An Analysis of Fixed-Priority Schedulability on a Multiprocessor

A new feasibility test for preemptive scheduling of periodic or sporadic real-time tasks on a single-queue m-server system allows for arbitrary fixed task priorities and arbitrary deadlines. For the special case when deadline equals period and priorities are rate monotonic, any set of tasks with maximum individual task utilization u_max and minimum individual task utilization u_min is feasible if the total utilization does not exceed . Ted Baker received the Ph.D. in Computer Science from Cornell University in 1973. He is a Professor in the Department of Computer Science at the Florida State University, which he chaired from 1998 to 2005. After spending several years doing research in computational complexity theory, he moved on to more practical aspects of computing and has worked in the area of both Ada compilation and real-time systems for the last two decades. A group he organized at FSU in 1979 produced one of the first validated Ada cross-compilers for embedded systems. Since then, he has done research, development, and consulting related to real-time embedded computing, from basic research on scheduling and concurrency control through development of kernels and run-time system support for real-time programming languages. He has also been active in IEEE (POSIX) and ISO standards work related to real-time systems. Dr. Baker was a member of the SEI Rate Monotonic Analysis group, served as real-time area expert for the Ada 9X language mapping and revision team. He directed the FSU teams that developed several software products, including the FSU POSIX threads library, the Florist implementation of IEEE Std 1003.5b-c (the POSIX/Ada API), a set of validation tests for the 1003.5b standards, and the multitasking run-time system for the Gnu Ada (GNAT) compiler. He directed the porting of the latter to several environments, including the Java Virtual Machine and RT Linux. His current research interests are real-time multiprocessor scheduling and real-time device driver architecture.     

Deep Fluids: A Generative Network for Parameterized Fluid Simulations

This paper presents a novel generative model to synthesize fluid simulations from a set of reduced parameters. A convolutional neural network is trained on a collection of discrete, parameterizable fluid simulation velocity fields. Due to the capability of deep learning architectures to learn representative features of the data, our generative model is able to accurately approximate the training data set, while providing plausible interpolated in‐betweens. The proposed generative model is optimized for fluids by a novel loss function that guarantees divergence‐free velocity fields at all times. In addition, we demonstrate that we can handle complex parameterizations in reduced spaces, and advance simulations in time by integrating in the latent space with a second network. Our method models a wide variety of fluid behaviors, thus enabling applications such as fast construction of simulations, interpolation of fluids with different parameters, time re‐sampling, latent space simulations, and compression of fluid simulation data. Reconstructed velocity fields are generated up to 700× faster than re‐simulating the data with the underlying CPU solver, while achieving compression rates of up to 1300×.     

Inhibition of CCL2 Signaling in Combination with Docetaxel Treatment Has Profound Inhibitory Effects on Prostate Cancer Growth in Bone

The C-C chemokine ligand 2 (CCL2) stimulates migration, proliferation, and invasion of prostate cancer (PCa) cells, and its signaling also plays a role in the activation of osteoclasts. Therefore targeting CCL2 signaling in regulation of tumor progression in bone metastases is an area of intense research. The objective of our study was to investigate the efficacy of CCL2 blockade by neutralizing antibodies to inhibit the growth of PCa in bone. We used a preclinical model of cancer growth in the bone in which PCa C4-2B cells were injected directly into murine tibiae. Animals were treated for ten weeks with neutralizing anti-CCL2 antibodies, docetaxel, or a combination of both, and then followed an additional nine weeks. CCL2 blockade inhibited the growth of PCa in bone, with even more pronounced inhibition in combination with docetaxel. CCL2 blockade also resulted in increases in bone mineral density. Furthermore, our results showed that the tumor inhibition lasted even after discontinuation of the treatment. Our data provide compelling evidence that CCL2 blockade slows PCa growth in bone, both alone and in combination with docetaxel. These results support the continued investigations of CCL2 blockade as a treatment for advanced metastatic PCa.     

Automated design process modelling and analysis using immersive virtual reality

The capture of engineering design processes and associated knowledge has traditionally been extremely difficult due to the high overhead associated with current intrusive and time-consuming manual methods used in industry, usually involving interruption of the designer during the design task and relying on them to remember how a design solution was developed after the event. This paper presents novel research which demonstrates how the detailed logging and analysis of an individual designer’s actions in a cable harness virtual reality (VR) design and manufacturing system permits automated design task analysis with process mapping. Based on prior research, which utilised user-logging to automatically analyse design activities and generate assembly plans, this work involves the automatic capture of extracted design knowledge embedded within the log files and subsequently represented using IDEF0 diagrams, DRed graphs, PSL, XML, annotated movie clips and storyboard representations. Using this design knowledge, an online help system has been demonstrated which helps users to carry out design tasks similar to those performed previously by expert users. This is triggered by monitoring the designer’s actions and functions in real time and pushes knowledge and advice to the user which was captured from experts and subsequently formalised during earlier design sessions.     

Modeling and manipulating the structure of hierarchical schemas for the web

The Semantic Web is the next step of the current Web where information will become more machine-understandable to support effective data discovery and integration. Hierarchical schemas, either in the form of tree-like structures (e.g., DTDs, XML schemas), or in the form of hierarchies on a category/subcategory basis (e.g., thematic hierarchies of portal catalogs), play an important role in this task. They are used to enrich semantically the available information. Up to now, hierarchical schemas have been treated rather as sets of individual elements, acting as semantic guides for browsing or querying data. Under that view, queries like “find the part of a portal catalog which is not present in another catalog” can be answered only in a procedural way, specifying which nodes to select and how to get them. For this reason, we argue that hierarchical schemas should be treated as full-fledged objects so as to allow for their manipulation. This work proposes models and operators to manipulate the structural information of hierarchies, considering them as first-class citizens. First, we explore the algebraic properties of trees representing hierarchies, and define a lattice algebraic structure on them. Then, turning this structure into a boolean algebra, we present the operators S-union, S-intersection and S-difference to support structural manipulation of hierarchies. These operators have certain algebraic properties to provide clear semantics and assist the transformation, simplification and optimization of sequences of operations using laws similar to those of set theory. Also, we identify the conditions under which this framework is applicable. Finally, we demonstrate an application of our framework for manipulating hierarchical schemas on tree-like hierarchies encoded as RDF/s files.     

Real-time prediction of order flowtimes using support vector regression

In a make-to-order production system, a due date must be assigned to new orders that arrive dynamically, which requires predicting the order flowtime in real-time. This study develops a support vector regression model for real-time flowtime prediction in multi-resource, multi-product systems. Several combinations of kernel and loss functions are examined, and results indicate that the linear kernel and the ε$\epsilon$-insensitive loss function yield the best generalization performance. The prediction error of the support vector regression model for three different multi-resource systems of varying complexity is compared to that of classic time series models (exponential smoothing and moving average) and to a feedforward artificial neural network. Results show that the support vector regression model has lower flowtime prediction error and is more robust. More accurately predicting flowtime using support vector regression will improve due-date performance and reduce expenses in make-to-order production environments.     

Graphlet decomposition: framework,algorithms, and applications

From social science to biology, numerous applications often rely on graphlets for intuitive and meaningful characterization of networks. While graphlets have witnessed a tremendous success and impact in a variety of domains, there has yet to be a fast and efficient framework for computing the frequencies of these subgraph patterns. However, existing methods are not scalable to large networks with billions of nodes and edges. In this paper, we propose a fast, efficient, and parallel framework as well as a family of algorithms for counting k-node graphlets. The proposed framework leverages a number of theoretical combinatorial arguments that allow us to obtain significant improvement on the scalability of graphlet counting. For each edge, we count a few graphlets and obtain the exact counts of others in constant time using the combinatorial arguments. On a large collection of \(300+\) networks from a variety of domains, our graphlet counting strategies are on average \(460{\times }\) faster than existing methods. This brings new opportunities to investigate the use of graphlets on much larger networks and newer applications as we show in the experiments. To the best of our knowledge, this paper provides the largest graphlet computations to date.     

The study of atmospheric ice-nucleating particles via microfluidically generated droplets

Ice-nucleating particles (INPs) play a significant role in the climate and hydrological cycle by triggering ice formation in supercooled clouds, thereby causing precipitation and affecting cloud lifetimes and their radiative properties. However, despite their importance, INP often comprise only 1 in 10³–10⁶ ambient particles, making it difficult to ascertain and predict their type, source, and concentration. The typical techniques for quantifying INP concentrations tend to be highly labour-intensive, suffer from poor time resolution, or are limited in sensitivity to low concentrations. Here, we present the application of microfluidic devices to the study of atmospheric INPs via the simple and rapid production of monodisperse droplets and their subsequent freezing on a cold stage. This device offers the potential for the testing of INP concentrations in aqueous samples with high sensitivity and high counting statistics. Various INPs were tested for validation of the platform, including mineral dust and biological species, with results compared to literature values. We also describe a methodology for sampling atmospheric aerosol in a manner that minimises sampling biases and which is compatible with the microfluidic device. We present results for INP concentrations in air sampled during two field campaigns: (1) from a rural location in the UK and (2) during the UK’s annual Bonfire Night festival. These initial results will provide a route for deployment of the microfluidic platform for the study and quantification of INPs in upcoming field campaigns around the globe, while providing a benchmark for future lab-on-a-chip-based INP studies.