particles in a box of 2.0 Gpc/h, and the mass of each particle is $3.27 \times 10^{8}$ Msun/h. The highest resolution simulation, called Shin-Uchuu, consists of 262 billion ($6400^3$) particles in a box of 140 Mpc/h, with a particle mass of $8.97 \times 10^{5}$ Msun/h. Combining these simulations we can follow the evolution of dark matter haloes (and subhaloes) spanning from dwarf galaxies to massive galaxy cluster hosts. We present basic statistics, dark matter power spectra and halo (subhalo) mass function, to demonstrate the huge dynamic range and superb statistics of the Uchuu simulations. From the analysis of the evolution of the power spectra we conclude that our simulations are accurate enough from the Baryon Acoustic Oscillations up to very small scales. We also provide parameters of a mass-concentration model, which describes the evolution of halo concentrations, that reproduces our simulation data within 5% error. We make publicly available various $N$-body products, as part of Uchuu Data Release 1, on the Skies & Universes site. We also plan to release gravitational lensing maps, mock galaxy, X-ray cluster and active galactic nuclei catalogues in the near future.
Staling in Starch Bread: the Effect of Gluten Additions on Specific Loaf Volume and Firming Rate

Hypotheses on the role of gluten in bread staling range from gluten having an anti-firming effect, or no effect on firming, to gluten-starch interactions being essential for bread firming. To test these hypotheses, the firming rate of starch bread made from protein-free synthetic flour was compared with that of starch-gluten breads made from synthetic flours containing 1-15% gluten (Fig. I ). Only loaves of similar specific loaf volume and crumb moisture content were compared to eliminate these parameters as variables that might influence firming rate. The starch breads clearly increased in firmness up to six days, indicating that gluten was not essential to the firming process, starch alone causing bread to firm with time. The starch-10% gluten breads and starch-15 % gluten breads had very similar specific loaf volumes, moisture contents and firming rates to that of the starch breads. This indicates that protein possibly has some role in firming, because if only starch has a role in firming then adding gluten would effectively dilute the starch and reduce the rate of firming. We propose that increasing bread firmness results from glucan chains of partially leached amylose and amylopectin attached to swollen starch granules forming hydrogen bonds with other starch granules and, to a smaller extent, with gluten fibrils.
Measuring Corporate Culture Using Machine Learning

We create a culture dictionary using one of the latest machine learning techniques—the word embedding model—and 209,480 earnings call transcripts. We score the five corporate cultural values of innovation, integrity, quality, respect, and teamwork for 62,664 firm-year observations over the period 2001–2018. We show that an innovative culture is broader than the usual measures of corporate innovation – R&D expenses and the number of patents. Moreover, we show that corporate culture correlates with business outcomes, including operational efficiency, risk-taking, earnings management, executive compensation design, firm value, and deal making, and that the culture-performance link is more pronounced in bad times. Finally, we present suggestive evidence that corporate culture is shaped by major corporate events, such as mergers and acquisitions.
Strangeonium-like hybrids on the lattice

The strangeonium-like $s\bar{s}g$ hybrids are investigated from lattice QCD in the quenched approximation. In the Coulomb gauge, spatially extended operators are constructed for $1^{--}$ and $(0,1,2)^{-+}$ states with the color octet $s\bar{s}$ component being separated from the chromomagnetic field strength by spatial distances $r$, whose matrix elements between the vacuum and the corresponding states are interpreted as Bethe-Salpeter (BS) wave functions. In each of the $(1,2)^{-+}$ channels, the masses and the BS wave functions are reliably derived. The $1^{-+}$ ground state mass is around 2.1-2.2 GeV, and that of $2^{-+}$ is around 2.3-2.4 GeV, while the masses of the first excited states are roughly 1.4 GeV higher. This mass splitting is much larger than the expectation of the phenomenological flux-tube model or constituent gluon model for hybrids, which is usually a few hundred MeV. The BS wave functions with respect to $r$ show clear radial nodal structures of non-relativistic two-body system, which imply that $r$ is a meaningful dynamical variable for these hybrids and motivate a color halo picture of hybrids that the color octet $s\bar{s}$ is surrounded by gluonic degrees of freedom. In the $1^{--}$ channel, the properties of the lowest two states comply with those of $\phi(1020)$ and $\phi(1680)$. We have not obtained convincing information relevant to $\phi(2170)$ yet, however, we argue that whether $\phi(2170)$ is a conventional $s\bar{s}$ meson or a $s\bar{s}g$ hybrid within the color halo scenario, the ratio of partial decay widths $\Gamma(\phi \eta)$ and $\Gamma (\phi \eta')$ observed by BESIII can be understood by the mechanism of hadronic transition of a strangeonium-like meson along with the $\eta-\eta'$ mixing.