Miracle-dz/UniFAR-FTUs huggingface dataset:
A prognostic scoring model based on the HPV status of oropharyngeal carcinoma patients treated with postoperative radiotherapy in China.

OBJECTIVES Prognostic models that can predict prognosis and guide postoperative radiotherapy (PRT) and that are based on the human papillomavirus (HPV) status of patients with oropharyngeal carcinoma (OPSCC) in China are rare. METHODS Survival was analyzed by performing a Kaplan-Meier analysis and log-rank test. A Cox regression analysis was performed for the multivariate analyses. A prognostic scoring model was constructed according to the regression coefficient obtained from the Cox regression model. RESULTS A prognostic model that included gender, clinical stage, histologic stage, metastasis, and HPV status was created and used to divide patients into high-risk (PI ≥ -0.008) and low-risk (PI < -0.008) groups. The results showed that the patients who received PRT had a longer overall survival time than those who did not receive PRT (47.31 months vs. 28.31 months). Furthermore, the patients who received PRT in the high-risk group had a longer survival time when the survival was greater than 20 months (P = 0.024), and PRT may indicate a worse prognosis in the low-risk group (P = 0.071). CONCLUSION This model will contribute to the formulation of individualized treatment programs for OPSCC patients. PRT should be administered to high-risk patients.
Sentence Encoder Sentence Decoder Tree Encoder Tree Decoder Sentence Vocabulary Tree Vocabulary MLP MLP N μ 2 σ 2

We present a syntax-infused variational autoencoder (SIVAE), that integrates sentences with their syntactic trees to improve the grammar of generated sentences. Distinct from existing VAE-based text generative models, SIVAE contains two separate latent spaces, for sentences and syntactic trees. The evidence lower bound objective is redesigned correspondingly, by optimizing a joint distribution that accommodates two encoders and two decoders. SIVAE works with long shortterm memory architectures to simultaneously generate sentences and syntactic trees. Two versions of SIVAE are proposed: one captures the dependencies between the latent variables through a conditional prior network, and the other treats the latent variables independently such that syntactically-controlled sentence generation can be performed. Experimental results demonstrate the generative superiority of SIVAE on both reconstruction and targeted syntactic evaluations. Finally, we show that the proposed models can be used for unsupervised paraphrasing given different syntactic tree templates.
INPP 4 B suppresses prostate cancer cell invasion

Background: INPP4B and PTEN dual specificity phosphatases are frequently lost during progression of prostate cancer to metastatic disease. We and others have previously shown that loss of INPP4B expression correlates with poor prognosis in multiple malignancies and with metastatic spread in prostate cancer. Results: We demonstrate that de novo expression of INPP4B in highly invasive human prostate carcinoma PC-3 cells suppresses their invasion both in vitro and in vivo. Using global gene expression analysis, we found that INPP4B regulates a number of genes associated with cell adhesion, the extracellular matrix, and the cytoskeleton. Importantly, de novo expressed INPP4B suppressed the proinflammatory chemokine IL-8 and induced PAK6. These genes were regulated in a reciprocal manner following downregulation of INPP4B in the independently derived INPP4B-positive LNCaP prostate cancer cell line. Inhibition of PI3K/Akt pathway, which is highly active in both PC-3 and LNCaP cells, did not reproduce INPP4B mediated suppression of IL-8 mRNA expression in either cell type. In contrast, inhibition of PKC signaling phenocopied INPP4B-mediated inhibitory effect on IL-8 in either prostate cancer cell line. In PC-3 cells, INPP4B overexpression caused a decline in the level of metastases associated BIRC5 protein, phosphorylation of PKC, and expression of the common PKC and IL-8 downstream target, COX-2. Reciprocally, COX-2 expression was increased in LNCaP cells following depletion of endogenous INPP4B. Conclusion: Taken together, we discovered that INPP4B is a novel suppressor of oncogenic PKC signaling, further emphasizing the role of INPP4B in maintaining normal physiology of the prostate epithelium and suppressing metastatic potential of prostate tumors.
Some antinutrient compositions and in vitro antioxidant properties of milled Carica papaya ( pawpaw ) peels and seeds

Received: 28 October, 2016 Accepted: 5 January, 2017 Published: 20 March, 2017 This study evaluated some antinutrient compositions and antioxidant properties of milled Carica papaya peels and seeds, using standard methods. The result revealed the preponderance of phenol in the Carica papaya peels (184.72± 0.02 mg/100 g) which was higher than the other determined antinutrients in the peels and seeds. This was followed by oxalates in the peels (28.81±0.15 %), phenols in the seeds (28.32±0.02 mg/100 g) and oxalates in the seeds (11.08±0.17 %) while the least of the determined antinutrients was hydrogen cyanide in the peels (0.15±0.20 μg/g). The 2,2-Diphenyl-1-picryhydrazyl (DPPH) radical scavenging ability of the Carica papaya peels (48.03 ± 0.69 %) was higher than that of the seeds (43.70±0.78 %) while the chelating ability of the peels (27.83 ± 0.81 %) and the reducing ability of the peels (36.24 ± 0.23 %) were lower than that of the seeds (32.60±1.21 % and 52.33±0.34 %, respectively). The difference between the Carica papaya peels and seeds for the determined parameters, apart from that of flavonoids, phytates and saponins, was significant (p<0.05). Thus, the study revealed that the Carica papaya peels and seeds contain varied anti-nutrients with varied and sample specific antioxidant properties. Further studies, including the determination of the other antioxidant compounds in and the concerted antioxidant capacity of the Carica papaya peels and seeds are warranted and recommended. © 2017 PSCI Publisher All rights reserved.
Effects of fuel structure on soot precursors in a laminar co-flow flame

The goal of this work is to aid in understanding the effects of fuel molecular structure on the spatial development of polycyclic aromatic hydrocarbons (PAH), or soot precursors, in an axisymmetric, co-flow, laminar flame configuration at atmospheric pressure. Two fuels with varying molecular structure are investigated: iso-octane/ndodecane mixture and m-xylene/n-dodecane mixture. The flames investigated are non-premixed and rich premixed (jet equivalence ratio of 6) flames, and the total carbon flow rate is kept constant to facilitate comparison between fuels. A laser-induced fluorescence technique is used to obtain spatially-resolved PAH in the jet flames. The PAH are identified into two classes: single/two ring aromatics (small) and molecules having three/four rings (large). The experimental results indicate that the level of aromatics for m-xylene/n-dodecane fuel is higher compared to isooctane/n-dodecane fuel. The comparison of PAH in non-premixed and premixed flames show significant differences in the spatial development of PAH along the downstream direction. These results are compared to initial simulation results and a methodology for using the PAH-LIF technique for validating soot models in laminar jet flames is proposed.
A FAST SEMI-SUPERVISED SINGLE-LINK CLUSTERING METHOD

Semi-supervised single-link (SSl) clustering method, can give better results than unsupervised single-link method. This paper proposes constrained leader-based SSl method to achieve almost similar clustering result, but with much reduced time by by employing Leaders clustering method working with leader data patterns, instead of working with every individual data pattern in the dataset. The proposed method basically a two step process.First it derives a set of prototypes by using a fast clustering method along with constraints called the Constrained leaders clustering method with threshold parameter α. The second step is by applying the SSL method in the presence of a few labeled data with respecting the constraints(Must-link and Cannot-link) are presented in this paper. The ‘must-link’ constraints (a constraint of this kind says, two particular data patterns must be grouped together),this type of constraints can be merged in the process easily, but “can-not link” constraints (e.g., two particular data patterns must not be grouped together) or “distinct label constraints” having their own difficulties. Both benchmark and synthetic datasets are used in the experimental study. Experimentally, the proposed Constrained leader-based SSl method is shown to work at-least 10 factors faster than SSl method, and achieves almost similar clustering result.
MODELING OF BIOMASS GASIFICATION

Optimal conversion of chemical energy of the biomass or other solid fuel into the desired gas depends on proper configuration, sizing, and choice of gasifier operating conditions. Optimum operating conditions are often derived through trials on the unit or by experiments on pilot plants. Simulation, or mathematical modeling, allows the designer or plant engineer to reasonably optimize the operation or design of the plant. The good mathematical model can: find optimum operating conditions or a design for the gasifier , provide information on extreme operating conditions (high temperature, high pressure) where experiments are difficult to perform , provide information over a much wider range of conditions than those obtained experimentally, better interpret experimental results and analyze abnormal behavior of a gasifier, if that occurs. The equilibrium model is independent of the gasifier design which can make them more suitable for a system study of the most important process parameters. The use of an equilibrium model assumes that the residence time of the reactants in the gasifier is high enough to reach chemical equilibrium. For established biomass ultimate analysis, tem perature of gasification air and temperature of produced gas, combining the mass balance equations with the equations for the equilibrium constants and equation of energy balance, the equivalence ratio (ER) and composition of produced gas can be obtained. A mathematical model for investigation of the influence of temperature of the produced gas and temperature of gasification air on the process parameters was developed. It can be used for estimation and design of gasification equipment.
Radar interferometry as an innovative solution for monitoring the construction of the Grand Paris Express metro network – First results

The Grand Paris Express (GPE) is an unprecedented urban development project centred on a major expansion of the existing public transport network for the whole Paris metropolitan area. Overall, 68 new stations and 200 km of metro lines will be gradually put into service. Among numerous aspects, the project is driven by risk management and neighbourhood disturbance reduction. These considerations play a key role in the design and the construction process and monitoring is a major part of the contractors’ missions. Among the monitoring methods implemented in the framework of the project, there is an innovative technology, Synthetic Aperture Radar Interferometry or InSAR. This technique, deployed over the 200 km of metro line during the construction, is based on the use of SqueeSARTM, a processing chain and proprietary multi-interferogram technique developed and patented by TRE ALTAMIRA.
Bond Relative Value Models and Term Structure of Credit Spreads : A Practitioner ’ s Approach 1

Bond relative value models to detect mispriced bonds are widely used in the investment community. These range from simple yield to maturity comparisons to sophisticated stochastic models. The first step for many of these models is the determination of reference yield curves. There are numerous publications on these yield curve fitting approaches with related empirical research yet few actually document practical implementations for operational purposes. Accordingly, the first part of this article describes and then illustrates implementation of a number of these benchmarking models. Within such a fitting framework, bonds subject to credit risk can often not be handled since the number of bonds of equivalent credit quality is simply too small to derive reliable reference curves. Here the article proposes a novel approach to parameterize the term structure of credit spread. Its main benefit are intuitive model parameters that relate to the concept of how market practitioners like traders and asset manager tend to measure credit risk of fixed income securities. Many of the models described herein have been implemented in EXCEL/VBA, some of which are generalized versions of models that have been developed for practical bond relative value research. The files containing the models can be downloaded from the following website: http://www.mngt. waikato.ac. nz/kurt/
An SDR implementation of an OFDM transmitter and receiver in GNU Radio

The new level of connectivity among machines and people promised by the forthcoming 5G mobile communication system will shape diverse application areas from healthcare and education to smart factories and autonomous driving. These applications have a variety of different sets of requirements on data rates, latency, and reliability which are expected to be supported by an enhanced version of the OFDM modulation used today in the physical layer of 4G LTE systems. Thus, a new waveform design that allows multiplex flexibly different communication services within a common framework is an ongoing research topic. On the other hand, software defined radio is an appealing concept to introduce such flexibility since the functionality of the system relies on the software implemented on a programmable device. In order to investigate the performance of new OFDMbased systems, this work presents a preliminary prototype of an OFDM communication link for video transmission using the open-source tool GNU radio and the RF hardware interface NI USRP and proves the feasibility of employing SDR for real-time signal processing of configurable radios.
Can Industrial Co-Agglomeration between Producer Services and Manufacturing Reduce Carbon Intensity in China?

Climate change poses unprecedented challenges for humanity. Reducing carbon intensity is an inevitable choice for tackling climate change and promoting sustainable development. China has made some emission reduction commitments in the international community to promote the decoupling of China’s economic development from carbon emissions. The realization of the industrial structure from the “single-wheel drive” of the manufacturing to the “two-wheel drive” economic development model of the service industry and the manufacturing has become a key measure to achieve China’s economic intensive development. According to resource misallocation situation in different regions, this paper explored the impact of the collaborative agglomeration between producer services and manufacturing (hereinafter referred to as industrial co-agglomeration) on carbon intensity. The research results show that the carbon intensity is decreasing year by year, and the degree of intensification of China’s economic growth continues to increase. Moreover, the effect of industrial co-agglomeration to promote carbon emission reduction is significantly limited by the degree of misallocated resources, and there is a double threshold effect. Specifically, in areas where resource allocation is reasonable, industrial co-agglomeration can produce significant agglomeration effects and promote carbon intensity reduction. Once the degree of misallocated resources exceeds a threshold level, the agglomeration effect will turn into a crowding effect, resulting in an inability to reduce carbon intensity. We comprehensively analyzed the driving factors for reducing carbon intensity and proposed policy pathways for achieving China’s carbon intensity target.
High-Level Techniques for Landscape Creation

A complete solution for creating an entire planet by combining different types of elements from an intuitive specification is still an open problem. High-level techniques are at the core of the solution for this issue. In this context, our work aims to contribute for the development of high-level techniques for landscape creation by the proposal of two approaches. The first deals with the manipulation of the position of landscape elements (according to some criteria), and the second is related to the generation of terrains from two new data-driven methods. In particular, we introduced a flexible method for resizing a landscape specification keeping the overall appearance. It is based on the insertion and removal of objects followed by scene adjustment (enlargement or shrinking). Furthermore, we introduced two data-driven techniques for terrain synthesis. They are developed over the same intuitive control approach, and they take advantage of the geometric nature of the data for improving the synthesis. The first technique is a patch-based algorithm, with new optimization structures for patch choice, and a new patch insertion approach (both based on the geometric nature of the data). The second is a pixel-based method, based on a graph of exemplars in multiresolution. Keywords-Landscape Creation; Terrain Synthesis. 1
Earnings Management and Stock Price Crashes: The Deteriorating Information Environment Post Cross-Delisting

We test whether cross-delisted firms from the major U.S. stock exchanges experience an increase in crash risk associated with earnings management. Consistent with our prediction, we find that earnings management have a greater positive impact on stock price crash risk post-cross-delisting when compared to a sample of still cross-listed firms. Moreover, our results suggest that this effect is more pronounced for cross-delisted firms from countries with weaker investor protection and poorer quality of their information environment. We further examine stock price reaction to earnings announcements in the preand post-cross-delisting period. Our evidence shows that post-cross-delisted firms experience higher abnormal returns around earnings announcements, especially those from countries with weaker institutional quality and firms with poorer information environment. Classifications: F30; F31; G15; G30
A Survey on Research on Clustering In Homogeneous and Heterogeneous Wireless Sensor Networks

Cellular Sensor Network is usually a network of little battery driven sensing devices (referred to as sensing nodes), with the capacity of sensing the physical parameters like temperature, humidity, pressure, motion, etc. from its environment of deployment and statement it to a central node called foundation station. These nodes collaborate with one another to satisfy their job. As the power source is usually constrained in WSNs, it must be used correctly. Clustering is among the methods which are becoming used effectively to reduce energy dissipation in WSNs. Clustering protocols result in improvement in the intake of energy, lower cost of conversation in the network and improved effectiveness. In this paper, we present an excellent grained study on LEACH centered hierarchical clustering protocols, specifically in a heterogeneous network where in fact the energy of the nodes is usually different and at the end we present assessment in tabular type, based on base station area, contribution, advantages, drawbacks and improvement accomplished with the compared protocols.
Supplemental Material for Stochastic Blockmodels meet Graph Neural Networks 1

The framework proposed uses a stick-breaking IBP prior which has two parameters: α and K. The parameter α is the initial guess of the number of non-zero entries in the binary vector bn and K is the truncation parameter. In the experiments, α ∈ {5, 10, 20, 50, 100}. In general, a higher value of the α parameter worked better for DGLFRM-B and LFRM, as compared to the α value in the DGLFRM model. This difference in α reflects the inherent capacity of the latent space of these models. The embedding learned by DGLFRM, while being highly sparse, are in real space resulting in more capacity to represent data as compared to the binary latent space in DGLFRM-B and LFRM.
Deep Motion Exploitation for Video Object Segmentation

In this paper, we propose a novel MoNet model to deeply exploit motion cues for boosting video object segmentation performance from two aspects, i.e., frame representation learning and segmentation refinement. Concretely, MoNet exploits computed motion cue (i.e., optical flow) to reinforce the representation of the target frame by aligning and integrating representations from its neighbors. The new representation provides valuable temporal contexts for segmentation and improves robustness to various common contaminating factors, e.g., motion blur, appearance variation and deformation of video objects. Moreover, MoNet exploits motion inconsistency and transforms such motion cue into foreground/background prior to eliminate distraction from confusing instances and noisy regions. By introducing a distance transform layer, MoNet can effectively separate motion-inconstant instances/regions and thoroughly refine segmentation results. Integrating the proposed two motion exploitation components with a standard segmentation network, MoNet provides new state-of-the-art performance on three competitive benchmark datasets.
Spring 5-29-2015 Investigating the Usage of Enterprise Architecture Artifacts

Enterprise architecture (EA) is a description of an enterprise from an integrated business and IT perspective intended to improve business and IT alignment. EA literature describes many detailed EA methodologies, artifacts and frameworks to organize them. However, establishing EA practice still remains a challenging endeavor with a low success rate. On the other hand, this is not surprising since most organizations successfully practicing EA do not follow EA methodologies and frameworks strictly but adapt them to their own needs or even use them only as idea contributors. Therefore, in order to improve the success rate of EA initiatives, it is necessary to understand better how exactly successful companies adapt EA methodologies in practice. In particular, we argue that the most significant problems in EA practice arise because the usage of individual EA artifacts in practice is poorly understood. In this paper we describe how we are going to investigate the usage of EA artifacts to close this gap.
Timescales of membrane fusion revealed by direct imaging of vesicle fusion with high temporal resolution

This Supporting Text contains details about the two experimental fusion protocols, part of the dimensional analysis for the time scales that govern the opening of the fusion neck in the early and later stages, the description of the four supporting movies, and the chemical structure of the ligand used in the ligand-mediated fusion protocol. The Supporting Movies display the time evolution of single fusion events obtained from two protocols: (i) fusion of vesicles that are functionalized with fusogenic ligands (Movies S1 and S2), and (ii) electrofusion (Movies S3 and S4).
Inflation Forecasting Using the Output Gap : The Case of the Czech Republic , Slovakia , Hungary and Poland

This article examines the ability of the output gap to forecast inflation, while analyzing Czech, Slovak, Hungarian and Polish economies. Output gaps are estimated using the Hodrick-Prescott filter and the production function. A simple Phillips curve model is chosen to explore their forecasting capabilities. Forecasting errors are then compared with an AR(1) process. In general, it holds true for all economies in question that integration of the output gap into models enhances their ability to forecast inflation.