Miracle-dz/UniFAR-FTUs huggingface dataset:
The optical near-field speckles and their first order statistics on the basis of the integral equations of electromagnetic field

From Helmholtz equation of the harmonic electromagnetic waves, the integral equations of the light field at the medium boundaries are obtained by use of the Green's theorem and are discretized into linear equation set with the values of the light field and its derivative as the unknowns. On solving the linear equation set, we realize the rigorous computations of the light fields at the boundaries. Then the intensities of the light waves scattered by the random self-affine fractal surfaces in the optical near-field are calculated, and the propagation characteristics, the evolutions of the contrast and the intensity probability density function of the near-field speckles are studied in detail. The near-field speckles are much different from the conventional speckles in the diffraction regions and in the imaging systems. There are obvious local fluctuations in the intensity distributions of the near-field speckles and such fluctuations disappear after propagating a distance of one wavelength from the medium surfaces. For the random surfaces with smaller lateral correlation lengths, the speckle contrasts approach the saturation values and the speckle fields approach Gaussian distribution within the near-field, while for the random surfaces with larger lateral correlation lengths, such evolutions become comparatively slow.
A Lumped-Parameter Off-Axis-Capable Dynamic Switched Reluctance Machine Model Including Unbalanced Radial Forces

A detailed procedure for reducing a switched reluctance machine (SRM) design to a dynamic lumped-parameter model accounting for all important magnetic interactions is given. Although the rotor is assumed mass-balanced, its center is allowed to move with respect to the stator center, which introduces unbalanced radial electromagnetic forces (URFs). The developed model is then integrated with dynamic power converter, controller, and mechanical system models to facilitate integrated-system studies for understanding the impacts of rotor off-axis dynamics. Detailed simulation results are given to highlight the utility of the model, and the undesirable negative stiffness characteristics of the SRM caused by the URFs are described and examined.
Different diffusion behavior of Cu, Ni and Zn atoms in Cu/Sn-9Zn/Ni interconnects during liquid-solid electromigration

Different diffusion behavior of Cu, Ni and Zn atoms in Cu/Sn-9Zn/Ni interconnects during liquid-solid electromigration (L-S EM) were investigated under a current density of 5.0 × 103 A/cm2 at 230 oC. When Cu atoms were under downwind diffusion, L-S EM enhanced the cross-solder diffusion of Cu atoms to the opposite Ni side compared with the liquid-solid reaction case, resulting in the formation of interfacial Cu5Zn8 at Ni/Sn-Zn interface, and its thickness increased at the beginning and then decreased. For the Ni atoms, L-S EM significantly enhanced the diffusion of Ni atoms to the Cu side when Ni atoms were under downwind diffusion, resulting in the formation of a large amount of (Ni, Cu)3(Sn, Zn)4 at the Cu side. Under the combined effect of chemical potential gradient and electronic wind force, the Zn atoms with positive effective charge number would directional diffuse towards Cu side under both downwind and upwind diffusion conditions, as a result, the interfacial Cu5Zn8 formed at the Cu side, and its thickness continuously increased.
The GEF1 Proton-Chloride Exchanger Affects Tombusvirus Replication via Regulation of Copper Metabolism in Yeast

ABSTRACT Replication of plus-strand RNA viruses [(+)RNA viruses] is performed by viral replicases, whose function is affected by many cellular factors in infected cells. In this paper, we demonstrate a surprising role for Gef1p proton-chloride exchanger in replication of Tomato bushy stunt virus (TBSV) model (+)RNA virus. A genetic approach revealed that Gef1p, which is the only proton-chloride exchanger in Saccharomyces cerevisiae, is required for TBSV replication in the yeast model host. We also show that the in vitro activity of the purified tombusvirus replicase from gef1Δ yeast was low and that the in vitro assembly of the viral replicase in a cell extract was inhibited by the cytosolic fraction obtained from gef1Δ yeast. Altogether, our data reveal that Gef1p modulates TBSV replication via regulating Cu2+ metabolism in the cell. This conclusion is supported by several lines of evidence, including the direct inhibitory effect of Cu2+ ions on the in vitro assembly of the viral replicase, on the activity of the viral RNA-dependent RNA polymerase, and an inhibitory effect of deletion of CCC2 copper pump on TBSV replication in yeast, while altered iron metabolism did not reduce TBSV replication. In addition, applying a chloride channel blocker impeded TBSV replication in Nicotiana benthamiana protoplasts or in whole plants. Overall, blocking Gef1p function seems to inhibit TBSV replication through altering Cu2+ ion metabolism in the cytosol, which then inhibits the normal functions of the viral replicase.
Secondary structure alone is generally not statistically significant for the detection of noncoding RNAs

MOTIVATION Several results in the literature suggest that biologically interesting RNAs have secondary structures that are more stable than expected by chance. Based on these observations, we developed a scanning algorithm for detecting noncoding RNA genes in genome sequences, using a fully probabilistic version of the Zuker minimum-energy folding algorithm. RESULTS Preliminary results were encouraging, but certain anomalies led us to do a carefully controlled investigation of this class of methods. Ultimately, our results argue that for the probabilistic model there is indeed a statistical effect, but it comes mostly from local base-composition bias and not from RNA secondary structure. For the thermodynamic implementation (which evaluates statistical significance by doing Monte Carlo shuffling in fixed-length sequence windows, thus eliminating the base-composition effect) the signals for noncoding RNAs are still usually indistinguishable from noise, especially when certain statistical artifacts resulting from local base-composition inhomogeneity are taken into account. We conclude that although a distinct, stable secondary structure is undoubtedly important in most noncoding RNAs, the stability of most noncoding RNA secondary structures is not sufficiently different from the predicted stability of a random sequence to be useful as a general genefinding approach.
Wireless sensor network design for landslide warning system in IoT architecture

Landslide disaster is frequently happened in Indonesia, which has climate characteristic, area topography, and geological structure that make this country has many landslide potential areas. During the rainy season, the load on the slopes grows due to the increase in water content in the soil, which can lead to landslide. Landslide early warning system can be built based on geological structure and some physical environment parameters using sensors using the wireless sensor network. The communication networks with Internet technology support a substantial backbone for communication. IoT architecture can accommodate wireless sensor network for landslide early warning system. This paper describes the system uses an Arduino ATMega 2560 microcontroller to collect data from many sensors. The measured and actual physical parameters are obtained, which indicates the system succeeds in providing sensed data.
Visualizing Dynamic Brain Networks Using an Animated Dual-Representation

Dynamic network visualization has been a challenging topic for dynamic networks analysis, especially for spatially embedded networks like brain networks. In this paper, we present an animated interactive visualization design that combines enhanced node-link diagrams and distance matrix layouts to assist neuroscientists in their exploration of dynamic brain networks and that enables them to understand how functional connections relate to the spatial structure of the brain. Our visualization also provides the ability to observe the evolution of a network, the change in the community identities, and node behavior over time.
Improved Interferometric Synthetic Aperture Radar processing via advanced co-registration and phase correction techniques

Interferometric Synthetic Aperture Radar (InSAR) applies the interferograms of two or more SAR images to generate maps of surface deformation or digital elevation models. InSAR operational processing chain for displacement map generation comprises of five major stages: co-registration and re-sampling, interferogram generation, flat-earth correction, topography correction and phase unwrapping. This paper discusses and evaluates the authors' proposed algorithms for SAR image matching and for topographic and reference-phase corrections, which improve conventional InSAR processing techniques in terms of increasing efficiency and reducing the time and computational effort. Our proposed algorithms are implemented in MATLAB and evaluated with respect to the conventional InSAR processing performed by DORIS software for a pair of Envisat ASAR data associated with the 2003 BAM earthquake.
Spatial variation in forest growth after disturbances in a northern hardwood ecosystem and its relationship with underlying environmental factors

Executive summary: Disturbances can change the structure and function of forest stands. How forests respond to disturbance depends on species differences and spatially varying environmental factors. In this study, I analyzed the spatial variation in the growth of two forest stands in Hubbard Brook Experimental Forest. A calcium addition was introduced to one forest stand, and a clear-cut disturbance was introduced to the other. There are hundreds of permanent plots in each forest stand. Basal area of each species in each plot was used as an indicator of forest growth. To analyze the relationship between forest growth and environmental factors, geographic variables (elevation, slope, aspect, distance to stream) and soil chemistry variables (carbon concentration, nitrogen concentration) were considered potentially influential in this study. A forest stand level analysis was first conducted to measure the overall forest change during the time of interest. The calcium addition disturbance was not found to have changed the forest growth trend. The clear-cut disturbance decreased the basal area, followed by recovery at a nearly constant rate over the following 25 years. A stand level species-specific analysis revealed that there were large interspecific differences in basal area, and in basal area change. The interspecific differences in physiological properties influenced recovery. To focus on the influence of environmental factors, I removed the interspecific differences by using a " Forest Change Index " (FCI) to indicate the forest growth condition. The FCI is calculated by summing up the standardized basal area increment of all species in a plot. The FCI measures the combined effect of richness, abundance, and mean individual tree growth. Comparing the inter-plot differences in FCI before and after disturbance, I found that spatial variation patterns to be highly persistent through time, both for calcium addition and clearcutting. After the clear-cut, the spatial variation pattern in forest growth was changed during the first two decades following disturbance, but subsequently reemerged. This result suggests that spatial reorganization with disturbance was temporary. -2-A regression analysis on the environmental variables shows that geographic variables control the spatial variation in forest growth. Soil carbon concentration was only significant in the clear-cut stand, while nitrogen concentration was not influential in either stand. Furthermore, a generalized joint attribute model (GJAM) analysis showed species differences in response to environmental variables, which explains the persistence in spatial variation. The persistent spatial pattern likely resulted from nearly constant restrictions on resources. Although species composition and growth …
Supporting Regenerative Medicine by Integrative Dimensionality Reduction

Summary Objective: The assessment of the developmental potential of stem cells is a crucial step towards their clinical application in regenerative medicine. It has been demonstrated that genome-wide expression profiles can predict the cellular differentiation stage by means of dimensionality reduction methods. Here we show that these techniques can be further strengthened to support decision making with i) a novel strategy for gene selection; ii) methods for combining the evidence from multiple data sets. Methods: We propose to exploit dimensionality reduction methods for the selection of genes specifically activated in different stages of differentiation. To obtain an integrated predictive model, the expression values of the selected genes from multiple data sets are combined. We investigated distinct approaches that either aggregate data sets or use learning ensembles. Results: We analyzed the performance of the proposed methods on six publicly available data sets. The selection procedure identified a reduced subset of genes whose expression values gave rise to an accurate stage prediction. The assessment of predictive accuracy demonstrated a high quality of predictions for most of the data integration methods presented. Conclusion: The experimental results highlighted the main potentials of proposed approaches. These include the ability to predict the true staging by combining multiple training data sets when this could not be inferred from a single data source, and to focus the analysis on a reduced list of genes of similar predictive performance.
Neural Compensations After Lesion of the Cerebral Cortex

Functional improvement after cortical injury can be stimulated by various factors including experience, psychomotor stimulants, gonadal hormones, and neurotrophic factors. The, timing of the administration of these factors may be critical, however. For example, factors such as gonadal hormones, nerve growth factor, or psychomotor stimulants may act to either enhance or retard recovery, depending upon the timing of administration. Nicotine, for instance, stimulates recovery if given after an injury but is without neuroprotective effect and may actually retard recovery if it is given only preinjury. A related timing problem concerns the interaction of different treatments. For example, behavioral therapies may act, in part, via their action in stimulating the endogenous production of trophic factors. Thus, combining behavioral therapies with pharmacological administration of compounds to increase the availability of trophic factors enhances functional outcome. Finally, anatomical evidence suggests that the mechanism of action of many treatments is through changes in dendritic arborization, which presumably reflects changes in synaptic organization. Factors that enhance dendritic change stimulate functional compensation, whereas factors that retard or block dendritic change block or retard compensation.
Sparse Grid Tutorial

The sparse grid method is a special discretization technique, which allows to cope with the curse of dimensionality of grid based approaches to some extent. It is based on a hierarchical basis [Fab09, Yse86, Yse92], a representation of a discrete function space which is equivalent to the conventional nodal basis, and a sparse tensor product construction. The method was originally developed for the solution of partial differential equations [Zen91, Gri91, Bun92, Bal94, Ach03] and is now also successfully used for integral equations [FHP96, GOS99], interpolation and approximation [Bas85, Tem89, SS99, GK00, Kna00, KW05b]. Furthermore there is work on stochastic differential equations [ST03a, ST03b], differential forms in the context of the Maxwell-equation [GH03] and with a wavelet-based sparse grid discretization parabolic problems are treated in [vPS04]. Besides Galerkin finite element approaches there are also finite differences on sparse grids [Gri98, Sch99, Kos02, GK03] and finite volume approaches [Hem95]. Besides working directly in the hierarchical basis a sparse grid representation of a function can also be computed using the combination technique [GSZ92], where a certain sequence of partial functions is linearly combined. Applications include eigenvalue problems [GG00], numerical integration [GG98, BD03, GG03], parabolic equations for options pricing [Rei04], machine learning [GGT01, GG02, Gar04, GG05, Gar06] and data analysis [LNSH05] and it is used for solving the stochastic master equation applied to gene regulatory networks [HBS+06]. The underlying idea of sparse grids can be traced back to the Russian mathematician Smolyak [Smo63], who used it for numerical integration. The concept is also closely related to hyperbolic crosses [Bab60, Tem89, Tem93a, Tem93b], boolean methods [Del82, DS89], discrete blending methods [BDJ92] and splitting extrapolation methods [LLS95]. For the representation of a function f defined over a d-dimensional domain the sparse grid approach employs O(h−1 n ·log(h −1 n ) d−1) grid points in the discretization process, where hn := 2 −n denotes the mesh size. It can be shown that the order of approximation to describe a function f , under certain smoothness conditions, is O(hn · log(h −1 n ) d−1). This is in contrast to conventional grid methods, which need O(h−d n ) for an accuracy of O(h 2 n). Since the curse of dimensionality of full grid method arises for sparse grids at this much smaller extent they can be used for higher dimensional problems.
Quenched Free Energy and Large Deviations for Random Walks in Random Potentials

We study quenched distributions on random walks in a random potential on integer lattices of arbitrary dimension and with an arbitrary finite set of admissible steps. The potential can be unbounded and can depend on a few steps of the walk. Directed, undirected, and stretched polymers, as well as random walk in random environment, are covered. The restriction needed is on the moment of the potential, in relation to the degree of mixing of the ergodic environment. We derive two variational formulas for the limiting quenched free energy and prove a process‐level quenched large deviation principle (LDP) for the empirical measure. As a corollary we obtain LDPs for types of random walks in random environments not covered by earlier results. © 2012 Wiley Periodicals, Inc.
Investigating the Effects of Task Scheduling on Thermal Behavior

 Thermal behavior is expected to be an important design consideration for the next generation of microprocessors. Most dynamic thermal management techniques are based on the principle of ‘reactive throttling’, where some form of performance throttling (such as voltage or frequency scaling) is invoked, after a pre-architected temperature threshold has been exceeded. As such, there is performance degradation with each such DTM technique. In some cases, if the temperature threshold is set to a conservative value for package/cooling cost considerations, the degradation can be quite severe. In this paper, we investigate the potential benefits of temperature-aware task scheduling with minimum or no performance overhead. We explored policies based on metrics such as average temperature; dynamic profiling of threads and localized heating, instead of IPC-based metrics. Our preliminary results indicate that thermal-aware task scheduling provides significant alleviation on chip temperatures. We observed up to 52% reduction in the number of cycles above the thermal threshold, compared to a thermally-oblivious policy. On average, our MinTemp policy yields less than 3% thermally critical cycles, even on a challenging benchmark set such as SPEC2000 with 12 of the 25 benchmarks above 360K. As we employ already existing scheduling infrastructure, there was no considerable change in net performance.
Modulo Multiplier by using Radix-8 Modified Booth Algorithm

Modular arithmetic operations (inversion, multiplication and exponentiation) are used in several cryptography applications. RSA and elliptic curve cryptography (ECC) are two of the most well established and widely used public key cryptographic (PKC) algorithms. The encryption and decryption of these PKC algorithms are performed by repeated modulo multiplications. These multiplications differ from those encountered in signal processing and general computing applications in their sheer operand size. Key sizes in the range of 512~1024 bits and 160~512 bits are typical in RSA and ECC, respectively. Hence, the long carry propagation of large integer multiplication is the bottleneck in hardware implementation of PKC. The residue number system (RNS) has emerged as a promising alternative number representation for the design of faster and low power multipliers owing to its merit to distribute a long integer multiplication into several shorter and independent modulo multiplications. RNS has also been successfully employed to design fault tolerant digital circuits. A special moduli set of forms {2n-1, 2n, 2n +1} are preferred over the generic moduli due to the ease of hardware implementation of modulo arithmetic functions as well as system-level intermodulo operations, such as RNS-to-binary conversion and sign detections. To facilitate design of highspeed full-adder based modulo arithmetic units, it is worthwhile to keep the moduli of a high-DR RNS in forms of {2n-1, 2n, 2n +1}. The modulo 2n-1 multiplier is usually the noncritical datapath among all modulo multipliers in such high-DR RNS multiplier. With this precept, a family of radix-8 Booth encoded modulo 2n1 multipliers, with delay adaptable to the RNS multiplier delay, is proposed. The modulo 2n-1 multiplier delay is made scalable by controlling the word-length of the ripple carry adder, employed for radix-8 hard multiple generation.