Miracle-dz/UniFAR-FTUs huggingface dataset:
TensorFlow 1.x Deep Learning Cookbook: Over 90 unique recipes to solve artificial-intelligence driven problems with Python

Take the next step in implementing various common and not-so-common neural networks with Tensorflow 1.xKey FeaturesSkill up and implement tricky neural networks using Google's TensorFlow 1.xAn easy-to-follow guide that lets you explore reinforcement learning, GANs, autoencoders, multilayer perceptrons and more. Hands-on recipes to work with Tensorflow on desktop, mobile, and cloud environmentBook DescriptionDeep neural networks (DNNs) have achieved a lot of success in the field of computer vision, speech recognition, and natural language processing. The entire world is filled with excitement about how deep networks are revolutionizing artificial intelligence. This exciting recipe-based guide will take you from the realm of DNN theory to implementing them practically to solve the real-life problems in artificial intelligence domain. In this book, you will learn how to efficiently use TensorFlow, Google's open source framework for deep learning. You will implement different deep learning networks such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Deep Q-learning Networks (DQNs), and Generative Adversarial Networks (GANs) with easy to follow independent recipes. You will learn how to make Keras as backend with TensorFlow. With a problem-solution approach, you will understand how to implement different deep neural architectures to carry out complex tasks at work. You will learn the performance of different DNNs on some popularly used data sets such as MNIST, CIFAR-10, Youtube8m, and more. You will not only learn about the different mobile and embedded platforms supported by TensorFlow but also how to set up cloud platforms for deep learning applications. Get a sneak peek of TPU architecture and how they will affect DNN future. By using crisp, no-nonsense recipes, you will become an expert in implementing deep learning techniques in growing real-world applications and research areas such as reinforcement learning, GANs, autoencoders and more. What you will learn Install TensorFlow and use it for CPU and GPU operations Implement DNNs and apply them to solve different AI-driven problems. Leverage different data sets such as MNIST, CIFAR-10, and Youtube8m with TensorFlow and learn how to access and use them in your code. Use TensorBoard to understand neural network architectures, optimize the learning process, and peek inside the neural network black box. Use different regression techniques for prediction and classification problems Build single and multilayer perceptrons in TensorFlow Implement CNN and RNN in TensorFlow, and use it to solve real-world use cases. Learn how restricted Boltzmann Machines can be used to recommend movies. Understand the implementation of Autoencoders and deep belief networks, and use them for emotion detection. Master the different reinforcement learning methods to implement game playing agents. GANs and their implementation using TensorFlow.
On the Generalized Degrees of Freedom of the MIMO Interference Channel With Delayed CSIT

The generalized degrees of freedom (GDoF) of the two-user multiple-input multiple-output interference channel is studied under the assumption of delayed channel state information at the transmitters. In particular, with <inline-formula> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula> antennas at each transmitter and <inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> antennas at each receiver, and in the non-trivial case when <inline-formula> <tex-math notation="LaTeX">$M>N$ </tex-math></inline-formula> (with the case of <inline-formula> <tex-math notation="LaTeX">$M\leq N$ </tex-math></inline-formula> not needing any CSIT), new lower and upper bounds on the symmetric GDoF are obtained that are parameterized by <inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula>, which links the interference-to-noise ratio (INR) and the signal-to-noise ratio (SNR) at each receiver via <inline-formula> <tex-math notation="LaTeX">$\mathrm {INR}=\mathrm {SNR}^{{\alpha }}$ </tex-math></inline-formula>. A new upper bound for the symmetric GDoF is obtained by maximizing a bound on the weighted sum rate, which in turn is obtained from a combination of genie-aided side-information and an extremal inequality. The maximum weighted sum rate in the high SNR regime is shown to occur when the transmit covariance matrix at each transmitter is full rank. An achievability scheme is developed that is based on block-Markov encoding and backward decoding, and which incorporates channel statistics through interference quantization and digital multicasting. This symmetric GDoF lower bound is maximized separately for different ranges of <inline-formula> <tex-math notation="LaTeX">${\alpha }$ </tex-math></inline-formula>, by optimizing the transmit power levels in the achievability scheme separately in the very weak <inline-formula> <tex-math notation="LaTeX">$[0\leq {\alpha }\leq ({1}/{2})]$ </tex-math></inline-formula>, weak <inline-formula> <tex-math notation="LaTeX">$[({1}/{2}) < {\alpha }\leq 1]$ </tex-math></inline-formula>, and strong <inline-formula> <tex-math notation="LaTeX">$({\alpha }>1)$ </tex-math></inline-formula> interference regimes. The lower and upper bounds coincide when <inline-formula> <tex-math notation="LaTeX">${\alpha }\geq [({{r}+1})/({{r}+2})]$ </tex-math></inline-formula>, where <inline-formula> <tex-math notation="LaTeX">${r}=\min (2,{M}/{N})$ </tex-math></inline-formula>, thus characterizing the symmetric GDoF completely for strong interference and a range of values of weak interference. It is also shown that treating interference as noise is strictly sub-optimal from a GDoF perspective even when the interference is very weak.
Cell death induced by methanolic extract of Prosopis cineraria leaves in MCF-7 breast cancer cell line

Apoptosis is a programmed cell death and is an active, defined process. It plays an important role in the development of multicellular organisms, regulation and maintenance of the cell populations in tissues upon physiological and pathological conditions. Cancer is one of the scenarios where too little apoptosis occurs, resulting in malignant cells that will not die and continue to proliferate. Apoptosis is one of the main therapeutic targets in cancer research. In the present study we did a comparative study on the apoptosis influencing activity of methanolic extract of Prosopis cineraria leaves in breast cancer cell line MCF-7 and non cancerous cell line HBL 100. Various staining techniques like Giemsa, ethidium bromide, Propidium iodide and Hoechst were performed both in cancerous cell line MCF-7 and noncancerous cell line HBL 100. The results revealed that plant extract caused a steep increase in apoptotic ratio in cancer cell line and not in HBL 100.
Incorporating External Knowledge into Machine Reading for Generative Question Answering

Commonsense and background knowledge is required for a QA model to answer many nontrivial questions. Different from existing work on knowledge-aware QA, we focus on a more challenging task of leveraging external knowledge to generate answers in natural language for a given question with context. In this paper, we propose a new neural model, Knowledge-Enriched Answer Generator (KEAG), which is able to compose a natural answer by exploiting and aggregating evidence from all four information sources available: question, passage, vocabulary and knowledge. During the process of answer generation, KEAG adaptively determines when to utilize symbolic knowledge and which fact from the knowledge is useful. This allows the model to exploit external knowledge that is not explicitly stated in the given text, but that is relevant for generating an answer. The empirical study on public benchmark of answer generation demonstrates that KEAG improves answer quality over models without knowledge and existing knowledge-aware models, confirming its effectiveness in leveraging knowledge.
2289 – 4489 THE MEDIATING EFFECT OF SCHOOL-BASED MANAGEMENT ON SCHOOL CLIMATE , BUREAUCRACY AND EFFECTIVENESS IN SECONDARY SCHOOLS

School effectiveness is a global issue among education stakeholders particularly in developing countries where difficulties in delivering quality education is widespread. However, internal and external factors in schools make school outcomes unpredictable, thus making bureaucracy an effective managerial and analytical tool which can be used to examine and direct organizational structure. This study examined the mediating effect of school-based management on school climate, bureaucracy and effectiveness in secondary schools. A quantitative research of cross-sectional survey with population of 7,533 teachers was employed. Four sets of instruments were adapted and administered on sample of 350 teachers in Nigeria secondary schools through stratified random sampling of the proportionate method. Structural Equation Modelling (SEM) analysis was employed to test the fitness of data in relation to constructs in the model and further confirm hypotheses generated for this study. The findings of revealed that underlying predictors were true measure of their respective constructs. There is a mediation effect of school-based management on bureaucracy and school effectiveness while other path analysis revealed that school-based management did not mediate between school climate and school effectiveness. This study expands theory on bureaucracy as bright side and validates the assertion that, bureaucracy is required in public schools.
Plasmodium falciparum Hep 1 is required to prevent the self aggregation of Pf Hsp 70-3 D

The majority of mitochondrial proteins are encoded in the nucleus and need to be imported from the cytosol into the mitochondria, and molecular chaperones play a key role in the efficient translocation and proper folding of these proteins in the matrix. One such molecular chaperone is the eukaryotic mitochondrial heat shock protein 70 (Hsp70); however, it is prone to self-aggregation and requires the presence of an essential zinc-finger protein, Hsp70-escort protein 1 (Hep1), to maintain its structure and function. PfHsp70-3, the only Hsp70 predicted to localize in the mitochondria of P. falciparum, may also rely on a Hep1 orthologue to prevent self-aggregation. In this study, we identified a putative Hep1 orthologue in P. falciparum and co-expression of PfHsp70-3 and PfHep1 enhanced the solubility of PfHsp70-3. PfHep1 suppressed the thermally induced aggregation of PfHsp70-3 but not the aggregation of malate dehydrogenase or citrate synthase, thus showing specificity for PfHsp70-3. Zinc ions were indeed essential for maintaining the function of PfHep1, as EDTA chelation abrogated its abilities to suppress the aggregation of PfHsp70-3. Soluble and functional PfHsp70-3, acquired by co-expression with PfHep-1, will facilitate the biochemical characterisation of this particular Hsp70 protein and its evaluation as a drug target for the treatment of malaria.
Estimating North American background ozone in U . S . surface air with two independent global models : Variability , uncertainties , and recommendations

Department of Earth and Environmental Sciences and Lamont-Doherty Earth Observatory, Columbia University, 61 Route 9W, Palisades, NY, USA; amfiore@ldeo.columbia. edu; oclifton@ldeo.columbia. edu Nelson Institute Center for Sustainability and the Global Environment (SAGE), University of Wisconsin-Madison, Madison, WI, USA; oberman.wisc@gmail.com NOAA Geophysical Fluid Dynamics Laboratory and Atmospheric and Oceanic Sciences, Princeton University, 201 Forrestal Road, Princeton, NJ, USA; meiyun.lin@noaa.gov; larry.horowitz@noaa.gov School of Engineering and Applied Sciences, Harvard University, 29 Oxford Street, Cambridge, MA, USA; djacob@fas.harvard. edu Department of Atmospheric and Oceanic Sciences & Laboratory for Climate and OceanAtmosphere Studies, School of Physics, Peking University, China; zhanglg@pku.edu. cn UCAR/NOAA Geophysical Fluid Dynamics Laboratory, Princeton, NJ, USA; Vaishali.Naik@noaa.gov U.S. EPA, National Center for Environmental Assessment, Research Triangle Park, NC, USA; Pinto.Joseph@epa.gov *Corresponding author; phone 1-845-365-8580; fax 1-845-365-8157
Electronic Structure and Enhanced Charge-Density Wave Order of Monolayer VSe 2

Jiagui Feng, 2, ∗ Deepnarayan Biswas, ∗ Akhil Rajan, ∗ Matthew D. Watson, Federico Mazzola, Oliver J. Clark, Kaycee Underwood, Igor Marković, 3 Martin McLaren, Andrew Hunter, David M. Burn, Liam B. Duffy, 6 Sourabh Barua, † Geetha Balakrishnan, François Bertran, Patrick Le Fèvre, Timur K. Kim, Gerrit van der Laan, Thorsten Hesjedal, Peter Wahl, and Phil D. C. King ‡ SUPA, School of Physics and Astronomy, University of St. Andrews, St. Andrews KY16 9SS, United Kingdom Suzhou Institute of Nano-Tech. and Nanobionics (SINANO), CAS, 398 Ruoshui Road, SEID, SIP, Suzhou, 215123, China Max Planck Institute for Chemical Physics of Solids, Nöthnitzer Straße 40, 01187 Dresden, Germany Magnetic Spectroscopy Group, Diamond Light Source, Didcot, OX11 0DE, United Kingdom Department of Physics, University of Oxford, Oxford, OX1 3PU, United Kingdom ISIS, STFC, Rutherford Appleton Laboratory, Didcot, OX11 0QX, United Kingdom Department of Physics, University of Warwick, Coventry CV4 7AL, United Kingdom Synchrotron SOLEIL, CNRS-CEA, L’Orme des Merisiers, Saint-Aubin-BP48, 91192 Gif-sur-Yvette, France Diamond Light Source, Harwell Campus, Didcot, OX11 0DE, United Kingdom
Do security-di erentiated water rights improve e ciency ?

Most existing water markets combine water rights trading and water allocation trading. O ering di erent levels of security for rights can make the market more sophisticated and allow water users to manage the risks of supply uncertainty better. We compare results from a laboratory experiment with two water right designs, one with a unique security level and another with two security levels. We nd that a two security levels system improves both allocative e ciency and risk management, but only when transactions costs are higher in the market for water allocation than in the market for water rights.
Full Convergence of the Iterative Bayesian Update and Applications to Mechanisms for Privacy Protection

The iterative Bayesian update (IBU) and the matrix inversion (INV) are the main methods to retrieve the original distribution from noisy data resulting from the application of privacy protection mechanisms. We show that the theoretical foundations of the IBU established in the literature are flawed, as they rely on an assumption that in general is not satisfied in typical real datasets. We then fix the theory of the IBU, by providing a general convergence result for the underlying Expectation-Maximization method. Our framework does not rely on the above assumption, and also covers a more general local privacy model. Finally we evaluate the precision of the IBU on data sanitized with the Geometric, $k$-RR, and RAPPOR mechanisms, and we show that it outperforms INV in the first case, while it is comparable to INV in the other two cases.
Protein Analysis Report of wild type yeast Saccharomyces cerevisiae and mutant MGA 2

Protein from wild-type Saccharomyces cerevisiae and mutant MGA2 strains were evaluated semi-quantitatively using a variety of biological techniques. Proteins were assayed using a bovine serum albumin (BSA) standard curve, loaded onto sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) and analyzed for tubulin protein using western immunoblotting. The BSA standard curve showed protein concentration extractions of 2.9 mg/mL for wild-type and 2.2 mg/mL for the mutant. SDS-PAGE showed that 25μg wild-type and MGA2 protein bands were darker than 15μg bands. Western immunoblotting revealed tubulin at approximately 50 kDa in both wild-type and MGA2 strains, with the same overall banding pattern for wild-type and MGA2; thus showing MGA2 had no effect on tubulin production. Unexpected doublebanding patterns were seen in MGA2 and wild-type lanes, as well as the positive control lane.
Positron Emission Tomography in the Study of Regional Cerebral Blood Flow in the Premature Infant with Major Intraventricular Hemorrhage and in the Term Newborn with Asphyxia

The two major causes of neurological morbidity and mortality related to definable events in the neonatal period are intraventricular hemorrhage (IVH) with hemorrhagic intracerebral involvement in the preterm infant, and hypoxic-ischemic encephalopathy in the asphyxiated term infant. This chapter reviews studies of regional cerebral blood flow (CBF) by positron emission tomography (PET) in these two important groups of infants.
Driving Control Technologies of New High-Efficient Motors

Although induction motor is the most commonly used actuator in the industry, its efficiencies are difficult to upgrade due to the existence of rotor copper losses. Permanent-magnet synchronous motor (PMSM) and synchronous reluctance motor (SynRM) are the two new motors being able to achieve the IE4-class and even the IE5-class efficiency. As to the switched reluctance motor (SRM), it lacks commercialized motors and power modules being available. For establishing high-performance PMSM and SynRM drives, many key affairs must be properly treated, which are introduced in this article. The contents presented in this chapter include: (1) interdisciplinary affairs of motor drives, (2) comparative characteristics and applications of commonly used motors, (3) reversible and generator operations for specific motors, (4) power electronic converters for motor drives, (5) possible new high-efficient motor drives, (6) PMSM drives, (7) SynRM drives, (8) some key issues of PMSM drives, (9) some key issues of SynRM drives, (10) example of PMSM drive, and (11) example of SynRM drive.
Optimizing Cone Beam Computed Tomography (CBCT) System for Image Guided Radiation Therapy

Cone Beam Computed Tomography (CBCT) system is the most widely used imaging device in image guided radiation therapy (IGRT), where set of 3D volumetric image of patient can be reconstructed to identify and correct position setup errors prior to the radiation treatment. This CBCT system can significantly improve precision of on -line setup errors of patient position and tumor target localization prior to the treatment. However, there are still a number of issues that needs to be investigated with CBCT system such as 1) progressively increasing defective pixels in imaging detectors by its frequent usage, 2) hazardous radiation exposure to patients during the CBCT imaging, 3) degradation of image quality due to patients' respiratory motion when CBCT is acquired and 4) unknown knowledge of certain anatomical features such as liver, due to lack of soft-tissue contrast which makes tumor motion verification challenging. In this dissertation, we explore on optimizing the use of cone beam computed tomography (CBCT) system under such circumstances. We begin by introducing general concept of IGRT. We then present the development of automated defective pixel detection algorithm for X-ray imagers that is used for CBCT imaging using wavelet analysis. We next investigate on developing fast and efficient low-dose volumetric reconstruction techniques which includes 1) fast digital tomosynthesis reconstruction using general- purpose graphics processing unit (GPGPU) programming and 2) fast low-dose CBCT image reconstruction based on the Gradient-Projection-Barzilai-Borwein formulation (GP-BB). We further developed two efficient approaches that could reduce the degradation of CBCT images from respiratory motion. First, we propose reconstructing four dimensional (4D) CBCT and DTS using respiratory signal extracted from fiducial markers implanted in liver. Second, novel motion- map constrained image reconstruction (MCIR) is proposed that allows reconstruction of high quality and high phase resolution 4DCBCT images with no more than the imaging dose used in a standard Free Breathing 3DCBCT (FB-3DCBCT) scan. Finally, we demonstrate a method to analyze motion characteristics of liver that are particularly important for image guided stereotactic body radiation therapy (IG- SBRT). It is anticipated that all the approaches proposed in this study, which are both technically and clinically feasible, will allow much improvement in IGRT process
Preference for energy futures in Scotland

In the next two decades Scotland is facing tough greenhouse gas emissions reduction targets as well as the upcoming shutdown of a number of existing thermal plants. Given the limited timeframe it would seem imperative that Scotland’s energy policy is developed with public preferences in mind, as political unpopularity and public objections, with the associated need for lengthy public enquires, are likely to mean that targets are more likely to be missed. As such, appraisal of any potential energy option should not be limited to consideration of financial viability but should also take full account of environmental and social costs. The primary aim of our study was to determine public preferences and willingness to pay for alternative energy options, such as wind, nuclear and biomass against the current generation mix, all of which may form an integral part of the future generation portfolio for Scotland. One method of determining social costs is through stated preference techniques, one of which is choice experiments – the method applied in the current study. Our analysis is based on a postal survey sent out to a random sample of 1000 households across Scotland. People were asked to choose between four energy options: wind, biomass, nuclear and current energy mix, depending on which energy option and associated mix of attributes they prefer. Attributes were: distance from respondent’s home, carbon emissions reduction, local biodiversity impacts, land requirements (a fixed attribute) and an annual electricity bill increase (the cost attribute). Our results suggest that carbon-neutral energy options tend to have a positive willingness to pay associated with them and be more favoured by the population over the current energy mix with distance from the respondent’s home, increases in biodiversity and increases in energy bill all having a significant impact on preferences. We also found variation in preferences according to socio-economic groupings, for example respondents with children tend to have a higher preference towards renewable technologies than those without. In addition to the overall sample, we also investigated divergence in preferences between three areas of Scotland (Highlands and Islands; Central; and South). The results indicate that, depending on the geographical location, people’s preferences for energy generation technologies vary. Our results suggest that Scottish energy policy need not only be planned accounting for public preferences for different energy options but also regional divergence of preferences within the country.