gebhart/patrepeval huggingface dataset:
NAVI: Category-Agnostic Image Collections with High-Quality 3D Shape and Pose Annotations (2023)

Recent advances in neural reconstruction enable high-quality 3D object reconstruction from casually captured image collections. Current techniques mostly analyze their progress on relatively simple image collections where Structure-from-Motion (SfM) techniques can provide ground-truth (GT) camera poses. We note that SfM techniques tend to fail on in-the-wild image collections such as image search results with varying backgrounds and illuminations. To enable systematic research progress on 3D reconstruction from casual image captures, we propose NAVI: a new dataset of category-agnostic image collections of objects with high-quality 3D scans along with per-image 2D-3D alignments providing near-perfect GT camera parameters. These 2D-3D alignments allow us to extract accurate derivative annotations such as dense pixel correspondences, depth and segmentation maps. We demonstrate the use of NAVI image collections on different problem settings and show that NAVI enables more thorough evaluations that were not possible with existing datasets. We believe NAVI is beneficial for systematic research progress on 3D reconstruction and correspondence estimation. Project page: https://navidataset.github.io
Ultrasound transmission tomography image reconstruction with a fully convolutional neural network (2020)

Image reconstruction of ultrasound computed tomography based on the wave equation is able to show much more structural details than simpler ray-based image reconstruction methods. However, to invert the wave-based forward model is computationally demanding. To address this problem, we develop an efficient fully learned image reconstruction method based on a convolutional neural network. The image is reconstructed via one forward propagation of the network given input sensor data, which is much faster than the reconstruction using conventional iterative optimization methods. To transform the ultrasound measured data in the sensor domain into the reconstructed image in the image domain, we apply multiple down-scaling and up-scaling convolutional units to efficiently increase the number of hidden layers with a large receptive and projective field that can cover all elements in inputs and outputs, respectively. For dataset generation, a paraxial approximation forward model is used to simulate ultrasound measurement data. The neural network is trained with a dataset derived from natural images in ImageNet and tested with a dataset derived from medical images in OA-Breast Phantom dataset. Test results show the superior efficiency of the proposed neural network to other reconstruction algorithms including popular neural networks. When compared with conventional iterative optimization algorithms, our neural network can reconstruct a 110 × 86 image more than 20 times faster on a CPU and 1000 times faster on a GPU with comparable image quality and is also more robust to noise.
Longitudinal analysis of antibody decay in convalescent COVID-19 patients (2021)

Determining the sustainability of antibodies targeting severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is essential for predicting immune response against the Coronavirus disease 2019 (COVID-19). To quantify the antibody decay rates among the varying levels of anti-nucleocapsid (anti-N) Immunoglobulin G (IgG) in convalescent COVID-19 patients and estimate the length of time they maintained SARS-CoV-2 specific antibodies, we have collected longitudinal blood samples from 943 patients over the course of seven months after their initial detection of SARS-CoV-2 virus by RT-PCR. Anti-N IgG levels were then quantified in these blood samples. The primary study outcome was the comparison of antibody decay rates from convalescent patients with high or low initial levels of antibodies using a mixed linear model. Additional measures include the length of time that patients maintain sustainable levels of anti-N IgG. Antibody quantification of blood samples donated by the same subject multiple times shows a gradual decrease of IgG levels to the cutoff index level of 1.4 signal/cut-off (S/C) on the Abbott Architect SARS-CoV-2 IgG test. In addition, this study shows that antibody reduction rate is dependent on initial IgG levels, and patients with initial IgG levels above 3 S/C show a significant 1.68-fold faster reduction rate compared to those with initial IgG levels below 3 S/C. For a majority of the donors naturally occurring anti-N antibodies were detected above the threshold for only four months after infection with SARS-CoV-2. This study is clinically important for the prediction of immune response capacity in COVID-19 patients.
<title>Landing trajectory measurement using onboard video sensor and runway landmarks</title> (1995)

This paper describes experiments that prove the feasibility of using a video sensor for image- based landing trajectory measurement for both data reduction and auto-landing guidance purpose. Video footage recorded with a forward-looking CCD camera mounted under the nose of a Boeing 737 was analyzed post test on a specially designed data reduction workstation. The combined image analysis and photogrammetric algorithms are capable of estimating and automatically tracking the six degrees of freedom (six DOF, or roll, pitch, yaw, and position coordinates) of a rigid body from a single camera view. The six DOF tracking algorithm locates fixed features of the runway in each video frame to estimate and track the six DOF of the camera in the runway reference coordinates. Runway lights were used as the fixed features in this case. The random measurement errors without temporal smoothing were estimated from measurement results. Although the vision-based approach has much smaller systematic error than GPS or inertial system, it can use these as secondary sensors for initial acquisition and maintaining continuous track. The approach is shown to be viable for a real-time auto- landing guidance vision system employing commercially-available hardware technology.© (1995) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
Correlation between the structure and the spin state inR1−xSrxCoO3(R=La, Pr, and Nd) (2007)

The crystal structures of the perovskites ${R}_{1\ensuremath{-}x}{\mathrm{Sr}}_{x}\mathrm{Co}{\mathrm{O}}_{3}$ ($R=\mathrm{La}$, Pr, and Nd) with $0\ensuremath{\leqslant}x\ensuremath{\leqslant}0.3$ were refined from x-ray powder diffraction spectra with the Rietveld method. The result of the Rietveld analyses indicates the presence of three unequal Co-O bonds with different lengths for all samples, and we found that the difference between the two Co-O(2) bond lengths increases with temperature and Sr content. The intermediate-spin (IS) state of the ${\mathrm{Co}}^{3+}$ ion is Jahn-Teller (JT) active, and the structural data show that the concentration of ${\mathrm{Co}}^{3+}$ ions increases with temperature and Sr content. For the non-Sr-doped $R=\mathrm{La}$ sample, we observed only a small distortion at $90\phantom{\rule{0.3em}{0ex}}\mathrm{K}$ while the JT distortion was large at $300\phantom{\rule{0.3em}{0ex}}\mathrm{K}$. With substitution of Sr for La, the distortion at $90\phantom{\rule{0.3em}{0ex}}\mathrm{K}$ was found to increase. This observation can be accounted for by the increased stability of the IS state with Sr doping. On the other hand, the distortion was small even at $300\phantom{\rule{0.3em}{0ex}}\mathrm{K}$ for nondoped $\mathrm{Pr}\mathrm{Co}{\mathrm{O}}_{3}$ and $\mathrm{Nd}\mathrm{Co}{\mathrm{O}}_{3}$, indicating that the low-spin state is stable up to higher temperatures than in $\mathrm{La}\mathrm{Co}{\mathrm{O}}_{3}$. The distortion at $90\phantom{\rule{0.3em}{0ex}}\mathrm{K}$ was larger for the Sr-doped samples as in the $R=\mathrm{La}$ system, but the Sr content at which the JT distortion started to increase significantly was larger than that in the $R=\mathrm{La}$ system.
Distributed adaptive patching strategies against malware propagation: A passivity approach (2016)

A computer malware is a malicious code that compromises a node and then attempts to infect the node's neighbors in order to mount further attacks. Strategies for mitigating malware propagation attacks are based on patching each node at a certain rate, which is selected based on a trade-off between removing the viruses and the cost of patching. This selection, however, implicitly assumes that the propagation rate is known, whereas in practice the propagation rate depends on the inherently uncertain goals and capabilities of the attacker. In this paper, we propose and analyze adaptive defense strategies against malware with unknown propagation rates from a control-theoretic perspective. We introduce a distributed defense strategy in which each host increases its patching rate when a malware is detected, and decreases its patching rate when the host is not infected. The proposed patching strategies can drive the probability of infection to an arbitrarily low value at steady-state by varying the patching update parameters. Using a passivity-based approach, we prove that, when each node has the same patching parameters, the adaptive defense strategy ensures that the infection probabilities converge to any desired positive steady-state value. When the parameters are heterogeneous among nodes, we prove local stability of the adaptive patching dynamics, analyze the convergence rate of the infection probability, and formulate an optimization problem for selecting the infection probabilities based on a trade-off between the cost of patching and the cost of infection at steady-state. Our results are illustrated through a numerical study.
Markov Chain Monte Carlo Detection for Frequency-Selective Channels Using List Channel Estimates (2011)

In this paper, we develop a statistical approach based on Markov chain Monte Carlo (MCMC) techniques for joint data detection and channel estimation over time-varying frequency-selective channels. The proposed detector, that we call MCMC with list channel estimates (MCMC-LCE), adopts the Gibbs sampler to find a list of mostly likely transmitted sequences and matching channel estimates/impulse responses (CIR), to compute the log-likelihood ratio (LLR) of transmitted bits. The MCMC-LCE provides a low-complexity means to approximate the optimal maximum a posterior (MAP) detection in a statistical fashion and is applicable to channels with long memory. Promising behavior of the MCMC-LCE is presented using both synthetic channels and real data collected from underwater acoustic (UWA) channels whose large delay spread and time variation have been the main motivation for the developed system. We also adopt an adaptive variable step-size least mean-square (VSLMS) algorithm for channel tracking. We find that this choice, which does not require prior knowledge on the CIR statistics, is a good fit for UWA channels. Superior performance of the MCMC-LCE over turbo minimum mean-square-error (MMSE) equalizers is demonstrated for a variety of channels examined in this work.
Differentially Private CutMix for Split Learning with Vision Transformer (2022)

Recently, vision transformer (ViT) has started to outpace the conventional CNN in computer vision tasks. Considering privacy-preserving distributed learning with ViT, federated learning (FL) communicates models, which becomes ill-suited due to ViT' s large model size and computing costs. Split learning (SL) detours this by communicating smashed data at a cut-layer, yet suffers from data privacy leakage and large communication costs caused by high similarity between ViT' s smashed data and input data. Motivated by this problem, we propose DP-CutMixSL, a differentially private (DP) SL framework by developing DP patch-level randomized CutMix (DP-CutMix), a novel privacy-preserving inter-client interpolation scheme that replaces randomly selected patches in smashed data. By experiment, we show that DP-CutMixSL not only boosts privacy guarantees and communication efficiency, but also achieves higher accuracy than its Vanilla SL counterpart. Theoretically, we analyze that DP-CutMix amplifies Rényi DP (RDP), which is upper-bounded by its Vanilla Mixup counterpart.
Genome sequencing of disease and carriage isolates of nontypeable Haemophilus influenzae identifies discrete population structure (2014)

One of the main hurdles for the development of an effective and broadly protective vaccine against nonencapsulated isolates of Haemophilus influenzae (NTHi) lies in the genetic diversity of the species, which renders extremely difficult the identification of cross-protective candidate antigens. To assess whether a population structure of NTHi could be defined, we performed genome sequencing of a collection of diverse clinical isolates representative of both carriage and disease and of the diversity of the natural population. Analysis of the distribution of polymorphic sites in the core genome and of the composition of the accessory genome defined distinct evolutionary clades and supported a predominantly clonal evolution of NTHi, with the majority of genetic information transmitted vertically within lineages. A correlation between the population structure and the presence of selected surface-associated proteins and lipooligosaccharide structure, known to contribute to virulence, was found. This high-resolution, genome-based population structure of NTHi provides the foundation to obtain a better understanding, of NTHi adaptation to the host as well as its commensal and virulence behavior, that could facilitate intervention strategies against disease caused by this important human pathogen.
Supermolecular Structure of Poly(Butylene Terephthalate) Fibers Formed with the Addition of Reduced Graphene Oxide (2020)

Nanocomposite fibers based on poly(butylene terephthalate) (PBT) and reduced graphene oxide (rGO) were prepared using a method able to disperse graphene in one step into a polymer matrix. The studies were performed for fibers containing four different concentrations of rGO at different take-up velocities. The supermolecular structures of the fibers at the crystallographic and lamellar levels were examined by means of calorimetric and X-ray scattering methods (DSC, WAXS, and SAXS). It was found that the fiber structure is mainly influenced by the take-up velocity. Fibers spun at low and medium take-up velocities contained a crystalline α-form, whereas the fibers spun at a high take-up velocity contained a smectic mesophase. During annealing, the smectic phase transformed into its α-form. The degree of transformation depended on the rGO content. Reduced graphene mainly hindered the crystallization of PBT by introducing steric obstacles confining the ordering of the macromolecules of PBT.
Power spectral density as template for modeling a metal-oxide nanocluster resist to obtain accurate resist roughness profiles (2021)

Background: The metal-oxide resist (MOR) is a promising type of nonchemically amplified resist (CAR) for EUV lithography. This family of resists shows some advantages over the conventional CARs. Even though a prior MOR model exists, no documented references for the application described could be found in open literature to develop a physically rational lithographic model that can accurately simulate and predict lithographic results for these resists. Aim: Increase the fundamental understanding of this class of resists by creating a model that also considers these aspects of the resist. Approach: Model the metal-oxo clusters of the MOR as nanoparticles with an effective radius that operate under excluded volume. Results: We show the possibility to include both the packing noise effect, as well as accurate roughness (characteristics) predictions by utilizing power spectral density (PSD) plots. Conclusions: Varying the calibrated model parameters has a clear effect on the overall roughness of the resist lines and is reflected in the PSD behavior. In contrast to experimental data, changing the resist film thickness did not result in a change in PSD behavior.
Biological validation that SF3b is a target of the antitumor macrolide pladienolide (2011)

Pladienolide is a naturally occurring macrolide that binds to the SF3b complex to inhibit mRNA splicing. It has not been fully validated whether the splicing impairment is a relevant mechanism for the potent antitumor activity of pladienolide. We established pladienolide-resistant clones from WiDr and DLD1 colorectal cancer cells that were insensitive to the inhibitory action of pladienolide on cell proliferation and splicing. An mRNA-Seq differential analysis revealed that these two cell lines have an identical mutation at Arg1074 in the gene for SF3B1, which encodes a subunit of the SF3b complex. Reverse expression of the mutant protein transferred pladienolide resistance to WiDr cells. Furthermore, immunoprecipitation analysis using a radiolabeled probe showed that the mutation impaired the binding affinity of paldienolide to its target. These results clearly demonstrate that pladienolide exerts its potent activity by targeting SF3b and also suggest that inhibition of SF3b is a promising drug target for anticancer therapy.
Fanconi anemia gene mutations in young-onset pancreatic cancer. (2003)

Genes of the Fanconi complementation groups [Fanconi anemia (FA) genes] are suggested to be involved in homologous DNA recombination and produce FA when two allelic mutations are inherited. BRCA2 is an FA gene and additionally conveys an inherited risk for breast, ovarian, and pancreatic cancer for individuals carrying a single mutated allele [N. G. Howlett et al., Science (Wash. DC), 297: 606-609, 2002]. Here we report inherited and somatic mutations of FANCC and FANCG present in young-onset pancreatic cancer. This may imply a general involvement of Fanconi genes with an inherited risk of cancer. The known hypersensitivity of Fanconi cells to mitomycin and other therapeutic agents [M. S. Sasaki, Nature (Lond.), 257: 501-503, 1975] suggests a therapeutic utility for a more complete characterization of the DNA repair defects and their causative genetic mutations in pancreatic cancer.
Practical guides for x-ray photoelectron spectroscopy (XPS): Interpreting the carbon 1s spectrum (2021)

The carbon 1s photoelectron spectrum is the most widely fit and analyzed narrow scan in the x-ray photoelectron spectroscopy (XPS) literature. It is, therefore, critically important to adopt well-established protocols based on best practices for its analysis, since results of these efforts affect research outcomes in a wide range of different application areas across materials science. Unfortunately, much XPS peak fitting in the scientific literature is inaccurate. In this guide, we describe and explain the most common problems associated with C 1s narrow scan analysis in the XPS literature. We then provide an overview of rules, principles, and considerations that, taken together, should guide the approach to the analysis of C 1s spectra. We propose that following this approach should result in (1) the avoidance of common problems and (2) the extraction of reliable, reproducible, and meaningful information from experimental data.
Phantom study and accuracy evaluation of an image-to-world registration approach used with electro-magnetic tracking system for neurosurgery (2015)

Minimally invasive neurosurgery needs intraoperative imaging updates and high efficient image guide system to facilitate the procedure. An automatic image guided system utilized with a compact and mobile intraoperative CT imager was introduced in this work. A tracking frame that can be easily attached onto the commercially available skull clamp was designed. With known geometry of fiducial and tracking sensor arranged on this rigid frame that was fabricated through high precision 3D printing, not only was an accurate, fully automatic registration method developed in a simple and less-costly approach, but also it helped in estimating the errors from fiducial localization in image space through image processing, and in patient space through the calibration of tracking frame. Our phantom study shows the fiducial registration error as 0.348&plusmn;0.028mm, comparing the manual registration error as 1.976&plusmn;0.778mm. The system in this study provided a robust and accurate image-to-patient registration without interruption of routine surgical workflow and any user interactions involved through the neurosurgery.
Lithography simulation in DfM: achievable accuracy versus requirements (2007)

Lithography simulation remains one of the primary aspects of most DfM flows, along with critical area analysis and chem-mech-polish (CMP) modeling. Often, the accuracy of the DfM flow is judged solely on the accuracy of the lithographic simulation. In this paper we attempt to refute that viewpoint and highlight the many sources of error in a DfM flow. We examine the factors that impact accuracy and attempt to quantify their effect. Differences between rigorous simulation, which includes full mask data preparation along with lithography simulation, and the use of compact models are explored. Required and achievable DfM accuracy over time and across multiple fabs is examined and the use of a "closed-loop" DfM flow is proposed.
Concurrent error detection using monitoring machines (1995)

In a monitored FSM controller, an auxiliary monitoring machine operates in lockstep with the main FSM, so that any fault in either machine is immediately detected. The monitoring machine provides a uniform mechanism for the detection of stuck-at faults as well as delay faults. The technique yields designs that compare favorably with previous implementations. Not only is fault coverage higher, the hardware cost of the monitored sequential circuit is significantly lower.
Isolation and Characterization of Acyl Coenzyme A Carboxylases from Mycobacterium tuberculosis and Mycobacterium bovis , Which Produce Multiple Methyl-Branched Mycocerosic Acids (1982)

Mycobacterium tuberculosis H37Ra and M. bovis BCG produce multiple methyl-branched fatty acids called mycocerosic acids, presumably from methyl-malonyl coenzyme A (CoA). An acyl-CoA carboxylase was isolated from these organisms at a 30 to 50% yield by a purification procedure involving ammonium sulfate fractionation, gel filtration, and affinity chromatography with a monomeric avidin-Sepharose 4B-CL gel with d-biotin as the eluant. Sodium dodecyl sulfate electrophoresis and avidin binding indicate that each enzyme is probably composed of two dissimilar subunits with a covalently bound biotin in the larger subunit. The enzyme preparations from H37Ra and BCG had specific activities of 2.1 and 5.5 mumol min(-1) mg(-1), respectively, when propionyl-CoA was the substrate. The enzymes from the two species displayed striking similarities in their kinetic parameters. They showed maximal activity at pH 8.0 when propionyl-CoA was the substrate, but displayed a relatively broad pH-activity profile when acetyl-CoA was the substrate. With both substrates, potassium phosphate buffer gave maximal activity. Apparent K(m) values for propionyl-CoA, ATP, Mg(2+), and NaHCO(3) were 70 muM, 100 muM, 5.4 mM, and 2.2 mM, respectively. The enzyme also carboxylated acetyl-CoA and butyryl-CoA, and high-performance liquid chromatography showed the expected products of carboxylation. However, with these substrates, the K(m) was higher and the V(max) was lower than those of propionyl-CoA. The enzyme was shown to be stereospecific, synthesizing exclusively (S)-methylmalonyl-CoA from propionyl-CoA. No other acyl-CoA carboxylase was observed during the purification procedure, indicating that the present carboxylase may provide malonyl-CoA for the synthesis of n-fatty acids as well as methylmalonyl-CoA for the synthesis of mycocerosic acids.