Miracle-dz/UniFAR-FTUs huggingface dataset:
Viscosity Limit of Stationary Distributions for the Random Forced Burgers Equation

We prove convergence of stationary distributions for the randomly forced Burgers and Hamilton–Jacobi equations in the limit when viscosity tends to zero. It turns out that for all values of the viscosity ν there exists a unique (up to an additive constant) global stationary solution to the randomly forced Hamilton–Jacobi equation. The main result follows from the convergence of these solutions in a limit when ν tends to zero without changing its sign. The two limiting solutions (for different signs of the viscosity term) correspond to unique backward and forward viscosity solutions. Our approach, which is an extension of the previous work [1], [6], is based on the stochastic version of Lax formula for solutions to the initial and final value problems for the viscous Hamilton–Jacobi equation. 2000 Math. Subj. Class. 35L65, 37H10, 37D99.
of Nebraska - Lincoln DigitalCommons@University of Nebraska - Lincoln Blind River Dolphin: First Side-Swimming Cetacean Blind River Dolphin: First Side-Swimming Cetacean

The blind river dolphin (Platanista first written about was found to be the first known side-swimming cetacean. The rudimentary eye lacks the lens, but anatomical evidence suggests that the eye may serve as a light sensor. The underwater sound emissions of this species, although similar to those of the Amazon River dolphin (Inia geof-frensis), appear to be produced constantly. As part of a study of the only river dolphin long recognized by native peo-ples as "blind" (Platanista gangetica), E. S. Herald, R. F. L. Frye, and E. J. Morris spent 11 days on the River near Sukkur, km of Karachi, West Pakistan We found that the blind dolphin has a variety of native names, the most common of which are buhlaan (West Pakistan) and susu (India). The river dolphin family, Platanisti-dae, contains only four species; all are long-snouted and similar in appearance. The least known species occurs in Tung Lake, vexillifer). Another is widely distributed in the Amazon and Orinoco basins (Inia N -) to :3 cr LL. C: L- iL
Functional Enhancers at the Gene-Poor 8q24 Cancer-Linked Locus

Multiple discrete regions at 8q24 were recently shown to contain alleles that predispose to many cancers including prostate, breast, and colon. These regions are far from any annotated gene and their biological activities have been unknown. Here we profiled a 5-megabase chromatin segment encompassing all the risk regions for RNA expression, histone modifications, and locations occupied by RNA polymerase II and androgen receptor (AR). This led to the identification of several transcriptional enhancers, which were verified using reporter assays. Two enhancers in one risk region were occupied by AR and responded to androgen treatment; one contained a single nucleotide polymorphism (rs11986220) that resides within a FoxA1 binding site, with the prostate cancer risk allele facilitating both stronger FoxA1 binding and stronger androgen responsiveness. The study reported here exemplifies an approach that may be applied to any risk-associated allele in non-protein coding regions as it emerges from genome-wide association studies to better understand the genetic predisposition of complex diseases.
New results on robust l2 – l∞ filtering for uncertain linear discrete-time systems

This paper investigates the problem of energy-to-peak (l2 - l∞) filtering for discrete-time uncertain systems with polyhedral uncertainties in the state-space equations. By increasing the flexible dimensions in the solution space for the energy-to-peak optimization, less conservative results on the robust energy-to-peak filtering are obtained. The filter parameters can be readily designed by solving a set of parameter-dependent linear matrix inequalities (LMIs). In comparison with the existing methods, the improvement of the proposed method over the existing results is shown via a numerical example.
Backprop KF: Learning Discriminative Deterministic State Estimators

Generative state estimators based on probabilistic filters and smoothers are one of the most popular classes of state estimators for robots and autonomous vehicles. However, generative models have limited capacity to handle rich sensory observations, such as camera images, since they must model the entire distribution over sensor readings. Discriminative models do not suffer from this limitation, but are typically more complex to train as latent variable models for state estimation. We present an alternative approach where the parameters of the latent state distribution are directly optimized as a deterministic computation graph, resulting in a simple and effective gradient descent algorithm for training discriminative state estimators. We show that this procedure can be used to train state estimators that use complex input, such as raw camera images, which must be processed using expressive nonlinear function approximators such as convolutional neural networks. Our model can be viewed as a type of recurrent neural network, and the connection to probabilistic filtering allows us to design a network architecture that is particularly well suited for state estimation. We evaluate our approach on synthetic tracking task with raw image inputs and on the visual odometry task in the KITTI dataset. The results show significant improvement over both standard generative approaches and regular recurrent neural networks.
Inventory, Discounts, and the Timing Effect

We introduce and analyze a model that explicitly considers the timing effect of intertemporal pricing---the concept, found in practice, that demand during a sale is increasing in the time since the last sale. We present structural results that characterize the interaction between the decision to hold a sale and the inventory-ordering decision. We show that the optimal inventory-ordering policy is a state-dependent base-stock policy; however, the optimal pricing policy can be quite complicated due to both the value and the cost of holding inventory and delaying sales. In our computational analysis, we find that compared to a fixed-price policy, we see an average gain in profit of almost 5% from optimally varying promotion and inventory decisions accounting for intertemporal demand, and we find that this potential profit gain increases as demand variability decreases. We also develop a heuristic based on deterministic pricing and find that it performs well relative to the optimal policy.
Complexes of Vesicular Stomatitis Virus Matrix Protein with Host Rae1 and Nup98 Involved in Inhibition of Host Transcription

Vesicular stomatitis virus (VSV) suppresses antiviral responses in infected cells by inhibiting host gene expression at multiple levels, including transcription, nuclear cytoplasmic transport, and translation. The inhibition of host gene expression is due to the activity of the viral matrix (M) protein. Previous studies have shown that M protein interacts with host proteins Rae1 and Nup98 that have been implicated in regulating nuclear-cytoplasmic transport. However, Rae1 function is not essential for host mRNA transport, raising the question of how interaction of a viral protein with a host protein that is not essential for gene expression causes a global inhibition at multiple levels. We tested the hypothesis that there may be multiple M protein-Rae1 complexes involved in inhibiting host gene expression at multiple levels. Using size exclusion chromatography and sedimentation velocity analysis, it was determined that Rae1 exists in high, intermediate, and low molecular weight complexes. The intermediate molecular weight complexes containing Nup98 interacted most efficiently with M protein. The low molecular weight form also interacted with M protein in cells that overexpress Rae1 or cells in which Nup98 expression was silenced. Silencing Rae1 expression had little if any effect on nuclear accumulation of host mRNA in VSV-infected cells, nor did it affect VSV's ability to inhibit host translation. Instead, silencing Rae1 expression reduced the ability of VSV to inhibit host transcription. M protein interacted efficiently with Rae1-Nup98 complexes associated with the chromatin fraction of host nuclei, consistent with an effect on host transcription. These results support the idea that M protein-Rae1 complexes serve as platforms to promote the interaction of M protein with other factors involved in host transcription. They also support the idea that Rae1-Nup98 complexes play a previously under-appreciated role in regulation of transcription.
Random forests versus Neural Networks — What's best for camera localization?

This work addresses the task of camera localization in a known 3D scene given a single input RGB image. State-of-the-art approaches accomplish this in two steps: firstly, regressing for every pixel in the image its 3D scene coordinate and subsequently, using these coordinates to estimate the final 6D camera pose via RANSAC. To solve the first step. Random Forests (RFs) are typically used. On the other hand. Neural Networks (NNs) reign in many dense regression tasks, but are not test-time efficient. We ask the question: which of the two is best for camera localization? To address this, we make two method contributions: (1) a test-time efficient NN architecture which we term a ForestNet that is derived and initialized from a RF, and (2) a new fully-differentiable robust averaging technique for regression ensembles which can be trained end-to-end with a NN. Our experimental findings show that for scene coordinate regression, traditional NN architectures are superior to test-time efficient RFs and ForestNets, however, this does not translate to final 6D camera pose accuracy where RFs and ForestNets perform slightly better. To summarize, our best method, a ForestNet with a robust average, which has an equivalent fast and lightweight RF, improves over the state-of-the-art for camera localization on the 7-Scenes dataset [1]. While this work focuses on scene coordinate regression for camera localization, our innovations may also be applied to other continuous regression tasks.
Dimensionality of Carbon Nanomaterials Determines the Binding and Dynamics of Amyloidogenic Peptides: Multiscale Theoretical Simulations

Experimental studies have demonstrated that nanoparticles can affect the rate of protein self-assembly, possibly interfering with the development of protein misfolding diseases such as Alzheimer's, Parkinson's and prion disease caused by aggregation and fibril formation of amyloid-prone proteins. We employ classical molecular dynamics simulations and large-scale density functional theory calculations to investigate the effects of nanomaterials on the structure, dynamics and binding of an amyloidogenic peptide apoC-II(60-70). We show that the binding affinity of this peptide to carbonaceous nanomaterials such as C60, nanotubes and graphene decreases with increasing nanoparticle curvature. Strong binding is facilitated by the large contact area available for π-stacking between the aromatic residues of the peptide and the extended surfaces of graphene and the nanotube. The highly curved fullerene surface exhibits reduced efficiency for π-stacking but promotes increased peptide dynamics. We postulate that the increase in conformational dynamics of the amyloid peptide can be unfavorable for the formation of fibril competent structures. In contrast, extended fibril forming peptide conformations are promoted by the nanotube and graphene surfaces which can provide a template for fibril-growth.
6H-5 Design, Elaboration and Characterization of Coupled Resonator Filters for WCDMA Applications

This work presents the design, manufacturing and characterization of bulk acoustic wave coupled resonator filter (BAW CRF) for W-CDMA applications. Aluminum nitride resonators with molybdenum electrodes are acoustically coupled through a W/SiO2 multi-layered stack and insulated by a fully dielectric SiN/SiOC Bragg mirror. This configuration provides galvanic isolation between input and output making CRF suitable for single-to-balanced conversion. Measurements of both single-ended and single-to-balanced filters display bandwidth and out-of-band attenuation close to W-CDMA specifications. Minimum insertion loss of 2.9 dB is measured whereas strong spurious resonances are observed. Experimental results on spurious modes suppression using specific border region are discussed
Nonstructural Protein 5A Is Incorporated into Hepatitis C Virus Low-Density Particle through Interaction with Core Protein and Microtubules during Intracellular Transport

Nonstructural protein 5A (NS5A) of hepatitis C virus (HCV) serves dual functions in viral RNA replication and virus assembly. Here, we demonstrate that HCV replication complex along with NS5A and Core protein was transported to the lipid droplet (LD) through microtubules, and NS5A-Core complexes were then transported from LD through early-to-late endosomes to the plasma membrane via microtubules. Further studies by cofractionation analysis and immunoelectron microscopy of the released particles showed that NS5A-Core complexes, but not NS4B, were present in the low-density fractions, but not in the high-density fractions, of the HCV RNA-containing virions and associated with the internal virion core. Furthermore, exosomal markers CD63 and CD81 were also detected in the low-density fractions, but not in the high-density fractions. Overall, our results suggest that HCV NS5A is associated with the core of the low-density virus particles which exit the cell through a preexisting endosome/exosome pathway and may contribute to HCV natural infection.
Prion protein and susceptibility to kainate-induced seizures

Prion protein (PrP) is a cell surface glycoprotein which is required for susceptibility to prion infection and disease. However, PrP is expressed in many different cell types located in numerous organs. Therefore, in addition to its role in prion diseases, PrP may have a large variety of other biological functions involving the nervous system and other systems. We recently showed that susceptibility to kainate-induced seizures differed in Prnp−/− and Prnp+/+ mice on the C57BL/10SnJ background. However, in a genetic complementation experiment a PrP expressing transgene was not able to rescue the Prnp+/+ phenotype. Thus the apparent effect of PrP on seizures was actually due to genes flanking the Prnp−/− gene rather that the Prnp deletion itself. We discuss here several pitfalls in the use of Prnp−/− genotypes expressed in various mouse genetic backgrounds to determine the functions of PrP. In particular, the use of Prnp−/− mice with heterogeneous mixed genetic backgrounds may have weakened the conclusions of many previous experiments. Use of either co-isogenic mice or congenic mice with more homogeneous genetic backgrounds is now feasible. For congenic mice, the potential problem of flanking genes can be mitigated by the use of appropriate transgene rescue experiments to confirm the conclusions.
Composite hot subdwarf binaries - I. The spectroscopically confirmed sdB sample

Hot subdwarf-B (sdB) stars in long-period binaries are found to be on eccentric orbits, even though current binary-evolution theory predicts that these objects are circularized before the onset of Roche lobe overflow (RLOF). To increase our understanding of binary interaction processes during the RLOF phase, we started a long-term observing campaign to study wide sdB binaries. In this paper, we present a sample of composite binary sdBs, and the results of the spectral analysis of nine such systems. The grid search in stellar parameters (gssp) code is used to derive atmospheric parameters for the cool companions. To cross-check our results and also to characterize the hot subdwarfs, we used the independent XTgrid code, which employs Tlusty non-local thermodynamic equilibrium models to derive atmospheric parameters for the sdB component and phoenix synthetic spectra for the cool companions. The independent gssp and XTgrid codes are found to show good agreement for three test systems that have atmospheric parameters available in the literature. Based on the rotational velocity of the companions, we make an estimate for the mass accreted during the RLOF phase and the minimum duration of that phase. We find that the mass transfer to the companion is minimal during the subdwarf formation.
The Relationship between Manufacturer and Distributors: Knowledge Transfer and Performance

This study explored the relationship between knowledge transfer from manufacturer to distributor. The research question is related to knowledge transfer from the manufacturer to distributor and potential improvement in the performance of the last. This issue is important for companies that are expanding their markets. A survey was conducted in business units that belong to the transport equipment sector. The study's sample comprised 199 companies. Measuring instrument followed a rigorous sequence of methodological procedures. Measurements were validated by confirmatory factor analysis, and hypotheses were tested using structural equation modeling. Results suggested positive effects of the manufacturer's performance monitoring and knowledge transfer on the distributor's performance. By having formal and informal mechanisms to follow distributor activities, it is possible to identify operational activities performed by the manufacturer that might improve distributor performance.
Hashing for Similarity Search: A Survey

Similarity search (nearest neighbor search) is a problem of pursuing the data items whose distances to a query item are the smallest from a large database. Various methods have been developed to address this problem, and recently a lot of efforts have been devoted to approximate search. In this paper, we present a survey on one of the main solutions, hashing, which has been widely studied since the pioneering work locality sensitive hashing. We divide the hashing algorithms two main categories: locality sensitive hashing, which designs hash functions without exploring the data distribution and learning to hash, which learns hash functions according the data distribution, and review them from various aspects, including hash function design and distance measure and search scheme in the hash coding space.
Adaptive Robust Control – Theory and Application to Precision and Energy Saving Control of Electro-Hydraulic Systems

The salient features of the proposed adaptive robust control (ARC) lie in the seamless integration of (i) appropriate nonlinear robust feedback controls that are physically realizable while having targeted local high-gain feedback so that the effect of various model uncertainties can be sufficiently attenuated to guarantee certain consistent robust control performance and (ii) controlled learning to reduce model uncertainties to maximize the achievable control performance and facilitate the construction of certain built-in intelligences. Such a design philosophy is well in-line with how human beings utilize feedback information and makes it well suited for the integrated design of intelligent and precision mechatronic systems, as demonstrated through various applications including the electrical motor driven mechanical systems with control accuracy down to sub-micrometer levels and piezo-electrical actuator driven nano-positioning systems for nanotechnology applications. This paper is to introduce researchers and practicing engineers to the essences of such an advanced nonlinear control design methodology. The energy-saving control of electrohydraulic systems using novel programmable valves are used as an application example and comparative experimental results are presented to illustrate the effectiveness of the presented ARC approach.
Kip3, the yeast kinesin-8, is required for clustering of kinetochores at metaphase

In Saccharomyces cerevisiae, chromosome congression clusters kinetochores on either side of the spindle equator at metaphase. Many organisms require one or more kinesin-8 molecular motors to achieve chromosome alignment. The yeast kinesin-8, Kip3, has been well studied in vitro but a role in chromosome congression has not beenreported. We investigated Kip3's role in this process using semi-automated, quantitative fluorescence microscopy and time-lapse imaging and found that Kip3 is required for congression. Deletion of KIP3 increases inter-kinetochore distances and increases the variability in the position of sister kinetochores along the spindle axis during metaphase. Kip3 does not regulate spindle length and is not required for kinetochore-microtubule attachment. Instead, Kip3 clusters kinetochores on the metaphase spindle by tightly regulating kinetochore microtubule lengths.
miR-183 regulates autophagy and apoptosis in colorectal cancer through targeting of UVRAG

Ultraviolet radiation resistance-associated gene (UVRAG) is a well-known regulator of autophagy by promoting autophagosome formation and maturation. Multiple studies have implicated UVRAG in the pathogenesis of colorectal cancer. However, the mechanisms underlying the regulation of UVRAG are unclear. Here, we describe miR-183 as a new autophagy-inhibiting miRNA. Our results showed that induction of autophagy lead to down-regulation of miR-183 in colorectal cancer cells. And, over-expression of miR-183 resulted in the attenuation of rapamycin- or starvation-induced autophagy in cancer cells, whereas inhibition of endogenous miR-183 stimulated autophagy and apoptosis. Additionally, either autophagy inhibitor 3-MA or pan-caspase inhibitor Z-VAD-FMK respectively or both treatments reversed AMO-183-induced cell death. Further studies showed that UVRAG is a target of miR-183 and as a key regulator promotes autophagy and apoptosis. More importantly, over-expression of UVRAG rescued autophagic activity and induced apoptosis in presence of miR-183. Therefore, the present study investigated the promoting effect of miR-183 on colorectal cancer progression, which was considered to be mediated by autophagy and apoptosis through targeting of UVRAG.