Paleoethnobotany of the Late Woodland Mason Phase in the Elk and Duck River Valleys, Tennessee

A substantial sample of paleobotanical residues from two Late Woodland Mason phase components in southeastern Middle Tennessee were examined. The data were analyzed to determine what plants were available to Mason populations, which of those plants were utilized, the local geographic areas exploited, and the impact of man\u27s procurement practices on the environment. Inferences derived from paleobotanical analysis are used to suggest a pattern of Mason plant utilization that can be integrated with other subsystems to provide definitive statements concerning the cultural whole. These statements provide a basis for comparing Mason with other cultural manifestations in the local cultural sequence of the Eastern Highland Rim

On Lightweight Privacy-Preserving Collaborative Learning for IoT Objects

The Internet of Things (IoT) will be a main data generation infrastructure for achieving better system intelligence. This paper considers the design and implementation of a practical privacy-preserving collaborative learning scheme, in which a curious learning coordinator trains a better machine learning model based on the data samples contributed by a number of IoT objects, while the confidentiality of the raw forms of the training data is protected against the coordinator. Existing distributed machine learning and data encryption approaches incur significant computation and communication overhead, rendering them ill-suited for resource-constrained IoT objects. We study an approach that applies independent Gaussian random projection at each IoT object to obfuscate data and trains a deep neural network at the coordinator based on the projected data from the IoT objects. This approach introduces light computation overhead to the IoT objects and moves most workload to the coordinator that can have sufficient computing resources. Although the independent projections performed by the IoT objects address the potential collusion between the curious coordinator and some compromised IoT objects, they significantly increase the complexity of the projected data. In this paper, we leverage the superior learning capability of deep learning in capturing sophisticated patterns to maintain good learning performance. Extensive comparative evaluation shows that this approach outperforms other lightweight approaches that apply additive noisification for differential privacy and/or support vector machines for learning in the applications with light data pattern complexities.Comment: 12 pages,IOTDI 201

Haptic Displayof Realistic Tool Contact via Dynamically Compensated Control of a Dedicated Actuator

High frequency contact accelerations convey important information that the vast majority of haptic interfaces cannot render. Building on prior work, we present an approach to haptic interface design that uses a dedicated linear voice coil actuator and a dynamic system model to allow the user to feel these signals. This approach was tested through use in a bilateral teleoperation experiment where a user explored three textured surfaces under three different acceleration control architectures: none, constant gain, and dynamic compensation. The controllers that use the dedicated actuator vastly outperform traditional position-position control at conveying realistic contact accelerations. Analysis of root mean square error, linear regression, and discrete Fourier transforms of the acceleration data also indicate a slight performance benefit for dynamic compensation over constant gain

Agrin isoforms and their role in synaptogenesis

Agrin is thought to mediate the motor neuron-induced aggregation of synaptic proteins on the surface of muscle fibers at neuromuscular junctions. Recent experiments provide direct evidence in support of this hypothesis, reveal the nature of agrin immunoreactivity at sites other than neuromuscular junctions, and have resulted in findings that are consistent with the possibility that agrin plays a role in synaptogenesis throughout the nervous system

Revisiting the Core Ontology and Problem in Requirements Engineering

In their seminal paper in the ACM Transactions on Software Engineering and Methodology, Zave and Jackson established a core ontology for Requirements Engineering (RE) and used it to formulate the "requirements problem", thereby defining what it means to successfully complete RE. Given that stakeholders of the system-to-be communicate the information needed to perform RE, we show that Zave and Jackson's ontology is incomplete. It does not cover all types of basic concerns that the stakeholders communicate. These include beliefs, desires, intentions, and attitudes. In response, we propose a core ontology that covers these concerns and is grounded in sound conceptual foundations resting on a foundational ontology. The new core ontology for RE leads to a new formulation of the requirements problem that extends Zave and Jackson's formulation. We thereby establish new standards for what minimum information should be represented in RE languages and new criteria for determining whether RE has been successfully completed.Comment: Appears in the proceedings of the 16th IEEE International Requirements Engineering Conference, 2008 (RE'08). Best paper awar

The agrin gene codes for a family of basal lamina proteins that differ in function and distribution

We isolated two cDNAs that encode isoforms of agrin, the basal lamina protein that mediates the motor neuron-induced aggregation of acetylcholine receptors on muscle fibers at the neuromuscular junction. Both proteins are the result of alternative splicing of the product of the agrin gene, but, unlike agrin, they are inactive in standard acetylcholine receptor aggregation assays. They lack one (agrin-related protein 1) or two (agrin-related protein 2) regions in agrin that are required for its activity. Expression studies provide evidence that both proteins are present in the nervous system and muscle and that, in muscle, myofibers and Schwann cells synthesize the agrin-related proteins while the axon terminals of motor neurons are the sole source of agrin

An efficient algorithm for learning with semi-bandit feedback

We consider the problem of online combinatorial optimization under semi-bandit feedback. The goal of the learner is to sequentially select its actions from a combinatorial decision set so as to minimize its cumulative loss. We propose a learning algorithm for this problem based on combining the Follow-the-Perturbed-Leader (FPL) prediction method with a novel loss estimation procedure called Geometric Resampling (GR). Contrary to previous solutions, the resulting algorithm can be efficiently implemented for any decision set where efficient offline combinatorial optimization is possible at all. Assuming that the elements of the decision set can be described with d-dimensional binary vectors with at most m non-zero entries, we show that the expected regret of our algorithm after T rounds is O(m sqrt(dT log d)). As a side result, we also improve the best known regret bounds for FPL in the full information setting to O(m^(3/2) sqrt(T log d)), gaining a factor of sqrt(d/m) over previous bounds for this algorithm.Comment: submitted to ALT 201

Spectral Properties of delta-Plutonium: Sensitivity to 5f Occupancy

By combining the local density approximation (LDA) with dynamical mean field theory (DMFT), we report a systematic analysis of the spectral properties of $\delta$-plutonium with varying $5f$ occupancy. The LDA Hamiltonian is extracted from a tight-binding (TB) fit to full-potential linearized augmented plane-wave (FP-LAPW) calculations. The DMFT equations are solved by the exact quantum Monte Carlo (QMC) method and the Hubbard-I approximation. We have shown for the first time the strong sensitivity of the spectral properties to the $5f$ occupancy, which suggests using this occupancy as a fitting parameter in addition to the Hubbard $U$. By comparing with PES data, we conclude that the ``open shell'' $5f^{5}$ configuration gives the best agreement, resolving the controversy over $5f$ ``open shell'' versus ``close shell'' atomic configurations in $\delta$-Pu.Comment: 6 pages, 2 embedded color figures, to appear in Physical Review

Dimensional Reduction of High-Frequencey Accelerations for Haptic Rendering

Haptics research has seen several recent efforts at understanding and recreating real vibrations to improve the quality of haptic feedback in both virtual environments and teleoperation. To simplify the modeling process and enable the use of single-axis actuators, these previous efforts have used just one axis of a three-dimensional vibration signal, even though the main vibration mechanoreceptors in the hand are know to detect vibrations in all directions. Furthermore, the fact that these mechanoreceptors are largely insensitive to the direction of high-frequency vibrations points to the existence of a transformation that can reduce three-dimensional high-frequency vibration signals to a one-dimensional signal without appreciable perceptual degradation. After formalizing the requirements for this transformation, this paper describes and compares several candidate methods of varying degrees of sophistication, culminating in a novel frequency-domain solution that performs very well on our chosen metrics