Uncaring universe

Depicting the mythical and chaotic, my work revisits traditional and pop-cultural icons. I borrow my framing of absurdity from Camus’s The Myth of Sisyphus: “In a universe suddenly divested of illusions and lights, man feels an alien, a stranger. [...]This divorce between man and his life, the actor and his setting, is properly the feeling of absurdity.” I grew up as the only child in a family of diplomats, a learning journey that mutated across geographies. Born in China, I lived in Russia, Mongolia and Korea before coming to the USA. My world is an aesthetic amalgamation of dissonant cultures. Constant moving bred insecurity: the feeling of detachment surrounded me tightly. Over time, I stopped adjusting and became comfortable simply observing. If life is a comic strip, I’m outside the frame. My paintings are uncanny, examining myths, cartoons and memories. They rely on rich texture, bright colors, and clear lines: to me, absurdity is out of context; loss of connection; detachment from familiar settings. All the helpless animals, lifeless trees, Mahjong cards, increscent moons, and trapped souls in my work are entities divorced from their settings. By layering and recombining cultural icons, I create visual barriers that mimic and illustrate the dissonance I experience as an immigrant within the transnational Chinese diaspora. Fundamentally, these paintings are an indictment: an expression of my desire to assimilate and inform, expressing the uncanny through juxtaposition and dark humor to construct new intersectional imaginaries

C0C^{0}-regularity for solutions of elliptic equations with distributional coefficients

In this paper, the continuity of solutions for elliptic equations in divergence form with distributional coefficients is considered. Inspired by the discussion on necessary and sufficient conditions for the form boundedness of elliptic operators by Maz'ya and Verbitsky (Acta Math., 188, 263-302, 2002 and Comm. Pure Appl. Math., 59, 1286-1329, 2006), we propose two kinds of sufficient conditions, which are some Dini decay conditions and some integrable conditions named Kato class or $K^{1}$ class, to show that the weak solution of the Schr\"{o}dinger type elliptic equation with distributional coefficients is continuous and give an almost optimal priori estimate. These estimates can clearly show that how the coefficients and nonhomogeneous terms influence the regularity of solutions. The $\ln$-Lipschitz regularity and H\"{o}lder regularity are also obtained as corollaries which cover the classical De Giorgi's H\"{o}lder estimates

Characterization of Self-Assembling Quinoline- Based Foldamers by Fluorescence Anisotropy

Foldamers represent a family of synthetic macromolecules which, like their biological counterparts, are able to adopt a well-defined conformation in solution. Oligoquinoline-carboxamides (Qn) are a group of foldamers that adopt a helical conformation in solution. A series of Qn foldamers were prepared by chromatography-free large-scale synthesis and segment-doubling strategy. The C-terminal ester group of the Qn foldamers could be hydrolyzed to yield acid-functionalized foldamers (QnA) which could self-assemble into larger ((QnA)2-Na) complexes by metal coordination with a sodium cation. Moreover, the addition of a bis-acid functionalized tetramer (AQ2PQ2A) to a solution of (QnA)2-Na complexes resulted in insertion oligomeric products. To characterize these complexes in solution, both Qn and QnA were end-labeled with an oligo(phenylene vinylene) dye (OPV) at their N-terminus via a rigid amide bond to yield the OPV-Qn and OPV-QnA fluorescent equivalents. OPV was used to conduct time-resolved fluorescence anisotropy (TRFA) measurements on the OPV-Qn and OPV-QnA foldamers, the (OPV-QnA)2-Na complexes, and the OPV-Qn-Na-(AQ2PQ2A)n oligomers. Analysis of the TRFA of the OPV-Qn foldamers yielded the rotational time () of the fluorescent species, which was found to reflect the hydrodynamic volume (Vh) of the foldamers. The straight line obtained by plotting as a function of the number of (quinoline) units (NUs) demonstrated that the foldamers behaved in solution as rigid cylinders for all lengths examined. The linearity of the -vs-NU plot was employed as a calibration curve against which the rotational time of the QnA-complexes could be compared. Within experimental error, the rotational time of a Qn+m complex was found to equal the sum of the rotational times obtained for Qn and Qm. This result suggests that the complexation of two acid-functionalized oligoquinoline foldamers in solution generated a fully stacked foldamer with a NU equal to the sum of the NUs of its constituting elements. Hetero-complexes between OPV-Q8A and Q16A were also produced by adding a 10-fold excess of Q16A to an OPV-Q8A solution. Complexation was demonstrated by the value of the mixture, that equaled that of an OPV-Q24 foldamer. Dilution experiments on a solution of OPV-Q8A-Na-Q16A complexes led to the dissociation of the complexes into their OPV-Q8A and Q16A constituting elements, as evidenced by the progressive decrease in from the value obtained for OPV-Q24 to that of OPV-Q8 upon decreasing foldamer concentration. Similarly, the addition of increasing amounts of AQ2PQ2A to a solution of OPV-Q8A in chloroform resulted in an increase in demonstrating the formation of complexes between OPV-Q8A and AQ2PQ2A until reached a plateau for large OPV-Q8A/AQ2PQ2A molar ratios. In the plateau region, the rotational time of the oligomeric complexes generated from OPV-Q8A and AQ2PQ2A stabilized by isobutyl or hexyl side chains was equal to that of an OPV-Q¬n foldamer with n equal to 24 or 30, respectively. The apparent absence of further polymerization, evidenced by the constant value reached for high OPV-Q8A/AQ2PQ2A molar ratios, was attributed to aggregation of longer complexes and their precipitation. This study represents the first example in the scientific literature where TRFA was applied to characterize the NU of helical self-assembling foldamers in solution

Efficient Anomaly Detection with Budget Annotation Using Semi-Supervised Residual Transformer

Anomaly Detection is challenging as usually only the normal samples are seen during training and the detector needs to discover anomalies on-the-fly. The recently proposed deep-learning-based approaches could somehow alleviate the problem but there is still a long way to go in obtaining an industrial-class anomaly detector for real-world applications. On the other hand, in some particular AD tasks, a few anomalous samples are labeled manually for achieving higher accuracy. However, this performance gain is at the cost of considerable annotation efforts, which can be intractable in many practical scenarios. In this work, the above two problems are addressed in a unified framework. Firstly, inspired by the success of the patch-matching-based AD algorithms, we train a sliding vision transformer over the residuals generated by a novel position-constrained patch-matching. Secondly, the conventional pixel-wise segmentation problem is cast into a block-wise classification problem. Thus the sliding transformer can attain even higher accuracy with much less annotation labor. Thirdly, to further reduce the labeling cost, we propose to label the anomalous regions using only bounding boxes. The unlabeled regions caused by the weak labels are effectively exploited using a highly-customized semi-supervised learning scheme equipped with two novel data augmentation methods. The proposed method outperforms all the state-of-the-art approaches using all the evaluation metrics in both the unsupervised and supervised scenarios. On the popular MVTec-AD dataset, our SemiREST algorithm obtains the Average Precision (AP) of 81.2% in the unsupervised condition and 84.4% AP for supervised anomaly detection. Surprisingly, with the bounding-box-based semi-supervisions, SemiREST still outperforms the SOTA methods with full supervision (83.8% AP) on MVTec-AD.Comment: 20 pages,6 figure

Persistence of an SEIR Model with Immigration Dependent on the Prevalence of Infection

We incorporate the immigration of susceptible individuals into an SEIR epidemic model, assuming that the immigration rate decreases as the spread of infection increases. For this model, the basic reproduction number, R0, is found, which determines that the disease is either extinct or persistent ultimately. The obtained results show that the disease becomes extinct as R01

Leveraging syntactic and semantic graph kernels to extract pharmacokinetic drug drug interactions from biomedical literature

BACKGROUND: Information about drug-drug interactions (DDIs) supported by scientific evidence is crucial for establishing computational knowledge bases for applications like pharmacovigilance. Since new reports of DDIs are rapidly accumulating in the scientific literature, text-mining techniques for automatic DDI extraction are critical. We propose a novel approach for automated pharmacokinetic (PK) DDI detection that incorporates syntactic and semantic information into graph kernels, to address the problem of sparseness associated with syntactic-structural approaches. First, we used a novel all-path graph kernel using shallow semantic representation of sentences. Next, we statistically integrated fine-granular semantic classes into the dependency and shallow semantic graphs. RESULTS: When evaluated on the PK DDI corpus, our approach significantly outperformed the original all-path graph kernel that is based on dependency structure. Our system that combined dependency graph kernel with semantic classes achieved the best F-scores of 81.94 % for in vivo PK DDIs and 69.34 % for in vitro PK DDIs, respectively. Further, combining shallow semantic graph kernel with semantic classes achieved the highest precisions of 84.88 % for in vivo PK DDIs and 74.83 % for in vitro PK DDIs, respectively. CONCLUSIONS: We presented a graph kernel based approach to combine syntactic and semantic information for extracting pharmacokinetic DDIs from Biomedical Literature. Experimental results showed that our proposed approach could extract PK DDIs from literature effectively, which significantly enhanced the performance of the original all-path graph kernel based on dependency structure

Equivariant Contrastive Learning for Sequential Recommendation

Contrastive learning (CL) benefits the training of sequential recommendation models with informative self-supervision signals. Existing solutions apply general sequential data augmentation strategies to generate positive pairs and encourage their representations to be invariant. However, due to the inherent properties of user behavior sequences, some augmentation strategies, such as item substitution, can lead to changes in user intent. Learning indiscriminately invariant representations for all augmentation strategies might be suboptimal. Therefore, we propose Equivariant Contrastive Learning for Sequential Recommendation (ECL-SR), which endows SR models with great discriminative power, making the learned user behavior representations sensitive to invasive augmentations (e.g., item substitution) and insensitive to mild augmentations (e.g., featurelevel dropout masking). In detail, we use the conditional discriminator to capture differences in behavior due to item substitution, which encourages the user behavior encoder to be equivariant to invasive augmentations. Comprehensive experiments on four benchmark datasets show that the proposed ECL-SR framework achieves competitive performance compared to state-of-the-art SR models. The source code is available at https://github.com/Tokkiu/ECL.Comment: Accepted by RecSys 202