Computing Dynamic Output Feedback Laws

The pole placement problem asks to find laws to feed the output of a plant governed by a linear system of differential equations back to the input of the plant so that the resulting closed-loop system has a desired set of eigenvalues. Converting this problem into a question of enumerative geometry, efficient numerical homotopy algorithms to solve this problem for general Multi-Input-Multi-Output (MIMO) systems have been proposed recently. While dynamic feedback laws offer a wider range of use, the realization of the output of the numerical homotopies as a machine to control the plant in the time domain has not been addressed before. In this paper we present symbolic-numeric algorithms to turn the solution to the question of enumerative geometry into a useful control feedback machine. We report on numerical experiments with our publicly available software and illustrate its application on various control problems from the literature.Comment: 20 pages, 3 figures; the software described in this paper is publicly available via http://www.math.uic.edu/~jan/download.htm

Conjugated Polymer and oligomer based nanosensors

Ph.DDOCTOR OF PHILOSOPH

The Future of Generic Biologics: Should the United States “Follow-On” the European Pathway?

The United States is embarking on a biotechnology drug revolution. In the last few decades, biotech drugs have saved millions of lives, and the market for these miracle cures continues to grow at an astronomical rate. Unfortunately, as the market for biotech drugs is skyrocketing, drug prices are following suit. As Congress strives to make these new drugs more affordable, it must not ignore significant safety concerns unique to these revolutionary therapies. Congress should follow the lead of the European Union to create an accessible pathway for generic forms of biotech drugs that includes strict regulatory measures to ensure drug safety and efficacy

USD: Unknown Sensitive Detector Empowered by Decoupled Objectness and Segment Anything Model

Open World Object Detection (OWOD) is a novel and challenging computer vision task that enables object detection with the ability to detect unknown objects. Existing methods typically estimate the object likelihood with an additional objectness branch, but ignore the conflict in learning objectness and classification boundaries, which oppose each other on the semantic manifold and training objective. To address this issue, we propose a simple yet effective learning strategy, namely Decoupled Objectness Learning (DOL), which divides the learning of these two boundaries into suitable decoder layers. Moreover, detecting unknown objects comprehensively requires a large amount of annotations, but labeling all unknown objects is both difficult and expensive. Therefore, we propose to take advantage of the recent Large Vision Model (LVM), specifically the Segment Anything Model (SAM), to enhance the detection of unknown objects. Nevertheless, the output results of SAM contain noise, including backgrounds and fragments, so we introduce an Auxiliary Supervision Framework (ASF) that uses a pseudo-labeling and a soft-weighting strategies to alleviate the negative impact of noise. Extensive experiments on popular benchmarks, including Pascal VOC and MS COCO, demonstrate the effectiveness of our approach. Our proposed Unknown Sensitive Detector (USD) outperforms the recent state-of-the-art methods in terms of Unknown Recall, achieving significant improvements of 14.3\%, 15.5\%, and 8.9\% on the M-OWODB, and 27.1\%, 29.1\%, and 25.1\% on the S-OWODB

Competitive Consequences of Using a Category Captain

Many retailers designate one national brand manufacturer in each product category as a “category captain” to help manage the entire category. A category captain may perform demand-enhancing services such as better shelf arrangements, shelf-space management, and design and management of in-store displays. In this paper, we examine when and why a retailer may engage one manufacturer exclusively as a category captain to provide such service and the implications. We find that demand substitutability of competing brands gives rise to a service efficiency effect—service that expands the category is more effective in increasing a manufacturer\u27s sales and margin than service that shifts demand from a rival\u27s brand. We show that the service efficiency effect may motivate a category captain to provide a service that benefits all brands in the category even though doing so is more costly. We further show that, in categories that are less price competitive, there is higher competition between manufacturers to become the category captain. Consequently, a retailer may obtain better service by using a category captain than by engaging both manufacturers simultaneously. Our findings may help explain why a retailer may rely on a category captain despite concerns regarding opportunism and why there is limited empirical evidence of harm to rival manufacturers

Competitive Consequences of Using a Category Captain

Many retailers designate one national brand manufacturer in each product category as a “category captain” to help manage the entire category. A category captain may perform demand-enhancing services such as better shelf arrangements, shelf-space management, and design and management of in-store displays. In this paper, we examine when and why a retailer may engage one manufacturer exclusively as a category captain to provide such service and the implications. We find that demand substitutability of competing brands gives rise to a service efficiency effect—service that expands the category is more effective in increasing a manufacturer\u27s sales and margin than service that shifts demand from a rival\u27s brand. We show that the service efficiency effect may motivate a category captain to provide a service that benefits all brands in the category even though doing so is more costly. We further show that, in categories that are less price competitive, there is higher competition between manufacturers to become the category captain. Consequently, a retailer may obtain better service by using a category captain than by engaging both manufacturers simultaneously. Our findings may help explain why a retailer may rely on a category captain despite concerns regarding opportunism and why there is limited empirical evidence of harm to rival manufacturers

Ambient conditions disordered-ordered phase transition of two-dimensional interfacial water molecules dependent on charge dipole moment

Phase transitions of water molecules are commonly expected to occur only under extreme conditions, such as nanoconfinement, high pressure, or low temperature. We herein report the disordered-ordered phase transition of two-dimensional interfacial water molecules under ambient conditions using molecular-dynamics simulations. This phase transition is greatly dependent on the charge dipole moment, production of both charge values, and the dipole length of the solid surface. The phase transition can be identified by a sharp change in water-water interaction energies and the order parameters of the two-dimensional interfacial water monolayer, under a tiny dipole moment change near the critical dipole moment. The critical dipole moment of the solid material surface can classify a series of materials that can induce distinct ordered phases of surface water, which may also result in surface wetting, friction, and other properties

Self-assembled micellar structures of Lipopeptides with variable number of attached lipid chains revealed by atomistic molecular dynamics simulations

We present atomistic molecular dynamics simulation study of the self-assembly behavior of toll-like agonist lipopeptides (PamnCSK4) in aqueous solutions. The variable number of hexadecyl lipid chains (n = 1, 2, 3) per molecule has been experimentally suggested to have remarkable influence on their self-assembled nanostructures. Starting from pre-assembled spherical or bilayer configurations, the aggregates of lipopeptides, PamCSK4 and Pam2CSK4, which contain peptide sequences CSK4 linked to either mono- or di-lipid chains (Pam), evolve into spherical-like micelles within 30 ns, whereas the self-assembled structure of tri-lipidated lipopeptides, Pam3CSK4, relaxes much slower and reaches an equilibrium state of flattened wormlike micelle with a bilayer packing structure. The geometric shapes and sizes, namely the gyration radii of spherical micelles and thickness of the flattened wormlike micelle, are found to be in good agreement with experimental measurements, which effectively validates the simulation models and employed force fields. Detailed analyses of molecular packing reveal that these self-assembled nanostructures all consist of a hydrophobic core constructed by lipid chains, a transitional layer and a hydrophilic interfacial layer composed of peptide sequences. The average area per peptide head at the interfaces is found to be nearly constant for all micellar structures studied. The packing parameter of the lipopeptide molecules thus increases with the increase of the number of linked lipid chains, giving rise to the distinct micellar shape transition from spherical-like to flattened wormlike geometry with bilayer stacking, which is qualitatively different from the shape transitions of surfactant micelles induced by variation of concentration or salt type. To facilitate the close-packing of the lipid chains in the hydrophobic core, the lipopeptide molecules typically take the bent conformation with average tilt angles between the peptide sequences and the lipid chains ranging from 110° to 140°. This consequently affects the orientation angles of the lipid chains with respect to the radial or normal direction of the spherical-like or flattened wormlike micelles. In addition, the secondary structures of the peptides may also be altered by the number of lipid chains they are linked to and the resultant micellar structures. Our simulation results on the microscopic structural features of the lipopeptide nanostructures may provide potential insights into their bioactivities and contribute to the design of bioactive medicines or drug carriers. The force fields built for these lipopeptides and the geometric packing discussions could also be adopted for simulating and understanding the self-assembly behavior of other bioactive amiphiphiles with similar chemical compositions

An ensemble of VisNet, Transformer-M, and pretraining models for molecular property prediction in OGB Large-Scale Challenge @ NeurIPS 2022

In the technical report, we provide our solution for OGB-LSC 2022 Graph Regression Task. The target of this task is to predict the quantum chemical property, HOMO-LUMO gap for a given molecule on PCQM4Mv2 dataset. In the competition, we designed two kinds of models: Transformer-M-ViSNet which is an geometry-enhanced graph neural network for fully connected molecular graphs and Pretrained-3D-ViSNet which is a pretrained ViSNet by distilling geomeotric information from optimized structures. With an ensemble of 22 models, ViSNet Team achieved the MAE of 0.0723 eV on the test-challenge set, dramatically reducing the error by 39.75% compared with the best method in the last year competition