Gradient Bounds for Solutions of Stochastic Differential Equations Driven by Fractional Brownian Motions

We study some functional inequalities satisfied by the distribution of the solution of a stochastic differential equation driven by fractional Brownian motions. Such functional inequalities are obtained through new integration by parts formulas on the path space of a fractional Brownian motion.Comment: The paper is dedicated to Pr. David Nualart 60th's birthda

Visualization of the distribution of autophosphorylated calcium/calmodulin-dependent protein kinase II after tetanic stimulation in the CA1 area of the hippocampus

Autophosphorylation of calcium/calmodulin-dependent protein kinase II (CaMKII) at threonine-286 produces Ca2+-independent kinase activity and has been proposed to be involved in induction of long-term potentiation by tetanic stimulation in the hippocampus. We have used an immunocytochemical method to visualize and quantify the pattern of autophosphorylation of CaMKII in hippocampal slices after tetanization of the Schaffer collateral pathway. Thirty minutes after tetanic stimulation, autophosphorylated CaM kinase II (P-CaMKII) is significantly increased in area CA1 both in apical dendrites and in pyramidal cell somas. In apical dendrites, this increase is accompanied by an equally significant increase in staining for nonphosphorylated CaM kinase II. Thus, the increase in P-CaMKII appears to be secondary to an increase in the total amount of CaMKII. In neuronal somas, however, the increase in P-CaMKII is not accompanied by an increase in the total amount of CaMKII. We suggest that tetanic stimulation of the Schaffer collateral pathway may induce new synthesis of CaMKII molecules in the apical dendrites, which contain mRNA encoding its alpha-subunit. In neuronal somas, however, tetanic stimulation appears to result in long-lasting increases in P-CaMKII independent of an increase in the total amount of CaMKII. Our findings are consistent with a role for autophosphorylation of CaMKII in the induction and/or maintenance of long-term potentiation, but they indicate that the effects of tetanus on the kinase and its activity are not confined to synapses and may involve induction of new synthesis of kinase in dendrites as well as increases in the level of autophosphorylated kinase

Catalytic hollow fiber membranes prepared using layer-by-layer adsorption of polyelectrolytes and metal nanoparticles

Immobilization of metalnanoparticles in hollowfibermembranes via alternating adsorption of polyelectrolytes and negatively charged Au nanoparticles yields catalytic reactors with high surface areas. SEM images show that this technique deposits a high density of unaggregated metalnanoparticles both on the surfaces and in the pores of the hollowfibers. Catalytic reduction of 4-nitrophenol with NaBH4, which can be easily monitored by UV–vis spectrophotometry, demonstrates that the nanoparticles in the hollowfibermembrane are highly catalytically active. In a single pass through the membrane, >99% of the 4-nitrophenol is reduced to 4-aminophenol, but this conversion decreases over time. The conversion decline may stem from catalyst fouling caused by by-products of 4-aminophenol oxidation

UrbanFM: Inferring Fine-Grained Urban Flows

Urban flow monitoring systems play important roles in smart city efforts around the world. However, the ubiquitous deployment of monitoring devices, such as CCTVs, induces a long-lasting and enormous cost for maintenance and operation. This suggests the need for a technology that can reduce the number of deployed devices, while preventing the degeneration of data accuracy and granularity. In this paper, we aim to infer the real-time and fine-grained crowd flows throughout a city based on coarse-grained observations. This task is challenging due to two reasons: the spatial correlations between coarse- and fine-grained urban flows, and the complexities of external impacts. To tackle these issues, we develop a method entitled UrbanFM based on deep neural networks. Our model consists of two major parts: 1) an inference network to generate fine-grained flow distributions from coarse-grained inputs by using a feature extraction module and a novel distributional upsampling module; 2) a general fusion subnet to further boost the performance by considering the influences of different external factors. Extensive experiments on two real-world datasets, namely TaxiBJ and HappyValley, validate the effectiveness and efficiency of our method compared to seven baselines, demonstrating the state-of-the-art performance of our approach on the fine-grained urban flow inference problem

Compilation by stochastic Hamiltonian sparsification

Simulation of quantum chemistry is expected to be a principal application of quantum computing. In quantum simulation, a complicated Hamiltonian describing the dynamics of a quantum system is decomposed into its constituent terms, where the effect of each term during time-evolution is individually computed. For many physical systems, the Hamiltonian has a large number of terms, constraining the scalability of established simulation methods. To address this limitation we introduce a new scheme that approximates the actual Hamiltonian with a sparser Hamiltonian containing fewer terms. By stochastically sparsifying weaker Hamiltonian terms, we benefit from a quadratic suppression of errors relative to deterministic approaches. Relying on optimality conditions from convex optimisation theory, we derive an appropriate probability distribution for the weaker Hamiltonian terms, and compare its error bounds with other probability ansatzes for some electronic structure Hamiltonians. Tuning the sparsity of our approximate Hamiltonians allows our scheme to interpolate between two recent random compilers: qDRIFT and randomized first order Trotter. Our scheme is thus an algorithm that combines the strengths of randomised Trotterisation with the efficiency of qDRIFT, and for intermediate gate budgets, outperforms both of these prior methods.Comment: 17 pages, 1 figure, 1 algorith

Multimodal Foundation Models For Echocardiogram Interpretation

Multimodal deep learning foundation models can learn the relationship between images and text. In the context of medical imaging, mapping images to language concepts reflects the clinical task of diagnostic image interpretation, however current general-purpose foundation models do not perform well in this context because their training corpus have limited medical text and images. To address this challenge and account for the range of cardiac physiology, we leverage 1,032,975 cardiac ultrasound videos and corresponding expert interpretations to develop EchoCLIP, a multimodal foundation model for echocardiography. EchoCLIP displays strong zero-shot (not explicitly trained) performance in cardiac function assessment (external validation left ventricular ejection fraction mean absolute error (MAE) of 7.1%) and identification of implanted intracardiac devices (areas under the curve (AUC) between 0.84 and 0.98 for pacemakers and artificial heart valves). We also developed a long-context variant (EchoCLIP-R) with a custom echocardiography report text tokenizer which can accurately identify unique patients across multiple videos (AUC of 0.86), identify clinical changes such as orthotopic heart transplants (AUC of 0.79) or cardiac surgery (AUC 0.77), and enable robust image-to-text search (mean cross-modal retrieval rank in the top 1% of candidate text reports). These emergent capabilities can be used for preliminary assessment and summarization of echocardiographic findings

Spin dynamics and level structure of quantum-dot quantum wells

We have characterized CdS/CdSe/CdS quantum-dot quantum wells using time-resolved Faraday rotation (TRFR). The spin dynamics show that the electron g-factor varies as a function of quantum well width and the transverse spin lifetime of several nano-seconds is robust up to room temperature. As a function of probe energy, the amplitude of the TRFR signal shows pronounced resonances, which allow one to identify individual exciton transitions. While the TRFR data are inconsistent with the conduction and valence band level scheme of spherical quantum-dot quantum wells, a model in which broken spherical symmetry is taken into account captures the essential features.Comment: 5 pages, 3 figure

Evolutionary Signatures of Common Human Cis-Regulatory Haplotypes

Variation in gene expression may give rise to a significant fraction of inter-individual phenotypic variation. Studies searching for the underlying genetic controls for such variation have been conducted in model organisms and humans in recent years. In our previous effort of assessing conserved underlying haplotype patterns across ethnic populations, we constructed common haplotypes using SNPs having conserved linkage disequilibrium (LD) across ethnic populations. These common haplotypes cluster into a simple evolutionary structure based on their frequencies, defining only up to three conserved clusters termed ‘haplotype frameworks’. One intriguing preliminary finding was that a significant portion of reported variants strongly associated with cis-regulation tags these globally conserved haplotype frameworks. Here we expand the investigation by collecting genes showing stringently determined cis-association between genotypes and expression phenotypes from major studies. We conducted phylogenetic analysis of current major haplotypes along with the corresponding haplotypes derived from chimpanzee reference sequences. Our analysis reveals that, for the vast majority of such cis-regulatory genes, the tagging SNPs showing the strongest association also tag the haplotype lineages directly separated from ancestry, inferred from either chimpanzee reference sequences or the allele frequency-derived haplotype frameworks, suggesting that the differentially expressed phenotypes were evolved relatively early in human history. Such evolutionary signatures provide keys for a more effective identification of globally-conserved candidate regulatory haplotypes across human genes in future epidemiologic and pharmacogenetic studies

Analysis of Bridges for Seismic Hazard Mitigation in Kentucky

The priority routes have been selected for Western Kentucky which shares the most hazardous New Madrid seismic zone. As the vital links on the priority routes, bridges need to be protected from collapse during earthquakes in order to maintain the access to the route for subsequent emergency traffic. In this paper, a support-loss type of bridge collapse due to earthquake induced abutment sliding is analyzed and corresponding criteria to this type of collapse is established. The analysis methods for existing bridge abutment are advanced. A computer program based on the methods is developed and applied to evaluate the potential earthquake induced damage of 276 bridges on the priority routes