Miracle-dz/UniFAR-FTUs huggingface dataset:
Expectation-Maximization Regularized Deep Learning for Weakly Supervised Tumor Segmentation for Glioblastoma

We present an Expectation-Maximization (EM) Regularized Deep Learning (EMReDL) model for weakly supervised tumor segmentation. The proposed framework is tailored to glioblastoma, a type of malignant tumor characterized by its diffuse infiltration into the surrounding brain tissue, which poses significant challenge to treatment target and tumor burden estimation using conventional structural MRI. Although physiological MRI provides more specific information regarding tumor infiltration, the relatively low resolution hinders a precise full annotation. This has motivated us to develop a weakly supervised deep learning solution that exploits the partial labelled tumor regions. EMReDL contains two components: a physiological prior prediction model and EM-regularized segmentation model. The physiological prior prediction model exploits the physiological MRI by training a classifier to generate a physiological prior map. This map is passed to the segmentation model for regularization using the EM algorithm. We evaluated the model on a glioblastoma dataset with the pre-operative multiparametric and recurrence MRI available. EMReDL showed to effectively segment the infiltrated tumor from the partially labelled region of potential infiltration. The segmented core tumor and infiltrated tumor demonstrated high consistency with the tumor burden labelled by experts. The performance comparisons showed that EMReDL achieved higher accuracy than published state-of-the-art models. On MR spectroscopy, the segmented region displayed more aggressive features than other partial labelled region. The proposed model can be generalized to other segmentation tasks that rely on partial labels, with the CNN architecture flexible in the framework.
High-field spin-flop state in green dioptase

O. Prokhnenko,1, ∗ G. Marmorini,2 S. E. Nikitin,3, 4, † D. Yamamoto,2 A. Gazizulina,1 M. Bartkowiak,1 A. N. Ponomaryov,5, ‡ S. A. Zvyagin,5 H. Nojiri,6 I. F. Díaz-Ortega,6 L. M. Anovitz,7 A. I. Kolesnikov,8 and A. Podlesnyak8 1Helmholtz-Zentrum Berlin für Materialien und Energie, D-14109 Berlin, Germany 2Department of Physics and Mathematics, Aoyama-Gakuin University, Sagamihara, Kanagawa 252-5258, Japan 3Max Planck Institute for Chemical Physics of Solids, Nöthnitzer Str. 40, D-01187 Dresden, Germany 4Institut für Festkörperund Materialphysik, Technische Universität Dresden, D-01069 Dresden, Germany 5Dresden High Magnetic Field Laboratory (HLD-EMFL), Helmholtz-Zentrum Dresden-Rossendorf, 01328 Dresden, Germany 6Institute for Materials Research, Tohoku University, Sendai, 980-8578, Japan 7Chemical Sciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, USA 8Neutron Scattering Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, USA (Dated: January 22, 2021)
Mechanical Properties and Sensory Evaluation of Selected Apple Cultivars

Abstract Selected commercial apple cultivars (Golden Delicious, Stark Delicious, Renetta, Annurca, Granny Smith, Emperor) were studied to discriminate among the cultivars and the different levels of merceological quality for their mechanical and texture characteristics, by instrumental and sensory methods. Single compression, double compression and penetration tests were carried out to determine mechanical and texture properties of the apple flesh (failure stress and strain, modulus of deformability, juiciness, cohesiveness and firmness). Sensory responses were obtained by the descriptive technique of texture profile analysis and by the pairwise ranking test (paired comparisons of more than two samples) for the following parameters: fracturability, firmness, cohesiveness, mealiness and juiciness. Annurca and Granny Smith cultivars showed the highest values for almost all the instrumental and sensory parameters measured. The instrumental methods indicated that the internal variability of flesh structure influenced the differences among the cultivars, although some significant differences were clear. Results from the correlation study between instrumental and sensorial parameters were affected by the non-homogeneous distribution of the values in the correlation plots, probably due to the low number of samples considered. Principal component analysis applied to the instrumental results showed that the first two components described about 85% of the total variance. The first component was a linear combination of five variables, excluding the failure strain, while the second was a failure strain factor. It seems that only two parameters, the failure stress for instance and the failure strain, would be sufficient to point out differences among the cultivars and the merceological quality levels considered.
Blocked and Hierarchical Disentangled Representation From Information Theory Perspective

We propose a novel and theoretical model, blocked and hierarchical variational autoencoder (BHiVAE), to get better-disentangled representation. It is well known that information theory has an excellent explanatory meaning for the network, so we start to solve the disentanglement problem from the perspective of information theory. BHiVAE mainly comes from the information bottleneck theory and information maximization principle. Our main idea is that (1) Neurons block not only one neuron node is used to represent attribute, which can contain enough information; (2) Create a hierarchical structure with different attributes on different layers, so that we can segment the information within each layer to ensure that the final representation is disentangled. Furthermore, we present supervised and unsupervised BHiVAE, respectively, where the difference is mainly reflected in the separation of information between different blocks. In supervised BHiVAE, we utilize the label information as the standard to separate blocks. In unsupervised BHiVAE, without extra information, we use the Total Correlation (TC) measure to achieve independence, and we design a new prior distribution of the latent space to guide the representation learning. It also exhibits excellent disentanglement results in experiments and superior classification accuracy in representation learning.
Changing views about remote working during the COVID-19 pandemic: Evidence using panel data from Japan

COVID-19 has led to school closures in Japan to cope with the pandemic. Under the state of emergency, in addition to school closure, after-school care has not been sufficiently supplied. We independently collected individual level data through internet surveys to construct short panel data from mid-March to mid-June 2020, which covered before and after the state of emergency. We analyze how the presence of school-aged children influences their parents views about working from home. After controlling for various factors using a fixed effects model, we find that in cases where parents were workers, and the children are (1) in primary school, parents are willing to promote working from home. If children are (2) in junior high school, the parents view is hardly affected. (3) Surprisingly, workers whose children are primary school pupils are most likely to support promotion of working from home after schools reopen. Due to school closure and a lack of after-school care, parents need to work from home, and this experience motivated workers with small children to continue doing so to improve work-life balance even after schools reopen.
Overfitting for Fun and Profit: Instance-Adaptive Data Compression

Neural data compression has been shown to outperform classical methods in terms of $RD$ performance, with results still improving rapidly. At a high level, neural compression is based on an autoencoder that tries to reconstruct the input instance from a (quantized) latent representation, coupled with a prior that is used to losslessly compress these latents. Due to limitations on model capacity and imperfect optimization and generalization, such models will suboptimally compress test data in general. However, one of the great strengths of learned compression is that if the test-time data distribution is known and relatively low-entropy (e.g. a camera watching a static scene, a dash cam in an autonomous car, etc.), the model can easily be finetuned or adapted to this distribution, leading to improved $RD$ performance. In this paper we take this concept to the extreme, adapting the full model to a single video, and sending model updates (quantized and compressed using a parameter-space prior) along with the latent representation. Unlike previous work, we finetune not only the encoder/latents but the entire model, and - during finetuning - take into account both the effect of model quantization and the additional costs incurred by sending the model updates. We evaluate an image compression model on I-frames (sampled at 2 fps) from videos of the Xiph dataset, and demonstrate that full-model adaptation improves $RD$ performance by ~1 dB, with respect to encoder-only finetuning.
Mindless Attractor: A False-Positive Resistant Intervention for Drawing Attention Using Auditory Perturbation

Explicitly alerting users is not always an optimal intervention, especially when they are not motivated to obey. For example, in video-based learning, learners who are distracted from the video would not follow an alert asking them to pay attention. Inspired by the concept of Mindless Computing, we propose a novel intervention approach, Mindless Attractor, that leverages the nature of human speech communication to help learners refocus their attention without relying on their motivation. Specifically, it perturbs the voice in the video to direct their attention without consuming their conscious awareness. Our experiments not only confirmed the validity of the proposed approach but also emphasized its advantages in combination with a machine learning-based sensing module. Namely, it would not frustrate users even though the intervention is activated by false-positive detection of their attentive state. Our intervention approach can be a reliable way to induce behavioral change in human–AI symbiosis.
AT 2019avd: a novel addition to the diverse population of nuclear transients

We report on SRG/eROSITA, ZTF, ASAS-SN, Las Cumbres, NEOWISE-R, and Swift XRT/UVOT observations of the unique ongoing event AT 2019avd, located in the nucleus of a previously inactive galaxy at z = 0.029. eROSITA first observed AT 2019avd on 2020-04-28 during its first all sky survey, when it was detected as an ultra-soft X-ray source (kT ~ 85 eV) that was ≳90 times brighter in the 0.2−2 keV band than a previous 3σ upper flux detection limit (with no archival X-ray detection at this position). The ZTF optical light curve in the ~450 days preceding the eROSITA detection is double peaked, and the eROSITA detection coincides with the rise of the second peak. Follow-up optical spectroscopy shows the emergence of a Bowen fluorescence feature and high-ionisation coronal lines ([Fe X] 6375 Å, [Fe XIV] 5303 Å), along with persistent broad Balmer emission lines (FWHM ~ 1400 km s−1). Whilst the X-ray properties make AT 2019avd a promising tidal disruption event (TDE) candidate, the optical properties are atypical for optically selected TDEs. We discuss potential alternative origins that could explain the observed properties of AT 2019avd, such as a stellar binary TDE candidate, or a TDE involving a super massive black hole binary.
Quadratic Residual Networks: A New Class of Neural Networks for Solving Forward and Inverse Problems in Physics Involving PDEs

We propose quadratic residual networks (QRes) as a new type of parameter-efficient neural network architecture, by adding a quadratic residual term to the weighted sum of inputs before applying activation functions. With sufficiently high functional capacity (or expressive power), we show that it is especially powerful for solving forward and inverse physics problems involving partial differential equations (PDEs). Using tools from algebraic geometry, we theoretically demonstrate that, in contrast to plain neural networks, QRes shows better parameter efficiency in terms of network width and depth thanks to higher non-linearity in every neuron. Finally, we empirically show that QRes shows faster convergence speed in terms of number of training epochs especially in learning complex patterns.
The Role of Strong Gravity and the Nuclear Equation of State on Neutron-star Common-envelope Accretion

Common-envelope evolution is important in the formation of neutron star binaries within the isolated binary formation channel. As a neutron star inspirals within the envelope of a primary massive star, it accretes and spins up. Because neutron stars are in the strong-gravity regime, they have a substantial relativistic mass deficit, i.e., their gravitational mass is less than their baryonic mass. This effect causes some fraction of the accreted baryonic mass to convert into neutron star binding energy. The relativistic mass deficit also depends on the nuclear equation of state, since more compact neutron stars will have larger binding energies. We model the mass growth and spin-up of neutron stars inspiraling within common-envelope environments and quantify how different initial binary conditions and hadronic equations of state affect the post-common-envelope neutron star’s mass and spin. From these models, we find that neutron star mass growth is suppressed by ≈15%–30%. We also find that for a given amount of accreted baryonic mass, more compact neutron stars will spin-up faster while gaining less gravitational mass, and vice versa. This work demonstrates that a neutron star’s strong gravity and nuclear microphysics plays a role in neutron-star-common-envelope evolution, in addition to the macroscopic astrophysics of the envelope. Strong gravity and the nuclear equation of state may thus affect both the population properties of neutron star binaries and the cosmic double neutron star merger rate.
Learning rich touch representations through cross-modal self-supervision

The sense of touch is fundamental in several manipulation tasks, but rarely used in robot manipulation. In this work we tackle the problem of learning rich touch features from cross-modal self-supervision. We evaluate them identifying objects and their properties in a few-shot classification setting. Two new datasets are introduced using a simulated anthropomorphic robotic hand equipped with tactile sensors on both synthetic and daily life objects. Several self-supervised learning methods are benchmarked on these datasets, by evaluating few-shot classification on unseen objects and poses. Our experiments indicate that cross-modal self-supervision effectively improves touch representation, and in turn has great potential to enhance robot manipulation skills.
Reconstruction of twisted Steinberg algebras

We show how to recover a discrete twist over an ample Hausdorff groupoid from a pair consisting of an algebra and what we call a quasi-Cartan subalgebra. We identify precisely which twists arise in this way (namely, those that satisfy the local bisection hypothesis), and we prove that the assignment of twisted Steinberg algebras to such twists and our construction of a twist from a quasi-Cartan pair are mutually inverse. We identify the algebraic pairs that correspond to effective groupoids and to principal groupoids. We also indicate the scope of our results by identifying large classes of twists for which the local bisection hypothesis holds automatically.
Knowledge-Preserving Incremental Social Event Detection via Heterogeneous GNNs

Social events provide valuable insights into group social behaviors and public concerns and therefore have many applications in fields such as product recommendation and crisis management. The complexity and streaming nature of social messages make it appealing to address social event detection in an incremental learning setting, where acquiring, preserving, and extending knowledge are major concerns. Most existing methods, including those based on incremental clustering and community detection, learn limited amounts of knowledge as they ignore the rich semantics and structural information contained in social data. Moreover, they cannot memorize previously acquired knowledge. In this paper, we propose a novel Knowledge-Preserving Incremental Heterogeneous Graph Neural Network (KPGNN) for incremental social event detection. To acquire more knowledge, KPGNN models complex social messages into unified social graphs to facilitate data utilization and explores the expressive power of GNNs for knowledge extraction. To continuously adapt to the incoming data, KPGNN adopts contrastive loss terms that cope with a changing number of event classes. It also leverages the inductive learning ability of GNNs to efficiently detect events and extends its knowledge from previously unseen data. To deal with large social streams, KPGNN adopts a mini-batch subgraph sampling strategy for scalable training, and periodically removes obsolete data to maintain a dynamic embedding space. KPGNN requires no feature engineering and has few hyperparameters to tune. Extensive experiment results demonstrate the superiority of KPGNN over various baselines.
The Buildup of the Intracluster Light of A85 as Seen by Subaru’s Hyper Suprime-Cam

The study of low surface brightness light in large, deep imaging surveys is still uncharted territory as automated data reduction pipelines over-subtract or eliminate this light. Using archival data of the A85 cluster of galaxies taken with the Hyper Suprime-Cam on the Subaru Telescope, we show that using careful data processing can unveil the diffuse light within the cluster, the intracluster light. We reach surface brightness limits of (3σ, 10″ × 10″) = 30.9 and (3σ, 10″ × 10″) = 29.7 mag arcsec−2. We measured the radial surface brightness profiles of the brightest cluster galaxy out to the intracluster light (radius ∼215 kpc) for the g and i bands. We found that both the surface brightness and the color profiles become shallower beyond ∼75 kpc suggesting that a distinct component, the intracluster light, starts to dominate at that radius. The color of the profile at ∼100 kpc suggests that the buildup of the intracluster light of A85 occurs by the stripping of massive (∼1010 M ⊙) satellites. The measured fraction of this light ranges from 8%–30% in g, depending on the definition of intracluster light chosen.
Influence of hydrophilic and hydrophobic aerosil particles on the molecular modes in the liquid crystal 4‐n‐pentyl‐4′‐cyanobiphenyl

Dielectric spectroscopy in the frequency range 106–109 Hz was applied to investigate the influence of hydrophilic and hydrophobic aerosil particles on molecular processes in the liquid crystal 4‐n‐pentyl‐4′‐cyanobiphenyl. The dynamics of the molecular process in the isotropic phase is non‐Arrhenius; it weakly depends on the aerosil density and shows critical temperature dependence of the activation energy near the isotropic–nematic phase transition. The relaxation rate of the process related to the hindered rotation of the molecule around its molecular short axis (slower process) follows an Arrhenius law over about thirty degrees in the nematic range (except close to the phase transition) with an activation energy comparable to the bulk and is almost independent of the aerosil density. The relaxation rate of the process originating from the fluctuation of the molecular long axis around the director (librational mode, faster process), however, follows the Vogel–Fulcher–Tammann law. With increasing disorder achieved by increasing the aerosil density, the relative dielectric strength of the librational mode increases in comparison with the bulk. The relaxation frequency of the slower process increases but that of the faster process decreases with increasing aerosil density. Both these effects are less pronounced for hydrophobic than for hydrophilic aerosils.
Oxygen diffusion mechanism in the mixed ion-electron conductor NdBaCo2O5+x

Double perovskite cobaltites were recently presented as promising cathode materials for solid oxide fuel cells. While an atomistic mechanism was proposed for oxygen diffusion in this family of materials, no direct experimental proof has been presented so far. We report here the first study that directly compares experimental and theoretical diffusion pathways of oxygen in an oxide, namely in the double cobaltite compound, NdBaCo2O5+x. Model-free experimental nuclear density maps are obtained from the maximum entropy method combined with Rietveld refinement against high resolution neutron diffraction data collected at 1173 K. They are then compared to theoretical maps resulting from classical molecular dynamics calculations. The analysis of 3D maps of atomic densities allows identifying unambiguously the pathways and the mechanisms involved in the oxide ion diffusion. It is shown that oxygen diffusion occurs along a complex trajectory between Nd- and Co-containing a,b planes. The study also reveals that Ba-containing planes act as a barrier for oxygen diffusion. The diffusion mechanism is also supported through the oxygen sites occupancy analysis that confirms the increase of oxygen vacancies in the cobalt-planes on heating. The use of such combined experimental and theoretical analysis should be considered as a very powerful approach for materials design.
An exploration of the experience of trainee integrative psychotherapists on therapeutic alliance in the light of their attachment style

Therapeutic Relationship has been regarded as a profound element of the psychotherapeutic procedure and crucial for the outcome of therapy. Research evidence so far claims that both therapist’s and client’s personality can impact the Therapeutic Relationship. Based on the well-researched Attachment Theory there has been some research mostly focused on how clients’ attachment patterns can affect the Alliance. Limited research, though, on how therapists experience the Relationship in relation with their Attachment Styles do exist. The current study investigates trainee therapists’ experience of the Therapeutic Relationship in the light of their Attachment Style. A qualitative approach was used for this research to investigate in depth the experience of five trainee Integrative psychotherapists who were recruited to take part in semi-structured interviews. Interviews were transcribed and verbatim was divided into superordinate and subordinate themes and analyzed by Interpretative Phenomenological Analysis method. Four master themes were identified. Findings were then compared and discussed according to existing literature. All participants referred to the relevance of Attachment Theory in psychotherapy. They also agreed on the activation of their Attachment Styles during the therapeutic process. The securely attached therapist confirms previous studies on her capability to offer a secure base for her clients. On the other hand therapists with insecure Attachment Styles appeared sensitive in their collaboration mostly with insecure clients. Anxiously attached therapists proved to experience difficulty in the initial stages of the Therapeutic Relationship while the combination of avoidant therapist-avoidant client appeared to be the most demanding one, regarding the concept of trust and relationship ruptures. We suggest that the exploration of trainee therapists’ Attachment Styles as well as the study of the Attachment Theory should be added to all Counseling and Psychotherapy curricula regardless the therapeutic orientation because of its contribution to forming positive Therapeutic Relationships.