Showing papers by "National University of Singapore published in 2017"

Neural Collaborative Filtering

Abstract: In recent years, deep neural networks have yielded immense success on speech recognition, computer vision and natural language processing. However, the exploration of deep neural networks on recommender systems has received relatively less scrutiny. In this work, we strive to develop techniques based on neural networks to tackle the key problem in recommendation --- collaborative filtering --- on the basis of implicit feedback. Although some recent work has employed deep learning for recommendation, they primarily used it to model auxiliary information, such as textual descriptions of items and acoustic features of musics. When it comes to model the key factor in collaborative filtering --- the interaction between user and item features, they still resorted to matrix factorization and applied an inner product on the latent features of users and items. By replacing the inner product with a neural architecture that can learn an arbitrary function from data, we present a general framework named NCF, short for Neural network-based Collaborative Filtering. NCF is generic and can express and generalize matrix factorization under its framework. To supercharge NCF modelling with non-linearities, we propose to leverage a multi-layer perceptron to learn the user-item interaction function. Extensive experiments on two real-world datasets show significant improvements of our proposed NCF framework over the state-of-the-art methods. Empirical evidence shows that using deeper layers of neural networks offers better recommendation performance.

Energy-Efficient UAV Communication With Trajectory Optimization

Abstract: Wireless communication with unmanned aerial vehicles (UAVs) is a promising technology for future communication systems. In this paper, assuming that the UAV flies horizontally with a fixed altitude, we study energy-efficient UAV communication with a ground terminal via optimizing the UAV’s trajectory, a new design paradigm that jointly considers both the communication throughput and the UAV’s energy consumption. To this end, we first derive a theoretical model on the propulsion energy consumption of fixed-wing UAVs as a function of the UAV’s flying speed, direction, and acceleration. Based on the derived model and by ignoring the radiation and signal processing energy consumption, the energy efficiency of UAV communication is defined as the total information bits communicated normalized by the UAV propulsion energy consumed for a finite time horizon. For the case of unconstrained trajectory optimization, we show that both the rate-maximization and energy-minimization designs lead to vanishing energy efficiency and thus are energy-inefficient in general. Next, we introduce a simple circular UAV trajectory, under which the UAV’s flight radius and speed are jointly optimized to maximize the energy efficiency. Furthermore, an efficient design is proposed for maximizing the UAV’s energy efficiency with general constraints on the trajectory, including its initial/final locations and velocities, as well as minimum/maximum speed and acceleration. Numerical results show that the proposed designs achieve significantly higher energy efficiency for UAV communication as compared with other benchmark schemes.

SCA-CNN: Spatial and Channel-Wise Attention in Convolutional Networks for Image Captioning

Abstract: Visual attention has been successfully applied in structural prediction tasks such as visual captioning and question answering. Existing visual attention models are generally spatial, i.e., the attention is modeled as spatial probabilities that re-weight the last conv-layer feature map of a CNN encoding an input image. However, we argue that such spatial attention does not necessarily conform to the attention mechanism — a dynamic feature extractor that combines contextual fixations over time, as CNN features are naturally spatial, channel-wise and multi-layer. In this paper, we introduce a novel convolutional neural network dubbed SCA-CNN that incorporates Spatial and Channel-wise Attentions in a CNN. In the task of image captioning, SCA-CNN dynamically modulates the sentence generation context in multi-layer feature maps, encoding where (i.e., attentive spatial locations at multiple layers) and what (i.e., attentive channels) the visual attention is. We evaluate the proposed SCA-CNN architecture on three benchmark image captioning datasets: Flickr8K, Flickr30K, and MSCOCO. It is consistently observed that SCA-CNN significantly outperforms state-of-the-art visual attention-based image captioning methods.

Development and Validation of a Deep Learning System for Diabetic Retinopathy and Related Eye Diseases Using Retinal Images From Multiethnic Populations With Diabetes.

Abstract: Importance A deep learning system (DLS) is a machine learning technology with potential for screening diabetic retinopathy and related eye diseases. Objective To evaluate the performance of a DLS in detecting referable diabetic retinopathy, vision-threatening diabetic retinopathy, possible glaucoma, and age-related macular degeneration (AMD) in community and clinic-based multiethnic populations with diabetes. Design, Setting, and Participants Diagnostic performance of a DLS for diabetic retinopathy and related eye diseases was evaluated using 494 661 retinal images. A DLS was trained for detecting diabetic retinopathy (using 76 370 images), possible glaucoma (125 189 images), and AMD (72 610 images), and performance of DLS was evaluated for detecting diabetic retinopathy (using 112 648 images), possible glaucoma (71 896 images), and AMD (35 948 images). Training of the DLS was completed in May 2016, and validation of the DLS was completed in May 2017 for detection of referable diabetic retinopathy (moderate nonproliferative diabetic retinopathy or worse) and vision-threatening diabetic retinopathy (severe nonproliferative diabetic retinopathy or worse) using a primary validation data set in the Singapore National Diabetic Retinopathy Screening Program and 10 multiethnic cohorts with diabetes. Exposures Use of a deep learning system. Main Outcomes and Measures Area under the receiver operating characteristic curve (AUC) and sensitivity and specificity of the DLS with professional graders (retinal specialists, general ophthalmologists, trained graders, or optometrists) as the reference standard. Results In the primary validation dataset (n = 14 880 patients; 71 896 images; mean [SD] age, 60.2 [2.2] years; 54.6% men), the prevalence of referable diabetic retinopathy was 3.0%; vision-threatening diabetic retinopathy, 0.6%; possible glaucoma, 0.1%; and AMD, 2.5%. The AUC of the DLS for referable diabetic retinopathy was 0.936 (95% CI, 0.925-0.943), sensitivity was 90.5% (95% CI, 87.3%-93.0%), and specificity was 91.6% (95% CI, 91.0%-92.2%). For vision-threatening diabetic retinopathy, AUC was 0.958 (95% CI, 0.956-0.961), sensitivity was 100% (95% CI, 94.1%-100.0%), and specificity was 91.1% (95% CI, 90.7%-91.4%). For possible glaucoma, AUC was 0.942 (95% CI, 0.929-0.954), sensitivity was 96.4% (95% CI, 81.7%-99.9%), and specificity was 87.2% (95% CI, 86.8%-87.5%). For AMD, AUC was 0.931 (95% CI, 0.928-0.935), sensitivity was 93.2% (95% CI, 91.1%-99.8%), and specificity was 88.7% (95% CI, 88.3%-89.0%). For referable diabetic retinopathy in the 10 additional datasets, AUC range was 0.889 to 0.983 (n = 40 752 images). Conclusions and Relevance In this evaluation of retinal images from multiethnic cohorts of patients with diabetes, the DLS had high sensitivity and specificity for identifying diabetic retinopathy and related eye diseases. Further research is necessary to evaluate the applicability of the DLS in health care settings and the utility of the DLS to improve vision outcomes.

Progress in Exosome Isolation Techniques.

Abstract: Exosomes are one type of membrane vesicles secreted into extracellular space by most types of cells. In addition to performing many biological functions particularly in cell-cell communication, cumulative evidence has suggested that several biological entities in exosomes like proteins and microRNAs are closely associated with the pathogenesis of most human malignancies and they may serve as invaluable biomarkers for disease diagnosis, prognosis, and therapy. This provides a commanding impetus and growing demands for simple, efficient, and affordable techniques to isolate exosomes. Capitalizing on the physicochemical and biochemical properties of exosomes, a number of techniques have been developed for the isolation of exosomes. This article summarizes the advances in exosome isolation techniques with an emphasis on their isolation mechanism, performance, challenges, and prospects. We hope that this article will provide an overview of exosome isolation techniques, opening up new perspectives towards the development more innovative strategies and devices for more time saving, cost effective, and efficient isolations of exosomes from a wide range of biological matrices.

Parton distributions from high-precision collider data

Abstract: We present a new set of parton distributions, NNPDF31, which updates NNPDF30, the first global set of PDFs determined using a methodology validated by a closure test The update is motivated by recent progress in methodology and available data, and involves both On the methodological side, we now parametrize and determine the charm PDF alongside the light-quark and gluon ones, thereby increasing from seven to eight the number of independent PDFs On the data side, we now include the D0 electron and muon W asymmetries from the final Tevatron dataset, the complete LHCb measurements of W and Z production in the forward region at 7 and 8 TeV, and new ATLAS and CMS measurements of inclusive jet and electroweak boson production We also include for the first time top-quark pair differential distributions and the transverse momentum of the Z bosons from ATLAS and CMS We investigate the impact of parametrizing charm and provide evidence that the accuracy and stability of the PDFs are thereby improved We study the impact of the new data by producing a variety of determinations based on reduced datasets We find that both improvements have a significant impact on the PDFs, with some substantial reductions in uncertainties, but with the new PDFs generally in agreement with the previous set at the one-sigma level The most significant changes are seen in the light-quark flavor separation, and in increased precision in the determination of the gluon We explore the implications of NNPDF31 for LHC phenomenology at Run II, compare with recent LHC measurements at 13 TeV, provide updated predictions for Higgs production cross-sections and discuss the strangeness and charm content of the proton in light of our improved dataset and methodology The NNPDF31 PDFs are delivered for the first time both as Hessian sets, and as optimized Monte Carlo sets with a compressed number of replicas

Association analysis identifies 65 new breast cancer risk loci

Abstract: Breast cancer risk is influenced by rare coding variants in susceptibility genes, such as BRCA1, and many common, mostly non-coding variants. However, much of the genetic contribution to breast cancer risk remains unknown. Here we report the results of a genome-wide association study of breast cancer in 122,977 cases and 105,974 controls of European ancestry and 14,068 cases and 13,104 controls of East Asian ancestry. We identified 65 new loci that are associated with overall breast cancer risk at P < 5 × 10-8. The majority of credible risk single-nucleotide polymorphisms in these loci fall in distal regulatory elements, and by integrating in silico data to predict target genes in breast cells at each locus, we demonstrate a strong overlap between candidate target genes and somatic driver genes in breast tumours. We also find that heritability of breast cancer due to all single-nucleotide polymorphisms in regulatory features was 2-5-fold enriched relative to the genome-wide average, with strong enrichment for particular transcription factor binding sites. These results provide further insight into genetic susceptibility to breast cancer and will improve the use of genetic risk scores for individualized screening and prevention.

Recent Trends in Deep Learning Based Natural Language Processing

Abstract: Deep learning methods employ multiple processing layers to learn hierarchical representations of data and have produced state-of-the-art results in many domains. Recently, a variety of model designs and methods have blossomed in the context of natural language processing (NLP). In this paper, we review significant deep learning related models and methods that have been employed for numerous NLP tasks and provide a walk-through of their evolution. We also summarize, compare and contrast the various models and put forward a detailed understanding of the past, present and future of deep learning in NLP.

Topological-insulator-based terahertz modulator

Abstract: Three dimensional topological insulators, as a new phase of quantum matters, are characterized by an insulating gap in the bulk and a metallic state on the surface. Particularly, most of the topological insulators have narrow band gaps, and hence have promising applications in the area of terahertz optoelectronics. In this work, we experimentally demonstrate an electronically-tunable terahertz intensity modulator based on Bi1:5Sb0:5Te1:8Se1:2 single crystal, one of the most insulating topological insulators. A relative frequency-independent modulation depth of ~62% over a wide frequency range from 0.3 to 1.4 THz has been achieved at room temperature, by applying a bias current of 100 mA. The modulation in the low current regime can be further enhanced at low temperature. We propose that the extraordinarily large modulation is a consequence of thermally-activated carrier absorption in the semiconducting bulk states. Our work provides a new application of topological insulators for terahertz technology.

Chemotherapy for advanced gastric cancer

Abstract: Background Gastric cancer is the fifth most common cancer worldwide. In "Western" countries, most people are either diagnosed at an advanced stage, or develop a relapse after surgery with curative intent. In people with advanced disease, significant benefits from targeted therapies are currently limited to HER-2 positive disease treated with trastuzumab, in combination with chemotherapy, in first-line. In second-line, ramucirumab, alone or in combination with paclitaxel, demonstrated significant survival benefits. Thus, systemic chemotherapy remains the mainstay of treatment for advanced gastric cancer. Uncertainty remains regarding the choice of the regimen. Objectives To assess the efficacy of chemotherapy versus best supportive care (BSC), combination versus single-agent chemotherapy and different chemotherapy combinations in advanced gastric cancer. Search methods We searched the Cochrane Central Register of Controlled Trials, MEDLINE and Embase up to June 2016, reference lists of studies, and contacted pharmaceutical companies and experts to identify randomised controlled trials (RCTs). Selection criteria We considered only RCTs on systemic, intravenous or oral chemotherapy versus BSC, combination versus single-agent chemotherapy and different chemotherapy regimens in advanced gastric cancer. Data collection and analysis Two review authors independently identified studies and extracted data. A third investigator was consulted in case of disagreements. We contacted study authors to obtain missing information. Main results We included 64 RCTs, of which 60 RCTs (11,698 participants) provided data for the meta-analysis of overall survival. We found chemotherapy extends overall survival (OS) by approximately 6.7 months more than BSC (hazard ratio (HR) 0.3, 95% confidence intervals (CI) 0.24 to 0.55, 184 participants, three studies, moderate-quality evidence). Combination chemotherapy extends OS slightly (by an additional month) versus single-agent chemotherapy (HR 0.84, 95% CI 0.79 to 0.89, 4447 participants, 23 studies, moderate-quality evidence), which is partly counterbalanced by increased toxicity. The benefit of epirubicin in three-drug combinations, in which cisplatin is replaced by oxaliplatin and 5-FU is replaced by capecitabine is unknown.Irinotecan extends OS slightly (by an additional 1.6 months) versus non-irinotecan-containing regimens (HR 0.87, 95% CI 0.80 to 0.95, 2135 participants, 10 studies, high-quality evidence).Docetaxel extends OS slightly (just over one month) compared to non-docetaxel-containing regimens (HR 0.86, 95% CI 0.78 to 0.95, 2001 participants, eight studies, high-quality evidence). However, due to subgroup analyses, we are uncertain whether docetaxel-containing combinations (docetaxel added to a single-agent or two-drug combination) extends OS due to moderate-quality evidence (HR 0.80, 95% CI 0.71 to 0.91, 1466 participants, four studies, moderate-quality evidence). When another chemotherapy was replaced by docetaxel, there is probably little or no difference in OS (HR 1.05; 0.87 to 1.27, 479 participants, three studies, moderate-quality evidence). We found there is probably little or no difference in OS when comparing capecitabine versus 5-FU-containing regimens (HR 0.94, 95% CI 0.79 to 1.11, 732 participants, five studies, moderate-quality evidence) .Oxaliplatin may extend (by less than one month) OS versus cisplatin-containing regimens (HR 0.81, 95% CI 0.67 to 0.98, 1105 participants, five studies, low-quality evidence). We are uncertain whether taxane-platinum combinations with (versus without) fluoropyrimidines extend OS due to very low-quality evidence (HR 0.86, 95% CI 0.71 to 1.06, 482 participants, three studies, very low-quality evidence). S-1 regimens improve OS slightly (by less than an additional month) versus 5-FU-containing regimens (HR 0.91, 95% CI 0.83 to 1.00, 1793 participants, four studies, high-quality evidence), however since S-1 is used in different doses and schedules between Asian and non-Asian population, the applicability of this finding to individual populations is uncertain. Authors' conclusions Chemotherapy improves survival (by an additional 6.7 months) in comparison to BSC, and combination chemotherapy improves survival (by an additional month) compared to single-agent 5-FU. Testing all patients for HER-2 status may help to identify patients with HER-2-positive tumours, for whom, in the absence of contraindications, trastuzumab in combination with capecitabine or 5-FU in combination with cisplatin has been shown to be beneficial. For HER-2 negative people, all different two-and three-drug combinations including irinotecan, docetaxel, oxaliplatin or oral 5-FU prodrugs are valid treatment options for advanced gastric cancer, and consideration of the side effects of each regimen is essential in the treatment decision. Irinotecan-containing combinations and docetaxel-containing combinations (in which docetaxel was added to a single-agent or two-drug (platinum/5-FUcombination) show significant survival benefits in the comparisons studied above. Furthermore, docetaxel-containing three-drug regimens have increased response rates, but the advantages of the docetaxel-containing three-drug combinations (DCF, FLO-T) are counterbalanced by increased toxicity. Additionally, oxaliplatin-containing regimens demonstrated a benefit in OS as compared to the same regimen containing cisplatin, and there is a modest survival improvement of S-1 compared to 5-FU-containing regimens.Whether the survival benefit for three-drug combinations including cisplatin, 5-FU, and epirubicin as compared to the same regimen without epirubicin is still valid when second-line therapy is routinely administered and when cisplatin is replaced by oxaliplatin and 5-FU by capecitabine is questionable. Furthermore, the magnitude of the observed survival benefits for the three-drug regimens is not large enough to be clinically meaningful as defined recently by the American Society for Clinical Oncology (Ellis 2014). In contrast to the comparisons in which a survival benefit was observed by adding a third drug to a two-drug regimen at the cost of increased toxicity, the comparison of regimens in which another chemotherapy was replaced by irinotecan was associated with a survival benefit (of borderline statistical significance), but without increased toxicity. For this reason irinotecan/5-FU-containing combinations are an attractive option for first-line treatment. Although they need to be interpreted with caution, subgroup analyses of one study suggest that elderly people have a greater benefit form oxaliplatin, as compared to cisplatin-based regimens, and that people with locally advanced disease or younger than 65 years might benefit more from a three-drug regimen including 5-FU, docetaxel, and oxaliplatin as compared to a two-drug combination of 5-FU and oxaliplatin, a hypothesis that needs further confirmation. For people with good performance status, the benefit of second-line chemotherapy has been established in several RCTs.

Neural Factorization Machines for Sparse Predictive Analytics

Abstract: Many predictive tasks of web applications need to model categorical variables, such as user IDs and demographics like genders and occupations. To apply standard machine learning techniques, these categorical predictors are always converted to a set of binary features via one-hot encoding, making the resultant feature vector highly sparse. To learn from such sparse data effectively, it is crucial to account for the interactions between features. Factorization Machines (FMs) are a popular solution for efficiently using the second-order feature interactions. However, FM models feature interactions in a linear way, which can be insufficient for capturing the non-linear and complex inherent structure of real-world data. While deep neural networks have recently been applied to learn non-linear feature interactions in industry, such as the Wide&Deep by Google and DeepCross by Microsoft, the deep structure meanwhile makes them difficult to train. In this paper, we propose a novel model Neural Factorization Machine (NFM) for prediction under sparse settings. NFM seamlessly combines the linearity of FM in modelling second-order feature interactions and the non-linearity of neural network in modelling higher-order feature interactions. Conceptually, NFM is more expressive than FM since FM can be seen as a special case of NFM without hidden layers. Empirical results on two regression tasks show that with one hidden layer only, NFM significantly outperforms FM with a 7.3% relative improvement. Compared to the recent deep learning methods Wide&Deep and DeepCross, our NFM uses a shallower structure but offers better performance, being much easier to train and tune in practice.

An atlas of human long non-coding RNAs with accurate 5′ ends

Abstract: Long non-coding RNAs (lncRNAs) are largely heterogeneous and functionally uncharacterized. Here, using FANTOM5 cap analysis of gene expression (CAGE) data, we integrate multiple transcript collections to generate a comprehensive atlas of 27,919 human lncRNA genes with high-confidence 5' ends and expression profiles across 1,829 samples from the major human primary cell types and tissues. Genomic and epigenomic classification of these lncRNAs reveals that most intergenic lncRNAs originate from enhancers rather than from promoters. Incorporating genetic and expression data, we show that lncRNAs overlapping trait-associated single nucleotide polymorphisms are specifically expressed in cell types relevant to the traits, implicating these lncRNAs in multiple diseases. We further demonstrate that lncRNAs overlapping expression quantitative trait loci (eQTL)-associated single nucleotide polymorphisms of messenger RNAs are co-expressed with the corresponding messenger RNAs, suggesting their potential roles in transcriptional regulation. Combining these findings with conservation data, we identify 19,175 potentially functional lncRNAs in the human genome.

Placement Optimization of UAV-Mounted Mobile Base Stations

Abstract: In terrestrial communication networks without fixed infrastructure, unmanned aerial vehicle-mounted mobile base stations (MBSs) provide an efficient solution to achieve wireless connectivity. This letter aims to minimize the number of MBSs needed to provide wireless coverage for a group of distributed ground terminals (GTs), ensuring that each GT is within the communication range of at least one MBS. We propose a polynomial-time algorithm with successive MBS placement, where the MBSs are placed sequentially starting on the area perimeter of the uncovered GTs along a spiral path toward the center, until all GTs are covered. Numerical results show that the proposed algorithm performs favorably compared with other schemes in terms of the number of required MBSs as well as time complexity.

Discovery of a rich gene pool of bat SARS-related coronaviruses provides new insights into the origin of SARS coronavirus

Abstract: A large number of SARS-related coronaviruses (SARSr-CoV) have been detected in horseshoe bats since 2005 in different areas of China. However, these bat SARSr-CoVs show sequence differences from SARS coronavirus (SARS-CoV) in different genes (S, ORF8, ORF3, etc) and are considered unlikely to represent the direct progenitor of SARS-CoV. Herein, we report the findings of our 5-year surveillance of SARSr-CoVs in a cave inhabited by multiple species of horseshoe bats in Yunnan Province, China. The full-length genomes of 11 newly discovered SARSr-CoV strains, together with our previous findings, reveals that the SARSr-CoVs circulating in this single location are highly diverse in the S gene, ORF3 and ORF8. Importantly, strains with high genetic similarity to SARS-CoV in the hypervariable N-terminal domain (NTD) and receptor-binding domain (RBD) of the S1 gene, the ORF3 and ORF8 region, respectively, were all discovered in this cave. In addition, we report the first discovery of bat SARSr-CoVs highly similar to human SARS-CoV in ORF3b and in the split ORF8a and 8b. Moreover, SARSr-CoV strains from this cave were more closely related to SARS-CoV in the non-structural protein genes ORF1a and 1b compared with those detected elsewhere. Recombination analysis shows evidence of frequent recombination events within the S gene and around the ORF8 between these SARSr-CoVs. We hypothesize that the direct progenitor of SARS-CoV may have originated after sequential recombination events between the precursors of these SARSr-CoVs. Cell entry studies demonstrated that three newly identified SARSr-CoVs with different S protein sequences are all able to use human ACE2 as the receptor, further exhibiting the close relationship between strains in this cave and SARS-CoV. This work provides new insights into the origin and evolution of SARS-CoV and highlights the necessity of preparedness for future emergence of SARS-like diseases.

Rational Design of Metal‐Organic Framework Derived Hollow NiCo2O4 Arrays for Flexible Supercapacitor and Electrocatalysis

Abstract: Metal-organic frameworks (MOFs) are promising porous precursors for the construction of various functional materials for high-performance electrochemical energy storage and conversion. Herein, a facile two-step solution method to rational design of a novel electrode of hollow NiCo2O4 nanowall arrays on flexible carbon cloth substrate is reported. Uniform 2D cobalt-based wall-like MOFs are first synthesized via a solution reaction, and then the 2D solid nanowall arrays are converted into hollow and porous NiCo2O4 nanostructures through an ion-exchange and etching process with an additional annealing treatment. The as-obtained NiCo2O4 nanostructure arrays can provide rich reaction sites and short ion diffusion path. When evaluated as a flexible electrode material for supercapacitor, the as-fabricated NiCo2O4 nanowall electrode shows remarkable electrochemical performance with excellent rate capability and long cycle life. In addition, the hollow NiCo2O4 nanowall electrode exhibits promising electrocatalytic activity for oxygen evolution reaction. This work provides an example of rational design of hollow nanostructured metal oxide arrays with high electrochemical performance and mechanical flexibility, holding great potential for future flexible multifunctional electronic devices.

Reference component analysis of single-cell transcriptomes elucidates cellular heterogeneity in human colorectal tumors

Abstract: Intratumoral heterogeneity is a major obstacle to cancer treatment and a significant confounding factor in bulk-tumor profiling. We performed an unbiased analysis of transcriptional heterogeneity in colorectal tumors and their microenvironments using single-cell RNA-seq from 11 primary colorectal tumors and matched normal mucosa. To robustly cluster single-cell transcriptomes, we developed reference component analysis (RCA), an algorithm that substantially improves clustering accuracy. Using RCA, we identified two distinct subtypes of cancer-associated fibroblasts (CAFs). Additionally, epithelial-mesenchymal transition (EMT)-related genes were found to be upregulated only in the CAF subpopulation of tumor samples. Notably, colorectal tumors previously assigned to a single subtype on the basis of bulk transcriptomics could be divided into subgroups with divergent survival probability by using single-cell signatures, thus underscoring the prognostic value of our approach. Overall, our results demonstrate that unbiased single-cell RNA-seq profiling of tumor and matched normal samples provides a unique opportunity to characterize aberrant cell states within a tumor.

Electrocatalysis of polysulfide conversion by sulfur-deficient MoS2 nanoflakes for lithium–sulfur batteries

Abstract: Lithium–sulfur batteries are promising next-generation energy storage devices due to their high energy density and low material cost. Efficient conversion of lithium polysulfides to lithium sulfide (during discharge) and to sulfur (during recharge) is a performance-determining factor for lithium–sulfur batteries. Here we show that MoS2−x/reduced graphene oxide (MoS2−x/rGO) can be used to catalyze the polysulfide reactions to improve the battery performance. It was confirmed, through microstructural characterization of the materials, that sulfur deficiencies on the surface participated in the polysulfide reactions and significantly enhanced the polysulfide conversion kinetics. The fast conversion of soluble polysulfides decreased their accumulation in the sulfur cathode and their loss from the cathode by diffusion. Hence in the presence of a small amount of MoS2−x/rGO (4 wt% of the cathode mass), high rate (8C) performance of the sulfur cathode was improved from a capacity of 161.1 mA h g−1 to 826.5 mA h g−1. In addition, MoS2−x/rGO also enhanced the cycle stability of the sulfur cathode from a capacity fade rate of 0.373% per cycle (over 150 cycles) to 0.083% per cycle (over 600 cycles) at a typical 0.5C rate. These results provide direct experimental evidence for the catalytic role of MoS2−x/rGO in promoting the polysulfide conversion kinetics in the sulfur cathode.

Electromagnetic reprogrammable coding-metasurface holograms.

Abstract: Metasurfaces have enabled a plethora of emerging functions within an ultrathin dimension, paving way towards flat and highly integrated photonic devices. Despite the rapid progress in this area, simultaneous realization of reconfigurability, high efficiency, and full control over the phase and amplitude of scattered light is posing a great challenge. Here, we try to tackle this challenge by introducing the concept of a reprogrammable hologram based on 1-bit coding metasurfaces. The state of each unit cell of the coding metasurface can be switched between ‘1’ and ‘0’ by electrically controlling the loaded diodes. Our proof-of-concept experiments show that multiple desired holographic images can be realized in real time with only a single coding metasurface. The proposed reprogrammable hologram may be a key in enabling future intelligent devices with reconfigurable and programmable functionalities that may lead to advances in a variety of applications such as microscopy, display, security, data storage, and information processing. Realizing metasurfaces with reconfigurability, high efficiency, and control over phase and amplitude is a challenge. Here, Li et al. introduce a reprogrammable hologram based on a 1-bit coding metasurface, where the state of each unit cell of the coding metasurface can be switched electrically.

Attentive Collaborative Filtering: Multimedia Recommendation with Item- and Component-Level Attention

Abstract: Multimedia content is dominating today's Web information. The nature of multimedia user-item interactions is 1/0 binary implicit feedback (e.g., photo likes, video views, song downloads, etc.), which can be collected at a larger scale with a much lower cost than explicit feedback (e.g., product ratings). However, the majority of existing collaborative filtering (CF) systems are not well-designed for multimedia recommendation, since they ignore the implicitness in users' interactions with multimedia content. We argue that, in multimedia recommendation, there exists item- and component-level implicitness which blurs the underlying users' preferences. The item-level implicitness means that users' preferences on items (e.g. photos, videos, songs, etc.) are unknown, while the component-level implicitness means that inside each item users' preferences on different components (e.g. regions in an image, frames of a video, etc.) are unknown. For example, a 'view'' on a video does not provide any specific information about how the user likes the video (i.e.item-level) and which parts of the video the user is interested in (i.e.component-level). In this paper, we introduce a novel attention mechanism in CF to address the challenging item- and component-level implicit feedback in multimedia recommendation, dubbed Attentive Collaborative Filtering (ACF). Specifically, our attention model is a neural network that consists of two attention modules: the component-level attention module, starting from any content feature extraction network (e.g. CNN for images/videos), which learns to select informative components of multimedia items, and the item-level attention module, which learns to score the item preferences. ACF can be seamlessly incorporated into classic CF models with implicit feedback, such as BPR and SVD++, and efficiently trained using SGD. Through extensive experiments on two real-world multimedia Web services: Vine and Pinterest, we show that ACF significantly outperforms state-of-the-art CF methods.

Projecting social contact matrices in 152 countries using contact surveys and demographic data.

Abstract: Heterogeneities in contact networks have a major effect in determining whether a pathogen can become epidemic or persist at endemic levels. Epidemic models that determine which interventions can successfully prevent an outbreak need to account for social structure and mixing patterns. Contact patterns vary across age and locations (e.g. home, work, and school), and including them as predictors in transmission dynamic models of pathogens that spread socially will improve the models' realism. Data from population-based contact diaries in eight European countries from the POLYMOD study were projected to 144 other countries using a Bayesian hierarchical model that estimated the proclivity of age-and-location-specific contact patterns for the countries, using Markov chain Monte Carlo simulation. Household level data from the Demographic and Health Surveys for nine lower-income countries and socio-demographic factors from several on-line databases for 152 countries were used to quantify similarity of countries to estimate contact patterns in the home, work, school and other locations for countries for which no contact data are available, accounting for demographic structure, household structure where known, and a variety of metrics including workforce participation and school enrolment. Contacts are highly assortative with age across all countries considered, but pronounced regional differences in the age-specific contacts at home were noticeable, with more inter-generational contacts in Asian countries than in other settings. Moreover, there were variations in contact patterns by location, with work-place contacts being least assortative. These variations led to differences in the effect of social distancing measures in an age structured epidemic model. Contacts have an important role in transmission dynamic models that use contact rates to characterize the spread of contact-transmissible diseases. This study provides estimates of mixing patterns for societies for which contact data such as POLYMOD are not yet available.

BLOCKBENCH: A Framework for Analyzing Private Blockchains

Abstract: Blockchain technologies are taking the world by storm. Public blockchains, such as Bitcoin and Ethereum, enable secure peer-to-peer applications like crypto-currency or smart contracts. Their security and performance are well studied. This paper concerns recent private blockchain systems designed with stronger security (trust) assumption and performance requirement. These systems target and aim to disrupt applications which have so far been implemented on top of database systems, for example banking, finance and trading applications. Multiple platforms for private blockchains are being actively developed and fine tuned. However, there is a clear lack of a systematic framework with which different systems can be analyzed and compared against each other. Such a framework can be used to assess blockchains' viability as another distributed data processing platform, while helping developers to identify bottlenecks and accordingly improve their platforms. In this paper, we first describe BLOCKBENCH, the first evaluation framework for analyzing private blockchains. It serves as a fair means of comparison for different platforms and enables deeper understanding of different system design choices. Any private blockchain can be integrated to BLOCKBENCH via simple APIs and benchmarked against workloads that are based on real and synthetic smart contracts. BLOCKBENCH measures overall and component-wise performance in terms of throughput, latency, scalability and fault-tolerance. Next, we use BLOCKBENCH to conduct comprehensive evaluation of three major private blockchains: Ethereum, Parity and Hyperledger Fabric. The results demonstrate that these systems are still far from displacing current database systems in traditional data processing workloads. Furthermore, there are gaps in performance among the three systems which are attributed to the design choices at different layers of the blockchain's software stack. We have released BLOCKBENCH for public use.

Local-Global Parcellation of the Human Cerebral Cortex From Intrinsic Functional Connectivity MRI

Abstract: A central goal in systems neuroscience is the parcellation of the cerebral cortex into discrete neurobiological “atoms”. Resting-state functional magnetic resonance imaging (rs-fMRI) offers the possibility of in-vivo human cortical parcellation. Almost all previous parcellations relied on one of two approaches. The local gradient approach detects abrupt transitions in functional connectivity patterns. These transitions potentially reflect cortical areal boundaries defined by histology or visuotopic fMRI. By contrast, the global similarity approach clusters similar functional connectivity patterns regardless of spatial proximity, resulting in parcels with homogeneous (similar) rs-fMRI signals. Here we propose a gradient-weighted Markov Random Field (gwMRF) model integrating local gradient and global similarity approaches. Using task-fMRI and rs-fMRI across diverse acquisition protocols, we found gwMRF parcellations to be more homogeneous than four previously published parcellations. Furthermore, gwMRF parcellations agreed with the boundaries of certain cortical areas defined using histology and visuotopic fMRI. Some parcels captured sub-areal (somatotopic and visuotopic) features that likely reflect distinct computational units within known cortical areas. These results suggest that gwMRF parcellations reveal neurobiologically meaningful features of brain organization and are potentially useful for future applications requiring dimensionality reduction of voxel-wise fMRI data. Multi-resolution parcellations generated from 1489 participants are available at FREESURFER_WIKI LINK_TO_BE_ADDED.

Epigenome-wide association study of body mass index, and the adverse outcomes of adiposity

Abstract: Approximately 1.5 billion people worldwide are overweight or affected by obesity, and are at risk of developing type 2 diabetes, cardiovascular disease and related metabolic and inflammatory disturbances1,2. Although the mechanisms linking adiposity to associated clinical conditions are poorly understood, recent studies suggest that adiposity may influence DNA methylation3,4,5,6, a key regulator of gene expression and molecular phenotype7. Here we use epigenome-wide association to show that body mass index (BMI; a key measure of adiposity) is associated with widespread changes in DNA methylation (187 genetic loci with P < 1 × 10−7, range P = 9.2 × 10−8 to 6.0 × 10−46; n = 10,261 samples). Genetic association analyses demonstrate that the alterations in DNA methylation are predominantly the consequence of adiposity, rather than the cause. We find that methylation loci are enriched for functional genomic features in multiple tissues (P < 0.05), and show that sentinel methylation markers identify gene expression signatures at 38 loci (P < 9.0 × 10−6, range P = 5.5 × 10−6 to 6.1 × 10−35, n = 1,785 samples). The methylation loci identify genes involved in lipid and lipoprotein metabolism, substrate transport and inflammatory pathways. Finally, we show that the disturbances in DNA methylation predict future development of type 2 diabetes (relative risk per 1 standard deviation increase in methylation risk score: 2.3 (2.07–2.56); P = 1.1 × 10−54). Our results provide new insights into the biologic pathways influenced by adiposity, and may enable development of new strategies for prediction and prevention of type 2 diabetes and other adverse clinical consequences of obesity.