3,080 research outputs found
A PVT-TYPE ALGORITHM FOR MINIMIZING A NONSMOOTH CONVEX FUNCTION
A general framework of the (parallel variable transformation) PVT-type algorithm, called the PVT-MYR algorithm, for minimizing a nonsmooth convex function is proposed, via the Moreau-Yosida regularization. As a particular scheme of this framework an Īµ-scheme is also presented. The global convergence of this algorithm is given under the assumptions of strong convexity of the objective function and an Īµ-descent condition determined by an Īµ-forced function. An appendix stating the proximal point algorithm is recalled in the last section
MetaboAnalystR 3.0: Toward an Optimized Workflow for Global Metabolomics.
Liquid chromatography coupled to high-resolution mass spectrometry platforms are increasingly employed to comprehensively measure metabolome changes in systems biology and complex diseases. Over the past decade, several powerful computational pipelines have been developed for spectral processing, annotation, and analysis. However, significant obstacles remain with regard to parameter settings, computational efficiencies, batch effects, and functional interpretations. Here, we introduce MetaboAnalystR 3.0, a significantly improved pipeline with three key new features: (1) efficient parameter optimization for peak picking; (2) automated batch effect correction; and 3) more accurate pathway activity prediction. Our benchmark studies showed that this workflow was 20~100X faster compared to other well-established workflows and produced more biologically meaningful results. In summary, MetaboAnalystR 3.0 offers an efficient pipeline to support high-throughput global metabolomics in the open-source R environment
A superlinear space decomposition algorithm for constrained nonsmooth convex program
AbstractA class of constrained nonsmooth convex optimization problems, that is, piecewise C2 convex objectives with smooth convex inequality constraints are transformed into unconstrained nonsmooth convex programs with the help of exact penalty function. The objective functions of these unconstrained programs are particular cases of functions with primalādual gradient structure which has connection with VU space decomposition. Then a VU space decomposition method for solving this unconstrained program is presented. This method is proved to converge with local superlinear rate under certain assumptions. An illustrative example is given to show how this method works
Temperature Effects and Compensation-Control Methods
In the analysis of the effects of temperature on the performance of microgyroscopes, it is found that the resonant frequency of the microgyroscope decreases linearly as the temperature increases, and the quality factor changes drastically at low temperatures. Moreover, the zero bias changes greatly with temperature variations. To reduce the temperature effects on the microgyroscope, temperature compensation-control methods are proposed. In the first place, a BP (Back Propagation) neural network and polynomial fitting are utilized for building the temperature model of the microgyroscope. Considering the simplicity and real-time requirements, piecewise polynomial fitting is applied in the temperature compensation system. Then, an integral-separated PID (Proportion Integration Differentiation) control algorithm is adopted in the temperature control system, which can stabilize the temperature inside the microgyrocope in pursuing its optimal performance. Experimental results reveal that the combination of microgyroscope temperature compensation and control methods is both realizable and effective in a miniaturized microgyroscope prototype
Pathologically Activated Neuroprotection via Uncompetitive Blockade of \u3cem\u3eN\u3c/em\u3e-Methyl-d-aspartate Receptors with Fast Off-rate by Novel Multifunctional Dimer Bis(propyl)-cognitin
Uncompetitive N-methyl-d-aspartate (NMDA) receptor antagonists with fast off-rate (UFO) may represent promising drug candidates for various neurodegenerative disorders. In this study, we report that bis(propyl)-cognitin, a novel dimeric acetylcholinesterase inhibitor and Ī³-aminobutyric acid subtype A receptor antagonist, is such an antagonist of NMDA receptors. In cultured rat hippocampal neurons, we demonstrated that bis(propyl)-cognitin voltage-dependently, selectively, and moderately inhibited NMDA-activated currents. The inhibitory effects of bis(propyl)-cognitin increased with the rise in NMDA and glycine concentrations. Kinetics analysis showed that the inhibition was of fast onset and offset with an off-rate time constant of 1.9 s. Molecular docking simulations showed moderate hydrophobic interaction between bis(propyl)-cognitin and the MK-801 binding region in the ion channel pore of the NMDA receptor. Bis(propyl)-cognitin was further found to compete with [3H]MK-801 with a Ki value of 0.27 Ī¼m, and the mutation of NR1(N616R) significantly reduced its inhibitory potency. Under glutamate-mediated pathological conditions, bis(propyl)-cognitin, in contrast to bis(heptyl)-cognitin, prevented excitotoxicity with increasing effectiveness against escalating levels of glutamate and much more effectively protected against middle cerebral artery occlusion-induced brain damage than did memantine. More interestingly, under NMDA receptor-mediated physiological conditions, bis(propyl)-cognitin enhanced long-term potentiation in hippocampal slices, whereas MK-801 reduced and memantine did not alter this process. These results suggest that bis(propyl)-cognitin is a UFO antagonist of NMDA receptors with moderate affinity, which may provide a pathologically activated therapy for various neurodegenerative disorders associated with NMDA receptor dysregulation
HumanMAC: Masked Motion Completion for Human Motion Prediction
Human motion prediction is a classical problem in computer vision and
computer graphics, which has a wide range of practical applications. Previous
effects achieve great empirical performance based on an encoding-decoding
style. The methods of this style work by first encoding previous motions to
latent representations and then decoding the latent representations into
predicted motions. However, in practice, they are still unsatisfactory due to
several issues, including complicated loss constraints, cumbersome training
processes, and scarce switch of different categories of motions in prediction.
In this paper, to address the above issues, we jump out of the foregoing style
and propose a novel framework from a new perspective. Specifically, our
framework works in a masked completion fashion. In the training stage, we learn
a motion diffusion model that generates motions from random noise. In the
inference stage, with a denoising procedure, we make motion prediction
conditioning on observed motions to output more continuous and controllable
predictions. The proposed framework enjoys promising algorithmic properties,
which only needs one loss in optimization and is trained in an end-to-end
manner. Additionally, it accomplishes the switch of different categories of
motions effectively, which is significant in realistic tasks, e.g., the
animation task. Comprehensive experiments on benchmarks confirm the superiority
of the proposed framework. The project page is available at
https://lhchen.top/Human-MAC
Kinematic-aware Prompting for Generalizable Articulated Object Manipulation with LLMs
Generalizable articulated object manipulation is essential for home-assistant
robots. Recent efforts focus on imitation learning from demonstrations or
reinforcement learning in simulation, however, due to the prohibitive costs of
real-world data collection and precise object simulation, it still remains
challenging for these works to achieve broad adaptability across diverse
articulated objects. Recently, many works have tried to utilize the strong
in-context learning ability of Large Language Models (LLMs) to achieve
generalizable robotic manipulation, but most of these researches focus on
high-level task planning, sidelining low-level robotic control. In this work,
building on the idea that the kinematic structure of the object determines how
we can manipulate it, we propose a kinematic-aware prompting framework that
prompts LLMs with kinematic knowledge of objects to generate low-level motion
trajectory waypoints, supporting various object manipulation. To effectively
prompt LLMs with the kinematic structure of different objects, we design a
unified kinematic knowledge parser, which represents various articulated
objects as a unified textual description containing kinematic joints and
contact location. Building upon this unified description, a kinematic-aware
planner model is proposed to generate precise 3D manipulation waypoints via a
designed kinematic-aware chain-of-thoughts prompting method. Our evaluation
spanned 48 instances across 16 distinct categories, revealing that our
framework not only outperforms traditional methods on 8 seen categories but
also shows a powerful zero-shot capability for 8 unseen articulated object
categories. Moreover, the real-world experiments on 7 different object
categories prove our framework's adaptability in practical scenarios. Code is
released at
https://github.com/GeWu-Lab/LLM_articulated_object_manipulation/tree/main.Comment: Accepted by ICRA 202
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