A Novel Document Generation Process for Topic Detection based on Hierarchical Latent Tree Models

We propose a novel document generation process based on hierarchical latent tree models (HLTMs) learned from data. An HLTM has a layer of observed word variables at the bottom and multiple layers of latent variables on top. For each document, we first sample values for the latent variables layer by layer via logic sampling, then draw relative frequencies for the words conditioned on the values of the latent variables, and finally generate words for the document using the relative word frequencies. The motivation for the work is to take word counts into consideration with HLTMs. In comparison with LDA-based hierarchical document generation processes, the new process achieves drastically better model fit with much fewer parameters. It also yields more meaningful topics and topic hierarchies. It is the new state-of-the-art for the hierarchical topic detection

SECaps: A Sequence Enhanced Capsule Model for Charge Prediction

Automatic charge prediction aims to predict appropriate final charges according to the fact descriptions for a given criminal case. Automatic charge prediction plays a critical role in assisting judges and lawyers to improve the efficiency of legal decisions, and thus has received much attention. Nevertheless, most existing works on automatic charge prediction perform adequately on high-frequency charges but are not yet capable of predicting few-shot charges with limited cases. In this paper, we propose a Sequence Enhanced Capsule model, dubbed as SECaps model, to relieve this problem. Specifically, following the work of capsule networks, we propose the seq-caps layer, which considers sequence information and spatial information of legal texts simultaneously. Then we design a attention residual unit, which provides auxiliary information for charge prediction. In addition, our SECaps model introduces focal loss, which relieves the problem of imbalanced charges. Comparing the state-of-the-art methods, our SECaps model obtains 4.5% and 6.4% absolutely considerable improvements under Macro F1 in Criminal-S and Criminal-L respectively. The experimental results consistently demonstrate the superiorities and competitiveness of our proposed model.Comment: 13 pages, 3figures, 5 table

A quasi-Monte Carlo method for computing areas of point-sampled surfaces

A novel and efficient quasi-Monte Carlo method for computing the area of a point-sampled surface with associated surface normal for each point is presented. Our method operates directly on the point cloud without any surface reconstruction procedure. Using the Cauchy–Crofton formula, the area of the point-sampled surface is calculated by counting the number of intersection points between the point cloud and a set of uniformly distributed lines generated with low-discrepancy sequences. Based on a clustering technique, we also propose an effective algorithm for computing the intersection points of a line with the point-sampled surface. By testing on a number of point-based models, experiments suggest that our method is more robust and more efficient than those conventional approaches based on surface reconstruction.postprin

A cross-center smoothness prior for variational Bayesian brain tissue segmentation

Suppose one is faced with the challenge of tissue segmentation in MR images, without annotators at their center to provide labeled training data. One option is to go to another medical center for a trained classifier. Sadly, tissue classifiers do not generalize well across centers due to voxel intensity shifts caused by center-specific acquisition protocols. However, certain aspects of segmentations, such as spatial smoothness, remain relatively consistent and can be learned separately. Here we present a smoothness prior that is fit to segmentations produced at another medical center. This informative prior is presented to an unsupervised Bayesian model. The model clusters the voxel intensities, such that it produces segmentations that are similarly smooth to those of the other medical center. In addition, the unsupervised Bayesian model is extended to a semi-supervised variant, which needs no visual interpretation of clusters into tissues.Comment: 12 pages, 2 figures, 1 table. Accepted to the International Conference on Information Processing in Medical Imaging (2019

Dual Drug-Loaded Biofunctionalized Amphiphilic Chitosan Nanoparticles: Enhanced Synergy between Cisplatin and Demethoxycurcumin against Multidrug-Resistant Stem-Like Lung Cancer Cells

Lung cancer kills more humans than any other cancer and multidrug resistance (MDR) in cancer stem-like cells (CSC) is emerging as a reason for failed treatments. One concept which addresses this root cause of treatment failure is the utilization of nanoparticles to simultaneously deliver dual drugs to cancer cells with synergistic performance, easy to envision - hard to achieve. It is challenging to simultaneously load drugs of highly different physicochemical properties into one nanoparticle, release kinetics may differ between drugs and general requirements for biomedical nanoparticles apply. Here self-assembled nanoparticles of amphiphilic carboxymethyl-hexanoyl chitosan (CHC) were shown to present nano-microenvironments enabling simultaneous loading of hydrophilic and hydrophobic drugs. This was expanded into a dual-drug nano-delivery system to treat lung CSC. CHC nanoparticles were loaded/chemically modified with the anticancer drug cisplatin and the MDR-suppressing Chinese herbal extract demethoxycurcumin, followed by biofunctionalization with CD133 antibody for enhanced uptake by lung CSC, all in a feasible one-pot preparation. The nanoparticles were characterized with regard to chemistry, size, zeta potential and drug loading/release. Biofunctionalized and non-functionalized nanoparticles were investigated for uptake by lung CSC. Subsequently the cytotoxicity of single and dual drugs, free in solution or in nanoparticles, was evaluated against lung CSC at different doses. From the dose response at different concentrations the degree of synergy was determined through Chou-Talalay's Plot. The biofunctionalized nanoparticles promoted synergistic effects between the drugs and were highly effective against MDR lung CSC. The efficacy and feasible one-pot preparation suggest preclinical studies using relevant disease models to be justified

The carbon border adjustment mechanism is inefficient in addressing carbon leakage and results in unfair welfare losses

The European Commission has proposed a Carbon Border Adjustment Mechanism (CBAM) to reduce carbon leakage and create a level playing field for its domestic products and imported goods. Nevertheless, the effectiveness of the proposal remains unclear, especially when it triggers threats of retaliation from trading partners of the European Union (EU). We apply a Computable General Equilibrium (CGE) model - Global Trade Analysis Project (GTAP) - to assess the economic and environmental impacts of different CBAM schemes. Here we show that the effectiveness of the CBAM to address carbon leakage risks is rather limited, and the CBAM raises concerns over global welfare costs, GDP losses, and violation of equality principles. Trade retaliation leads to multiplied welfare losses, which would mostly be borne by poor countries. Our results question the carbon leakage reduction effect of a unilateral trade policy and suggest that climate change mitigation still needs to be performed within the framework of international cooperation

Convergence of the waste and water sectors: risks, opportunities and future trends – discussion paper

The aim of this discussion paper is to bring to light the increasing convergence of the water and waste sectors and the associated risks, benefits, and future trends already on the horizon. Current examples of convergence in managing coal seam gas (CSG), food waste, fats, oils and grease (FOG) and biosolids, provide insights into not only the risks to public and environmental health of waste streams that cross sectoral boundaries but also potential opportunities for the water and waste sectors to seize as business opportunities. What is clear is that convergence between these sectors is already happening and in some cases there are adverse environmental consequences and associated health impacts. A key message from this research is the need to take an integrated and coordinated approach to planning and regulating the convergence of the water and waste sectors. Key recommendations to manage the risks associated with cross sector convergence of the water and waste sectors include facilitating: (1) increased engagement between regulators of each sector, (2) greater communication across sectors (3) a co-ordinated approach and plan to managing waste streams, (4) the development of monitoring and evaluation frameworks that cross sectors and (5) a coordinated approach to the assessment of research needs

Higher Derivative Corrections to R-charged Black Holes: Boundary Counterterms and the Mass-Charge Relation

We carry out the holographic renormalization of Einstein-Maxwell theory with curvature-squared corrections. In particular, we demonstrate how to construct the generalized Gibbons-Hawking surface term needed to ensure a perturbatively well-defined variational principle. This treatment ensures the absence of ghost degrees of freedom at the linearized perturbative order in the higher-derivative corrections. We use the holographically renormalized action to study the thermodynamics of R-charged black holes with higher derivatives and to investigate their mass to charge ratio in the extremal limit. In five dimensions, there seems to be a connection between the sign of the higher derivative couplings required to satisfy the weak gravity conjecture and that violating the shear viscosity to entropy bound. This is in turn related to possible constraints on the central charges of the dual CFT, in particular to the sign of c-a.Comment: 30 pages. v2: references added, some equations simplifie

A Genome-wide gene-expression analysis and database in transgenic mice during development of amyloid or tau pathology

We provide microarray data comparing genome-wide differential expression and pathology throughout life in four lines of "amyloid" transgenic mice (mutant human APP, PSEN1, or APP/PSEN1) and "TAU" transgenic mice (mutant human MAPT gene). Microarray data were validated by qPCR and by comparison to human studies, including genome-wide association study (GWAS) hits. Immune gene expression correlated tightly with plaques whereas synaptic genes correlated negatively with neurofibrillary tangles. Network analysis of immune gene modules revealed six hub genes in hippocampus of amyloid mice, four in common with cortex. The hippocampal network in TAU mice was similar except that Trem2 had hub status only in amyloid mice. The cortical network of TAU mice was entirely different with more hub genes and few in common with the other networks, suggesting reasons for specificity of cortical dysfunction in FTDP17. This Resource opens up many areas for investigation. All data are available and searchable at http://www.mouseac.org

Enhanced efficiency of solid-state NMR investigations of energy materials using an external automatic tuning/matching (eATM) robot.

We have developed and explored an external automatic tuning/matching (eATM) robot that can be attached to commercial and/or home-built magic angle spinning (MAS) or static nuclear magnetic resonance (NMR) probeheads. Complete synchronization and automation with Bruker and Tecmag spectrometers is ensured via transistor-transistor-logic (TTL) signals. The eATM robot enables an automated "on-the-fly" re-calibration of the radio frequency (rf) carrier frequency, which is beneficial whenever tuning/matching of the resonance circuit is required, e.g. variable temperature (VT) NMR, spin-echo mapping (variable offset cumulative spectroscopy, VOCS) and/or in situ NMR experiments of batteries. This allows a significant increase in efficiency for NMR experiments outside regular working hours (e.g. overnight) and, furthermore, enables measurements of quadrupolar nuclei which would not be possible in reasonable timeframes due to excessively large spectral widths. Additionally, different tuning/matching capacitor (and/or coil) settings for desired frequencies (e.g. $^{7}$Li and $^{31}$P at 117 and 122MHz, respectively, at 7.05 T) can be saved and made directly accessible before automatic tuning/matching, thus enabling automated measurements of multiple nuclei for one sample with no manual adjustment required by the user. We have applied this new eATM approach in static and MAS spin-echo mapping NMR experiments in different magnetic fields on four energy storage materials, namely: (1) paramagnetic $^{7}$Li and $^{31}$P MAS NMR (without manual recalibration) of the Li-ion battery cathode material LiFePO$_{4}$; (2) paramagnetic $^{17}$O VT-NMR of the solid oxide fuel cell cathode material La$_{2}$NiO$_{4+δ}$; (3) broadband $^{93}$Nb static NMR of the Li-ion battery material BNb$_{2}$O$_{5}$; and (4) broadband static $^{127}$I NMR of a potential Li-air battery product LiIO$_{3}$. In each case, insight into local atomic structure and dynamics arises primarily from the highly broadened (1-25MHz) NMR lineshapes that the eATM robot is uniquely suited to collect. These new developments in automation of NMR experiments are likely to advance the application of in and ex situ NMR investigations to an ever-increasing range of energy storage materials and systems.This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 655444 (O.P.). D.M.H. acknowledges funding from the Cambridge Commonwealth Trusts. J.L. gratefully acknowledges Trinity College, Cambridge (UK) for funding. K.J.G. gratefully acknowledges funding from the Winston Churchill Foundation of the United States and the Herchel Smith Scholarship. M.B. is the CEO of NMR Service GmbH (Erfurt, Germany), which manufactures the eATM device; M.B. acknowledges funding of the Central Innovation Programme for small and medium-sized enterprises (SMEs; Zentrales Innovationsprogramm Mittelstand, ZIM) of the German Federal Ministry of Economic Affairs and Energy (Bundesministerium für Wirtschaft und Energie, BMWi) under the Grant No. KF 2845501UWF. DFT calculations were performed on (1) the Darwin Supercomputer of the University of Cambridge High Performance Computing Service (http://www.hpc.cam.ac.uk), provided by Dell Inc. using Strategic Research Infrastructure Funding from the Higher Education Funding Council for England and funding from the Science and Technology Facilities Council and (2) the Center for Functional Nanomaterials cluster, Brookhaven National Laboratory, which is supported by the U.S. Department of Energy, Office of Basic Energy Sciences, under Contract No. DE-AC02-98CH10886