Miracle-dz/UniFAR-FTUs huggingface dataset:
From Ontologies to Learning-Based Programs

In this paper, we discuss the work in progress on designing a prototype for a novel declarative learningbased programming system and present our preliminary results. The main idea is to express learningbased programs in terms of declarative domain knowledge. Given that the existing ontologies contain rich domain and world knowledge, we propose to automatically generate the learning-based programs from the current ontology representation languages such as OWL. The ontological concepts and domain relationships are compiled to a graph which is a partial program. The nodes in the graph are connected to data sensors and learners. Local training algorithms can use data and train learners corresponding to each concept and domain relationship in the graph. Global inference mechanisms make the final decisions based on the local prediction of the learners and under the ontological constraints. We test our framework on the entitymention-relation extraction task.
The Even Start Family Literacy Program: The Rise and Fall of Family Literacy and the Need for its Return

II. EVEN START PURPOSE AND EVOLUTION . 429 A. The Rise of Family Literacy . 429 B. Legislative History and Program Design . 431 1. Creation of the Demonstration Program: Hawkins-Stafford Amendment . 431 2. Amending Even Start: National Literacy Act of 1991 . 433 3. Even Start’s First Reauthorization: Improving America’s Schools Act of 1994. 434 4. Amendments Between Reauthorizations. 434 5. Even Start Reauthorized Under the No Child Left Behind Act . 436 C. Even Start’s Demise. 436
Scale Invariant Constrained Deep Network for Head Pose Estimation

Head pose estimation plays a significant role in face recognition, facial expression analysis and so on. However, it still faces great challenges due to the initialization and facial feature points location accuracy. Inspired by the observation that head pose variations change tinily and nonlinearly but smoothly and continuously, we present a new approach based on scale invariant constrained deep network for head pose estimation. The proposed network architecture consists of three levels and each level has three networks. The first level is a global one. It predicts the head pose quickly as a preliminary estimation. The following two levels are local ones. They refine the estimation achieved from the previous layer step by step. At last, a multi-level regression is employed to combine the estimations from each level. The whole process is conducted in a deep structure to learn the three angles together directly. We perform large experiments on nine challenging datasets. The experimental results show that the proposed method perform remarkably better than the compared existing methods.
The Most Effective Digital Marketing Strategies & Approaches : A Review of Literature

The rise in popularity of organizations integrating technology into their marketing strategy, directs attention to the need for an in-depth review of digital marketing strategies. Making a strategic shift to client-centered marketing strategies, provide businesses the opportunity to engage in a new era of innovative marketing practices, that use digital marketing to meet their primary marketing requirements. A literature review of the most effective digital marketing approaches, provide companies with valuable tools to target a larger audience, using a combination of emerging technologies and some aspects of traditional marketing. The present study provided individuals, companies, organizations, businesses and researchers, with digital marketing strategies to increase visibility to their target market.
REPLACING DRUM TIMBRES AND FREQUENCY CHARACTERISTICS BETWEEN DIFFERENT MUSICAL PIECES

This paper presents a system that allows users to customize an audio signal of polyphonic music (target) without using musical scores by replacing the timbres of drum sounds and the frequency characteristics of harmonic sounds with those of another audio signal of polyphonic music (reference). To develop the system, we first use a method that can separate the amplitude spectra of the target and reference signals into harmonic and percussive spectra. We characterize frequency characteristics of the harmonic spectra by two envelopes tracing spectral dips and peaks roughly, and the target harmonic spectra are modified such that their envelopes become similar to those of the reference harmonic spectra. The target and reference percussive spectrograms are further decomposed into individual drum instruments, and we replace the timbres of those drum instruments in the target signal with those in the reference piece. The experimental results showed that our system can successfully let users perceive replacement of drum timbres and frequency characteristics.
Semantic Video Retrieval Using High Level Context 2008

Video retrieval – searching and retrieving videos relevant to a user defined query – is one of the most popular topics in both real life applications and multimedia research. This thesis employs concepts from Natural Language Understanding in solving the video retrieval problem. Our main contribution is the utilization of the semantic word similarity measures for video retrieval through the trained concept detectors, and the visual co-occurrence relations between such concepts. We propose two methods for content-based retrieval of videos: (1) A method for retrieving a new concept(a concept which is not known to the system, and no annotation is available) using semantic word similarity and visual co-occurrence, which is an unsupervised method. (2) A method for retrieval of videos based on their relevance to a user defined text query using the semantic word similarity and visual content of videos. For evaluation purposes, we mainly used the automatic search and the high level feature extraction test set of TRECVID’06 and TRECVID’07 benchmarks. These two data sets consist of 250 hours of multilingual news video captured from American, Arabic, German and Chinese TV channels. Although our method for retrieving a new concept is an unsupervised method, it outperforms the trained concept detectors (which are supervised) on 7 out of 20 test concepts, and overall it performs very close to the trained detectors. On the other hand, our visual content based semantic retrieval method performs more than 100%
Diversity measures and network centralities as indicators of interdisciplinarity: Case studies in BionanoScience

Mapping and evaluating interdisciplinarity poses major challenges, due to its multidimensional character and its inherent conflict with categorisation methods. Here we develop indicators of interdisciplinarity as cognitive diversity that are not dependent on categorisation. To do so, we integrate in a conceptual framework measures of diversity used in ecology and economics, the measures of similarity predominant in bibliometrics and recently proposed indicators of interdisciplinarity based on social network analysis. We carry out two case studies in bionanoscience which illustrate how these indicators do capture the diversity of research topics engaged by an author or a publication, in contrast to category-based indicators that fail to do so. We suggest that these simple and ready-to-use indicators of cognitive diversity may be of potential importance in comparative studies of emergent scientific and technological fields, where claims of novelty and interdisciplinarity are rife but not always justified.
Web Browser for Smartphone with New Optimized Power Consumption Technique

A smart phone is a mobile phone with an advanced mobile operating system which combines features of a personal computer operating system with other features useful for mobile or handheld use. Smartphone wastes its battery power for display and wireless interface. Smartphone web browser takes much more power for downloading the web pages, so it will drain the battery power completely for web browser. Here propose three techniques to solve the power consumption problem during web browsing that are, 1.changing of browser computation sequence, 2.Advanced Regression tree algorithm, 3.Less Energy Consumption Approach. Here proposed first algorithm is “changing of browser computation sequence”, it will rearrange the computation sequence of web browser, and first download the all object from server in DCH state, and DCH state consumes more battery power of the Smartphone. Now the radio interface is put into FACH state, the FACH state uses the half of the power of DCH state, here downlink or uplink takes place to perform remaining computation. Because remaining computation requires some upload and download process. Here propose second algorithm is “Advanced Regression Tree”, after downloading of all objects, average computation time was calculated by the ART, based on that time value only the smart phone still run in FACH state, if the computation takes more time, than Smartphone will be switched to idle state. Using these three algorithms we can reduce the power consumption of Smartphone by more than 30 percent during the web browsing. Keywords— smartphone browser, Advanced Regression Tree,DCH state, FACH state, IDLE state.
Study of agronomic traits of pinto bean ( Phaseolus vulgaris L . ) under nano TiO 2 spraying at various growth stages

Experiments were done on the effects of treatments of titanium dioxide spray on Pinto bean (Phaseolus vulgaris L. c.v ‘c.o.s.16’). The study was conducted as a factorial experiment in a randomized complete block design with four replications. Treatments consisted of two factors; the first factor was stage of plant growth that spraying was applied (rapid vegetative growth, flowering and pod fill); and the second factor was that of different concentrations of titanium dioxide nanoparticles (TiO2) that consisted of spray with water (control), nano titanium dioxide at concentrations of 0.01%, 0.02%, 0.03% and 0.05%. Results showed that effect of nano TiO2 was significant on chlorophyll content a, seed protein content, grain yield, biological yield and grain weight. The maximum amount of chlorophyll a, seed protein, biological yield, grain weight and grain yield were recorded from the treatment of nano TiO2 spray at the rapid vegetative growth by Nano particles 0.02 percentage, from the treatment of nano TiO2 spray at the rapid vegetative growth by Nano particles 0.05 percentage, from the treatment of nano TiO2 spray at the pod fill growth by Nano particles 0.05 percentage, from the treatment of nano TiO2 spray at pod fill growth by Nano particles 0.02 percentage and from the treatment of nano TiO2 spray at the flowering by Nano particles 0.03 percentage in comparison with other treatment. So, utilization of nanoparticles (nanao TiO2) can facilitate an increase in crop yield, particularly Pinto bean yield and we hope that the results of this research could provide nanoparticles as nanofertilizers to attain to stable performance in sustainable agriculture.
Chapter Aberrant Polycystin-1 Expression results in Modifi cation of AP-1 Activity , whereas Wnt Signaling remains unaffected 2

1Department of Human Genetics; 2Department of Molecular Cell Biology, Leiden University Medical Center, Leiden; 3Department of Immunology, Utrecht University Medical Center, Utrecht, The Netherlands; 4Dept. Biochemistry and Molecular Biology, University of Kansas Medical Center, Kansas City, USA; 5Sheffi eld Kidney Institute, Sheffi eld University, Sheffi eld, United Kingdom; 6Department of Pediatrics and Pediatric Research Institute, Saint Louis University, St. Louis, USA
Estimation of Optimal Channel Gain in Cognitive Radio Networks Using Bisectional Algorithm

------------------------------------------------------------------- ABSTRACT -------------------------------------------------------------- In Cognitive radio network, to carry out spectrum sharing between the primary users and cognitive users the interference temperature must be known. When Cognitive Transmitter (CT) knows the interference temperature, it will be able to share the spectrum along with the Primary Transmitter (PT) without affecting the quality of service of the primary users. So, to determine interference temperature from the cognitive transmitter, the primary channel gain must be calculated from the CT. To calculate the primary channel gain a maximum likelihood estimator (MLE) has been designed. In order to reduce the complexity of the maximum likelihood (ML) estimator, a Bisectional algorithm has been introduced to estimate the optimal channel gain from CT. The estimation error is very less of about -0.88 and it is reduced to -0.92 as the number of block increases to 100. The channel gain approaches to true value, when the number of blocks increases to 100. The achieved rate of CT is 18 kbps and the estimator approaches to true value as L block increases with perfect primary channel gain.
MORPHOLOGICAL CHARACTERIZATION OF SWEET SORGHUM GENOTYPES ACROSS ENVIRONMENTS

Sweet sorghum being a C4 crop accumulates more sugar in its stalks, also suitable for biofuel production and has high degree of tolerance to biotic and abiotic stresses. Morphological characteristics i.e., plant height, plant biomass, leaves on the stem, panicle length and yield of crude biomass of sweet sorghum genotypes were studied across different environments. Environments and genotype by environment interaction (GEI) had a significant effect on the yield of crude biomass. The smallest yield of crude biomass was achieved in 2016 (42.54 t ha-1), which was found significantly lower as compared to that in 2014 and 2015. The average plant height had a significant and positive correlation with the number of leaves ( 0.54) and number of leaves had highly a significant and positive correlation with the mass of stem (0.46) and panicle length (0.61). Biomass yield was positively and significantly correlated with precipitation (0.72) and negatively significantly correlated with temperature (-0.57). In breeding of sweet sorghum, the highest attention should be given to biomass yield as it manage the whole variation and controlled by polygenes.
Lombard Speech Synthesis Using Transfer Learning in a Tacotron Text-to-Speech System

Currently, there is increasing interest to use sequence-to-sequence models in text-to-speech (TTS) synthesis with attention like that in Tacotron models. These models are end-to-end, meaning that they learn both co-articulation and duration properties directly from text and speech. Since these models are entirely data-driven, they need large amounts of data to generate synthetic speech of good quality. However, in challenging speaking styles, such as Lombard speech, it is difﬁcult to record sufﬁciently large speech corpora. Therefore, we propose a transfer learning method to adapt a TTS system of normal speaking style to Lombard style. We also experiment with a WaveNet vocoder along with a traditional vocoder (WORLD) in the synthesis of Lombard speech. The subjective and objective evaluation results indicated that the proposed adaptation sys-tem coupled with the WaveNet vocoder clearly outperformed the conventional deep neural network based TTS system in the synthesis of Lombard speech.
Entity Resolution via Canonicalization

A Knowledge base (KB) is a machine-readable library of information created for computer systems to perform a range of tasks, such as question answering and language inference. Advancements in Open Information Extraction (OIE) techniques [13] have resulted in the construction of enormous open KB containing hundreds of millions of assertions. In order to utilize these KBs properly, one major problem that must be solved is entity resolution , the task of determining whether two given noun phrases refer to the same entity. Previous methods commonly devise a similarity function based on manual features or trained embeddings, and impose a one-o ﬀ threshold on the similarity score to determine equivalence. This project identiﬁes the composition of input instances and adopts a multi-level strategy to solve the problem in a coarse-to-ﬁne fashion. Extensive experiments on multiple datasets have shown the e ﬀ ectiveness of our method in improving the accuracy.
Topographic Identification of High-Risk Valvular Regions for Patients With Calcific Degenerative Aortic Valve Stenosis

Features of calcific leaflet distribution from CT valve images may allow prediction of hemodynamic disease severity in calcific degenerative aortic valve stenosis (DAS). The proposed study describes a signal processing scheme for selecting valve areas from maximum intensity projection valve images in a cohort of 52+43 patient images, diagnosed as having severe and moderate DAS. First the valve center and perimeter are approximated by a manually determined circle of radius r and center (x,y). The circle is then used to define eight masks, based on concentric circles of radius $\frac{r}{2},\frac{{r\sqrt 2 }}{2},\frac{{r\sqrt 3 }}{2}$ and r, each with center (x,y). The masks are used to define pixel regions within the valvular area, and statistical and textural descriptors are applied to each. Sensitivity/ specificity testing is performed with these descriptors, applied to the pixels within each mask, which show that disease severity is best predicted by using the smallest, most central mask and statistical features of skewness and kurtosis, providing area under the curve of 0.844 and 0.840 respectively. Our methodology was simple to implement and use, and provided good discriminatory power for disease severity. It also overcomes some difficulties in an earlier method, since our solution is scalable to variation in aortic valve size and tests a range ofstatistical and textural descriptors.
N ° 2017-01 Labor market consequences of trade openness and competi t ion in foreign markets February 2017

We analyze the labor market consequences of international trade, using the evidence provided by the behavior of Mexican labor markets after the introduction of NAFTA in the nineties and the accession of China to the WTO in 2001. Following an approach close to that proposed by Autor, Dorn and Hanson (2013), we use the local market variation on exposure to international markets to identify the effects of these events. We show that NAFTA integration reduced unemployment, and boosted employment and wages. Chinese competition tended to have the opposite effect. Additionally, we find that the labor market responses to international trade are heterogeneous across regions in the country, being significantly stronger in the regions closer to the U.S. border.
Mitsuba 2

Modern rendering systems are confronted with a dauntingly large and growing set of requirements: in their pursuit of realism, physically based techniques must increasingly account for intricate properties of light, such as its spectral composition or polarization. To reduce prohibitive rendering times, vectorized renderers exploit coherence via instruction-level parallelism on CPUs and GPUs. Differentiable rendering algorithms propagate derivatives through a simulation to optimize an objective function, e.g., to reconstruct a scene from reference images. Catering to such diverse use cases is challenging and has led to numerous purpose-built systems---partly, because retrofitting features of this complexity onto an existing renderer involves an error-prone and infeasibly intrusive transformation of elementary data structures, interfaces between components, and their implementations (in other words, everything). We propose Mitsuba 2, a versatile renderer that is intrinsically retargetable to various applications including the ones listed above. Mitsuba 2 is implemented in modern C++ and leverages template metaprogramming to replace types and instrument the control flow of components such as BSDFs, volumes, emitters, and rendering algorithms. At compile time, it automatically transforms arithmetic, data structures, and function dispatch, turning generic algorithms into a variety of efficient implementations without the tedium of manual redesign. Possible transformations include changing the representation of color, generating a "wide" renderer that operates on bundles of light paths, just-in-time compilation to create computational kernels that run on the GPU, and forward/reverse-mode automatic differentiation. Transformations can be chained, which further enriches the space of algorithms derived from a single generic implementation. We demonstrate the effectiveness and simplicity of our approach on several applications that would be very challenging to create without assistance: a rendering algorithm based on coherent MCMC exploration, a caustic design method for gradient-index optics, and a technique for reconstructing heterogeneous media in the presence of multiple scattering.
Title Sparsity Double Robust Inference of Average Treatment Effects Permalink

Many popular methods for building confidence intervals on causal effects under high-dimensional confounding require strong “ultra-sparsity” assumptions that may be difficult to validate in practice. To alleviate this difficulty, we here study a new method for average treatment effect estimation that yields asymptotically exact confidence intervals assuming that either the conditional response surface or the conditional probability of treatment allows for an ultra-sparse representation (but not necessarily both). This guarantee allows us to provide valid inference for average treatment effect in high dimensions under considerably more generality than available baselines. In addition, we showcase that our results are semi-parametrically efficient.
Investigations on Gas Flow in Cracked Granite Samples

In the case of rock-fluids interaction, numerous studies have mainly focused on field and numerical simulations regarding the existence of toxic gases and investigations about the possibility of gasburst. This paper is related to the development of experimental investigations for granite formations on samples obtained from Creighton mine, Sudbury, Canada. An experimental methodology was developed for intact samples submitted to uniaxial compression for a certain level of temperature, with the monitoring of gas releasing from the rock. A detailed description of the rock-gas interaction apparatus is made. The rock cores were obtained at 2,400 m deep from the mine and results from a sample are presented, as well as theoretical assumptions are discussed. Finally, some conclusions about the investigations on pre-existing fluids in fractured rocks are presented.
Design of a Wearable Textile Broadband RF Ambient Power Harvester

As the rapid development of communication industry, especially the 5G communication and IOT, there will be plenty of electromagnetic (EM) waves in the free space, which carry lots of energies. However, some of these energies are wasted in free space. To reuse these wasted energies, significantly growing interests are towards to the energy harvesting antennas. This paper is aimed to investigate a wearable antenna which can transfer RF energy from ambient sources to direct current by a soft and portable textile antenna. Among the numerous signals in mobile network, the GSM 1800, 3G, WiFi and 4G/5G will be chosen due to the city signal intensity distribution. Hence, a corresponding triple bands antenna has been designed to cover those frequency bands. The CST STUDIO SUITE is used in whole process of antenna design and simulation. The proposed antenna is a hooked dipole antenna with tuning bar (HATB) whose ends have small folded part in each side for better bandwidth performance. The presented antenna provides a wide operating band from 1.8 GHz to 2.5 GHz below -10 dB in return loss. And methods to overcome interference between antennas is found. Furthermore, using the corresponding rectifier to achieve the RF/DC conversion. The overall efficiency of whole rectifier is about 56.8 %, and output power level of the antenna system is 45.92nW. The experimental results could indicate that my textile hooked antenna harvester is a good choice for the charging system of personal wearable attachment, which could achieve low power absorbing for long distance anytime and anywhere.