The recent discovery of superconductivity in infinite-layer nickelates has motivated tremendous efforts to study these materials that are analogous to cuprates. However, superconductivity in infinite-layer nickelates has been realized only in thin fi
lms grown on SrTiO$_3$ substrates, thus raising the question whether it is interface-induced and the query into the role of SrTiO$_3$ substrate. Here, we report the observation of superconductivity in Pr$_{0.8}$Sr$_{0.2}$NiO$_2$ films prepared at almost the same conditions except they are grown on different substrates (LaAlO$_3$)$_{0.3}$(Sr$_2$AlTaO$_6$)$_{0.7}$ (LSAT) and SrTiO$_3$ with the corresponding onset of superconductivity maximized at 15 K and 9K, respectively. Our results not only suggest that the superconductivity in infinite-layer nickelates is unlikely an interface-induced phenomenon and that the SrTiO$_3$ substrate is not a necessary for the emergence of superconductivity, but also indicate that the compressive strain can possibly increase T$_c$ of Pr$_{0.8}$Sr$_{0.2}$NiO$_2$.
Trip recommendation is a significant and engaging location-based service that can help new tourists make more customized travel plans. It often attempts to suggest a sequence of point of interests (POIs) for a user who requests a personalized travel
demand. Conventional methods either leverage the heuristic algorithms (e.g., dynamic programming) or statistical analysis (e.g., Markov models) to search or rank a POI sequence. These procedures may fail to capture the diversity of human needs and transitional regularities. They even provide recommendations that deviate from tourists real travel intention when the trip data is sparse. Although recent deep recursive models (e.g., RNN) are capable of alleviating these concerns, existing solutions hardly recognize the practical reality, such as the diversity of tourist demands, uncertainties in the trip generation, and the complex visiting preference. Inspired by the advance in deep learning, we introduce a novel self-supervised representation learning framework for trip recommendation -- SelfTrip, aiming at tackling the aforementioned challenges. Specifically, we propose a two-step contrastive learning mechanism concerning the POI representation, as well as trip representation. Furthermore, we present four trip augmentation methods to capture the visiting uncertainties in trip planning. We evaluate our SelfTrip on four real-world datasets, and extensive results demonstrate the promising gain compared with several cutting-edge benchmarks, e.g., up to 4% and 12% on F1 and pair-F1, respectively.
Infrared small target detection plays an important role in the infrared search and tracking applications. In recent years, deep learning techniques were introduced to this task and achieved noteworthy effects. Following general object segmentation me
thods, existing deep learning methods usually processed the image from the global view. However, the imaging locality of small targets and extreme class-imbalance between the target and background pixels were not well-considered by these deep learning methods, which causes the low-efficiency on training and high-dependence on numerous data. A focally multi-patch network (FMPNet) is proposed in this paper to detect small targets by jointly considering the global and local properties of infrared small target images. From the global view, a supervised attention module trained by the small target spread map is proposed to suppress most background pixels irrelevant with small target features. From the local view, local patches are split from global features and share the same convolution weights with each other in a patch net. By synthesizing the global and local properties, the data-driven framework proposed in this paper has fused multi-scale features for small target detection. Extensive synthetic and real data experiments show that the proposed method achieves the state-of-the-art performance compared with existing both conventional and deep learning methods.
Image Quality Assessment (IQA) is important for scientific inquiry, especially in medical imaging and machine learning. Potential data quality issues can be exacerbated when human-based workflows use limited views of the data that may obscure digital
artifacts. In practice, multiple factors such as network issues, accelerated acquisitions, motion artifacts, and imaging protocol design can impede the interpretation of image collections. The medical image processing community has developed a wide variety of tools for the inspection and validation of imaging data. Yet, IQA of computed tomography (CT) remains an under-recognized challenge, and no user-friendly tool is commonly available to address these potential issues. Here, we create and illustrate a pipeline specifically designed to identify and resolve issues encountered with large-scale data mining of clinically acquired CT data. Using the widely studied National Lung Screening Trial (NLST), we have identified approximately 4% of image volumes with quality concerns out of 17,392 scans. To assess robustness, we applied the proposed pipeline to our internal datasets where we find our tool is generalizable to clinically acquired medical images. In conclusion, the tool has been useful and time-saving for research study of clinical data, and the code and tutorials are publicly available at https://github.com/MASILab/QA_tool.
Data from multi-modality provide complementary information in clinical prediction, but missing data in clinical cohorts limits the number of subjects in multi-modal learning context. Multi-modal missing imputation is challenging with existing methods
when 1) the missing data span across heterogeneous modalities (e.g., image vs. non-image); or 2) one modality is largely missing. In this paper, we address imputation of missing data by modeling the joint distribution of multi-modal data. Motivated by partial bidirectional generative adversarial net (PBiGAN), we propose a new Conditional PBiGAN (C-PBiGAN) method that imputes one modality combining the conditional knowledge from another modality. Specifically, C-PBiGAN introduces a conditional latent space in a missing imputation framework that jointly encodes the available multi-modal data, along with a class regularization loss on imputed data to recover discriminative information. To our knowledge, it is the first generative adversarial model that addresses multi-modal missing imputation by modeling the joint distribution of image and non-image data. We validate our model with both the national lung screening trial (NLST) dataset and an external clinical validation cohort. The proposed C-PBiGAN achieves significant improvements in lung cancer risk estimation compared with representative imputation methods (e.g., AUC values increase in both NLST (+2.9%) and in-house dataset (+4.3%) compared with PBiGAN, p$<$0.05).
To enhance the evacuation efficiency in partially observable asymmetric-exit evacuation, a general framework of dynamic guiding assistant system with density control algorithm is investigated. In this framework, several evacuation assistants are esta
blished to observe the partial information of pedestrians location and adjust the guiding signals of the dynamic guiding assistant systems. A simple on-off-based density control algorithm is proposed for the evacuation assistants according to the delayed data of the observed information (i.e., pedestrian densities in the observed regions near the corresponding exits). By involving a force-driven cellular automaton model, the strategic suggestions on how to set the observed region and the target density are given in this paper. It is observed that the proposed density control algorithm can control (positively affect) the global distribution of the pedestrians locations and suppress arching phenomena in the evacuation process even using the observed partial information under time delays. By imposing a moderate target density, the dynamic guiding assistant system also suppresses the triggers of collisions around the exits and avoids separating the pedestrians to an inefficient way. We reveal an interesting fact without loss of generality that to enhance the evacuation efficiency, we only need to observe the pedestrians location from a small region near the exit instead of a large region when the time delay of the observed information is small enough. Our numerical findings are expected to provide some new insights on designing the computer-aided guiding strategies in the real evacuations.
The Kagome superconductors AV$_3$Sb$_5$ (A=K, Rb, Cs) have received enormous attention due to their nontrivial topological electronic structure, anomalous physical properties and superconductivity. Unconventional charge density wave (CDW) has been de
tected in AV$_3$Sb$_5$ that is found to be intimately intertwined with the anomalous Hall effect and superconductivity. High-precision electronic structure determination is essential to understand the origin of the CDW transition and its interplay with electron correlation, topology and superconductivity, yet, little evidence has been found about the impact of the CDW state on the electronic structure in AV$_3$Sb$_5$. Here we unveil electronic nature of the CDW phase in our high-resolution angle-resolved photoemission (ARPES) measurements on KV$_3$Sb$_5$. We have observed CDW-induced Fermi surface reconstruction and the associated band structure folding. The CDW-induced band splitting and the associated gap opening have been revealed at the boundary of the pristine and reconstructed Brillouin zone. The Fermi surface- and momentum-dependent CDW gap is measured for the first time and the strongly anisotropic CDW gap is observed for all the V-derived Fermi surface sheets. In particular, we have observed signatures of the electron-phonon coupling for all the V-derived bands. These results provide key insights in understanding the nature of the CDW state and its interplay with superconductivity in AV$_3$Sb$_5$ superconductors.
High temperature superconductivity in cuprates arises from doping a parent Mott insulator by electrons or holes. A central issue is how the Mott gap evolves and the low-energy states emerge with doping. Here we report angle-resolved photoemission spe
ctroscopy measurements on a cuprate parent compound by sequential in situ electron doping. The chemical potential jumps to the bottom of the upper Hubbard band upon a slight electron doping, making it possible to directly visualize the charge transfer band and the full Mott gap region. With increasing doping, the Mott gap rapidly collapses due to the spectral weight transfer from the charge transfer band to the gapped region and the induced low-energy states emerge in a wide energy range inside the Mott gap. These results provide key information on the electronic evolution in doping a Mott insulator and establish a basis for developing microscopic theories for cuprate superconductivity.
The recent observation of superconductivity in infinite-layer nickelate Nd$_{0.8}$Sr$_{0.2}$NiO$_{2}$ has received considerable attention. Despite the many efforts to understand the superconductivity in infinite-layer nickelates, a consensus on the u
nderlying mechanism for the superconductivity has yet to be reached, partly owing to the challenges with the material synthesis. Here, we report the successful growth of superconducting infinite-layer Nd$_{0.8}$Sr$_{0.2}$NiO$_{2}$ films by pulsed-laser deposition and soft chemical reduction. The details on growth process will be discussed.
A major goal of lung cancer screening is to identify individuals with particular phenotypes that are associated with high risk of cancer. Identifying relevant phenotypes is complicated by the variation in body position and body composition. In the br
ain, standardized coordinate systems (e.g., atlases) have enabled separate consideration of local features from gross/global structure. To date, no analogous standard atlas has been presented to enable spatial mapping and harmonization in chest computational tomography (CT). In this paper, we propose a thoracic atlas built upon a large low dose CT (LDCT) database of lung cancer screening program. The study cohort includes 466 male and 387 female subjects with no screening detected malignancy (age 46-79 years, mean 64.9 years). To provide spatial mapping, we optimize a multi-stage inter-subject non-rigid registration pipeline for the entire thoracic space. We evaluate the optimized pipeline relative to two baselines with alternative non-rigid registration module: the same software with default parameters and an alternative software. We achieve a significant improvement in terms of registration success rate based on manual QA. For the entire study cohort, the optimized pipeline achieves a registration success rate of 91.7%. The application validity of the developed atlas is evaluated in terms of discriminative capability for different anatomic phenotypes, including body mass index (BMI), chronic obstructive pulmonary disease (COPD), and coronary artery calcification (CAC).
