Semantically-aware data discovery and placement in collaborative computing environments

As the size of scientific datasets and the demand for interdisciplinary collaboration grow in modern science, it becomes imperative that better ways of discovering and placing datasets generated across multiple disciplines be developed to facilitate interdisciplinary scientific research. For discovering relevant data out of large-scale interdisciplinary datasets. The development and integration of cross-domain metadata is critical as metadata serves as the key guideline for organizing data. To develop and integrate cross-domain metadata management systems in interdisciplinary collaborative computing environment, three key issues need to be addressed: the development of a cross-domain metadata schema; the implementation of a metadata management system based on this schema; the integration of the metadata system into existing distributed computing infrastructure. Current research in metadata management in distributed computing environment largely focuses on relatively simple schema that lacks the underlying descriptive power to adequately address semantic heterogeneity often found in interdisciplinary science. And current work does not take adequate consideration the issue of scalability in large-scale data management. Another key issue in data management is data placement, due to the increasing size of scientific datasets, the overhead incurred as a result of transferring data among different nodes also grow into a significant inhibiting factor affecting overall performance. Currently, few data placement strategies take into consideration semantic information concerning data content. In this dissertation, we propose a cross-domain metadata system in a collaborative distributed computing environment and identify and evaluate key factors and processes involved in a successful cross-domain metadata system with the goal of facilitating data discovery in collaborative environments. This will allow researchers/users to conduct interdisciplinary science in the context of large-scale datasets that will make it easier to access interdisciplinary datasets, reduce barrier to collaboration, reduce cost of future development of similar systems. We also investigate data placement strategies that involve semantic information about the hardware and network environment as well as domain information in the form of semantic metadata so that semantic locality could be utilized in data placement, that could potentially reduce overhead for accessing large-scale interdisciplinary datasets

Test platform design and control of a bicycle-type two-wheeled autonomous vehicle

Bicycle dynamics and behaviors have been vastly studied through modeling and simulation. Due to the complexity, software models are often assumed subjecting to di erent nonholonomic constraints in order to simplify the models and control algorithms. A real life autonomous bicycle faces perturbances from the road, wind, tire deformation, slipping among other external forces. Limitations of simulations will not always allow these to apply. All these issues make the autonomous bicycle research very challenging. To study the bicycle control problems a few research results from the literature are reviewed. A nonlinear bicycle model was used to conduct control simulations. Model based nonlinear controllers were applied to simulate the balance and path tracking control. A PID controller is more practical to replace the non-linear controller for the balance control. Simulation results of the di erent controllers are compared in order to decide the proper control strategies on the hardware platform. The controller design of the platform complies with practicality based on the hardware con guration. Two control schemes are implemented on the test platform; both are developed with PID algorithms. The rst scheme is a single PID control loop in which the controller takes the roll angle feedback and balances the running platform by means of steering. If the desired roll angle is zero the controller will try to hold the platform at the upright position. If the desired roll angle is non-zero the platform will be balanced at an equilibrium roll angle. A xed roll angle will lead to a xed steering angle as the result of balance control. The second scheme is directional control with balance consisting of two cascaded PID loops. Steering is the only means to control balance and direction. To do so the desired roll angle must be controlled to achieve the desired steering angle. The platform tilts to the desired side and steering follows to the same side of the tilt; the platform can then be lifted up by the centrifugal force and eventually balanced at an equilibrium roll angle. The direction can be controlled using a controlled roll angle. Many implementation issues have to be dealt with in order for the control algorithm to be functional. Dynamic roll angle measurement is implemented with complementary internal sensors (accelerometer and gyroscope). Directional information is obtained through a yaw rate gyroscope which operates on the principle of resonance. To monitor the speed of the platform, a rotational sensor was formed by using a hard drive stepper motor attached to the axis of the vehicle's driving motor. The optoelectronic circuit plays the vital role to ensure the system functionality by isolating the electromagnetic noise from the motors. Finally, in order to collect runtime data, the wireless communication is implemented through Bluetooth/RS232 serial interface. The data is then plotted and analyzed with Matlab. Controller gains are tuned through numerous road tests. Field test results show that the research has successfully achieved the goal of testing the low level control of autonomous bicycle. The developed algorithms are able to balance the platform on semi-smooth surfaces

Learning Compact Recurrent Neural Networks with Block-Term Tensor Decomposition

Recurrent Neural Networks (RNNs) are powerful sequence modeling tools. However, when dealing with high dimensional inputs, the training of RNNs becomes computational expensive due to the large number of model parameters. This hinders RNNs from solving many important computer vision tasks, such as Action Recognition in Videos and Image Captioning. To overcome this problem, we propose a compact and flexible structure, namely Block-Term tensor decomposition, which greatly reduces the parameters of RNNs and improves their training efficiency. Compared with alternative low-rank approximations, such as tensor-train RNN (TT-RNN), our method, Block-Term RNN (BT-RNN), is not only more concise (when using the same rank), but also able to attain a better approximation to the original RNNs with much fewer parameters. On three challenging tasks, including Action Recognition in Videos, Image Captioning and Image Generation, BT-RNN outperforms TT-RNN and the standard RNN in terms of both prediction accuracy and convergence rate. Specifically, BT-LSTM utilizes 17,388 times fewer parameters than the standard LSTM to achieve an accuracy improvement over 15.6\% in the Action Recognition task on the UCF11 dataset.Comment: CVPR201

On Gap-dependent Bounds for Offline Reinforcement Learning

This paper presents a systematic study on gap-dependent sample complexity in offline reinforcement learning. Prior work showed when the density ratio between an optimal policy and the behavior policy is upper bounded (the optimal policy coverage assumption), then the agent can achieve an $O\left(\frac{1}{\epsilon^2}\right)$ rate, which is also minimax optimal. We show under the optimal policy coverage assumption, the rate can be improved to $O\left(\frac{1}{\epsilon}\right)$ when there is a positive sub-optimality gap in the optimal $Q$-function. Furthermore, we show when the visitation probabilities of the behavior policy are uniformly lower bounded for states where an optimal policy's visitation probabilities are positive (the uniform optimal policy coverage assumption), the sample complexity of identifying an optimal policy is independent of $\frac{1}{\epsilon}$. Lastly, we present nearly-matching lower bounds to complement our gap-dependent upper bounds.Comment: 33 pages, 1 figure, submitted to NeurIPS 202

In Vivo Evaluation of the Nitroimidazole-Based Thioflavin-T Derivatives as Cerebral Ischemia Markers

Timely imaging and accurate interpretation of cerebral ischemia are required to identify patients who might benefit from more aggressive therapy, and nuclear medicine offers a noninvasive method for demonstrating cerebral ischemia. Three nitroimidazole-based thioflavin-T derivatives, N-[4-(benzothiazol-2-yl)phenyl]-3-(4-nitroimidazole-1-yl) propanamide (4NPBTA), N-[4-(benzothiazol-2-yl)phenyl]-3-(4-nitroimidazole-1-yl)-N-methylpropanamide (4NPBTA-1), and N-[4-(benzothiazol-2-yl)phenyl]-3-(2-nitroimidazole-1-yl) propanamide (2NPBTA), were radioiodinated and evaluated as possible cerebral ischemia markers. In normal mice, these compounds showed good permeation of the intact blood-brain barrier (BBB), high initial brain uptake, and rapid washout. In gerbil stroke models that had been subjected to right common carotid artery ligation to produce cerebral ischemia, [131I]2NPBTA, uptake in the right cerebral hemisphere decreased more slowly than that of the left, and the right/left hemisphere uptake ratios increased with time. Also, the right/left hemisphere uptake ratios correlated positively with the severity of the stroke. The results showed that [131I]2NPBTA had a specific location in the cerebral ischemic tissue. This represented a first step in finding new drugs and might provide a possible cerebral ischemic marker

Enhancing Drug Delivery Precision: Development and Optimization of Nanoparticle-Based Formulations for Targeted Therapy in Preclinical Models

In recent years, the utilization of nanoparticles has proliferated across a wide spectrum of clinical domains. Nanoparticles have been engineered to surmount the constraints associated with free therapeutics and negotiate biological barriers—systemic, microenvironmental, and cellular—that exhibit heterogeneity across diverse patient cohorts and diseases. Mitigating this patient heterogeneity has also been facilitated through precision therapeutics, where tailored interventions have augmented therapeutic effectiveness. Nonetheless, current nanoparticle development predominantly emphasizes the refinement of delivery platforms with a uniform approach. As lipid-based, polymeric, and inorganic nanoparticles undergo increasingly nuanced engineering, there arises the potential for tailoring them to drug delivery in a more personalized manner, ushering in the era of precision medicine. In this Review, we deliberate on sophisticated nanoparticle designs employed in both generalized and precision applications, offering insights into their potential for enhancing precision therapies. We concentrate on advancements in nanoparticle design that surmount heterogeneous barriers to delivery, positing that intelligent nanoparticle design can enhance efficacy in broad delivery applications while facilitating customized designs for precision applications, thereby ultimately enhancing overall patient outcomes

Scanning tunneling microscopy and spectroscopy of nanoscale twisted bilayer graphene

Nanoscale twisted bilayer graphene (TBG) is quite instable and will change its structure to Bernal (or AB-stacking) bilayer with a much lower energy. Therefore, the lack of nanoscale TBG makes its electronic properties not accessible in experiment up to now. In this work, a special confined TBG is obtained in the overlaid area of two continuous misoriented graphene sheets. The width of the confined region of the TBG changes gradually from about 22 nm to 0 nm. By using scanning tunnelling microscopy, we studied carefully the structure and the electronic properties of the nanoscale TBG. Our results indicate that the low-energy electronic properties, including twist-induced van Hove singularities (VHSs) and spatial modulation of local density-of-state, are strongly affected by the translational symmetry breaking of the nanoscale TBG. Whereas, the electronic properties above the energy of the VHSs are almost not influenced by the quantum confinement even when the width of the TBG is reduced to only a single moire spot.Comment: 4 Figure