Fuzzy Learning of Talbot Effect Guides Optimal Mask Design for Proximity Field Nanopatterning Lithography

Processing methods used in photonics and nanotechnology possess many limitations restricting their application areas such as high cost, inability to produce fine details, problems with scalability, and long processing time. Proximity field nanopatterning is a lithography method which surpasses these limitations. By using interference patterns produced by a two-dimensional phase mask, the technique is able to generate a submicron detailed exposure on a millimeter-size slab of light sensitive photopolymer, which is then developed like a photographic plate to reveal three-dimensional interference patterns from the phase mask. While it is possible to use simulations to obtain the interference patterns produced by a phase mask, realizing the mask dimensions necessary for producing a desired interference pattern is analytically challenging due to the intricacies of light interactions involved in producing the final interference pattern. An alternative method is to iteratively optimize the phase mask until the interference patterns obtained converge to the desired pattern. However, depending on the optimization technique used, one either risks a significant probability of failure or requires a prohibitive number of iterations. We argue that an optimization technique that is to take advantage of the physics of the problem using machine learning methods (here fuzzy learning) can lead to competent mask design. This technique is described in this letter.     

Phase I trial of weekly scheduling and pharmacokinetics of titanocene dichloride in patients with advanced cancer

PURPOSE: To determine the maximum-tolerated dose (MTD) and the dose-limiting toxicities (DLTs) of a weekly schedule of titanocene dichloride (TD) and to define the pharmacokinetics of titanium in plasma and urine. PATIENTS AND METHODS: Twenty patients with a median age of 58 years received 83 courses of TD. TD was given as 1-hour infusion at escalating doses from 70 to 185 mg/m2/wk. Pharmacokinetic analysis was performed in eight patients for total plasma titanium (TPTi) and in three patients for ultrafiltrable titanium (UFTi). RESULTS: At the fifth dose level (185 mg/m2/wk), a variety of DLTs were seen in five patients: fatigue in three, bilirubinemia in one, and hypokalemia in two. A further six patients were treated at 140 mg/m2; only one had dose-limiting creatinine elevation and this dose was therefore defined as the MTD. No myelosuppression or alopecia were observed. One patient with adenocarcinoma of unknown primary had a minor response. Pharmacokinetic analysis showed that TPTi maximum concentration (Cmax) values were linear with dose and elimination of TPTi was triphasic with a long terminal half-life (t1/2; median, 165 hours; range, 89 to 592). Between 7% and 24.3% of the total of administered titanium was eliminated in urine over the first 24 hours. In contrast, UFTi elimination was described by a one-compartment model with a t1/2 of 0.41 hours; peak levels of UFTi were 5.2% +/- 2.5% those of TPTi. CONCLUSION: The MTD of TD given on a weekly schedule is 140 mg/m2, with cumulative, but reversible creatinine and bilirubin elevation being the DLTs.     

Non-manual cues in automatic sign language recognition

Present work deals with the incorporation of non-manual cues in automatic sign language recognition. More specifically, eye gaze, head pose, and facial expressions are discussed in relation to their grammatical and syntactic function and means of including them in the recognition phase are investigated. Computer vision issues related to extracting facial features, eye gaze, and head pose cues are presented and classification approaches for incorporating these non-manual cues into the overall Sign Language recognition architecture are introduced.     

Efficiently adapting graphical models for selectivity estimation

Query optimizers rely on statistical models that succinctly describe the underlying data. Models are used to derive cardinality estimates for intermediate relations, which in turn guide the optimizer to choose the best query execution plan. The quality of the resulting plan is highly dependent on the accuracy of the statistical model that represents the data. It is well known that small errors in the model estimates propagate exponentially through joins, and may result in the choice of a highly sub-optimal query execution plan. Most commercial query optimizers make the attribute value independence assumption: all attributes are assumed to be statistically independent. This reduces the statistical model of the data to a collection of one-dimensional synopses (typically in the form of histograms), and it permits the optimizer to estimate the selectivity of a predicate conjunction as the product of the selectivities of the constituent predicates. However, this independence assumption is more often than not wrong, and is considered to be the most common cause of sub-optimal query execution plans chosen by modern query optimizers. We take a step towards a principled and practical approach to performing cardinality estimation without making the independence assumption. By carefully using concepts from the field of graphical models, we are able to factor the joint probability distribution over all the attributes in the database into small, usually two-dimensional distributions, without a significant loss in estimation accuracy. We show how to efficiently construct such a graphical model from the database using only two-way join queries, and we show how to perform selectivity estimation in a highly efficient manner. We integrate our algorithms into the PostgreSQL DBMS. Experimental results indicate that estimation errors can be greatly reduced, leading to orders of magnitude more efficient query execution plans in many cases. Optimization time is kept in the range of tens of milliseconds, making this a practical approach for industrial-strength query optimizers.     

Switching networked attitude control of an unmanned quadrotor

In this article, a switching networked attitude controller for an unmanned quadrotor over a wireless sensor network is presented. To deal with the network induced time varying delays, the quadrotor is being modeled as a switching time varying linear system, while the applied switching output feedback control scheme, is calculated based on Linear Matrix Inequalities, and is able to guarantee the stability of the quadrotor under arbitrary changes in the time delays.     

A New Neuro-FDS Definition for Indirect Adaptive Control of Unknown Nonlinear Systems Using a Method of Parameter Hopping

The indirect adaptive regulation of unknown nonlinear dynamical systems is considered in this paper. The method is based on a new neuro-fuzzy dynamical system (neuro-FDS) definition, which uses the concept of adaptive fuzzy systems (AFSs) operating in conjunction with high-order neural network functions (FHONNFs). Since the plant is considered unknown, we first propose its approximation by a special form of an FDS and then the fuzzy rules are approximated by appropriate HONNFs. Thus, the identification scheme leads up to a recurrent high-order neural network (RHONN), which however takes into account the fuzzy output partitions of the initial FDS. The proposed scheme does not require a priori experts' information on the number and type of input variable membership functions making it less vulnerable to initial design assumptions. Once the system is identified around an operation point, it is regulated to zero adaptively. Weight updating laws for the involved HONNFs are provided, which guarantee that both the identification error and the system states reach zero exponentially fast, while keeping all signals in the closed loop bounded. The existence of the control signal is always assured by introducing a novel method of parameter hopping, which is incorporated in the weight updating law. Simulations illustrate the potency of the method and comparisons with conventional approaches on benchmarking systems are given. Also, the applicability of the method is tested on a direct current (dc) motor system where it is shown that by following the proposed procedure one can obtain asymptotic regulation.     

Colloidal Quantum Dot Infrared Lasers Featuring Sub-Single-Exciton Threshold and Very High Gain

The use of colloidal quantum dots (CQDs) as a gain medium in infrared laser devices has been underpinned by the need for high pumping intensities, very short gain lifetimes, and low gain coefficients. Here, PbS/PbSSe core/alloyed-shell CQDs are employed as an infrared gain medium that results in highly suppressed Auger recombination with a lifetime of 485 ps, lowering the amplified spontaneous emission (ASE) threshold down to 300 µJ cm⁻², and showing a record high net modal gain coefficient of 2180 cm⁻¹. By doping these engineered core/shell CQDs up to nearly filling the first excited state, a significant reduction of optical gain threshold is demonstrated, measured by transient absorption, to an average-exciton population-per-dot 〈N^th〉_g of 0.45 due to bleaching of the ground state absorption. This in turn have led to a fivefold reduction in ASE threshold at 〈N^th〉_ASE = 0.70 excitons-per-dot, associated with a gain lifetime of 280 ps. Finally, these heterostructured QDs are used to achieve near-infrared lasing at 1670 nm at a pump fluences corresponding to sub-single-exciton-per-dot threshold (〈N^th〉_Las = 0.87). This work brings infrared CQD lasing thresholds on par to their visible counterparts, and paves the way toward solution-processed infrared laser diodes.     

On a class of optimal abstractions of finite-state machines

Anabstraction A of an fsmM consists in partitioning its states, inputs, and outputs into groups, thus turning it into a non-deterministic fsmM _A. For fixed sets of states, inputs, and outputs, and abstraction generally maps a number of machinesM defined on these sets into the sameM _A. We would like to find anoptimal abstractionA ^* which minimizes this number, while lumping the states, inputs, and outputs into a specified number of classes. We extend these ideas to an fsmM operating in a random environment, and show that the abstraction results in a probabilistic fsm . Thinking of changes inM's output map as resulting in machinesM≠MM, we want to find anA ^* that minimizes the number ofMM which are such that the transition probabilities of their abstracted version are identical to those of the specification machine . SuchMM arise from statistically-undetectable output faults inM. Abstractions are directly applicable to the monitoring of a complex system by an observer for deviations from correct behavior (faults). Complex systems are usually accessible through restricted interfaces, which do not allow the observer to distinguish among all states, inputs, and outputs, thus rendering some faulty transitions undetectable. An optimal interface design will minimize the number of such undetectable faults. Assuming that only single-transition output faults occur inM, we show that each of the classes into which the abstraction lumps the outputs contributes a number of undetectable output faults. We then show that the problem of partitioning the outputs into a given number of classes that minimizes the maximum of these numbers is NP-complete. However, we give (a) an approximate minimization algorithm, running in time linear in the number of classes and quadratic in the number ofM's outputs, and (b) a lower bound on the minimum, computable in the same amount of time. The concept of optimal abstractions is illustrated by numerical results on combinational logic circuits that perform arithmetical operations. The results shed light on the trade-off between model simplification and the ability to detect erroneous behaviors in complex systems.     

Distributed information service architecture for overlapping multiaccess networks

Multimedia delivery in mobile multiaccess network environments has emerged as a key area within the future Internet research domain. When network heterogeneity is coupled with the proliferation of multiaccess capabilities in mobile handheld devices, one can expect many new avenues for developing novel services and applications. New mechanisms for audio/video delivery over multiaccess networks will define the next generation of major distribution technologies, but will require significantly more information to operate according to their best potential. In this paper we present and evaluate a distributed information service, which can enhance media delivery over such multiaccess networks. We describe the proposed information service, which is built upon the new distributed control and management framework (DCMF) and the mobility management triggering functionality (TRG). We use a testbed which includes 3G/HSPA, WLAN and WiMAX network accesses to evaluate our proposed architecture and present results that demonstrate its value in enhancing video delivery and minimizing service disruption in an involved scenario.