Miracle-dz/UniFAR-FTUs huggingface dataset:
LO ] 26 M ay 2 01 5 On Counterexample Guided Quantifier Instantiation for Synthesis in CVC 4 ⋆ ⋆ ⋆

We introduce the first program synthesis engine implemented i si e an SMT solver. We present an approach that extracts solution functions from unsatisfiability proofs of the negated form of synthesis conje ctures. We also discuss novel counterexample-guided techniques for quantifier ins tantiation that we use to make finding such proofs practically feasible. A particul arly important class of specifications are single-invocation properties, for whic h we present a dedicated algorithm. To support syntax restrictions on generated sol utions, our approach can transform a solution found without restrictions into th e desired syntactic form. As an alternative, we show how to use evaluation functi o axioms to embed syntactic restrictions into constraints over algebraic da tatypes, and then use an algebraic datatype decision procedure to drive synthesis. Our experimental evaluation on syntax-guided synthesis benchmarks shows that ou r implementation in the CVC4 SMT solver is competitive with state-of-the-art to ols for synthesis.
An Automatic Extractor for Biomedical Terms in Spanish

Corpora: terms extracted semi-automatically [15] from the MultiMedica corpus [16]. Lexical resources: terms that were not found in the corpus were semi-automatically curated from general and specialized resources [17-20]. Terms are linguistic realizations of concepts in a specific domain [1, 2]. Automatic Term Recognition (ATR) aims at identifying candidate words in a text. ATR involves indentifying two features of a term: its termhood and its unithood [2]. Further difficulties arise when systems deal with variations [3] and homonymous words. We present a system that uses lexically based, tagger based, and ruled based methods.
University of Dundee Gender differences in cortical morphological networks

Cortical morphological networks (CMN), where each network models the relationship in morphology between different cortical brain regions quantified using a specific measurement (e.g., cortical thickness), have not been investigated with respect to gender differences in the human brain. Cortical processes are expected to involve complex interactions between different brain regions, univariate methods thus might overlook informative gender markers. Hence, by leveraging machine learning techniques with the potential to highlight multivariate interacting effects, we found that the most discriminative CMN connections betweenmales and females were derived from the left hemisphere using the mean sulcal depth as measurement. However, for both left and right hemispheres, the first most discriminative morphological connection revealed across all cortical attributes involved (entorhinal cortex↔ caudal anterior cingulate cortex) and (entorhinal cortex↔ transverse temporal cortex) respectively, which gives us new insights into behavioral gender differences from an omics perspective and might explain why males and females learn differently.
AIRLINES PASSENGER FORECASTING USING LSTM BASED RECURRENT NEURAL NETWORKS

Investors take huge risk in making investments because they lack the information about the future prospects of the company or we can also say that they lack forecasting. Due to which they may have to face loss in future. Similar is the case with transportation, especially when it comes to airline mode because of huge increase in investment and risk factor. Thus, this paper proposes a system using Long Short Term Memory (LSTM) based Recurrent Neural Networks for forecasting the number of airline passengers. Unlike the existing schemes, the proposed scheme is capable enough to minimize the Root Mean Square Error to maximum extent which ultimately would lead to better forecasting results. The proposed scheme consist of four steps: (i) LSTM model construction, (ii) data processing and making data suitable for LSTM model, (iii) fitting a stateful LSTM network model to the training data and (iv) evaluation of the static LSTM model on the test data and report the performance of forecast. Furthermore, the obtained results indicate that the proposed scheme reduces the chances of false positive to great extent and is practical enough to be implemented in real-time scenario.
EasyChair Preprint No 1392 Data Fusion Algorithms for Wireless Sensor Networks Based on BP Neural Network with Improved Particle Swarm Optimization

Data fusion algorithm of wireless sensor network based on Improved Particle Swarm Optimization BP neural network Aiming at the problem of slow convergence speed, sensitive to initial value and easy to fall into local optimal solution of traditional back propagation (BP) neural network in data fusion algorithm of wireless sensor network, this patent proposes WSN data fusion algorithm of improved particle swarm optimization BP neural network. Law. The particle swarm optimization (PSO) algorithm is improved by Beetle Antennae Search algorithm (BAS). The improved PSO algorithm is used to optimize the weights and thresholds of BP neural network. In WSN data fusion, the cluster head node extracts the features of the data through the optimized BP neural network and sends the fused features. Information to the sink node reduces redundant data transmission and prolongs the network life cycle. Keywords-Clustering routing; Data fusion; BP neural network; Beetle antennae search
Theses 5-2019 Design of a Flexible Schoenhage-Strassen FFT Polynomial Multiplier with High-Level Synthesis

Homomorphic Encryption (HE) is a promising field because it allows for encrypted data to be sent to and operated on by untrusted parties without the risk of privacy compromise. The benefits and applications of HE are far reaching, especially in regard to cloud computing. However, current HE solutions require resource intensive arithmetic operations such as high precision, high degree polynomial multiplication resulting in a minimum computational complexity of O(n log n) on standard CPUs though application of the Fast Fourier Transform (FFT). These operations result in poor overall performance for HE schemes in software and would benefit greatly from hardware acceleration. This work aims to accelerate the multi-precision arithmetic operations used in HE with specific focus on an implementation of the Schönhage-Strassen FFT based multiplication algorithm. It is to be incorporated into a larger HE library of arithmetic functions tuned for High-Level Synthesis (HLS) that enables flexible solutions for hardware/software systems on reconfigurable cloud resources. Although this project was inspired by HE, it could be incorporated within a generic mathematical library and support other domains. The developed FFT based polynomial multiplier exhibits flexibility in the selection of security parameters facilitating its use in a wide range of HE schemes and applications. The design also displayed substantial speedup over the polynomial multiplication functions implemented in the Number Theory Library (NTL) utilized by software based HE solutions.
Vindicating Synecdoche : A Study in Rhetoric and Cognitive Semantics

Synecdoche is a term used to refer to a classical rhetoric phenomenon that substitutes part for whole, genus for species or vice versa. It appears in different pictures that vary from a scholar to another ranging from a trope to a semantic mechanism. Some scholars identify synecdoche as a separate trope, others see it as a special form of metonymy and some expand it to be a master trope that includes most metonymical relations. More recently, cognitive semantics redefine synecdoche as an independent trope exclusively based on inclusion (taxonomic relations) in that they exclude the part-whole relation and subsume it entirely within metonymy. They, as the researcher sees, make a dangerous step that jeopardizes the concept of synecdoche and transforms it to an anemic trope or a merely semantic mechanism which belongs to a normal functioning of ordinary language. The present paper aims at defending the concept of synecdoche through conforming its independence without losing its fundamental part . Therefore, it tries its best to arrive at semantically based justifications to retrieve the lost part from metonymy to synecdoche. It, thus, announces the independence of synecdoche on condition that it preserves its most tropical part, viz. part-whole relation.
Analysis of Ultrasound Bone Mineral Density in 670 Retired Female Workers in Chengdu University

For hard work, research tasks or mental and physical stress, and lack of exercise, University Teachers become a high-risk groups and a blind spot of the National Fitness. In this article, only by testing 670 university retired teachers’ radius bone massl density, the autor monitors the osteoporosis signs of retired teachers, Observes the trends of T value, Z value and provides a useful reference for Early prevention and diagnosis of osteoporosis.
Model Probit pada Respons Biner Multivariat Menggunakan Simulated Maximum Likelihood Estimator Probit Model on Multivariate Binary Response Using Simulated Maximum Likelihood Estimator

In this paper, we discuss probit model on multivariate binary response. We assume that each of n individuals is observed in T responses. Yit is t response on i individual/subject and each response is binary. Each subject has covariate Xi (individual characteristic) and covariate Zijt (characteristic of alternative j). Response on individual i can be represented by Yi = (Yi1,.,YiT), Yit is t response on i individual/subject and each response is multinomial. In order to simplify, we choose one of individual characteristics and alternative characteristics. We use simulated maximum likelihood estimator (SMLE) methods to estimate the parameter based on GewekeHajivassiliou-Keane (GHK) simulator. We find the first derivative of likelihood function for multivariate binary probit. Then, we expand to multivariate multinomial response. The first derivative is used in the BHHH (Berndt, Hall, Hall, Hausman) iteration to obtain estimators.
ADDIS ABABA UNIVERSITY SCHOOL OF MECHANICAL & INDUSTRIAL ENGINEERING Performance Analysis of Acetal Nano-Silica composite spur gear

It is well known that gears are one of the most important and common machine components, and un alternative, light weight and durable, material for gears has been the focus of many researchers, especially in the last two decades. This thesis work will focus on assessing the capability of polymer composite spur gear, acetal, which is the most commonly used polymer for gear manufacturing due to its manufacturability, low cost, and good wear resistance, reinforced with nano-silica (spherical), one of the most easily available and widely used nano particle to reinforce thermoplastics, for medium power transmission(890W). Reinforcing polymers with nano-particles is found to be one of the most effective methods to improve the overall mechanical and thermal property of polymers. The composite gear performance is analyzed for wear and thermal resistance as well as fatigue life. The analysis is conducted with the aid of FEM software Abaqus/CAE 6.14 and the homogenized material property of the composite is predicted using the multi scale homogenization technique which was found to be reliable for calculating the homogenized mechanical property of polymer nano-composites. The results obtained after this work show that the addition of 4.5 volume percentage of nano-silica (SiO2) in to the polymer matrix (acetal) was found to increase the wear resistance by 300%, the thermal conductivity by 47% and the number of cycles to fatigue failure was found to be ten times more than pure acetal gear, where the change or increase in mass of the gear is only 15.86 grams.
COMPARISON OF VISUAL ACUITY AND CONTRAST SENSITIVITY BETWEEN UNILATERAL PRIMARY PTERYGIUM AND NORMAL ADULTS UTILIZING COMPUTERIZED

Introduction: This paper aimed to describe variation in oculovisual function between unilateral primary pterygium and normal adults using computerized M&S Smart System II. Methods: A total of 120 participants comprise 60 unilateral primary pterygium eyes from 60 pterygium patients and 60 normal adults were recruited in this study. Diagnosis and classification of primary pterygium were done by a consultant ophthalmologist (KMK). Standard optometric examinations were performed in all participants. Oculovisual function parameters were taken based on the best-corrected visual acuity (BCVA) and contrast sensitivity (CS) which was subjectively measured using computerized M&S Technologies Smart System II. Both measurements were done by single operator and performed on the same visit. Difference for both BCVA and CS parameters between primary pterygium and normal groups were determined via independent T-test. Results: Overall mean and standard deviation (n = 120) of BCVA and CS were found lower in primary pterygium group (0.73± 0.22LogMARand 45.00± 10.49%) compared to normal (0.12 ± 0.05 LogMAR and 6.40 ± 0.81 %) respectively. Independent T-test results showed significance difference in BCVAand CS values between primary pterygium groups and normal (both P< 0.001). Conclusions: Reduction of oculovisual function in pterygium patient is expected compared to normal. Both BCVA and CS can be used as indicator for surgical excision and prediction in early sign of reduction in visual performance.
Beyond Retail Stores: Managing Product Proliferation along the Supply Chain

Product proliferation occurs in supply chains when manufacturers respond to diverse market needs by trying to produce a range of products from a limited variety of raw materials. In such a setting, delaying the point of the proliferation helps manufacturers improve their responsiveness by postponing the ordering decisions of the final products until there is partial or full resolution of the demand uncertainty. This strategy can be implemented in two different ways: (i) redesigning the operations so that the point of proliferation is swapped with a downstream operation or (ii) reducing the lead times. To establish cost efficiency, manufacturers can systematically reduce their operational costs or postpone the high‐cost operations. We consider a multi‐echelon and multi‐product newsvendor problem with demand forecast evolution to analyze the value of each operational lever of the responsiveness and the efficiency. We use a generalized forecast‐evolution model to characterize the demand updating process, and develop a dynamic optimization model to determine the optimal order quantities at different echelons. We demonstrate that reducing the lead time of a downstream operation is more beneficial to manufacturers than reducing the lead time of an upstream operation by the same amount, whereas reducing the upstream operational costs is more favorable than reducing the downstream operational costs. We also indicate that delaying the proliferation may cause a loss of profit, even if it can be achieved with no additional costs. Finally, a decision typology is developed, which shows effective operational strategies depending on product/market characteristics and process flexibility.
Reading Anxiety Among Chinese Exchange Students in a Study-abroad Context

This study presents the results of an investigation into L2 reading anxiety among eight Chinese exchange students who participate in a one-year program at an American University. Foreign Language Reading Anxiety Scale (FLRAS) is used as a measurement of reading anxiety. Data are also collected from semi-structured interviews, syllabus of all six specialized courses participants have taken during the academic year and their final scores. The study finds: 1) all participants experience a rather high level of reading anxiety; 2) despite such anxiety, participants perform well in all major courses; 3) underlying causes for reading anxiety include content knowledge, unpreparedness of cultural difference and general negative feeling of studying a foreign language. Based on the research findings, suggestions are given to improve the overall efficacy of such exchange program, aiming to benefit all immediate parties involved.
Housing Markets and Automobile Policy

To alleviate air pollution and tra c congestion, cities around the world are beginning to implement automobile policies to restrict car use or car ownership. However, there is little empirical evidence on possible indirect impacts of these policies in connected markets. This paper investigates the distributional impacts of a unique car purchase lottery in Beijing on the housing market. I use a di erence-in-di erences approach to compare heterogeneous neighborhoods before and after implementation of the policy. At an aggregate scale, housing prices within Beijing's fourth ring road increase by 1 to 2% while those outside the fth ring road decrease by 5%. This is equivalent to transferring about 115,000 RMB ($17,000) from each apartment owner outside the fth ring to those within the fourth ring. The disaggregate e ects are even more pronounced: housing prices increase at locations close to common destinations (employment centers: 5%; primary schools: 3%) and alternative transportation (subway: 3%; buses: 4%). These changes re ect capitalization of the automobile policy and imply a large, and likely unexpected, redistribution across homeowners. The results are relevant to policy, both in the context of unintended consequences and for e orts to develop o setting measures. JEL: D12, D63, Q58, R32, R41, R48 ∗University of California, San Diego. Email: xulu@ucsd.edu. I am grateful to my advisor Mark Jacobsen for advice throughout the development of this paper. I would also like to thank Prashant Bharadwaj, Judson Boomhower, Richard Carson, Je rey Clemens, Gordon Dahl, Alexander Gelber, Roger Gordon, Josh Gra Zivin, Ruixue Jia, David Lagakos, Gordon McCord, Craig McIntosh, Michelle White, and participants at CMES (2017) and UCSD seminars for helpful feedbacks. Thanks to the Department of Economics and the Center for Environmental Economics for nancial support. All errors are my own.
Revisiting the added value of the school library

A cylinder for a printing press is adjusted away from a printing stock web or an adjacent cylinder by rotation of an eccentric element. The change in position caused by the movement of the eccentric element is compensated by an additional rotating movement superposed on the rotating movement of the cylinder such that the outer surface of the cylinder has no relative velocity relative to the adjacent cylinder or the printing stock web. Compensation is carried out by a regulating circuit to which is supplied the actual rotational angle of the cylinder with respect to the eccentric element and the actual rotational angle of the eccentric element with respect to the side wall or an angular function derived therefrom.
Title Towards Adaptive Robotic Sampling of Phytoplankton in the Coastal Ocean

Currents, wind, bathymetry, and freshwater run-off are some of the factors that make coastal waters heterogeneous and patchy, and an environment of scientific interest, where it is challenging to resolve the spatio-temporal variation within the water-column. We present methods and results from field experiments using an autonomous underwater vehicle (AUV) with embedded
Rationale for Targeting CD 6 as a Treatment for Autoimmune Diseases

CD6 is a 105–130 kDa surface glycoprotein expressed on the majority of T cells and a subset of B cells. The human cd6 gene maps to chromosome 11, and the expression of its protein product is tightly regulated. CD6 mediates cellular adhesion migration across the endothelial and epithelial cells. In addition, it participates in the antigen presentation by B cells and the subsequent proliferation of T cells. CD6 may bind in trans to surface glycoproteins (such as ALCAM and 3A11), or to microbial lipopolysaccharides, and may bind in cis to endogenous ligands (such as CD3 and CD5), and thereby deliver a costimulatory signal. Transinteractions are reinforced during autoimmune diseases (e.g., rheumatoid arthritis (RA), Sjögren’s syndrome, and multiple sclerosis) and some cancers. Based on experimental data and on clinical results in RA and psoriasis, we believe that the recent humanized anti-CD6specific mAb T1h may act as a regulator of the immunological response in addition to its function as an anti-Tand -B cell agent.
Estimation and Validation of the IGS Absolute Antenna Phase Center Variations

To validate the IGS proposed absolute antenna phase center variations (APCVs) and offsets for the GPS satellites, an independent APCV model is derived with the EPOS software using data of 90 IGS stations for the days 292 to 326 in 2003. The estimated block-type-specific models agree with the IGS ones within 1 mm rms for the APCVs and about 10 cm in the offsets. However, we found significant differences up to 0.8 m in the offsets among satellites belonging to the same block type. The APCVs for the individual satellite could be divided into 5 groups. Therefore, we suggest group-specific APCVs plus satellite-specific offset for IGS application. Furthermore, the same data set is also analyzed fixing the IGS proposed APCVs and offsets. Their impact is investigated by comparing the new results with those using the relative PCVs and offsets.
Investigating Deep Learning and Self Play in Artificial Intelligent Games

Deep learning, a technique used for learning in a more complex neural network with many layers of abstraction. It was able to produce best solutions in computer science problems such as computer vision, image recognition, speech recognition, and natural language processing. A deep learning network contains multiple layers of abstraction and as such tends to be more powerful than shallower networks. On the other hand, self play is an artificial game that plays itself repeatedly in order to acquire training and skills. It is enforced through a technique called, reinforcement learning. In this work, we investigate deep learning and self play and answer some promising questions. Moreover, we provide an analysis and conclusions for both deep learning and self play that address symbolic Artificial Intelligence look-ahead search, machine learning mechanisms, and uncertain reasoning.