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We investigate the transmission properties of a junction between a normal metal and a p-wave magnet in the ballistic regime. We introduce a two-dimensional square lattice that confirms the p-wave magnet criteria. The $\alpha$-vector of the magnet breaks the inversion symmetry of the parabolic dispersion, shifting them in $k$-space. These shifts alter the propagation direction of fermions passing through the junction interface. Depending on spin orientation, the transmission process exhibits anisotropic, angle-dependent behavior. We also demonstrate a mirror symmetry between fermions with opposite spin directions, leading to the emergence of a transverse spin current that flows parallel to the interface. Additionally, we show that the $\alpha$-vector acts as a source for the dynamics of the spin-density wave and observe the formation of an indirect gap in the conductance of the junction. Our findings highlight the unique transmission characteristics and spin transport phenomena in normal metal/p-wave magnet junctions, paving the way for potential applications in spintronic devices., Comment: 9 pages, 7 Figures

Stroke is a leading cause of mortality and disability. Emergent diagnosis and intervention are critical, and predicated upon initial brain imaging; however, existing clinical imaging modalities are generally costly, immobile, and demand highly specialized operation and interpretation. Low-energy microwaves have been explored as low-cost, small form factor, fast, and safe probes of tissue dielectric properties, with both imaging and diagnostic potential. Nevertheless, challenges inherent to microwave reconstruction have impeded progress, hence microwave imaging (MWI) remains an elusive scientific aim. Herein, we introduce a dedicated experimental framework comprising a robotic navigation system to translate blood-mimicking phantoms within an anatomically realistic human head model. An 8-element ultra-wideband (UWB) array of modified antipodal Vivaldi antennas was developed and driven by a two-port vector network analyzer spanning 0.6-9.0 GHz at an operating power of 1 mw. Complex scattering parameters were measured, and dielectric signatures of hemorrhage were learned using a dedicated deep neural network for prediction of hemorrhage classes and localization. An overall sensitivity and specificity for detection >0.99 was observed, with Rayliegh mean localization error of 1.65 mm. The study establishes the feasibility of a robust experimental model and deep learning solution for UWB microwave stroke detection.

Pentamodes (first conceived theoretically by Milton and Cherkaev) are a very interesting class of mechanical metamaterials where the bulk and shear moduli are decoupled. The pentamodes usually are composed of double cone-shaped struts with the middle diameter being large and the end diameters being tiny (ideally approaching zero). The cubic diamond geometry was proposed by Milton and Cherkaev as a suitable geometry for the unit cell and has since been used in the majority of the works on pentamodes. In this work, we aim to evaluate the degree to which the main unit cell design is contributing to high bulk to shear modulus ratio (known as FOM). In addition to the diamond unit cell, three other well-known unit cell designs are considered, and the effect of small diameter radius and the ratio of large-to-small diameter ratio, {\alpha}, on the FOM is evaluated. The results showed that regardless of the primitive unit cell shape, the FOM value is highly dependent on the d value, but its dependence on the D value is very weak. For d/h around 0.05 (h representing the linkage length), figures of merit in the range of 10^3 could be reached for all the studied topologies., Comment: 14 pages, 5 figures

The first multi-color light curve analysis of the AH Mic binary system is presented. This system has very few past observations from the southern hemisphere. We extracted the minima times from the light curves based on the Markov Chain Monte Carlo (MCMC) approach and obtained a new ephemeris. To provide modern photometric light curve solutions, we used the Physics of Eclipsing Binaries (Phoebe) software package and the MCMC approach. Light curve solutions yielded a system temperature ratio of 0.950, and we assumed a cold star-spot for the hotter star based on the O'Connell effect. This analysis reveals that AH Mic is a W-subtype W UMa contact system with a fill-out factor of 21.3% and a mass ratio of 2.32. The absolute physical parameters of the components are estimated by using the Gaia Early Data Release 3 (EDR3) parallax method to be M_h(M_Sun)=0.702(26), M_c(M_Sun)=1.629(104), R_h(R_Sun)=0.852(21), R_c(R_Sun)=1.240(28), L_h(L_Sun)=0.618(3) and L_c(L_Sun)=1.067(7). The orbital angular momentum of the AH Mic binary system was found to be 51.866(35). The components' positions of this system are plotted in the Hertzsprung-Russell (H-R) diagram., Comment: 6 figures, 4 tables, accepted by the Research in Astronomy and Astrophysics (RAA) journal

This paper contributes to a taxonomy of augmented reality and robotics based on a survey of 460 research papers. Augmented and mixed reality (AR/MR) have emerged as a new way to enhance human-robot interaction (HRI) and robotic interfaces (e.g., actuated and shape-changing interfaces). Recently, an increasing number of studies in HCI, HRI, and robotics have demonstrated how AR enables better interactions between people and robots. However, often research remains focused on individual explorations and key design strategies, and research questions are rarely analyzed systematically. In this paper, we synthesize and categorize this research field in the following dimensions: 1) approaches to augmenting reality; 2) characteristics of robots; 3) purposes and benefits; 4) classification of presented information; 5) design components and strategies for visual augmentation; 6) interaction techniques and modalities; 7) application domains; and 8) evaluation strategies. We formulate key challenges and opportunities to guide and inform future research in AR and robotics., Comment: CHI 2022

This work investigates a practical and novel method for automated unsupervised fault detection in vehicles using a fully convolutional autoencoder. The results demonstrate the algorithm we developed can detect anomalies which correspond to powertrain faults by learning patterns in the multivariate time-series data of hybrid-electric vehicle powertrain sensors. Data was collected by engineers at Ford Motor Company from numerous sensors over several drive cycle variations. This study provides evidence of the anomaly detecting capability of our trained autoencoder and investigates the suitability of our autoencoder relative to other unsupervised methods for automatic fault detection in this data set. Preliminary results of testing the autoencoder on the powertrain sensor data indicate the data reconstruction approach availed by the autoencoder is a robust technique for identifying the abnormal sequences in the multivariate series. These results support that irregularities in hybrid-electric vehicles' powertrains are conveyed via sensor signals in the embedded electronic communication system, and therefore can be identified mechanistically with a trained algorithm. Additional unsupervised methods are tested and show the autoencoder performs better at fault detection than outlier detectors and other novel deep learning techniques., Comment: SSCI2022, 7 pages, 3 Tables, 3 Figures

How do we imagine visual objects and combine them to create new forms? To answer this question, we need to explore the cognitive, computational and neural mechanisms underlying imagery and creativity. The body of research on deep learning models with creative behaviors is growing. However, in this paper we suggest that the complexity of such models and their training sets is an impediment to using them as tools to understand human aspects of creativity. We propose using simpler models, inspired by neural and cognitive mechanisms, that are trained with smaller data sets. We show that a standard deep learning architecture can demonstrate imagery by generating shape/color combinations using only symbolic codes as input. However, generating a new combination that was not experienced by the model was not possible. We discuss the limitations of such models, and explain how creativity could be embedded by incorporating memory mechanisms to combine the output of the network into new combinations and use that as new training data.

Jujube is a fruit mainly cultivated in India, China and Iran and has many health benefits. It is sold both fresh and dried. There are several factors in jujube pricing such as weight, wrinkles and defections. Some jujube farmers sell their product all at once, without any proper sorting or classification, for an average price. Our studies and experiences show that their profit can increase significantly if their product is sold after the sorting process. There are some traditional sorting methods for dried jujube fruit but they are costly, time consuming and can be inaccurate due to human error. Nowadays, computer vision combined with machine learning methods, is used increasingly in food industry for sorting and classification purposes and solve many of the traditional sorting methods' problems. In this paper we are proposing a computer vision-based method for grading jujube fruits using machine learning techniques which will take most of the important pricing factors into account and can be used to increase the profit of farmers. In this method we first acquire several images from different samples and then extract their visual features such as color features, shape and size features, texture features, defection and wrinkle features and then we select the most useful features using feature selection algorithms like PCA and CFS. A feature vector is obtained for each sample and we use these vectors to train our classifiers to be able to specify the corresponding pre-defined group for each of the samples. We used different classifiers and training methods in order to obtain the best result and by using decision tree we could reach 98.8% accuracy of the classification.

Light field imaging introduced the capability to refocus an image after capturing. Currently there are two popular methods for refocusing, shift-and-sum and Fourier slice methods. Neither of these two methods can refocus the light field in real-time without any pre-processing. In this paper we introduce a machine learning based refocusing technique that is capable of extracting 16 refocused images with refocusing parameters of \alpha=0.125,0.250,0.375,...,2.0 in real-time. We have trained our network, which is called RefNet, in two experiments. Once using the Fourier slice method as the training -- i.e., "ground truth" -- data and another using the shift-and-sum method as the training data. We showed that in both cases, not only is the RefNet method at least 134x faster than previous approaches, but also the color prediction of RefNet is superior to both Fourier slice and shift-and-sum methods while having similar depth of field and focus distance performance.

Light field (LF) imaging has gained significant attention due to its recent success in 3-dimensional (3D) displaying and rendering as well as augmented and virtual reality usage. Nonetheless, because of the two extra dimensions, LFs are much larger than conventional images. We develop a JPEG-assisted learning-based technique to reconstruct an LF from a JPEG bitstream with a bit per pixel ratio of 0.0047 on average. For compression, we keep the LF's center view and use JPEG compression with 50% quality. Our reconstruction pipeline consists of a small JPEG enhancement network (JPEG-Hance), a depth estimation network (Depth-Net), followed by view synthesizing by warping the enhanced center view. Our pipeline is significantly faster than using video compression on pseudo-sequences extracted from an LF, both in compression and decompression, while maintaining effective performance. We show that with a 1% compression time cost and 18x speedup for decompression, our methods reconstructed LFs have better structural similarity index metric (SSIM) and comparable peak signal-to-noise ratio (PSNR) compared to the state-of-the-art video compression techniques used to compress LFs.

RoomShift is a room-scale dynamic haptic environment for virtual reality, using a small swarm of robots that can move furniture. RoomShift consists of nine shape-changing robots: Roombas with mechanical scissor lifts. These robots drive beneath a piece of furniture to lift, move and place it. By augmenting virtual scenes with physical objects, users can sit on, lean against, place and otherwise interact with furniture with their whole body; just as in the real world. When the virtual scene changes or users navigate within it, the swarm of robots dynamically reconfigures the physical environment to match the virtual content. We describe the hardware and software implementation, applications in virtual tours and architectural design and interaction techniques., Comment: CHI 2020

Recognizing and understanding conversational groups, or F-formations, is a critical task for situated agents designed to interact with humans. F-formations contain complex structures and dynamics, yet are used intuitively by people in everyday face-to-face conversations. Prior research exploring ways of identifying F-formations has largely relied on heuristic algorithms that may not capture the rich dynamic behaviors employed by humans. We introduce REFORM (REcognize F-FORmations with Machine learning), a data-driven approach for detecting F-formations given human and agent positions and orientations. REFORM decomposes the scene into all possible pairs and then reconstructs F-formations with a voting-based scheme. We evaluated our approach across three datasets: the SALSA dataset, a newly collected human-only dataset, and a new set of acted human-robot scenarios, and found that REFORM yielded improved accuracy over a state-of-the-art F-formation detection algorithm. We also introduce symmetry and tightness as quantitative measures to characterize F-formations. Supplementary video: https://youtu.be/Fp7ETdkKvdA , Dataset available at: github.com/cu-ironlab/Babble, Comment: IROS 2020

We present PufferBot, an aerial robot with an expandable structure that may expand to protect a drone's propellers when the robot is close to obstacles or collocated humans. PufferBot is made of a custom 3D-printed expandable scissor structure, which utilizes a one degree of freedom actuator with rack and pinion mechanism. We propose four designs for the expandable structure, each with unique characterizations for different situations. Finally, we present three motivating scenarios in which PufferBot may extend the utility of existing static propeller guard structures. The supplementary video can be found at: https://youtu.be/XtPepCxWcBg, Comment: IROS 2020

In this paper, we present a new theoretical model for the dielectrophoresis (DEP) process of the single-walled carbon nanotubes (SWCNT). We obtain a different frequency interval for the alignment of wide energy gap semiconductor SWCNTs which shows a considerable difference with the prevalent model. For this, we study two specific models namely, the spherical model and the ellipsoid model to estimate the frequency interval. Then, we perform the DEP process and use the obtained frequencies (of spherical and ellipsoid models) for the alignment of the SWCNTs. Our empirical results declare the theoretical prediction, i.e., a crucial step toward the realization of carbon nanotube field-effect transistor (CNT-FET) with the DEP process based on the ellipsoid model.

Pentamode metamaterials are a type of extremal designer metamaterials which are able to demonstrate high rigidity in one direction and extremely high compliance in other directions. Pentamodes can, therefore, be considered as building blocks of exotic materials with any arbitrarily selected thermodynamically admissible elasticity tensor. The pentamode lattices can then be envisioned to be combined to construct intermediate extremal materials such as quadramodes, trimodes, and bimodes. In this study, we constructed several primary types of anisotropic pentamode lattices (with midpoint positioning of 10%, 15%, 20%, 25%, 30%, 35%, and 42% of the main unit cell diagonal) and then combined them mutually to explore the dependence of elastic properties of hybrid pentamodes on those of individual constructing lattices. Several anisotropic individual and hybrid pentamode lattice structures were produced using MultiJet Additive Manufacturing technique and then mechanically tested under compression. Finite element models were also created using COMSOL Multiphysics package. Two-component hybrid pentamode lattices composed of individual lattices with extensively different (as large as of 2 order of magnitudes) FOM ratios were constructed and analysed. It was demonstrated that it is possible to design and construct composite intermediate extremal materials with arbitrary eigen values in the elastic tensor. It is concluded that the elastic E, shear G, and bulk moduli B of the hybrid structure are the superpositions of the corresponding moduli of the individual lattice structures. The Poisson's ratio of the hybrid pentamode structure equals that of individual structure with higher Poisson's ratio. The yield stress of the hybrid pentamode lattice structure depends on the elastic moduli of the constructing lattice structures as well as yield stress of the weaker lattice structure.

The proliferation of fast, dense, byte-addressable nonvolatile memory suggests that data might be kept in pointer-rich "in-memory" format across program runs and even process and system crashes. For full generality, such data requires dynamic memory allocation, and while the allocator could in principle "rolled into" each data structure, it is desirable to make it a separate abstraction. Toward this end, we introduce recoverability, a correctness criterion for persistent allocators, together with a nonblocking allocator, Ralloc, that satisfies this criterion. Ralloc is based on the LRMalloc of Leite and Rocha, with three key innovations. First, we persist just enough information during normal operation to permit correct reconstruction of the heap after a full-system crash. Our reconstruction mechanism performs garbage collection (GC) to identify and remedy any failure-induced memory leaks. Second, we introduce the notion of filter functions, which identify the locations of pointers within persistent blocks to mitigate the limitations of conservative GC. Third, to allow persistent regions to be mapped at an arbitrary address, we employ position-independent (offset-based) pointers for both data and metadata. Experiments show Ralloc to be performance-competitive with both Makalu, the state-of-the-art lock-based persistent allocator, and such transient allocators as LRMalloc and JEMalloc. In particular, reliance on GC and offline metadata reconstruction allows Ralloc to pay almost nothing for persistence during normal operation.

In this work, protein-surface interactions were probed in terms of adsorption and desorption of a model protein, bovine serum albumin, on a low-fouling surface with single-molecule localization microscopy. Single-molecule experiments enable precise determination of both adsorption and desorption rates. Strikingly the experimental data show anomalous desorption kinetics, evident as a surface dwell time that exhibits a power-law distribution, i.e. a heavy-tailed rather than the expected exponential distribution. As a direct consequence of this heavy-tailed distribution, the average desorption rate depends upon the time scale of the experiment and the protein surface concentration does not reach equilibrium. Further analysis reveals that the observed anomalous desorption emerges due to the reversible formation of a small fraction of soluble protein multimers (small oligomers), such that each one desorbs from the surface with a different rate. The overall kinetics can be described by a series of elementary reactions, yielding simple scaling relations that predict experimental observations. This work reveals a mechanistic origin for anomalous desorption kinetics that can be employed to interpret observations where low-protein fouling surfaces eventually foul when in long-term contact with protein solutions. The work also provides new insights that can be used to define design principles for non-fouling surfaces and to predict their performance.

The increasing use of fossil fuels to produce energy is leading to environmental problems. Hence, it has led the human society to move towards the use of renewable energies, including solar energy. In recent years, one of the most popular methods to gain energy is using photovoltaic arrays to produce solar energy. Skyscrapers and different weather conditions cause shadings on these PV arrays, which leads to less power generation. Various methods such as TCT and Sudoku patterns have been proposed to improve power generation for partial shading PV arrays, but these methods have some problems such as not generating maximum power and being designed for a specific dimension of PV arrays. Therefore, we proposed a metaheuristic algorithm-based approach to extract maximum possible power in the shortest possible time. In this paper, five algorithms which have proper results in most of the searching problems are chosen from different groups of metaheuristic algorithms. Also, four different standard shading patterns are used for more realistic analysis. Results show that the proposed method achieves better results in maximum power generation compared to TCT arrangement (18.53%) and Sudoku arrangement (4.93%). Also, the results show that GWO is the fastest metaheuristic algorithm to reach maximum output power in PV arrays under partial shading condition. Thus, the authors believe that by using metaheuristic algorithms, an efficient, reliable, and fast solution is reached to solve partial shading PV arrays problem, Comment: 35 pages, 21 figures, 23 tables, Journal

The kernels of operating systems such as Windows, Linux, and MacOS are vulnerable to control-flow hijacking. Defenses exist, but many require efficient intra-address-space isolation. Execute-only memory, for example, requires read protection on code segments, and shadow stacks require protection from buffer overwrites. Intel's Protection Keys for Userspace (PKU) could, in principle, provide the intra-kernel isolation needed by such defenses, but, when used as designed, it applies only to user-mode application code. This paper presents an unconventional approach to memory protection, allowing PKU to be used within the operating system kernel on existing Intel hardware, replacing the traditional user/supervisor isolation mechanism and, simultaneously, enabling efficient intra-kernel isolation. We call the resulting mechanism Protection Keys for Kernelspace (PKK). To demonstrate its utility and efficiency, we present a system we call IskiOS: a Linux variant featuring execute-only memory (XOM) and the first-ever race-free shadow stacks for x86-64. Experiments with the LMBench kernel microbenchmarks display a geometric mean overhead of about 11% for PKK and no additional overhead for XOM. IskiOS's shadow stacks bring the total to 22%. For full applications, experiments with the system benchmarks of the Phoronix test suite display negligible overhead for PKK and XOM, and less than 5% geometric mean overhead for shadow stacks.

Determination of mass composition of high energy cosmic rays is one of the greatest challenge in modern astrophysics. All of previous methods for finding the mass composition of primary cosmic rays in a surface array at least require two independent measurements (e.g. muon and electron components) of extensive air showers (EAS). Here a new statistical parameter is introduced which can be used to determine the mass composition of vertical downward cosmic rays in a simple surface array. The most interesting thing about the new parameter is that it does not need two independent measurements and can be used in a simple surface array which does not have muon detectors.

A new accurate method for reconstructing the arrival direction of an extensive air shower (EAS) is described. Compared to existing methods, it is not subject to minimization of a function and, therefore, is fast and stable. This method also does not need to know detailed curvature or thickness structure of an EAS. It can have angular resolution of about 1 degree for a typical surface array in central regions. Also, it has better angular resolution than other methods in the marginal area of arrays., Comment: Accepted to be published in JCAP

Low level images representation in feature space performs poorly for classification with high accuracy since this level of representation is not able to project images into the discriminative feature space. In this work, we propose an efficient image representation model for classification. First we apply Hidden Markov Model (HMM) on ordered grids represented by different type of image descriptors in order to include causality of local properties existing in image for feature extraction and then we train up a separate classifier for each of these features sets. Finally we ensemble these classifiers efficiently in a way that they can cancel out each other errors for obtaining higher accuracy. This method is evaluated on 15 natural scene dataset. Experimental results show the superiority of the proposed method in comparison to some current existing methods, Comment: This paper has been withdrawn by the authors due to significant errors in equations 3 and 4

A large distance between true and reconstructed core locations of an extensive air shower (EAS) may results in great systematic mis-estimation of EAS parameters. The reconstruction of those EASs whose core locations are outside the boundary of a surface array (outside EAS (OEAS)) results in a large distance of the reconstructed core location from the true one, especially when the true core is far outside the array. Although it may not be mentioned, the identification of OEASs is a necessary and important step in the reconstruction procedure of an EAS. In this paper, an existing technique is optimized for the identification of OEASs. The simultaneous use of this technique and a recently developed approach for reconstructing the core location of an EAS can significantly increase the sensitive area of a surface array.

We study online learning under logarithmic loss with regular parametric models. Hedayati and Bartlett (2012b) showed that a Bayesian prediction strategy with Jeffreys prior and sequential normalized maximum likelihood (SNML) coincide and are optimal if and only if the latter is exchangeable, and if and only if the optimal strategy can be calculated without knowing the time horizon in advance. They put forward the question what families have exchangeable SNML strategies. This paper fully answers this open problem for one-dimensional exponential families. The exchangeability can happen only for three classes of natural exponential family distributions, namely the Gaussian, Gamma, and the Tweedie exponential family of order 3/2. Keywords: SNML Exchangeability, Exponential Family, Online Learning, Logarithmic Loss, Bayesian Strategy, Jeffreys Prior, Fisher Information1, Comment: 23 pages

In this study, calculations necessary to solve the large scale linear programming problems in two operating systems, Linux and Windows 7 (Win), are compared using two different methods. Relying on the interior-point methods, linear-programming interior point solvers (LIPSOL) software was used for the first method and relying on an augmented Lagrangian method-based algorithm, the second method used the generalized derivative. The performed calculations for various problems show the produced random in the Linux operating system (OS) and Win OS indicate the efficiency of the performed calculations in the Linux OS in terms of the accuracy and using of the optimum memory., Comment: 8 pages; This paper will be published in Appl. Math. Inf. Sci. Vol. 7 No. 1 (2013)

The most important goal of studying an extensive air shower is to find the energy, mass and arrival direction of its primary cosmic ray. In order to find these parameters, the core position and arrival direction of the shower should be determined. In this paper, a new method for finding core location has been introduced that utilizes trigger time information of particle detectors. We have also developed a simple technique to reconstruct the arrival direction. Our method is not based upon density-sensitive detectors which are sensitive to the number of crossing particles and is also independent of lateral distribution models. This model has been developed and examined by the analysis of simulated shower events generated by the CORSIKA package.

A relativistic non-local model for the inclusive photoproduction of $\eta$ mesons from complex nuclei is introduced. The model is based on the dominance of the S$_{11}$(1535) resonance. We compare the results of our calculations with the available data on inclusive cross sections for the nuclei C, Al and Cu. Assuming the resonance propagates freely in the nuclear medium, we find that the calculated angular distribution and energy dependence of the cross sections reproduce the data in a reasonable fashion. The present non-local model allows the inclusion of density dependent mass and width in the calculations. Including these in the calculations reveals that the presently available data do not show clear preference for the inclusion of such modifications of the properties of the S$_{11}$(1535) in the nuclear medium., Comment: 15 pages, 8 figures

A relativistic model for the quasifree exclusive photoproduction of $\eta$ mesons on nuclei is extended to include both non-local and medium effects. The reaction is assumed to proceed via the dominant contribution of the S$_{11}$(1535) resonance. The complicated integrals resulting from the non-locality are simplified using a modified version of a method given by Cooper and Maxwell. The non-locality effects are found to affect the magnitude of the cross section. Some possibilities reflecting the effects of the medium on the propagation and properties of the intermediate S$_{11}$ resonance are studied. The effects of allowing the S$_{11}$ to interact with the medium via mean field scalar and vector potentials are considered. Both broadening of width and reduction in mass of the resonance lead to a suppression of the calculated cross sections., Comment: 19 pages, 7 figures

A relativistic model for the quasifree photoproduction of eta meson from complex nuclei is developed. The interactions between fields are introduced through effective Lagrangians. Contributions from several nucleon resonances as well as nucleon Born terms and vector meson exchange diagrams are included. Nucleon and eta wavefunctions are solutions of Dirac and Klein-Gordon equations, respectively. Final state interactions of the outgoing particles are included via optical potentials. The effects of these interactions on the cross sections and photon asymmetries are studied and are found to be large. Calculations indicate that at energies near threshold the exclusive reaction takes place mainly through formation of the S_{11}(1535) resonance. Comparisons with the non-relativistic calculations show differences between the two approaches both for the cross sections and photon asymmetries. We give some detailed predictions for the reaction observables for exclusive photoproduction on ^{12}C, ^{16}O and ^{40}Ca., Comment: Latex, 27 pages, 13 uuencoded postscript figures. Accepted for publication in Phys. Rev. C

The relativistic amplitude for the direct knock-out contribution to $\left( \gamma, p \right)$ reactions on nuclei is reduced to a nonrelativistic form using an effective Pauli reduction scheme. The reduction is carried out to second order in the inverse nucleon mass. It is found that the interaction Hamiltonian appearing in the nonrelativistic amplitude has significant dependence, starting at second order, on the vector and scalar mean nuclear potentials. These strong medium modifications are absent in traditional nonrelativistic calculations. Detailed comparisons show that these modifications are crucial to understanding the differences between relativistic and nonrelativistic models. These differences are also examined through reduction of the relativistic amplitude via the Foldy-Wouthuysen transformation. Similar medium modifications are obtained in this case as well. We discuss the implications of these medium modifications for the consistency of existing nonrelativistic calculations., Comment: LaTeX, 31 pages including 5 uuencoded figures in eps files. Accepted for publication in Nuclear Physics A

Non-relativistic reduction of the S-matrix for the quasifree electron scattering process $A\left(~e, e'p~\right)A-1$ is studied in order to understand the source of differences between non-relativistic and relativistic models. We perform an effective Pauli reduction on the relativistic expression for the S-matrix in the one-photon exchange approximation. The reduction is applied to the nucleon current only; the electrons are treated fully relativistically. An expansion of the amplitude results in a power series in the nuclear potentials. The series is found to converge rapidly only if the nuclear potentials are included in the nuclear current operator. The results can be cast in a form which reproduces the non-relativistic amplitudes in the limit that the potentials are removed from the nuclear current operator. Large differences can be found between calculations which do and do not include the nuclear potentials in the different orders of the nuclear current operator. In the high missing momentum region we find that the non-relativistic calculations with potentials included in the nuclear current up to second order give results which are close to those of the fully relativistic calculation. This behavior is an indication of the importance of the medium modifications of the nuclear currents in this model, which are naturally built into the relativistic treatment of the reaction., Comment: Latex, 26 pages including 5 uuencoded postscript figures. accepted for publication in Phys. Rev. C.