Miracle-dz/UniFAR-FTUs huggingface dataset:
Iterative Registration of Multiple 3 D Data Sets using Covariance Matrix

In 3D data processing registration is the process of aligning multiple 3D data sets in a common coordinate system. Previous registration methods rely on accurate mechanical positioning devices, or on manual processing to estimate the viewpoints. In addition, most algorithms require many processes: feature extraction, matching, and surface segmentation. This paper presents an iterative method for automatically registering multiple 3D data sets using covariance matrix without a-prior knowledge about 3D transformation between views. For an accurate registration, our method uses both the 3D transformations giving a relative pose between the 3D data sets, and the projective matrix representing projection of 3D space to 2D image. By minimizing the difference of two covariance matrixes of the overlapping regions in two 3D sets, we can make a precise registration of multiple 3D sets without the above complex procedures that are prone to errors. In experimental results, we demonstrate the capabilities of the registration method on various complex 3D data sets.
Differentiation in populations of Hordeum spontaneum Koch along a gradient of environmental productivity and predictability: Intra- and interspecific competitive responses

We compared intra- and interspecific competitive responses of wild barley, Hordeum spontaneum, from four populations originating in distinct environments in Israel. The environments ranged along two parallel gradients of rainfall amount and predictability, from low (desert) to moderate (semi-steppe batha) to high (Mediterranean grassland and mountain, the latter also experiencing frost stress). The target barley plants grew under one of five densities (0, 4, 8, 16, and 32 surrounding plants per bucket) of either barley from the same population or oats (Avena sterilis) from a neutral population. The traits examined included estimates of fitness, reproductive traits, and resource allocation. The effect of intraspecific competition was stronger than interspecific competition at a high increment of neighbor density (from 4 to 32 neighbors). There was no difference in interspecific competitive responses of plants originating in the four environments at any neighbor density increments, but intraspecific competi.
Arbitrage Models and Mortgage Options Pricing

In this paper we examine the applicability of arbitrage theory to real estate. Arbitrage theory has been applied to the valuation of mortgages using partial differential equations, however the implicit assumptions made are problematic when applied to real estate. The latter is a very complex financial asset, and for instance, for the case of default options, one could produce very large errors (even up to 100%) by applying - unwisely - conventional arbitrage theory techniques. The consequences of real estate appraisal are in this paper studied in particular. Because one has encountered similar problems in real options theory as in real estate, the tools developed in that field could probably well adapt to real estate; we provide here an example. Finally the possibility of pricing contingent claims written on properties is discussed.
A Computational Approach for Simulating P-type Silicon Piezoresistor Using Four Point Bending Setup

The piezoresistance effect is defined as change in resistance due to applied stress. Silicon has a relatively large piezoresistance effect which has been known since 1954. A four point bending setup is proposed and designed to analyze the piezoresistance effect in p-type silicon. This setup is used to apply uniform and uniaxial stress along the crystal direction. The main aim of this work is to investigate the piezoresistive characteristic of p-type resistors as a function of doping concentrations using COMSOL Multiphysics. Simulation results are compared with experimental data.
Unbiased , Scalable Sampling of Constrained Kinematic Loops

We propose a Monte Carlo technique for generating conformations of a kinematic chain under loop closure and other severely restrictive constraints. The problem is cast as one of inference on a probabilistic graphical model that links configuration variables, spatial variables (e.g., atom positions), and constraints in a unified framework. We employ two technical contributions: first, a sparse model allows scalable inference to be performed on large kinematic chains, and second, the mathematical foundations for unbiased sampling of a probability density restricted to an implicit submanifold specified by loop closure constraints. The method is demonstrated to improve the speed of protein loop sampling when integrating prior from steric clashes, Ramachandran plots, and B-factors. Keywords-protein loops; conformation sampling; Markov chain Monte Carlo
In-line aligned and bottom-up Ag nanorods for surface-enhanced Raman spectroscopy

We have demonstrated surface-enhanced Raman spectroscopy on arrays of Ag nanorods aligned in line by a dynamic oblique deposition technique. For the light polarized along the major axis of the nanorods, the plasma resonance of the Ag nanorods has been tuned to a wavelength suitable for Raman spectroscopy. The average width and the length-to-width ratio of the resulting nanorods are 56nm and 3.5, respectively, and the nanorods align in line with small gaps of a few 10nm. The Raman scattering for the polarized light along the nanorods is enhanced significantly as compared with that perpendicular to the nanorods. This polarization dependent Raman enhancement is attributed to the local field concentration at the ends of the nanorods. Since the preparation process is physical and completely bottom up, it is robust in its selection of the materials and is useful in providing the surface-enhanced Raman scattering sensors at low cost.
The Internet as a medium for delivering treatment for depression. Possibilities and challenges highlighted by our experiences with MoodGYM

In this paper, I will present the current status of the use of the Internet as a medium for the treatment of depression. I will focus on the major challenges posed by the wide use of internet-based programmes. Recent and potential future developments of internet-based programmes for the treatment of depression will also be discussed. I am a member of a research group at UiT The Arctic University of Norway (UiT). For the past seven years, we have been working with the internet-based depression-treatment programme MoodGYM. Our motivation to perform this research was partly based on local shortage of treatment capacity. These local problems are not confined to northern Norway. The world is presently experiencing a worldwide epidemic of depression with vast personal, social, and economic costs. Health-care systems strive to cope with these challenges and investigate whether internet-based programmes can help alleviate these problems with no or limited involvement of the health-care system.
ACMAC ’ s PrePrint Repository Measuring the irreversibility of numerical schemes for reversible stochastic differential equations

For a Markov process the detailed balance condition is equivalent to the time-reversibility of the process. For stochastic differential equations (SDE’s) time discretization numerical schemes usually destroy the property of time-reversibility. Despite an extensive literature on the numerical analysis for SDE’s, their stability properties, strong and/or weak error estimates, large deviations and infinite-time estimates, no quantitative results are known on the lack of reversibility of the discrete-time approximation process. In this paper we provide such quantitative estimates by using the concept of entropy production rate, inspired by ideas from non-equilibrium statistical mechanics. The entropy production rate for a stochastic process is defined as the relative entropy (per unit time) of the path measure of the process with respect to the path measure of the time-reversed process. By construction the entropy production rate is nonnegative and it vanishes if and only if the process is reversible. Crucially, from a numerical point of view, the entropy production rate is an a posteriori quantity, hence it can be computed in the course of a simulation as the ergodic average of a certain functional of the process (the so-called Gallavotti-Cohen (GC) action functional). We compute the entropy production for various numerical schemes such as explicit Euler-Maruyama and explicit Milstein’s for reversible SDEs with additive or multiplicative noise. Additionally, we analyze the entropy production for the BBK integrator of the Langevin processes. We show that entropy production is an observable that distinguishes between different numerical schemes in terms of their discretization-induced irreversibility. Furthermore, our results show that the type of the noise critically affects the behavior of the entropy production rate.
Differential Meta-model and Particle Swarm Optimization

Optimization has been an active area of research for several decades. As many real-world optimization problems become increasingly complex, better optimization algorithms are always needs. Without loss of generally, the unconstrained optimization problem is identified with the discovery of the global minimizer of a real valued objective function (1)i.e. finding a point such that (2)Where is a nonempty compact set. There are two main categories for global optimization methods: deterministic and probabilistic methods. Traditionally, the deterministic methods need some information to determine the optima such as grads etc. However, for many real-world optimization problems, the optimization function may be discontinuous. Furthermore, the deterministic methods only can be applied into the problems with slow dimension because of the huge time-cost for large dimensionality. Therefore, from the 1960's, many researchers pay their attentions to the probabilistic methods. All of these rely on probabilistic judgements to determine whether or not search should depart from the neighbourhood of a local optimum (Forgo, 1988; Hansen, 1992; Rao, 1996). Different from adaptive stochastic search algorithms, evolutionary computation (EC) is a new kind of probabilistic method. They exploit a ser of potential solutions (called population), and detect the optimal problem solution through cooperation and competition among the individuals of the population. The well-known EC methods are all inspired from the evolution of nature such as: genetic algorithm (GA) (Goldberg, 1989; Michalewica, 1994), evolution strategy (ES) (Back, 1996; Schwefel, 1975), evolutionary programming (EP) (Fogel, 1996), and artificial life methods. Recently, particle swarm optimization (PSO) method (Kennedy and Eberhart, 1995) is proposed known as a member of the wide category of swarm intelligence methods
Hematological analysis of Molluscan species Bellamya bengalensis and Lamiellidens marginalis

Two molluscan species, freshwater snail Bellamya bengalensis and a bivalve Lamellidens marginalis were used for the hematological analysis. Hemolymph of both the species was collected from the body parts by using nonlethal technique in in-situ condition. Cells observed in the heamolymph were compared morphologically and quantitatively to discuss its role in the mechanism of circulation, transportation and also by means of defense mechanism in the body of both species. Cellular comparisons were made at the species level.
Circulating tumor cells and the metastatic process: the complexity of malignancy

Despite improvements achieved in terms of early detection and therapeutic approach, metastatic breast cancer remains one of the principal worldwide causes of death. In recent years, due to the heterogeneous response of each patient to chemotherapy, clinical research highlights the need of a personalized approach. Circulating tumor cells (CTCs) represents a promising tool for this purpose. Unfortunately, even if their correlation with severity, outcome and metastatic nature of the tumor has been established, several issues, mainly concerning their characterization and isolation, need to be solved. In this review, latest knowledge on CTCs and metastatic process in breast cancer were analyzed, aiming to understand their clinical utility and validity for a prospective ther-
Improvising in the primary mathematics classroom

If classrooms are dynamic, then mathematics teachers will need to improvise mid-lesson from time to time. Teachers' capacity to improvise is usually analysed via a cognitivist lens. This study contrasts a cognitivist analysis of a primary teacher of mathematics with an ecological analysis. The ecological approach was able to develop a more detailed characterisation of teacher improvisation where attention to, and manipulation of, environmental entities supported improvisation. This characterisation of improvisation is posited to have potential in developing novice teachers' capacity to 'think on their feet' in mathematics lessons.
Global manure nitrogen production and application in cropland during 1860–2014: a 5 arcmin gridded global dataset for Earth system modeling

Abstract. Given the important role of nitrogen input from livestock systems in terrestrial nutrient cycles and the atmospheric chemical composition, it is vital to have a robust estimation of the magnitude and spatiotemporal variation in manure nitrogen production and its application to cropland across the globe. In this study, we used the dataset from the Global Livestock Impact Mapping System (GLIMS) in conjunction with country-specific annual livestock populations to reconstruct the manure nitrogen production during 1860–2014. The estimated manure nitrogen production increased from 21.4 Tg N yr−1 in 1860 to 131.0 Tg N yr−1 in 2014 with a significant annual increasing trend (0.7 Tg N yr−1, p https://doi.org/10.1594/PANGAEA.871980 (Zhang et al., 2017).
Amplification of realistic Schrödinger-cat-state-like states by homodyne heralding

We present a scheme for the amplification of Schr\"odinger cat states that collapses two smaller states onto their constructive interference via a homodyne projection. We analyze the performance of the amplification in terms of fidelity and success rate when the input consists of either exact coherent state superpositions or of photon-subtracted squeezed vacua. The impact of imprecise homodyne detection and of impure squeezing is quantified. We also assess the scalability of iterated amplifications.
Bounds Analysis by Abstract Interpretation

Abstract interpretation is a framework that precisely relates programs to their formal models. A model of a program retains its control structure but computes in a data domain that is less detailed, i.e. more abstract. Symbolically evaluating programs over the abstract domain yields analysis results that have been traditionally used by compilers for optimization. This paper presents an abstract interpretation for a program language with settheoretic data structures that automatically and e ciently obtains bounds and containment data that may be used to help select e cient representations for these abstract types. In this work an abstract context, i.e. model, consists of a deductively-closed collection of formulas in a simple logical theory. The technique is easily extensible to other high-level data abstractions, and readily incorporable into compilers for very-high-level languages.interpretation is a framework that precisely relates programs to their formal models. A model of a program retains its control structure but computes in a data domain that is less detailed, i.e. more abstract. Symbolically evaluating programs over the abstract domain yields analysis results that have been traditionally used by compilers for optimization. This paper presents an abstract interpretation for a program language with settheoretic data structures that automatically and e ciently obtains bounds and containment data that may be used to help select e cient representations for these abstract types. In this work an abstract context, i.e. model, consists of a deductively-closed collection of formulas in a simple logical theory. The technique is easily extensible to other high-level data abstractions, and readily incorporable into compilers for very-high-level languages.
Content Aware Refresh: Exploiting the Asymmetry of DRAM Retention Errors to Reduce the Refresh Frequency of Less Vulnerable Data

DRAM refresh is responsible for significant performance and energy overheads in a wide range of computer systems, from mobile platforms to datacenters <xref ref-type="bibr" rid="ref1">[1]</xref> . With the growing demand for DRAM capacity and the worsening retention time characteristics of deeply scaled DRAM, refresh is expected to become an even more pronounced problem in future technology generations <xref ref-type="bibr" rid="ref2">[2]</xref> . This paper examines <italic>content aware refresh</italic>, a new technique that reduces the refresh frequency by exploiting the unidirectional nature of DRAM retention errors: assuming that a logical <bold>1</bold> and <bold>0</bold> respectively are represented by the presence and absence of charge, <bold>1</bold>-to-<bold>0</bold> failures are much more likely than <bold>0</bold>-to-<bold>1</bold> failures. As a result, in a DRAM system that uses a block error correcting code (ECC) to protect memory, blocks with fewer <bold>1</bold>s can attain a specified reliability target (i.e., mean time to failure) with a refresh rate lower than that which is required for a block with all <bold>1</bold>s. Leveraging this key insight, and without compromising memory reliability, the proposed content aware refresh mechanism refreshes memory blocks with fewer <bold>1</bold>s less frequently. To keep the overhead of tracking multiple refresh rates manageable, refresh groups—groups of DRAM rows refreshed together—are dynamically arranged into one of a predefined number of refresh bins and refreshed at the rate determined by the ECC block with the greatest number of <bold>1</bold>s in that bin. By tailoring the refresh rate to the actual content of a memory block rather than assuming a worst case data pattern, content aware refresh respectively outperforms DRAM systems that employ RAS-only Refresh, all-bank Auto Refresh, and per-bank Auto Refresh mechanisms by 12, 8, and 13 percent. It also reduces DRAM system energy by 15, 13, and 16 percent as compared to these systems.
Therapy for ADHD Directed Towards Addressing the Dual Imbalances in Mental Effort and Reward as Illustrated in the Mental Effort-Reward Imbalances Model (MERIM)

In this chapter we describe a clinical model for ADHD: the Mental Effort Reward Imbalances Model (MERIM). We use this model to explain some of the behaviour commonly observed in children with ADHD and to guide approaches to treatment. The MERIM views the behaviour associated with ADHD as an outcome of two unfavourable imbalances: 1.Imbalance of the level of mental effort required for achievement. 2.Imbalance in the level of reward experienced. These imbalances each contribute to lower levels of achievement and fewer rewarding experiences in ADHD. This results in a poorer mood with less tolerance for frustration, together with seeking rewards that do not involve high levels of effort. The concept of ADHD as dual imbalances in mental effort and reward gives a useful framework for understanding the behaviour and the strategies that individuals use to compensate and improve their mood.
LINKING CHILD MALTREATMENT HISTORY WITH CHILD ABUSE POTENTIAL: RELATIVE ROLES OF MALTREATMENT TYPES

The independent roles of each childhood maltreatment type on child abuse potential in adults have been insufficiently explored and are inconsistent, with dissociation as one of the possible suggested mediators of intergenerational child abuse. We investigated these effects among 164 non-clinical adult parents, who filled in general questionnaires: Childhood Trauma Questionnaire (CTQ), Child Abuse Potential Inventory (CAPI) and Dissociative Experience Scale (DES). Among all maltreatment types (emotional, physical and sexual abuse, emotional and physical neglect), emotional abuse was the only independent predictor in the regression model of child abuse potential. The relationship between emotional abuse history and child abuse potential was partially mediated by dissociation. The findings could speak in favor of the potentially unique detrimental role of emotional abuse in intergenerational maltreatment, with dissociation as one of the possible mechanisms.
A Preconditioned LMS adaptive filter

Inspired by the commonalities and differences between the Least Mean Square (LMS) and LMS/Newton algorithms, we propose a new adaptive filter algorithm, the Preconditioned LMS (PLMS) algorithm, which is optimized with respect to approximate a priori knowledge of the input autocorrelation signal. PLMS can result in significantly improved convergence speed, with negligible complexity increase, relative to the LMS algorithm.
Quantifying submarine eruptive flux from interpretation of hydroacoustic signals, West Mata Volcano, Lau Basin

West Mata is a submarine volcano in the northeast Lau Basin. Hydroacoustic data from a 5-month period provide insight into the nature of eruptive behavior at this volcano. Previous studies have used acoustic data to estimate the eruption velocity at subaerial volcanoes (Woulff and McGetchin, 1976; Vergniolle et al., 2004; Vergniolle and CaplanAuerbach, 2004, 2006; Caplan-Auerbach et al., 2010). In this study, the hydroacoustic data from West Mata are used to calculate eruption velocities and volumes (both lava and gas) for explosions during the 5-month hydrophone deployment. The method used in this study, developed by Vergniolle and Caplan-Auerbach (2006), was found to be most successful when events were hand-picked for a portion of the data during processing, and then extrapolated for explosions from the entire data set. However, there are two factors that contribute to an underestimation of the total eruptive volume. Only one signal type (explosions, representing discrete gas bubble bursts at the volcanic vent) could be used for estimating the eruptive velocities and volumes at West Mata, due to interference resulting from signal reflections. Another issue for accurately estimating cumulative eruptive volume resulted from raypaths reflecting on the sea surface, and arriving at the hydrophones 20 – 80 ms after the direct wave. This also caused hydroacoustic signals to overlap, limiting the calculations to the first 20 ms instead of the entire waveform. Eruptive velocities for vent explosions recorded at West Mata’s four hydrophones were found to range from 2.4 – 24.8 m/s. Eruptive flux ranged from 1.9 – 9.7 m/s for each event. The volumes erupted per explosion ranged from 0.03 – 0.15 m. Yearly eruption rates were calculated to be 5.7 x 10 – 9.1 x 10 m/yr for both gas and lava. However, if only lava is used (assumed to contain 25% vesicularity), then the yearly eruptive rate at West Mata is
Spectral Sensitivity of the NbN Single-Photon Superconducting Detector

SUMMARY We report our studies on the spectral sensitivity of superconducting NbN thin-film single-photon detectors (SPD’s)capable of GHz counting rates of visible and nearinfrared photons. In particular, it has been shown that a NbN SPD is sensitive to 1.55-µm wavelength radiation and can be used for quantum communication. Our SPD’s exhibit experimentally measured intrinsic quantum efficiencies from 20% at 800 nm up to ∼1% at 1.55-µm wavelength. The devices demonstrate picosecond response time (< 100 ps, limited by our readout system)and negligibly low dark counts. Spectral dependencies of photon counting of continuous-wave, 0.4-µm to 3.5-µm radiation, and 0.63-µm, 1.33-µm, and 1.55-µm laser-pulsed radiations are presented for the single-stripe-type and meander-type devices.