Image Cropping with Composition and Saliency Aware Aesthetic Score Map

Aesthetic image cropping is a practical but challenging task which aims at finding the best crops with the highest aesthetic quality in an image. Recently, many deep learning methods have been proposed to address this problem, but they did not reveal the intrinsic mechanism of aesthetic evaluation. In this paper, we propose an interpretable image cropping model to unveil the mystery. For each image, we use a fully convolutional network to produce an aesthetic score map, which is shared among all candidate crops during crop-level aesthetic evaluation. Then, we require the aesthetic score map to be both composition-aware and saliency-aware. In particular, the same region is assigned with different aesthetic scores based on its relative positions in different crops. Moreover, a visually salient region is supposed to have more sensitive aesthetic scores so that our network can learn to place salient objects at more proper positions. Such an aesthetic score map can be used to localize aesthetically important regions in an image, which sheds light on the composition rules learned by our model. We show the competitive performance of our model in the image cropping task on several benchmark datasets, and also demonstrate its generality in real-world applications.Comment: Accepted by AAAI 2

Identifying Dynamic Protein Complexes Based on Gene Expression Profiles and PPI Networks

Identification of protein complexes fromprotein-protein interaction networks has become a key problem for understanding cellular life in postgenomic era. Many computational methods have been proposed for identifying protein complexes. Up to now, the existing computational methods are mostly applied on static PPI networks. However, proteins and their interactions are dynamic in reality. Identifying dynamic protein complexes is more meaningful and challenging. In this paper, a novel algorithm, named DPC, is proposed to identify dynamic protein complexes by integrating PPI data and gene expression profiles. According to Core-Attachment assumption, these proteins which are always active in the molecular cycle are regarded as core proteins. The protein-complex cores are identified from these always active proteins by detecting dense subgraphs. Final protein complexes are extended from the protein-complex cores by adding attachments based on a topological character of “closeness” and dynamic meaning. The protein complexes produced by our algorithm DPC contain two parts: static core expressed in all the molecular cycle and dynamic attachments short-lived.The proposed algorithm DPC was applied on the data of Saccharomyces cerevisiae and the experimental results show that DPC outperforms CMC, MCL, SPICi, HC-PIN, COACH, and Core-Attachment based on the validation of matching with known complexes and hF-measures

Charge states, triple points and quadruple points in an InAs nanowire triple quantum dot revealed by an integrated charge sensor

A serial triple quantum dot (TQD) integrated with a quantum dot (QD) charge sensor is realized from an InAs nanowire via a fine finger-gate technique. The complex charge states and intriguing properties of the device are studied in the few-electron regime by direct transport measurements and by charge-sensor detection measurements. The measurements of the charge stability diagram for a capacitively coupled, parallel double-QD formed from a QD in the TQD and the sensor QD show a visible capacitance coupling between the TQD and the sensor QD, indicating a good sensitivity of the charge sensor. The charge stability diagrams of the TQD are measured by the charge sensor and the global features seen in the measured charge stability diagrams are well reproduced by the simultaneous measurements of the direct transport current through the TQD and by the simulation made based on an effective capacitance network model. The complex charge stability diagrams of the TQD are measured in detail with the integrated charge sensor in an energetically degenerate region, where all the three QDs are on or nearly on resonance, and the formations of quadruple points and of all possible eight charge states are observed. In addition, the operation of the TQD as a quantum cellular automata is demonstrated and discussed.Comment: 18 pages, 4 figures, Supplementary Information include

DeVLBert: Learning Deconfounded Visio-Linguistic Representations

In this paper, we propose to investigate the problem of out-of-domain visio-linguistic pretraining, where the pretraining data distribution differs from that of downstream data on which the pretrained model will be fine-tuned. Existing methods for this problem are purely likelihood-based, leading to the spurious correlations and hurt the generalization ability when transferred to out-of-domain downstream tasks. By spurious correlation, we mean that the conditional probability of one token (object or word) given another one can be high (due to the dataset biases) without robust (causal) relationships between them. To mitigate such dataset biases, we propose a Deconfounded Visio-Linguistic Bert framework, abbreviated as DeVLBert, to perform intervention-based learning. We borrow the idea of the backdoor adjustment from the research field of causality and propose several neural-network based architectures for Bert-style out-of-domain pretraining. The quantitative results on three downstream tasks, Image Retrieval (IR), Zero-shot IR, and Visual Question Answering, show the effectiveness of DeVLBert by boosting generalization ability.Comment: 10 pages, 4 figures, to appear in ACM MM 2020 proceeding

Prediction of recurrence of ischemic stroke within 1 year of discharge based on machine learning MRI radiomics

PurposeThis study aimed to investigate the value of a machine learning-based magnetic resonance imaging (MRI) radiomics model in predicting the risk of recurrence within 1 year following an acute ischemic stroke (AIS).MethodsThe MRI and clinical data of 612 patients diagnosed with AIS at the Second Affiliated Hospital of Nanchang University from March 1, 2019, to March 5, 2021, were obtained. The patients were divided into recurrence and non-recurrence groups according to whether they had a recurrent stroke within 1 year after discharge. Randomized splitting was used to divide the data into training and validation sets using a ratio of 7:3. Two radiologists used the 3D-slicer software to label the lesions on brain diffusion-weighted (DWI) MRI sequences. Radiomics features were extracted from the annotated images using the pyradiomics software package, and the features were filtered using the Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis. Four machine learning algorithms, logistic regression (LR), Support Vector Classification (SVC), LightGBM, and Random forest (RF), were used to construct a recurrence prediction model. For each algorithm, three models were constructed based on the MRI radiomics features, clinical features, and combined MRI radiomics and clinical features. The sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve (AUC) were used to compare the predictive efficacy of the models.ResultsTwenty features were selected from 1,037 radiomics features extracted from DWI images. The LightGBM model based on data with three different features achieved the best prediction accuracy from all 4 models in the validation set. The LightGBM model based solely on radiomics features achieved a sensitivity, specificity, and AUC of 0.65, 0.671, and 0.647, respectively, and the model based on clinical data achieved a sensitivity, specificity, and AUC of 0.7, 0.799, 0.735, respectively. The sensitivity, specificity, and AUC of the LightGBM model base on both radiomics and clinical features achieved the best performance with a sensitivity, specificity, and AUC of 0.85, 0.805, 0.789, respectively.ConclusionThe ischemic stroke recurrence prediction model based on LightGBM achieved the best prediction of recurrence within 1 year following an AIS. The combination of MRI radiomics features and clinical data improved the prediction performance of the model

Structural Characterization of Mesoporous Silica Nanofibers Synthesized Within Porous Alumina Membranes

Mesoporous silica nanofibers were synthesized within the pores of the anodic aluminum oxide template using a simple sol–gel method. Transmission electron microscopy investigation indicated that the concentration of the structure-directing agent (EO20PO70EO20) had a significant impact on the mesostructure of mesoporous silica nanofibers. Samples with alignment of nanochannels along the axis of mesoporous silica nanofibers could be formed under the P123 concentration of 0.15 mg/mL. When the P123 concentration increased to 0.3 mg/mL, samples with a circular lamellar mesostructure could be obtained. The mechanism for the effect of the P123 concentration on the mesostructure of mesoporous silica nanofibres was proposed and discussed

The Effect of Plasma Triglyceride-Lowering Therapy on the Evolution of Organ Function in Early Hypertriglyceridemia-Induced Acute Pancreatitis Patients With Worrisome Features (PERFORM Study): Rationale and Design of a Multicenter, Prospective, Observational, Cohort Study

Background: Acute pancreatitis (AP) is a potentially life-threatening inflammatory disease with multiple etiologies. The prevalence of hypertriglyceridemia-induced acute pancreatitis (HTG-AP) has been increasing in recent years. It is reported that early triglyceride (TG) levels were associated with the severity of the disease, and TG- lowering therapies, including medical treatment and blood purification, may impact the clinical outcomes. However, there is no consensus regarding the optimal TG-lowering therapy, and clinical practice varies greatly among different centers. Our objective is to evaluate the TG-lowering effects of different therapies and their impact on clinical outcomes in HTG-AP patients with worrisome features. Methods: This is a multicenter, observational, prospective cohort study. A total of approximately 300 patients with HTG-AP with worrisome features are planned to be enrolled. The primary objective of the study is to evaluate the relationship between TG decline and the evolution of organ failure, and patients will be dichotomized depending on the rate of TG decline. The primary outcome is organ failure (OF) free days to 14 days after enrollment. Secondary outcomes include new-onset organ failure, new-onset multiple-organ failure (MOF), new-onset persistent organ failure (POF), new receipt of organ support, requirement of ICU admission, ICU free days to day 14, hospital free days to day 14, 60-day mortality, AP severity grade (Based on the Revised Atlanta Classification), and incidence of systemic and local complications. Generalized linear model (GLM), Fine and Gray competing risk regression, and propensity score matching will be used for statistical analysis. Discussion: Results of this study will reveal the current practice of TG-lowering therapy in HTG-AP and provide necessary data for future trials

Search for new particles in events with energetic jets and large missing transverse momentum in proton-proton collisions at root s=13 TeV

A search is presented for new particles produced at the LHC in proton-proton collisions at root s = 13 TeV, using events with energetic jets and large missing transverse momentum. The analysis is based on a data sample corresponding to an integrated luminosity of 101 fb(-1), collected in 2017-2018 with the CMS detector. Machine learning techniques are used to define separate categories for events with narrow jets from initial-state radiation and events with large-radius jets consistent with a hadronic decay of a W or Z boson. A statistical combination is made with an earlier search based on a data sample of 36 fb(-1), collected in 2016. No significant excess of events is observed with respect to the standard model background expectation determined from control samples in data. The results are interpreted in terms of limits on the branching fraction of an invisible decay of the Higgs boson, as well as constraints on simplified models of dark matter, on first-generation scalar leptoquarks decaying to quarks and neutrinos, and on models with large extra dimensions. Several of the new limits, specifically for spin-1 dark matter mediators, pseudoscalar mediators, colored mediators, and leptoquarks, are the most restrictive to date.Peer reviewe