Deterministic annealing with Potts neurons for multi-robot routing

A deterministic annealing (DA) method is presented for solving the multi-robot routing problem with min–max objective. This is an NP-hard problem belonging to the multi-robot task allocation set of problems where robots are assigned to a group of sequentially ordered tasks such that the cost of the slowest robot is minimized. The problem is first formulated in a matrix form where the optimal solution of the problem is the minimum-cost permutation matrix without any loops. The solution matrix is then found using the DA method is based on mean field theory applied to a Potts spin model which has been proven to yield near-optimal results for NP-hard problems. Our method is bench-marked against simulated annealing and a heuristic search method. The results show that the proposed method is promising for small-medium sized problems in terms of computation time and solution quality compared to the other two methods.

     

String Methods for Stochastic Image and Shape Matching

Matching of images and analysis of shape differences is traditionally pursued by energy minimization of paths of deformations acting to match the shape objects. In the large deformation diffeomorphic metric mapping (LDDMM) framework, iterative gradient descents on the matching functional lead to matching algorithms informally known as Beg algorithms. When stochasticity is introduced to model stochastic variability of shapes and to provide more realistic models of observed shape data, the corresponding matching problem can be solved with a stochastic Beg algorithm, similar to the finite-temperature string method used in rare event sampling. In this paper, we apply a stochastic model compatible with the geometry of the LDDMM framework to obtain a stochastic model of images and we derive the stochastic version of the Beg algorithm which we compare with the string method and an expectation-maximization optimization of posterior likelihoods. The algorithm and its use for statistical inference is tested on stochastic LDDMM landmarks and images.     

Holistic security requirements analysis for socio-technical systems

Security has been a growing concern for large organizations, especially financial and governmental institutions, as security breaches in the systems they depend on have repeatedly resulted in billions of dollars in losses per year, and this cost is on the rise. A primary reason for these breaches is that the systems in question are “socio-technical” a mix of people, processes, technology, and infrastructure. However, such systems are designed in a piecemeal rather than a holistic fashion, leaving parts of the system vulnerable. To tackle this problem, we propose a three-layer security analysis framework consisting of a social layer (business processes, social actors), a software layer (software applications that support the social layer), and an infrastructure layer (physical and technological infrastructure). In our proposal, global security requirements lead to local security requirements, cutting across conceptual layers, and upper-layer security analysis influences analysis at lower layers. Moreover, we propose a set of analytical methods and a systematic process that together drive security requirements analysis across the three layers. To support analysis, we have defined corresponding inference rules that (semi-)automate the analysis, helping to deal with system complexity. A prototype tool has been implemented to support analysts throughout the analysis process. Moreover, we have performed a case study on a real-world smart grid scenario to validate our approach.     

Comparing Bar Chart Authoring with Microsoft Excel and Tangible Tiles

Providing tools that make visualization authoring accessible to visualization non‐experts is a major research challenge. Currently the most common approach to generating a visualization is to use software that quickly and automatically produces visualizations based on templates. However, it has recently been suggested that constructing a visualization with tangible tiles may be a more accessible method, especially for people without visualization expertise. There is still much to be learned about the differences between these two visualization authoring practices. To better understand how people author visualizations in these two conditions, we ran a qualitative study comparing the use of software to the use of tangible tiles, for the creation of bar charts. Close observation of authoring activities showed how each of the following varied according to the tool used: 1) sequences of action; 2) distribution of time spent on different aspects of the InfoVis pipeline; 3) pipeline task separation; and 4) freedom to manipulate visual variables. From these observations, we discuss the implications of the variations in activity sequences, noting tool design considerations and pointing to future research questions.     

Thermal and Electrical Properties of Nanocomposites Based on Self‐Assembled Pristine Graphene

Thermodynamically‐driven exfoliation and self‐assembly of pristine graphene sheets is shown to provide thermally and electrically functional polymer composites. The spreading of graphene sheets at a high energy liquid/liquid interface is driven by lowering the overall energy of the system, and provides for the formation of water‐in‐oil emulsions stabilized by overlapping graphene sheets. Polymerization of the oil phase, followed by removal of the dispersed water phase, produces inexpensive and porous composite foams. Contact between the graphene‐stabilized water droplets provides a pathway for electrical and thermal transport through the composite. Unlike other graphene foams, the graphite used to synthesize these composites is natural flake material, with no oxidation, reduction, sonication, high temperature thermal treatment, addition of surfactants, or high shear mixing required. The result is an inexpensive, low‐density material that exhibits Joule heating and displays increasing electrical conductivity with decreasing thermal conductivity.     

A Comparison of the Anti-Inflammatory Effects of <Emphasis Type="Italic">Cis</Emphasis>-9, <Emphasis Type="Italic">Trans</Emphasis>-11 Conjugated Linoleic Acid to Celecoxib in the Collagen-Induced Arthritis Model

Cyclooxygenase (COX)‐2 inhibitors, such as celecoxib, for chronic inflammatory disease are associated with adverse health events, while cis‐9, trans‐11 (c9t11) conjugated linoleic acid (CLA) is anti‐inflammatory without adverse events attributed to pure intake. Mechanistically, celecoxib and c9t11 disrupt the arachidonic acid cascade; however, the equivalency of anti‐inflammatory effects between these compounds is unknown. Therefore, to test the hypothesis that 0.5% dietary c9t11 reduces inflammation equivalently to a celecoxib dose intended to treat rheumatoid arthritis (RA; 5 mg/kg bw), arthritic mice received diets containing one of the following supplements: 1% corn oil (CO, w/w), 0.5% c9t11 (>91% purity) +0.5% CO, or 1% CO + 0.5, 5, or 50 mg/kg bw celecoxib, and were assessed for changes in arthritic severity over 6 weeks. Overall, arthritic severity in mice fed c9t11 was reduced (34%, P < 0.01) while celecoxib doses (0.5, 5, 50 mg/kg) reduced arthritic severity (16, 56, 48%, respectively) compared to CO‐fed arthritic mice. Linear regression of the celecoxib dose‐response showed 0.5% c9t11 (570 mg/kg bw) reduced arthritic severity equivalently to 1.5 mg/kg celecoxib. Interleukin‐6 (IL‐6) was increased in paws of arthritic mice fed CO compared to shams, but was decreased in arthritic groups fed 0.5% c9t11 and 5 mg/kg celecoxib, compared to arthritic mice fed CO (Ps ≤ 0.05). Additionally, paw and plasma IL‐10 levels in arthritic mice were decreased by 5 mg/kg celecoxib, but were unaffected by c9t11 compared to CO. Results suggest dietary c9t11 may be an effective adjunct to COX‐2 inhibition for treating chronic inflammation.     

Sand digging in Sepia officinalis: Assessment of a Cephalopod mollusc's "fixed" behavior pattern.

Examined the internal structure of a fixed behavior by 7 Cephalopod mollusks, digging into a sandy-bottom surface. Digging is a 5-sec sequence of 3 acts, blow forward, blow backward, and wiggle, initiated by exposure to light and a sandy bottom. Change in the grain size of sand into which the Ss dug, and in the amount of cover they could achieve, significantly changed the duration of digging and the number of acts. The transitional probabilities from one act to another within the sequence were calculated. Values were lower (the sequence was more variable) with large-grained sand and in too-shallow sand. Transition probabilities were calculated to behavioral acts one or more steps after blow forward and blow back in the sequence. There was a high probability of each act after the other, a stereotypy unaffected by changes in environment. A forward-backward-wiggle-stop sequence was likely in medium-sized sand, less so in other environments. The simple sand-digging behavior thus had both stereotyped and variable components that suggest a mixture of central programming and variation due to peripheral feedback. (26 ref) (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Poly(methylphenylphosphazene)–Graft–Poly(methyl Methacrylate) Copolymers Via Atom Transfer Radical Polymerization

Atom transfer radical polymerization (ATRP) was used to graft poly(methyl methacrylate), PMMA, onto poly(methylphenylphosphazene), [(Me)(Ph)PN] _n , PMPP. A two-step process was used to convert a portion of the methyl substituents on [(Me)(Ph)PN] _n to –CH₂C(CH₃)₂OH groups and then to bromoalkyl groups, –CH₂C(CH₃)₂OC(=O)C(CH₃)₂Br, the latter of which served as initiation sites for ATRP of methyl methacrylate (MMA) in the presence of CuCl/bipyridine. Variations in the length of the grafted chains were investigated and the graft copolymers were compared to the parent polymer and blends of similar composition. The new bromoalkyl derivatives of [(Me)(Ph)PN] _n and the PMPP–graft–PMMA copolymers were characterized by elemental analysis, ¹H and ³¹P NMR spectroscopy, size exclusion chromatography (SEC), and differential scanning calorimetry (DSC). We dedicate this paper to Professor Harry R. Allcock for consistently maintaining the highest standards in his creative, pioneering work in inorganic rings and polymers.     

SHORT AND LONG TERM FLUX DECLINE ANALYSIS IN ULTRAFILTRATION

Flux decline during stirred UF over a long time of operation can be classified into short and long time domains. In the short time, the decline in flux is basically osmotic pressure limited and this decline is extremely rapid. The limiting flux for the osmotic pressure controlled domain is obtained by successive substitution technique. This technique includes the variations of properties with concentrations. A modified Sherwood number correlation for determining the mass transfer coefficient is proposed for laminar flow in a stirred cell. The long term flux decline can be modeled semi-empirically by a two parameter model. One of these, the steady state polarized layer resistance, can be correlated to the operating conditions while the other constant of the model is found to be a characteristic of the solute only. The simplistic model developed here to account the flux decline during UF may be useful to the process engineers for design purposes.