Miracle-dz/UniFAR-FTUs huggingface dataset:
Federated Learning for Chronic Obstructive Pulmonary Disease Classification with Partial Personalized Attention Mechanism

Chronic Obstructive Pulmonary Disease (COPD) is the fourth leading cause of death worldwide. Yet, COPD diagnosis heavily relies on spirometric examination as well as functional airway limitation, which may cause a considerable portion of COPD patients underdiagnosed especially at the early stage. Recent advance in deep learning (DL) has shown their promising potential in COPD identification from CT images. However, with heterogeneous syndromes and distinct phenotypes, DL models trained with CTs from one data center fail to generalize on images from another center. Due to privacy regularizations, a collaboration of distributed CT images into one centralized center is not feasible. Federated learning (FL) approaches enable us to train with distributed private data. Yet, routine FL solutions suffer from performance degradation in the case where COPD CTs are not independent and identically distributed (Non-IID). To address this issue, we propose a novel personalized federated learning (PFL) method based on vision transformer (ViT) for distributed and heterogeneous COPD CTs. To be more specific, we partially personalize some heads in multiheaded self-attention layers to learn the personalized attention for local data and retain the other heads shared to extract the common attention. To the best of our knowledge, this is the first proposal of a PFL framework specifically for ViT to identify COPD. Our evaluation of a dataset set curated from six medical centers shows our method outperforms the PFL approaches for convolutional neural networks.
The sample complexity of sparse multi-reference alignment and single-particle cryo-electron microscopy

Multi-reference alignment (MRA) is the problem of recovering a signal from its multiple noisy copies, each acted upon by a random group element. MRA is mainly motivated by single-particle cryo-electron microscopy (cryo-EM) that has recently joined X-ray crystallography as one of the two leading technologies to reconstruct biological molecular structures. Previous papers have shown that in the high noise regime, the sample complexity of MRA and cryo-EM is $n=\omega(\sigma^{2d})$, where $n$ is the number of observations, $\sigma^2$ is the variance of the noise, and $d$ is the lowest-order moment of the observations that uniquely determines the signal. In particular, it was shown that in many cases, $d=3$ for generic signals, and thus the sample complexity is $n=\omega(\sigma^6)$. In this paper, we analyze the second moment of the MRA and cryo-EM models. First, we show that in both models the second moment determines the signal up to a set of unitary matrices, whose dimension is governed by the decomposition of the space of signals into irreducible representations of the group. Second, we derive sparsity conditions under which a signal can be recovered from the second moment, implying sample complexity of $n=\omega(\sigma^4)$. Notably, we show that the sample complexity of cryo-EM is $n=\omega(\sigma^4)$ if at most one third of the coefficients representing the molecular structure are non-zero; this bound is near-optimal. The analysis is based on tools from representation theory and algebraic geometry. We also derive bounds on recovering a sparse signal from its power spectrum, which is the main computational problem of X-ray crystallography.
Handwashing Action Detection System for an Autonomous Social Robot

Young children are at an increased risk of contracting contagious diseases such as COVID-19 due to improper hand hygiene. An autonomous social agent that observes children while handwashing and encourages good hand washing practices could provide an opportunity for handwashing behavior to become a habit. In this article, we present a human action recognition system, which is part of the vision system of a social robot platform, to assist children in developing a correct handwashing technique. A modified convolution neural network (CNN) architecture with Channel Spatial Attention Bilinear Pooling (CSAB) frame, with a VGG-16 architecture as the backbone is trained and validated on an augmented dataset. The modified architecture generalizes well with an accuracy of 90% for the WHO-prescribed handwashing steps even in an unseen environment. Our findings indicate that the approach can recognize even subtle hand movements in the video and can be used for gesture detection and classification in social robotics.
Statistics of Images of Galaxies with Particular Reference to Clustering

General Introduction . 75 I. OBSERVATIONAL STUDIES OF THE DISTRIBUTION OF IMAGES OF GALAXIES 1. Introductory remarks . 77 2. Existing surveys of galaxies . 77 3. Suggested program of surveys . 79 II. PROBABILISTIC STUDIES OF THE DISTRIBUTION OF IMAGES OF GALAXIES 4. General remarks . 80 5. Postulates underlying the stochastic model of clustering . 81 6. Generalization to a possibly expanding universe . 83 7. Postulate regarding the "visibility" of a galaxy on a photographic plate . 84 8. Basic formulas of the theory of simple clustering of galaxies . 86 9. Some numerical results . 88 10. Expected number of images of galaxies per square degree . 92 11. Generalization of the original model: model of multiple clustering . 96 12. Probability distribution of certain characteristics of images of clusters . 100 13. Problem of interlocking of clusters . 101 14. Model of fluctuating density of galaxies . 104 15. Test for expansion of clusters of galaxies . 106 16. Concluding section: some important outstanding problems . 106
ALMA observations of the kinematics and chemistry of disc formation

Context. The R CrA cloud hosts a handful of Class 0/I low-mass young stellar objects. The chemistry and physics at scales>500 AU in this cloud are dominated by the irradiation from the nearby Herbig Be star R CrA. The luminous large-scale emission makes it necessary to use high-resolution spectral imaging to study the chemistry and dynamics of the inner envelopes and discs of the protostars. Aims. We aim to better understand the structure of the inner regions of these protostars and, in particular, the interplay between the chemistry and the presence of discs. Methods. Using Atacama Large Millimeter/submillimeter Array (ALMA) high-resolution spectral imaging interferometry observations, we study the molecular line and dust continuum emission at submillimetre wavelengths. Results. We detect dust continuum emission from four circumstellar discs around Class 0/I objects within the R CrA cloud. Towards IRS7B we detect C 17 O emission showing a rotation curve consistent with a Keplerian disc with a well-defined edge that gives a good estimate for the disc radius at 50 AU. We derive the central object mass to 2:3 M and the disc mass to 0:024 M . The observations are also consistent with a model of material infalling under conservation of angular momentum; however, this model provides a worse fit to the data. We also report a likely detection of faint CH3OH emission towards this point source, as well as more luminous CH3OH emission in an outflow orthogonal to the major axis of the C 17 O emission. Conclusions. The faint CH3OH emission seen towards IRS7B can be explained by a flat density profile of the inner envelope caused by the disc with a radius < 50 AU. We propose that the regions of the envelopes where complex organic molecules are present in Class 0/I young stellar objects can become quenched as the disc grows.
Thermal Hall conductivity near field-suppressed magnetic order in a Kitaev-Heisenberg model

We investigate thermal Hall conductivity $\kappa_{xy}$ of a $J$-$K$ Kitaev-Heisenberg model with a Zeeman field in the $(111)$ direction in the light of the recent debate surrounding the possible re-emergence of Ising topological order (ITO) and half-quantized $\kappa_{xy}/T$ upon field-suppression of long-range magnetic order in Kitaev materials. We use the purification-based finite temperature Tensor Network approach making no prior assumptions about the nature of the excitations: Majorana, visons or spin waves. For purely Kitaev interactions and fields $h/K \gtrsim 0.02$ sufficient to degrade ITO, the peak $\kappa_{xy}/T$ monotonously decreases from half-quantization associated with lower fields - a behavior reminiscent of vison fluctuation corrections. For higher fields $h/K\gtrsim 0.1,$ we find the results qualitatively consistent with a spin-wave treatment. In our $J$-$K$ model (with ferro-$K$ and antiferro-$J$), in the vicinity of field-suppressed magnetic order, we found $\kappa_{xy}/T$ to be significant, with peak magnitudes exceeding half-quantization followed by a monotonous decrease with increasing $h. $ We thus conclude that half-quantized thermal Hall effect in the vicinity of field suppressed magnetic order in our model, is a fine-tuning effect and is not associated with a Majorana Hall state with ITO.
Spatial data modeling and mining using a graph-based representation

We propose a unique graph-based model to represent spatial data, non-spatial data and the spatial relations among spatial objects. We will generate datasets composed of graphs with a set of these three elements. We consider that by mining a dataset with these characteristics a graph-based mining tool can search patterns involving all these elements at the same time improving the results of the spatial analysis task. A significant characteristic of spatial data is that the attributes of the neighbors of an object may have an influence on the object itself. So, we propose to include in the model three relationship types (topological, orientation, and distance relations). In the model the spatial data (i. E. Spatial objects), non-spatial data (i. E. Non-spatial attributes), and spatial relations are represented as a collection of one or more directed graphs. A directed graph contains a collection of vertices and edges representing all these elements. Vertices represent either spatial objects, spatial relations between two spatial objects (binary relation), or non-spatial attributes describing the spatial objects. Edges represent a link between two vertices of any type. According to the type of vertices that an edge joins, it can represent either an attribute name or a spatial relation name. The attribute name can refer to a spatial object or a non-spatial entity. We use directed edges to represent directional information of relations among elements (i. E. Object x touches object y) and to describe attributes about objects (i. E. Object x has attribute z). We propose to adopt the Subdue system, a general graph-based data mining system developed at the University of Texas at Arlington, as our mining tool. A special feature named overlap has a primary role in the substructures discovery process and consequently a direct impact over the generated results. However, it is currently implemented in an orthodox way: all or nothing. Therefore, we propose a third approach: limited overlap, which gives the user the capability to set over which vertices the overlap will be allowed. We visualize directly three motivations issues to propose the implementation of the new algorithm: search space reduction, processing time reduction, and specialized overlapping pattern oriented search.
Translating Hanja Historical Documents to Contemporary Korean and English

The Annals of Joseon Dynasty (AJD) contain the daily records of the Kings of Joseon, the 500-year kingdom preceding the modern nation of Korea. The Annals were originally written in an archaic Korean writing system, `Hanja', and were translated into Korean from 1968 to 1993. The resulting translation was however too literal and contained many archaic Korean words; thus, a new expert translation effort began in 2012. Since then, the records of only one king have been completed in a decade. In parallel, expert translators are working on English translation, also at a slow pace and produced only one king's records in English so far. Thus, we propose H2KE, a neural machine translation model, that translates historical documents in Hanja to more easily understandable Korean and to English. Built on top of multilingual neural machine translation, H2KE learns to translate a historical document written in Hanja, from both a full dataset of outdated Korean translation and a small dataset of more recently translated contemporary Korean and English. We compare our method against two baselines: a recent model that simultaneously learns to restore and translate Hanja historical document and a Transformer based model trained only on newly translated corpora. The experiments reveal that our method significantly outperforms the baselines in terms of BLEU scores for both contemporary Korean and English translations. We further conduct extensive human evaluation which shows that our translation is preferred over the original expert translations by both experts and non-expert Korean speakers.
ImPosing: Implicit Pose Encoding for Efficient Visual Localization

We propose a novel learning-based formulation for visual localization of vehicles that can operate in real-time in city-scale environments. Visual localization algorithms determine the position and orientation from which an image has been captured, using a set of geo-referenced images or a 3D scene representation. Our new localization paradigm, named Implicit Pose Encoding (ImPosing), embeds images and camera poses into a common latent representation with 2 separate neural networks, such that we can compute a similarity score for each image-pose pair. By evaluating candidates through the latent space in a hierarchical manner, the camera position and orientation are not directly regressed but incrementally refined. Very large environments force competitors to store gigabytes of map data, whereas our method is very compact independently of the reference database size. In this paper, we describe how to effectively optimize our learned modules, how to combine them to achieve real-time localization, and demonstrate results on diverse large scale scenarios that significantly outperform prior work in accuracy and computational efficiency.
Temporal Sentence Grounding in Videos: A Survey and Future Directions

Temporal sentence grounding in videos (TSGV), a.k. a. , natural language video localization (NLVL) or video moment retrieval (VMR), aims to retrieve a temporal moment that semantically corresponds to a language query from an untrimmed video. Connecting computer vision and natural language, TSGV has drawn significant attention from researchers in both communities. This survey attempts to provide a summary of fundamental concepts in TSGV and current research status, as well as future research directions. As the background, we present a common structure of functional components in TSGV, in a tutorial style: from feature extraction from raw video and language query, to answer prediction of the target moment. Then we review the techniques for multimodal understanding and interaction, which is the key focus of TSGV for effective alignment between the two modalities. We construct a taxonomy of TSGV techniques and elaborate the methods in different categories with their strengths and weaknesses. Lastly, we discuss issues with the current TSGV research and share our insights about promising research directions.
Gas Perturbations in the Cool Cores of Galaxy Clusters: Effective Equation of State, Velocity Power Spectra, and Turbulent Heating

We present the statistical analysis of X-ray surface brightness and gas density fluctuations in the cool cores of 10 nearby, X-ray-bright galaxy clusters that have deep Chandra observations and show observational indications of radio-mechanical active galactic nucleus (AGN) feedback. Within the central parts of the cool cores, the total variance of fluctuations is dominated by isobaric and/or isothermal fluctuations on spatial scales ∼10–60 kpc, which are likely associated with slow gas motions and bubbles of relativistic plasma. Adiabatic fluctuations, associated with weak shocks and/or sonic turbulence, constitute less than 10% of the total variance in all clusters. The typical amplitude of density fluctuations is small, ∼10% or less on scales of ∼10–15 kpc. The observed subdominant contribution of adiabatic fluctuations and the small amplitude of density fluctuations support a model of gentle AGN feedback. The measured one-component velocities of gas motions are typically below 100–150 km s−1 on scales <50 kpc and can be up to ∼300 km s−1 on ∼100 kpc scales. The nonthermal energy is <12% of the thermal energy. Regardless of the source that drives these motions, the dissipation of the energy in such motions provides heat that is sufficient to balance radiative cooling on average, albeit with significant uncertainties. The results presented here support previous conclusions based on the analysis of the Virgo and Perseus Clusters and agree with the Hitomi measurements. With next-generation observatories like Athena and Lynx, these techniques will be yet more powerful.
Field-induced in a cuprate high temperature superconductor

We present a combined neutron diﬀraction and high-ﬁeld muon spin rotation ( µ SR) study of the magnetic and superconducting phases of the high-temperature superconductor La 1 . 94 Sr 0 . 06 CuO 4+ y ( T c = 37 . 5(2) K). We observe a linear dependence of the neutron diﬀraction signal from the modu-lated antiferromagnetic stripe order on the applied ﬁeld. The magnetic volume fraction measured with µ SR increases linearly from 0% to ∼ 40% with applied magnetic ﬁeld up to 8 T. This allows us to conclude, in contrast to earlier ﬁeld-dependent neutron diﬀraction studies, that the ordered stripe regions are induced by an applied ﬁeld, and that their ordered magnetic moment is ﬁeld-independent.
Synchrotron Polarization Radiative Transfer: Relativistic Thermal Electron Contribution

Relativistic thermal electrons moving in a large-scale magnetic field can produce synchrotron radiation. Linear synchrotron polarization can also be produced by the relativistic thermal electrons. In this paper, we utilize a hybrid thermal–nonthermal electron energy distribution to calculate circular synchrotron polarization. We further compute the radiative transfer of the synchrotron polarization in the optical and radio bands when we consider the contribution of the thermal electrons. We attempt to apply the polarization results to some astrophysical objects, such as kilonova like AT 2017gfo/GW170817, the fast radio burst (FRB), the gamma-ray burst afterglow, and the supernova remnant. The large optical depth of radiative transfer affects the small polarization degrees of these populations when the media surrounding the synchrotron sources take heavy absorption to the polarized photons. We need a strong magnetic field in our model to reproduce the linear and circular polarization properties that were observed in FRB 140514. This indicates that FRBs have a neutron star origin.
Pitot-Pressure Measurements in Flow Fields Behind a Rectangular Nozzle with Exhaust Jet for Free-Stream Mach Numbers of 0.00, 0.60, and 1.20

An investigation has been conducted in the Langley 16-Foot Transonic Tunnel to measure the flow field in and around the jet exhaust from a nonaxisymmetric nozzle configuration. The nozzle had a rectangular exit with a width-to-height ratio of 2.38. Pitot-pressure measurements were made at five longitudinal locations downstream of the nozzle exit. The maximum distance downstream of the exit was about 5 nozzle heights. These measurements were made at free-stream Mach numbers of 0.00, 0.60, and 1.20 with the nozzle operating at a ratio of nozzle total pressure to free-stream static pressure of 4.0. The jet exhaust was simulated with high-pressure air that had an exit total temperature essentially equal to the free-stream total temperature.
The atmosphere of WASP-17b: Optical high-resolution transmission spectroscopy

High-resolution transmission spectroscopy is a method for understanding the chemical and physical properties of upper exoplanetary atmospheres. Due to large absorption cross-sections, resonance lines of atomic sodium D-lines (at 5889.95 and 5895.92 Å) produce large transmission signals. Our aim is to unveil the physical properties of WASP-17b through an accurate measurement of the sodium absorption in the transmission spectrum. We analyze 37 high-resolution spectra observed during a single transit of WASP-17b with the MIKE instrument on the 6.5 m Magellan Telescopes. We exclude stellar flaring activity during the observations by analyzing the temporal variations of Hα and Ca II infrared triplet (IRT) lines. We then obtain the excess absorption light curves in wavelength bands of 0.75, 1, 1.5, and 3 Å around the center of each sodium line (i.e., the light curve approach). We model the effects of differential limb-darkening, and the changing planetary radial velocity on the light curves. We also analyze the sodium absorption directly in the transmission spectrum, which is obtained by dividing in-transit by out-of-transit spectra (i.e., the division approach). We then compare our measurements with a radiative transfer atmospheric model. Our analysis results in a tentative detection of exoplanetary sodium: we measure the width and amplitude of the exoplanetary sodium feature to be σNa = (0.128 ± 0.078) Å and ANa = (1.7 ± 0.9)% in the excess light curve approach and σNa = (0.850 ± 0.034) Å and ANa = (1.3 ± 0.6)% in the division approach. By comparing our measurements with a simple atmospheric model, we retrieve an atmospheric temperature of 15501550 −200+700 K and radius (at 0.1 bar) of 1.81 ± 0.02 RJup for WASP-17b.
Impact of prevention in primary care on costs in primary and secondary care for people with serious mental illness

Abstract A largely unexplored part of the financial incentive for physicians to participate in preventive care is the degree to which they are the residual claimant from any resulting cost savings. We examine the impact of two preventive activities for people with serious mental illness (care plans and annual reviews of physical health) by English primary care practices on costs in these practices and in secondary care. Using panel two‐part models to analyze patient‐level data linked across primary and secondary care, we find that these preventive activities in the previous year are associated with cost reductions in the current quarter both in primary and secondary care. We estimate that there are large beneficial externalities for which the primary care physician is not the residual claimant: the cost savings in secondary care are 4.7 times larger than the cost savings in primary care. These activities are incentivized in the English National Health Service but the total financial incentives for primary care physicians to participate were considerably smaller than the total cost savings produced. This suggests that changes to the design of incentives to increase the marginal reward for conducting these preventive activities among patients with serious mental illness could have further increased welfare.