Transitory States: Becoming and Continuity in the Drawing Process and Object

This thesis explores the influences and content of the visual artist Ming Ying Hong and in particular, examines her drawings created during her Master of Fine Arts degree program at Washington University in St. Louis. In theorizing about the practice of drawing, this document investigates the instability in meaning found in both her motifs of explosions and wounds, placing her research in larger philosophical context regarding the transformative potential of Giles Deleuze’s “becoming” and George Batailles’s “continuity.” Ultimately, the intersection of these two terms is exemplified in the in the paradoxical conflation of binaries, upsetting clear categorization and suspending concise meaning. As a result of these fluid boundaries, there is an inability to delineate abstraction from representation, calm from violence, and presence from absence. Furthermore, this document examines the practice of drawing as a means of obtaining an embodied state of becoming and continuity, enabling a sense of cohesiveness between self and world

Fast and Minimax Optimal Estimation of Low-Rank Matrices via Non-Convex Gradient Descent

We study the problem of estimating a low-rank matrix from noisy measurements, with the specific goal of achieving minimax optimal error. In practice, the problem is commonly solved using non-convex gradient descent, due to its ability to scale to large-scale real-world datasets. In theory, non-convex gradient descent is capable of achieving minimax error. But in practice, it often converges extremely slowly, such that it cannot even deliver estimations of modest accuracy within reasonable time. On the other hand, methods that improve the convergence of non-convex gradient descent, through rescaling or preconditioning, also greatly amplify the measurement noise, resulting in estimations that are orders of magnitude less accurate than what is theoretically achievable with minimax optimal error. In this paper, we propose a slight modification to the usual non-convex gradient descent method that remedies the issue of slow convergence, while provably preserving its minimax optimality. Our proposed algorithm has essentially the same per-iteration cost as non-convex gradient descent, but is guaranteed to converge to minimax error at a linear rate that is immune to ill-conditioning. Using our proposed algorithm, we reconstruct a 60 megapixel dataset for a medical imaging application, and observe significantly decreased reconstruction error compared to previous approaches

Generalized tt-jj Model

By parameterizing the t-j model we present a new electron correlation model with one free parameter for high-temperature superconductivity. This model is of $SU_{q}(1,2)$ symmetry. The energy spectrums are shown to be modulated by the free parameter in the model. The solution and symmetric structures of the Hilbert space, as well as the Bethe ansatz approach are discussed for special cases.Comment: 13 page, Latex, to appear in J. Phys.

Boty-like retrotransposons in the filamentous fungus Botrytis cinerea contain the additional antisense gene brtn

AbstractLong-terminal repeat (LTR) retrotransposons typically contain gag, pol, or gag–pol, and in some case env genes. In this work, we used data mining of the Botrytis cinerea genomic sequence and a molecular approach to identify Boty-like LTR retrotransposons in B. cinerea containing an antisense gene (brtn) between pol and the 3′-LTR. Reverse transcriptase PCR (RT-PCR) revealed that some brtn-like genes could be expressed, at least in B. cinerea T4. We conducted BLAST comparisons and conserved-domain analysis, but the function of putative BRTN is presently unknown. Boty-like LTR retrotransposons in Sclerotinia sclerotiorum, called ScscLRET and containing brtn homologs at positions similar to brtn, were detected by homology searches and data mining of the S. sclerotiorum 1980 genomic sequence. Thus, this study demonstrated that some fungal LTR retrotransposons contain additional antisense genes

Modeling meteorite craters by impacting melted tin on sand

To simulate the heated exterior of a meteorite, we impact a granular bed with melted tin. The morphology of tin remnant and crater is found to be sensitive to the temperature and solidification of tin. By employing deep learning and convolutional neural network, we can quantify and map the complex impact patterns onto network systems based on feature maps and Grad-CAM results. This gives us unprecedented details on how the projectile deforms and interacts with the granules, which information can be used to trace the development of different remnant shapes. Furthermore, full dynamics of granular system is revealed by the use of Particle Image Velocimetry. Kinetic energy, temperature and diameter of the projectile are used to build phase diagrams for the morphology of both crater and tin remnant. In addition to successfully reproducing key features of simple and complex craters, we are able to detect a possible artifact when compiling crater data from field studies. The depth of craters from high-energy impacts in our work is found to be independent of their width. However, when mixing data from different energy, temperature and diameter of projectile, a bogus power-law relationship appears between them. Like other controlled laboratory researches, our conclusions have the potential to benefit the study of paint in industry and asteroid sampling missions on the surface of celestial bodies.Comment: 6 pages, 5 figure

Statistical Origin of Constituent-Quark Scaling in the QGP hadronization

Nonextensive statistics in a Blast-Wave model (TBW) is implemented to describe the identified hadron production in relativistic p+p and nucleus-nucleus collisions. Incorporating the core and corona components within the TBW formalism allows us to describe simultaneously some of the major observations in hadronic observables at the Relativistic Heavy-Ion Collider (RHIC): the Number of Constituent Quark Scaling (NCQ), the large radial and elliptic flow, the effect of gluon saturation and the suppression of hadron production at high transverse momentum (pT) due to jet quenching. In this formalism, the NCQ scaling at RHIC appears as a consequence of non-equilibrium process. Our study also provides concise reference distributions with a least chi2 fit of the available experimental data for future experiments and models.Comment: 4 pages, 3 figures; added two tables, explained a little bit more on TBW_p

Classification of Bipartite and Tripartite Qutrit Entanglement under SLOCC

We classify biqutrit and triqutrit pure states under stochastic local operations and classical communication. By investigating the right singular vector spaces of the coefficient matrices of the states, we obtain explicitly two equivalent classes of biqutrit states and twelve equivalent classes of triqutrit states respectively.Comment: 10 page

Ultrasensitive amyloid β-protein quantification with high dynamic range using a hybrid graphene-gold surface-enhanced Raman spectroscopy platform.

Surface enhanced Raman spectroscopy (SERS) holds great promise in biosensing because of its single-molecule, label-free sensitivity. We describe here the use of a graphene-gold hybrid plasmonic platform that enables quantitative SERS measurement. Quantification is enabled by normalizing analyte peak intensities to that of the graphene G peak. We show that two complementary quantification modes are intrinsic features of the platform, and that through their combined use, the platform enables accurate determination of analyte concentration over a concentration range spanning seven orders of magnitude. We demonstrate, using a biologically relevant test analyte, the amyloid β-protein (Aβ), a seminal pathologic agent of Alzheimer's disease (AD), that linear relationships exist between (a) peak intensity and concentration at a single plasmonic hot spot smaller than 100 nm, and (b) frequency of hot spots with observable protein signals, i.e. the co-location of an Aβ protein and a hot spot. We demonstrate the detection of Aβ at a concentration as low as 10-18 M after a single 20 μl aliquot of the analyte onto the hybrid platform. This detection sensitivity can be improved further through multiple applications of analyte to the platform and by rastering the laser beam with smaller step sizes

The nucleolar protein NIFK promotes cancer progression via CK1α/β-catenin in metastasis and Ki-67-dependent cell proliferation.

Nucleolar protein interacting with the FHA domain of pKi-67 (NIFK) is a Ki-67-interacting protein. However, its precise function in cancer remains largely uninvestigated. Here we show the clinical significance and metastatic mechanism of NIFK in lung cancer. NIFK expression is clinically associated with poor prognosis and metastasis. Furthermore, NIFK enhances Ki-67-dependent proliferation, and promotes migration, invasion in vitro and metastasis in vivo via downregulation of casein kinase 1α (CK1α), a suppressor of pro-metastatic TCF4/β-catenin signaling. Inversely, CK1α is upregulated upon NIFK knockdown. The silencing of CK1α expression in NIFK-silenced cells restores TCF4/β-catenin transcriptional activity, cell migration, and metastasis. Furthermore, RUNX1 is identified as a transcription factor of CSNK1A1 (CK1α) that is negatively regulated by NIFK. Our results demonstrate the prognostic value of NIFK, and suggest that NIFK is required for lung cancer progression via the RUNX1-dependent CK1α repression, which activates TCF4/β-catenin signaling in metastasis and the Ki-67-dependent regulation in cell proliferation