Miracle-dz/UniFAR-FTUs huggingface dataset:
Book Review: Bayesian Analysis in Natural Language Processing by Shay Cohen

This approach enables an elegant and unified way to incorporate prior knowledge and manage uncertainty over parameters. It can also be used to provide capacity control for complex models as an alternative to smoothing. There have been many successful applications of Bayesian techniques in natural language processing (NLP). Some examples include: word segmentation (Goldwater et al. 2009), syntax (Johnson et al. 2007), morphology (Snyder & Barzilay 2008), coreference resolution (Haghighi & Klein 2007), and machine translation (Blunsom et al. 2009). Cohen’s book provides an accessible yet in-depth introduction to Bayesian techniques. It is aimed at a researcher or student who is already familiar with statistical modeling in natural language (i.e., at the level of introductory books such as Manning & Schütze [1999], Jurafsky & Martin [2009]). The stated goal of the book is to “cover the methods and algorithms that are needed to fluently read Bayesian learning papers in NLP and to do research in the area. ” I believe Cohen successfully achieves this goal, striking a nice balance between breadth and depth of material. Chapter 1 is a brief review of probability and statistics. It covers prerequisite concepts such as independence, conditional independence, and exchangeability of random variables. The differences between Bayesian and frequentist philosophies are discussed, albeit briefly. In general, the book maintains a pragmatic approach, focusing more on the mathematics and less on the philosophy.
Isothermal Conduction Calorimetry Analysis of Alkali Activated Slag Binder

*In this research, isothermal conduction calorimetry analysis has been conducted to investigate the reactivity of alkali activated slag binders. In order to secure the reactivity and workability of alkali activated slag binders, experiences with various types and concentrations of alkali activators were performed. Isothermal conduction calorimetry were measured with different alkali activators and mass ratio of SO3 to binders as variables, and sodium tripolyphosphate (Na2P3O10) and hydrated sodium borate (Na2B4O710H2O) were used to control setting time. As a results, alkali activated slag binders required alkali activators with 4 to 5 percent of concentration to accelerate the formation of calcium silicate hydrate(C-S-H) by alkali-activation, and overall heat generation rate delayed as accumulated heat decreased due to the high SO3 contents. Moreover, the use of hydrated sodium borate as setting retarder causes elongated setting time due to delaying heat generation, so it can be considered that setting retarder played an important role in delaying total heat generation rate.
Novel Avulaviruses in Penguins, Antarctica

We identified 3 novel and distinct avulaviruses from Gentoo penguins sampled in Antarctica. We isolated these viruses and sequenced their complete genomes; serologic assays demonstrated that the viruses do not have cross-reactivity between them. Our findings suggest that these 3 new viruses represent members of 3 novel avulavirus species.
Machine Floriography: Sentiment-inspired Flower Predictions over Gated Recurrent Neural Networks

The design of a flower bouquet often comprises a manual step of plant selection that follows an artistic style arrangement. Floral choices for a collection are typically founded on visual aesthetic principles that include shape, line, and color of petals. In this paper, we propose a novel framework that instead classifies sentences that describe sentiments and emotions typically conveyed by flowers, and predicts the bouquet content implicitly. Our work exploits the figurative Language of Flowers that formalizes an expandable list of translation records, each mapping a short-text sentiment sequence to a unique flower type we identify with the bouquet center-of-interest. Records are represented as word embeddings we feed into a gated recurrent neural-network, and a discriminative decoder follows to maximize the score of the lead flower and rank complementary flower types based on their posterior probabilities. Already normalized, these scores directly shape the mix weights in the final arrangement and support our intuition of a naturally formed bouquet. Our quantitative evaluation reviews both stand-alone and baseline comparative results.
Behavior of materials under conditions of thermal stress

A review is presented of available information on the behavior of brittle and ductile materials under conditions of thermal stress and thermal shock. For brittle materials, a simple formula relating physical properties to thermal-shock resistance is derived and used to determine the relative significance of two indices currently in use for rating materials. For ductile materials, thermal-shock resistance depends upon the complex interrelation among several metallurgical variables which seriously affect strength and ductility. These variables are briefly discussed and illustrated from literature sources. The importance of simulating operating conditions in tests for rating materials is especially to be emphasized because of the importance of testing conditions in metallurgy. A number of practical methods that have been used to minimize the deleterious effects of thermal stress and thermal shock are outlined.
Improving Graph Matching via Density Maximization

Graph matching has been widely used in various applications in computer vision due to its powerful performance. However, it poses three challenges to image sparse feature matching: (1) The combinatorial nature limits the size of the possible matches, (2) It is sensitive to outliers because the objective function prefers more matches, (3) It works poorly when handling many-to-many object correspondences, due to its assumption of one single cluster for each graph. In this paper, we address these problems with a unified framework-Density Maximization. We propose a graph density local estimator (DLE) to measure the quality of matches. Density Maximization aims to maximize the DLE values both locally and globally. The local maximization of DLE finds the clusters of nodes as well as eliminates the outliers. The global maximization of DLE efficiently refines the matches by exploring a much larger matching space. Our Density Maximization is orthogonal to specific graph matching algorithms. Experimental evaluation demonstrates that it significantly boosts the true matches and enables graph matching to handle both outliers and many-to-many object correspondences.
Hemophilia in focus.

Correspondence to: Univ.-Prof. Dr. Rüdiger E. Scharf, F.A. H.A. E-mail: rscharf@uni-duesseldorf.de Hämostaseologie 2017; 37: 93–95 Received and accepted: April 18, 2017 Every year on April 17, World Hemophilia Day is marked in the calendar of health professionals around the world. This theme and action day was established in 1989 by the World Federation of Hemophilia (WFH) to increase awareness of hemophilia and other congenital bleeding disorders (1). The date of April 17 was chosen to honor Frank Schnabel, the founder of WFH, whose birthday falls on the same date.
HyperNEAT-based flipper control for a crawler robot motion in 3D simulation environment

The paper presents neuroevolution approach to a crawler robot motion that autonomously solves sequences of navigation and flipper control tasks to overcome obstacles in 3D simulation domain. When modelling scenarios of robot locomotion, we used our model of a novel Russian crawler robot “Engineer” in ROS/Gazebo. The modelled robot measured obstacle's height by scanning a vertical profile of a terrain with 2D LIDAR and moved through 3D environment, adjusting its flippers to a relief by commands of the HyperNEAT neural network. As the result, our neuroevolution method was trained and tested on simulated data with a set of obstacles, demonstrating original solutions to robot navigation in 3D scene.
Polymorphisms, differentiation, and phylogeny of 10 Tibetan goat populations inferred from mitochondrial D-loop sequences

Abstract China has abundant population of Tibetan goats, but available information regarding genetic structure and phylogenetic status remains limited. Here, 130 mitochondrial D-loop sequences of individuals from 10 Tibetan goat populations located in distinct plateau areas were analyzed. Eighty-six haplotypes were defined, among which 97.7% were group-specific haplotypes. Haplotype and nucleotide diversity indices were 0.990 ± 0.003 and 0.0145 ± 0.0013, respectively. The pairwise Wright’s F-statistics ranged from −0.028 to 0.385, and over half of them were greater than 0.05, indicating apparent genetic differentiation among the populations. AMOVA analysis (FST = 0.0858) manifested that the genetic structure has become weak. Phylogenetic trees revealed four haplogroups (A, B, C, and D), suggesting that Tibetan goats had four origins. Mismatch distribution analyses and neutrality tests indicated that at least one population expansion event occurred during the demographic history of Tibetan goat. These results will provide a more complete understanding of Tibetan goat genetic resources.
Exploring question representation and adaptation with neural networks

Neural networks have recently been intensively explored for machine comprehension and question answering. Core to the problems is the involvement of questions and hence the understanding of them — questions play a central role in machine comprehension, questions answering, and many other problems (e.g., information retrieval and query-based summarization). In this paper, we explore better question understanding and representation. First, we propose enriched question representation by encoding syntactic information with neural networks and filtering question content to different channels with filter banks. Second, in addition to providing a unified representation scheme for all questions, we differentiate different types of questions to provide the models with the flexibility of modelling different question groups. We propose an adaptation framework for this purpose. On the Stanford Question Answering Dataset (SQuAD), we show that our approaches improve a very competitive baseline to attain the state-of-the-art performance.
Exploring communication during the journey from noticing bodily changes to a diagnosis of endometrial cancer

AIMS AND OBJECTIVES To understand women's perspectives about the trajectory from first bodily changes to diagnosis. BACKGROUND With endometrial cancer, as with all gynaecological cancers, early diagnosis is key to successful outcomes. However, women do not always seek clinical advice early. Previously, this gap has been referred to as a "delay," blamed on fear or refusal to acknowledge symptoms. METHODS A qualitative research project which involved face-to-face interviews with 16 women who had presented with symptoms of endometrial cancer. The paucity of research in the trajectory of women who experience a diagnosis of endometrial cancer required an exploratory overview of the data. Accordingly, an inductive thematic analysis was conducted using the framework of Braun and Clarke (Qualitative Research in Psychology, 3, 2006, 77). RESULTS Women and health professionals both resorted to satisficing, using heuristics to make decisions about the importance of symptoms depending on their severity and duration. Most women initially determined that the bodily changes were within the realms of normal. Time to diagnosis was also affected by the following: women's long-standing assumptions; communication with health professionals; liminality-women oscillating between their self-assessment that these changes were something significant or nothing important; and gaps in health literacy. CONCLUSIONS The journey from noticing bodily changes to diagnosis was a nonlinear trajectory. Women worked to make sense of what was happening to them, informed by their sociocultural environment. In particular, confusion about the purpose of cervical screening led a number of participants who had regular smears to assume they were "safe" from cancer worries. RELEVANCE TO CLINICAL PRACTICE Women and some health professionals may be unfamiliar with symptoms potentially indicative of endometrial cancer. There may be structural and communication barriers for women navigating healthcare systems. It is vital that nurses take time both to listen to women and to provide them with resources to enhance their health literacy.
Dynamic characterization of elastomers using impact testing

Dynamic characterization of elastomers, i.e. laboratory testing and fitting of constitutive models, is quite complicated. The demands on the laboratory equipment and the skill of the personal handling the test equipment are high. Stationary harmonic testing is an established method for evaluating dynamic properties of elastomers. However, it is demanding experimentally when it comes to high frequencies. The purpose of this investigation is to evaluate theoretically if impact testing can be an alternative to the harmonic test method. The impact behavior of an elastomer showing both rate and amplitude dependence is evaluated numerically. It is shown that the stress-strain behavior during impact show similarities to the stationary harmonic behavior of the material. Quantities comparable to the dynamic stiffness and phase angle can be derived from the impact behavior. It is also shown that amplitudes of 1-15% strain and frequency/rate dependence in the range 30-200 Hz, can be resolved with velocity and mass of the impacting body having realistic values. Moreover, a schematic experimental setup is also discussed. Impact testing has a potential to be a very simple method to obtain dynamic data for elastomers, to use in calibration of constitutive models. Alternatively it can be used as a simple quality control of dynamic properties in various elastomeric materials. (Less)
FPGA based parallelized architecture of efficient graph based image segmentation algorithm

Efficient and real time segmentation of color images has a variety of importance in many fields of computer vision such as image compression, medical imaging, mapping and autonomous navigation. Being one of the most computationally expensive operation, it is usually done through software implementation using high-performance processors. In robotic systems, however, with the constrained platform dimensions and the need for portability, low power consumption and simultaneously the need for real time image segmentation, we envision hardware parallelism as the way forward to achieve higher acceleration. Field-programmable gate arrays (FPGAs) are among the best suited for this task as they provide high computing power in a small physical area. They exceed the computing speed of software based implementations by breaking the paradigm of sequential execution and accomplishing more per clock cycle operations by enabling hardware level parallelization at an architectural level. In this paper, we propose three novel architectures of a well known Efficient Graph based Image Segmentation algorithm. These proposed implementations optimizes time and power consumption when compared to software implementations. The hybrid design proposed, has notable furtherance of acceleration capabilities delivering atleast 2X speed gain over other implementations, which henceforth allows real time image segmentation that can be deployed on Mobile Robotic systems.
Towards Automated Optimization of Web Interfaces and Application to E-commerce

In this paper, we present a system that supports the design of web graphical user interface by finding the optimal placement of interactive elements. The definition of optimal placement is context specific; it aims at maximizing measurable aspects of the user experience, and it is derived using expert knowledge embedded in the system, which is based on HCI principles, user studies, and data analytics. We use a novel modeling technique to represent the layout of web user interfaces. The model is unsupervisedly computed on information extracted by several image processing algorithms. The system identifies the website category, builds a layout model, compares it with the relevant optimal model, and recommends an alternative layout of interactive elements. To prove this concept, we present a study on e-commerce websites where placement of the checkout button has a significant impact on the online sale process conversion rate. The system identifies non-optimal placement, and it recommends an alternative position that is likely to improve the conversion rate. The results will be validated based on the Visa Checkout user experience. The system is implemented as an open-source software, and it currently supports the re-positioning of a single interactive element. In the paper, we discuss a further generalization of this approach to support a larger number of interactive objects in a wider spectrum of scenarios.
Thymidylate Synthase Genotype-Directed Chemotherapy for Patients with Gastric and Gastroesophageal Junction Cancers

Background Retrospective studies indicate associations between TSER (thymidylate synthase enhancer region) genotypes and clinical outcomes in patients receiving 5-FU based chemotherapy, but well-controlled prospective validation has been lacking. Methods In this phase II study (NCT00515216 registered through ClinicalTrials.gov, http://clinicaltrials.gov/show/NCT00515216), patients with “good risk” TSER genotypes (at least one TSER*2 allele) were treated with FOLFOX chemotherapy to determine whether prospective patient selection can improve overall response rates (ORR) in patients with gastric and gastroesophageal junction (GEJ) cancers, compared with historical outcomes in unselected patients (estimated 43%). Results The ORR in genotype-selected patients was 39.1% (9 partial responses out of 23 evaluable patients, 95% CI, 22.2 to 59.2), not achieving the primary objective of improving ORR. An encouraging disease control rate (DCR, consisting of partial responses and stable diseases) of 95.7% was noted and patients with homozygous TSER*2 genotype showed better tumor response. Conclusions In this first prospective, multi-institutional study in patients with gastric or GEJ cancers, selecting patients with at least one TSER*2 allele did not improve the ORR but led to an encouraging DCR. Further studies are needed to investigate the utility of selecting patients homozygous for the TSER*2 allele and additional genomic markers in improving clinical outcomes for patients with gastric and GEJ cancers. Trial Registration ClinicalTrials.gov NCT00515216
Comparison of Antibacterial Efficacy Chlorohexidine Gluconate and Saudi Myrrh Mouthwashes in the Oral Cavity

Myrrh have been traded in the Middle East and North Africa for 5,000 years, Myrrh is an Oleo-gum resin, phytotoxically safe raw material obtained from the stem of various species of the genus Commiphora, in recent years it has been used in pharmaceuticals, food industries. Chlorhexidine gluconate mouthwash is preferably prescribed by dentist as an effective mouthwash. Aim of the study to investigate the antibacterial effect of myrrh aqueous mouthwash (homemade) solution against some selected microbial flora and comparison of the activity with that of commercial chlorohexidine gluconate mouthwash by participation of 9 female volunteers. Antimicrobial activity of Myrrh was investigated and compared with 0.2% chlorohexidine gluconate mouthwash using agar diffusion method. Escherichia coli 25922, Salmonella 25566, Klebsiella pneumonia 13883, Pseudomonas 27853, Proteus, Staphylococcus aureus 25923, Bacillus, Streptococcus mutans and Candida albicans were used in this study and adjusted according to the Clinical and Laboratory Standards Institute (CLSI). The results revealed that Myrrh produced antimicrobial activity against oral flora and other species of microorganisms which was to some extent comparable with that of Chlorohexidine gluconate. Microbial load detection showed that home-made myrrh mouthwash aqueous solution is an effective as clinically proved mouthwash. keywords: Myrrh, Chlorohexidine, Mouthwash, Antibacterial.
In situ preparation of water-soluble ginsenoside Rh2-entrapped bovine serum albumin nanoparticles: in vitro cytocompatibility studies

The present study investigates a simple and convenient one-step procedure for the preparation of bovine serum albumin (BSA)-Rh2 nanoparticles (NPs) at room temperature. In this work, ginsenoside Rh2 was entrapped within the BSA protein to form BSA-Rh2 NPs to enhance the aqueous solubility, stability, and therapeutic efficacy of Rh2. The physiochemical characterization by high-performance liquid chromatography, nuclear magnetic resonance, Fourier transform infrared spectroscopy, field emission transmission electron microscopy, dynamic light scattering, and thermogravimetric analysis confirmed that the prepared BSA-Rh2 NPs were spherical, highly monodispersed, and stable in aqueous systems. In addition, the stability of NPs in terms of different time intervals, pHs, and temperatures (20°C–700°C) was analyzed. The results obtained with different pHs showed that the synthesized BSA-Rh2 NPs were stable in the physiological buffer (pH 7.4) for up to 8 days, but degraded under acidic conditions (pH 5.0) representing the pH inside tumor cells. Furthermore, comparative analysis of the water solubility of BSA-Rh2 NPs and standard Rh2 showed that the BSA nanocarrier enhanced the water solubility of Rh2. Moreover, in vitro cytotoxicity assays including cell viability assays and morphological analyses revealed that Rh2-entrapped BSA NPs, unlike the free Rh2, demonstrated better in vitro cell viability in HaCaT skin cell lines and that BSA enhanced the anticancer effect of Rh2 in A549 lung cell and HT29 colon cancer cell lines. Additionally, anti-inflammatory assay of BSA-Rh2 NPs and standard Rh2 performed using RAW264.7 cells revealed decreased lipopolysaccharide-induced nitric oxide production by BSA-Rh2 NPs. Collectively, the present study suggests that BSA can significantly enhance the therapeutic behavior of Rh2 by improving its solubility and stability in aqueous systems, and hence, BSA-Rh2 NPs may potentially be used as a ginsenoside delivery vehicle in cancer and inflammatory cell lines.
Super-FAN: Integrated Facial Landmark Localization and Super-Resolution of Real-World Low Resolution Faces in Arbitrary Poses with GANs

This paper addresses 2 challenging tasks: improving the quality of low resolution facial images and accurately locating the facial landmarks on such poor resolution images. To this end, we make the following 5 contributions: (a) we propose Super-FAN: the very first end-to-end system that addresses both tasks simultaneously, i.e. both improves face resolution and detects the facial landmarks. The novelty or Super-FAN lies in incorporating structural information in a GAN-based super-resolution algorithm via integrating a sub-network for face alignment through heatmap regression and optimizing a novel heatmap loss. (b) We illustrate the benefit of training the two networks jointly by reporting good results not only on frontal images (as in prior work) but on the whole spectrum of facial poses, and not only on synthetic low resolution images (as in prior work) but also on real-world images. (c) We improve upon the state-of-the-art in face super-resolution by proposing a new residual-based architecture. (d) Quantitatively, we show large improvement over the state-of-the-art for both face super-resolution and alignment. (e) Qualitatively, we show for the first time good results on real-world low resolution images like the ones of Fig. 1.