183 research outputs found
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
DroidBot-GPT: GPT-powered UI Automation for Android
This paper introduces DroidBot-GPT, a tool that utilizes GPT-like large
language models (LLMs) to automate the interactions with Android mobile
applications. Given a natural language description of a desired task,
DroidBot-GPT can automatically generate and execute actions that navigate the
app to complete the task. It works by translating the app GUI state information
and the available actions on the smartphone screen to natural language prompts
and asking the LLM to make a choice of actions. Since the LLM is typically
trained on a large amount of data including the how-to manuals of diverse
software applications, it has the ability to make reasonable choices of actions
based on the provided information. We evaluate DroidBot-GPT with a self-created
dataset that contains 33 tasks collected from 17 Android applications spanning
10 categories. It can successfully complete 39.39% of the tasks, and the
average partial completion progress is about 66.76%. Given the fact that our
method is fully unsupervised (no modification required from both the app and
the LLM), we believe there is great potential to enhance automation performance
with better app development paradigms and/or custom model training.Comment: 8 pages, 5 figure
scInterpreter: Training Large Language Models to Interpret scRNA-seq Data for Cell Type Annotation
Despite the inherent limitations of existing Large Language Models in
directly reading and interpreting single-cell omics data, they demonstrate
significant potential and flexibility as the Foundation Model. This research
focuses on how to train and adapt the Large Language Model with the capability
to interpret and distinguish cell types in single-cell RNA sequencing data. Our
preliminary research results indicate that these foundational models excel in
accurately categorizing known cell types, demonstrating the potential of the
Large Language Models as effective tools for uncovering new biological
insights.Comment: 4 pages, submitted to FC
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
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