Studies in hydrocarboxylation of styrene and derivatives using palladium complex catalysts

Carbonylation of aryl olefins and alcohols using homogeneous Pd catalysts has gained considerable interest due to their important applications in the synthesis the non-steroidal anti-inflammatory drugs consisting of 2-arylpropionic acids (e.g. Ibuprofen®, Naproxen®). In this work, different homogeneous palladium catalysts were compared for their performances in the hydrocarboxylation of styrene to identify the best performing catalyst system using Pd(pyca)(PPh3)(OTs) as a precursor, which shows above 99% regio-selectivity to 2-phenylpropionic acid as well as high activity. Therefore, this work mainly investigated the kinetics of hydrocarboxylation of styrene using Pd(pyca)(PPh3)(OTs)/PPh3/TsOH/LiCl catalyst system. Particularly, parametric study was carried out to understand the effects of different reaction parameters on the rate of hydrocarboxylation in a batch reactor as well as the concentration-time profiles. For interpretation of the reaction kinetics, a molecular level description of the reaction mechanism (catalytic cycle) was proposed to explain the unique observation of induction period at lower pressures of CO. The experimental concentration-time data for styrene, water and acid products were used to simulate the intrinsic rate parameters using an optimization program. The proposed reaction mechanism based on a Pd-hydride complex as an intermediate active species well explains the experimental data at different temperatures. The approach of micro-kinetic modeling does not require assumption of a rate determining step and provides good description of the complex trends with respect to reaction and catalyst parameters over a wide range of conditions. The approach is also useful to discriminate different reaction mechanisms and obtain intrinsic kinetic parameters for design and scale-up of reactors

Approximations and Bounds for (n, k) Fork-Join Queues: A Linear Transformation Approach

Compared to basic fork-join queues, a job in (n, k) fork-join queues only needs its k out of all n sub-tasks to be finished. Since (n, k) fork-join queues are prevalent in popular distributed systems, erasure coding based cloud storages, and modern network protocols like multipath routing, estimating the sojourn time of such queues is thus critical for the performance measurement and resource plan of computer clusters. However, the estimating keeps to be a well-known open challenge for years, and only rough bounds for a limited range of load factors have been given. In this paper, we developed a closed-form linear transformation technique for jointly-identical random variables: An order statistic can be represented by a linear combination of maxima. This brand-new technique is then used to transform the sojourn time of non-purging (n, k) fork-join queues into a linear combination of the sojourn times of basic (k, k), (k+1, k+1), ..., (n, n) fork-join queues. Consequently, existing approximations for basic fork-join queues can be bridged to the approximations for non-purging (n, k) fork-join queues. The uncovered approximations are then used to improve the upper bounds for purging (n, k) fork-join queues. Simulation experiments show that this linear transformation approach is practiced well for moderate n and relatively large k.Comment: 10 page

Zero-Shot Certified Defense against Adversarial Patches with Vision Transformers

Adversarial patch attack aims to fool a machine learning model by arbitrarily modifying pixels within a restricted region of an input image. Such attacks are a major threat to models deployed in the physical world, as they can be easily realized by presenting a customized object in the camera view. Defending against such attacks is challenging due to the arbitrariness of patches, and existing provable defenses suffer from poor certified accuracy. In this paper, we propose PatchVeto, a zero-shot certified defense against adversarial patches based on Vision Transformer (ViT) models. Rather than training a robust model to resist adversarial patches which may inevitably sacrifice accuracy, PatchVeto reuses a pretrained ViT model without any additional training, which can achieve high accuracy on clean inputs while detecting adversarial patched inputs by simply manipulating the attention map of ViT. Specifically, each input is tested by voting over multiple inferences with different attention masks, where at least one inference is guaranteed to exclude the adversarial patch. The prediction is certifiably robust if all masked inferences reach consensus, which ensures that any adversarial patch would be detected with no false negative. Extensive experiments have shown that PatchVeto is able to achieve high certified accuracy (e.g. 67.1% on ImageNet for 2%-pixel adversarial patches), significantly outperforming state-of-the-art methods. The clean accuracy is the same as vanilla ViT models (81.8% on ImageNet) since the model parameters are directly reused. Meanwhile, our method can flexibly handle different adversarial patch sizes by simply changing the masking strategy.Comment: 12 pages, 5 figure

Neural Network Control for the Probe Landing Based on Proportional Integral Observer

For the probe descending and landing safely, a neural network control method based on proportional integral observer (PIO) is proposed. First, the dynamics equation of the probe under the landing site coordinate system is deduced and the nominal trajectory meeting the constraints in advance on three axes is preplanned. Then the PIO designed by using LMI technique is employed in the control law to compensate the effect of the disturbance. At last, the neural network control algorithm is used to guarantee the double zero control of the probe and ensure the probe can land safely. An illustrative design example is employed to demonstrate the effectiveness of the proposed control approach

Multisensor Fault Identification Scheme Based on Decentralized Sliding Mode Observers Applied to Reconfigurable Manipulators

This paper concerns with a fault identification scheme in a class of nonlinear interconnected systems. The decentralized sliding mode observer is recruited for the investigation of position sensor fault or velocity sensor fault. First, a decentralized neural network controller is proposed for the system under fault-free state. The diffeomorphism theory is utilized to construct a nonlinear transformation for subsystem structure. A simple filter is implemented to convert the sensor fault into pseudo-actuator fault scenario. The decentralized sliding mode observer is then presented for multisensor fault identification of reconfigurable manipulators based on Lyapunov stable theory. Finally, two 2-DOF reconfigurable manipulators with different configurations are employed to verify the effectiveness of the proposed scheme in numerical simulation. The results demonstrate that one joint’s fault does not affect other joints and the sensor fault can be identified precisely by the proposed decentralized sliding mode observer

Serving MoE Models on Resource-constrained Edge Devices via Dynamic Expert Swapping

Mixture of experts (MoE) is a popular technique in deep learning that improves model capacity with conditionally-activated parallel neural network modules (experts). However, serving MoE models in resource-constrained latency-critical edge scenarios is challenging due to the significantly increased model size and complexity. In this paper, we first analyze the behavior pattern of MoE models in continuous inference scenarios, which leads to three key observations about the expert activations, including temporal locality, exchangeability, and skippable computation. Based on these observations, we introduce PC-MoE, an inference framework for resource-constrained continuous MoE model serving. The core of PC-MoE is a new data structure, Parameter Committee, that intelligently maintains a subset of important experts in use to reduce resource consumption. The optimal configuration of Parameter Committee is found offline by a profiling-guided committee planner, and expert swapping and request handling at runtime are managed by an adaptive committee scheduler. To evaluate the effectiveness of PC-MoE, we conduct experiments using state-of-the-art MoE models on common computer vision and natural language processing tasks. The results demonstrate optimal trade-offs between resource consumption and model accuracy achieved by PC-MoE. For instance, on object detection tasks with the Swin-MoE model, our approach can reduce memory usage and latency by 42.34% and 18.63% with only 0.10% accuracy degradation