Query: Piecewise linear spine for speed-energy efficiency trade-off in quadruped robots

Relevant passages:
Central pattern generators for locomotion control in animals and robots: A review <=SEP=> The problem of controlling locomotion is an area in which neuroscience and robotics can fruitfully interact. In this article, I will review research carried out on locomotor central pattern generators (CPGs), i.e. neural circuits capable of producing coordinated patterns of high-dimensional rhythmic output signals while receiving only simple, low-dimensional, input signals. The review will first cover neurobiological observations concerning locomotor CPGs and their numerical modelling, with a special focus on vertebrates. It will then cover how CPG models implemented as neural networks or systems of coupled oscillators can be used in robotics for controlling the locomotion of articulated robots. The review also presents how robots can be used as scientific tools to obtain a better understanding of the functioning of biological CPGs. Finally, various methods for designing CPGs to control specific modes of locomotion will be briefly reviewed. In this process, I will discuss different types of CPG models, the pros and cons of using CPGs with robots, and the pros and cons of using robots as scientific tools. Open research topics both in biology and in robotics will also be discussed.

Benefits of an active spine supported bounding locomotion with a small compliant quadruped robot <=SEP=> We studied the effect of the control of an active spine versus a fixed spine, on a quadruped robot running in bound gait. Active spine supported actuation led to faster locomotion, with less foot sliding on the ground, and a higher stability to go straight forward. However, we did no observe an improvement of cost of transport of the spine-actuated, faster robot system compared to the rigid spine.

Walking Robots and the Central and Peripheral Control of Locomotion in Insects <=SEP=> This paper outlines aspects of locomotor control in insects that may serve as the basis for the design of controllers for autonomous hexapod robots. Control of insect walking can be considered hierarchical and modular. The brain determines onset, direction, and speed of walking. Coordination is done locally in the ganglia that control leg movements. Typically, networks of neurons capable of generating alternating contractions of antagonistic muscles (termed central pattern generators, or CPGs) control the stepping movements of individual legs. The legs are coordinated by interactions between the CPGs and sensory feedback from the moving legs. This peripheral feedback provides information about leg load, position, velocity, and acceleration, as well as information about joint angles and foot contact. In addition, both the central pattern generators and the sensory information that feeds them may be modulated or adjusted according to circumstances. Consequently, locomotion in insects is extraordinarily robust and adaptable.

"Cheap" Rapid Locomotion of a Quadruped Robot: Self-Stabilization of Bounding Gait <=SEP=> The legged animals are capable of rapid, energy efficient, and adaptive locomotion in a complex environment. Toward a comprehensive understanding of the nature of such ecologically balanced legged locomotion, in this paper, we propose a novel method to achieve a form of bounding gait for a quadruped robot by using a minimalistic approach. Although this method uses a simple sinusoidal position control with no global sensory feedback, it is shown that the rapid bounding is possible in a relatively robust manner by properly exploiting the intrinsic dynamics and the interaction with the environment. The behavioral analyses with the robot experiments show that this relatively complicated dynamic locomotion is achieved even with a simple controller mainly because of a self-stabilization mechanism. Moreover, by exploiting this mechanism of self-stabilization, we propose a unique approach to control the forward velocity of the locomotion.

Modeling and Experiments of Untethered Quadrupedal Running with a Bounding Gait: The Scout II Robot <=SEP=> In this paper we compare models and experiments involving Scout II, an untethered four-legged running robot with only one actuator per compliant leg. Scout II achieves dynamically stable running of up to 1.3 m s−1 on flat ground via a bounding gait. Energetics analysis reveals a highly efficient system with a specific resistance of only 1.4. The running controller requires no task-level or body-state feedback, and relies on the passive dynamics of the mechanical system. These results contribute to the increasing evidence that apparently complex dynamically dexterous tasks may be controlled via simple control laws. We discuss general modeling issues for dynamically stable legged robots. Two simulation models are compared with experimental data to test the validity of common simplifying assumptions. The need for including motor saturation and non-rigid torque transmission characteristics in simulation models is demonstrated. Similar issues are likely to be important in other dynamically stable legged robots as well. An extensive suite of experimental results documents the robot’s performance and the validity of the proposed

Real-Time Tracking of Non-Rigid Objects Using Mean Shift <=SEP=> A new method for r eal-time tracking of non-rigid objects seen from a moving camera is proposed. The central computational module is based on the mean shift iterations and nds the most probable tar get p osition in the current frame. The dissimilarity between the target model (its c olor distribution) and the target candidates is expr essed by a metric derive d from the Bhattacharyya coe cient. The theoretical analysis of the approach shows that it relates to the Bayesian framework while providing a practical, fast and e cient solution. The capability of the tracker to handle in real-time partial occlusions, signi cant clutter, and target scale variations, is demonstrated for several image sequences.

A Survey on Data Mining Algorithm for Market Basket Analysis <=SEP=> s Association rule mining identifies the remarkable association or relationship between a large set of data items. With huge quantity of data constantly being obtained and stored in databases, several industries are becoming concerned in mining association rules from their databases. For example, the detection of interesting association relationships between large quantities of business transaction data can assist in catalog design, cross-marketing, lossleader analysis, and various business decision making processes. A typical example of association rule mining is market basket analysis. This method examines customer buying patterns by identifying associations among various items that customers place in their shopping baskets. The identification of such associations can assist retailers expand marketing strategies by gaining insight into which items are frequently purchased jointly by customers. It is helpful to examine the customer purchasing behavior and assists in increasing the sales and conserve inventory by focusing on the point of sale transaction data. This work acts as a broad area for the researchers to develop a better data mining algorithm. This paper presents a survey about the existing data mining algorithm for market basket analysis. Keyewords : Association Rule Mining, Apriori Algorithm, Market Basket Analysis GJCST Classification : H.2.8 A Survey on Data Mining Algorithm for Market Basket Analysis Strictly as per the compliance and regulations of: Global Journal of Computer Science and Technology Volume 11 Issue 11 Version 1.0 July 2011 Type: Double Blind Peer Reviewed International Research Journal Publisher: Global Journals Inc. (USA) Online ISSN: 0975-4172 & Print ISSN: 0975-4350

Functional connectivity in the resting brain: a network analysis of the default mode hypothesis. <=SEP=> Functional imaging studies have shown that certain brain regions, including posterior cingulate cortex (PCC) and ventral anterior cingulate cortex (vACC), consistently show greater activity during resting states than during cognitive tasks. This finding led to the hypothesis that these regions constitute a network supporting a default mode of brain function. In this study, we investigate three questions pertaining to this hypothesis: Does such a resting-state network exist in the human brain? Is it modulated during simple sensory processing? How is it modulated during cognitive processing? To address these questions, we defined PCC and vACC regions that showed decreased activity during a cognitive (working memory) task, then examined their functional connectivity during rest. PCC was strongly coupled with vACC and several other brain regions implicated in the default mode network. Next, we examined the functional connectivity of PCC and vACC during a visual processing task and show that the resultant connectivity maps are virtually identical to those obtained during rest. Last, we defined three lateral prefrontal regions showing increased activity during the cognitive task and examined their resting-state connectivity. We report significant inverse correlations among all three lateral prefrontal regions and PCC, suggesting a mechanism for attenuation of default mode network activity during cognitive processing. This study constitutes, to our knowledge, the first resting-state connectivity analysis of the default mode and provides the most compelling evidence to date for the existence of a cohesive default mode network. Our findings also provide insight into how this network is modulated by task demands and what functions it might subserve.

INTERACTIVE SAND-COVERED TERRAIN SURFACE MODEL WITH HAPTIC FEEDBACK <=SEP=> We describe a method for an intuitive haptic rendering and modeling of GIS terrain models represented as triangular irregular networks (TIN). The TIN terrain model does not allow representation of concavities and overhangs but it is sufficient for most GIS applications. Model editing is achieved by a set of virtual tools with various shapes which are controlled by a haptic device with force feedback. The haptic feedback makes manipulation with the terrain model much more intuitive than by the mouse, because it operates in 3D and it also provides better control with the force. The haptic device is point-based, so the forces provided by different tools must be mapped to a point. This, in effect, helps eliminating certain vibrations that could be caused by numerical errors and insufficient scene sampling. Our approach allows an adaptive adjustment of the level of detail of the model according to the terrain properties and user requirements. Haptic visualization in GIS models has been already used but such solutions are not common. Unlike existing approaches, our solution uses a general shape of the tool, it allows interactive editing of the TIN model, and it utilizes parallelism and coherence between consecutive states of the model that make haptic visualization faster and smoother.

That's So Annoying!!!: A Lexical and Frame-Semantic Embedding Based Data Augmentation Approach to Automatic Categorization of Annoying Behaviors using #petpeeve Tweets <=SEP=> We propose a novel data augmentation approach to enhance computational behavioral analysis using social media text. In particular, we collect a Twitter corpus of the descriptions of annoying behaviors using the #petpeeve hashtags. In the qualitative analysis, we study the language use in these tweets, with a special focus on the fine-grained categories and the geographic variation of the language. In quantitative analysis, we show that lexical and syntactic features are useful for automatic categorization of annoying behaviors, and frame-semantic features further boost the performance; that leveraging large lexical embeddings to create additional training instances significantly improves the lexical model; and incorporating frame-semantic embedding achieves the best overall performance.

The wave kernel signature: A quantum mechanical approach to shape analysis <=SEP=> We introduce the Wave Kernel Signature (WKS) for characterizing points on non-rigid three-dimensional shapes. The WKS represents the average probability of measuring a quantum mechanical particle at a specific location. By letting vary the energy of the particle, the WKS encodes and separates information from various different Laplace eigenfrequencies. This clear scale separation makes the WKS well suited for a large variety of applications. Both theoretically and in quantitative experiments we demonstrate that the WKS is substantially more discriminative and therefore allows for better feature matching than the commonly used Heat Kernel Signature (HKS). As an application of the WKS in shape analysis we show results on shape matching.

Orexins and Orexin Receptors: A Family of Hypothalamic Neuropeptides and G Protein-Coupled Receptors that Regulate Feeding Behavior <=SEP=> The hypothalamus plays a central role in the integrated control of feeding and energy homeostasis. We have identified two novel neuropeptides, both derived from the same precursor by proteolytic processing, that bind and activate two closely related (previously) orphan G protein-coupled receptors. These peptides, termed orexin-A and -B, have no significant structural similarities to known families of regulatory peptides. prepro-orexin mRNA and immunoreactive orexin-A are localized in neurons within and around the lateral and posterior hypothalamus in the adult rat brain. When administered centrally to rats, these peptides stimulate food consumption. prepro-orexin mRNA level is up-regulated upon fasting, suggesting a physiological role for the peptides as mediators in the central feedback mechanism that regulates feeding behavior.

BETTER GUN ENFORCEMENT , LESS CRIME * <=SEP=> Research Summary: Project Safe Neighborhoods (PSN), which for the past several years has been the major federal initiative to combat gun violence, includes several elements (such as gun locks and other efforts to reduce gun availability) that research suggests are likely to have at best modest effects on gun crime. In general, enforcement activities targeted at the “demand side” of the underground gun market currently enjoy stronger empirical support. However much of PSN’s budget has been devoted to increasing the severity of punishment, such as by federalizing gun cases, which seems to be less effective than targeted street-level enforcement designed to increase the probability of punishment for gun carrying or use in crime.

Modeling Document Novelty with Neural Tensor Network for Search Result Diversification <=SEP=> Search result diversification has attracted considerable attention as a means to tackle the ambiguous or multi-faceted information needs of users. One of the key problems in search result diversification is novelty, that is, how to measure the novelty of a candidate document with respect to other documents. In the heuristic approaches, the predefined document similarity functions are directly utilized for defining the novelty. In the learning approaches, the novelty is characterized based on a set of handcrafted features. Both the similarity functions and the features are difficult to manually design in real world due to the complexity of modeling the document novelty. In this paper, we propose to model the novelty of a document with a neural tensor network. Instead of manually defining the similarity functions or features, the new method automatically learns a nonlinear novelty function based on the preliminary representation of the candidate document and other documents. New diverse learning to rank models can be derived under the relational learning to rank framework. To determine the model parameters, loss functions are constructed and optimized with stochastic gradient descent. Extensive experiments on three public TREC datasets show that the new derived algorithms can significantly outperform the baselines, including the state-of-the-art relational learning to rank models.

An Experimental Comparison of RGB, YIQ, LAB, HSV, and Opponent Color Models <=SEP=> The increasing availability of affordable color raster graphics displays has made it important to develop a better understanding of how color can be used effectively in an interactive environment. Most contemporary graphics displays offer a choice of some 16 million colors; the user's problem is to find the right color.
Folklore has it that the RGB color space arising naturally from color display hardware is user-hostile and that other color models such as the HSV scheme are preferable. Until now there has been virtually no experimental evidence addressing this point.
We describe a color matching experiment in which subjects used one of two tablet-based input techniques, interfaced through one of five color models, to interactively match target colors displayed on a CRT.
The data collected show small but significant differences between models in the ability of subjects to match the five target colors used in this experiment. Subjects using the RGB color model matched quickly but inaccurately compared with those using the other models. The largest speed difference occurred during the early convergence phase of matching. Users of the HSV color model were the slowest in this experiment, both during the convergence phase and in total time to match, but were relatively accurate. There was less variation in performance during the second refinement phase of a match than during the convergence phase.
Two-dimensional use of the tablet resulted in faster but less accurate performance than did strictly one-dimensional usage.
Significant learning occurred for users of the Opponent, YIQ, LAB, and HSV color models, and not for users of the RGB color model.

Modeling the Diffuse Paintings of 'Sumie' <=SEP=> 

A globally stable adaptive congestion control scheme for Internet-style networks with delay <=SEP=> In this paper, we develop, analyze and implement a congestion control scheme in a noncooperative game framework, where each user's cost function is composed of a pricing function proportional to the queueing delay experienced by the user, and a fairly general utility function which captures the user demand for bandwidth. Using a network model based on fluid approximations and through a realistic modeling of queues, we establish the existence of a unique equilibrium as well as its global asymptotic stability for a general network topology, where boundary effects are also taken into account. We also provide sufficient conditions for system stability when there is a bottleneck link shared by multiple users experiencing nonnegligible communication delays. In addition, we study an adaptive pricing scheme using hybrid systems concepts. Based on these theoretical foundations, we implement a window-based, end-to-end congestion control scheme, and simulate it in ns-2 network simulator on various network topologies with sizable propagation delays.

Language Oriented Programming: The Next Programming Paradigm <=SEP=> Ideally, being a programmer means I can do anything on a computer. I have complete freedom, complete control. But in reality, programmers today have very It is time to begin the next technology revolution in software development, and the shape of this revolution is becoming more and more clear. The next programming paradigm is nearly upon us. It is not yet fully formed—different parts have different names: Intentional programming, MDA, generative programming, etc. I suggest uniting all of these new approaches under one name, ‘language-oriented programming’, and this article explains the main principles of this new programming paradigm. Today’s mainstream approach to programming has some crucial built-in assumptions which hold us back like chains around our necks, though most programmers don’t realize this. With all the progress made so far in programming, we are still in the Stone Age. We’ve got our trusty stone axe (object-oriented programming), which serves us well, but tends to chip and crack when used against the hardest problems. To advance beyond stone, we must tame fire. Only then can we forge new tools and spark a new age of invention and an explosion of new technologies. I’m talking about the limitations of programming which force the programmer to think like the computer rather than having the computer think more like the programmer. These are serious, deeply-ingrained limitations which will take a lot of effort to overcome. I’m not being pretentious when I say that this will be the next big paradigm shift in programming. We will need to completely redefine the way we write programs. In this article, I present my view and my current work toward Language Oriented Programming (LOP). First I will show what is wrong with mainstream programming today, then I’ll explain the concept of LOP by using the example of my existing implementation, the Meta Programming System (MPS). This article is intended to give you a bird’s-eye-view of LOP, to spark interest in the idea, and hopefully to generate feedback and discussion.

Estimating Phoneme Class Conditional Probabilities from Raw Speech Signal using Convolutional Neural Networks <=SEP=> In hybrid hidden Markov model/artificial neural networks (HMM/ANN) automatic speech recognition (ASR) system, the phoneme class conditional probabilities are estimated by first extracting acoustic features from the speech signal based on prior knowledge such as, speech perception or/and speech production knowledge, and, then modeling the acoustic features with an ANN. Recent advances in machine learning techniques, more specifically in the field of image processing and text processing, have shown that such divide and conquer strategy (i.e., separating feature extraction and modeling steps) may not be necessary. Motivated from these studies, in the framework of convolutional neural networks (CNNs), this paper investigates a novel approach, where the input to the ANN is raw speech signal and the output is phoneme class conditional probability estimates. On TIMIT phoneme recognition task, we study different ANN architectures to show the benefit of CNNs and compare the proposed approach against conventional approach where, spectral-based feature MFCC is extracted and modeled by a multilayer perceptron. Our studies show that the proposed approach can yield comparable or better phoneme recognition performance when compared to the conventional approach. It indicates that CNNs can learn features relevant for phoneme classification automatically from the raw speech signal.

Real-time motion planning methods for autonomous on-road driving: State-of-the-art and future research directions <=SEP=> Currently autonomous or self-driving vehicles are at the heart of academia and industry research becauseof itsmulti-faceted advantages that includes improved safety, reduced congestion, lower emissions and greater mobility. Software is the key driving factor underpinning autonomy within which planning algorithms that are responsible for mission-critical decision making hold a significant position. While transporting passengers or goods from a givenorigin to agivendestination,motionplanningmethods incorporate searching for apath to follow, avoiding obstacles and generating the best trajectory that ensures safety, comfort and efficiency. A range of different planning approaches have beenproposed in the literature. Thepurpose of this paper is to reviewexisting approaches and then compare and contrast different methods employed for the motion planning of autonomous on-road driving that consists of (1)findingapath, (2) searching for the safestmanoeuvre and (3)determining themost feasible trajectory. Methods developed by researchers in each of these three levels exhibit varying levels of complexity and performance accuracy. This paper presents a critical evaluation of each of these methods, in terms of their advantages/disadvantages, inherent limitations, feasibility, optimality, handling of obstacles and testing operational environments. Based on a critical review of existingmethods, research challenges to address current limitations are identified and future research directions are suggested so as to enhance the performanceofplanningalgorithmsat all three levels. Somepromising areasof future focushave been identified as the use of vehicular communications (V2V and V2I) and the incorporation of transport engineering aspects in order to improve the look-ahead horizon of current sensing technologies that are essential for planning with the aim of reducing the total cost of driverless vehicles. This critical reviewon planning techniques presented in this paper, along with theassociateddiscussions on their constraints and limitations, seek to assist researchers in accelerating development in the emerging field of autonomous vehicle research. Crown Copyright 2015 Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

A Self-Organizing Spatial Vocabulary <=SEP=> Language is a shared set of conventions for mapping meanings to utterances. This paper explores self-organization as the primary mechanism for the formation of a vocabulary. It reports on a computational experiment in which a group of distributed agents develop ways to identify each other using names or spatial descriptions. It is also shown that the proposed mechanism copes with the acquisition of an existing vocabulary by new agents entering the community and with an expansion of the set of meanings.

A New ADS-B Authentication Framework Based on Efficient Hierarchical Identity-Based Signature with Batch Verification <=SEP=> Automatic dependent surveillance-broadcast (ADS-B) has become a crucial part of next generation air traffic surveillance technology and will be mandatorily deployed for most of the airspaces worldwide by 2020. Each aircraft equipped with an ADS-B device keeps broadcasting plaintext messages to other aircraft and the ground station controllers once or twice per second. The lack of security measures in ADS-B systems makes it susceptible to different attacks. Among the various security issues, we investigate the integrity and authenticity of ADS-B messages. We propose a new framework for providing ADS-B with authentication based on three-level hierarchical identity-based signature (HIBS) with batch verification. Previous signature-based ADS-B authentication protocols focused on how to generate signatures efficiently, while our schemes can also significantly reduce the verification cost, which is critical to ADS-B systems, since at any time an ADS-B receiver may receive lots of signatures. We design two concrete schemes. The basic scheme supports partial batch verification and the extended scheme provides full batch verification. We give a formal security proof for the extended scheme. Experiment results show that our schemes with batch verification are tremendously more efficient in batch verifying <inline-formula><tex-math notation="LaTeX">$n$ </tex-math><alternatives><inline-graphic xlink:href="yang-ieq1-2459709.gif"/></alternatives></inline-formula> signatures than verifying <inline-formula><tex-math notation="LaTeX">$n$</tex-math><alternatives> <inline-graphic xlink:href="yang-ieq2-2459709.gif"/></alternatives></inline-formula> signatures independently. For example, the running time of verifying 100 signatures is 502 and 484 ms for the basic scheme and the extended scheme respectively, while the time is 2500 ms if verifying the signatures independently.

Entity Matching: How Similar Is Similar <=SEP=> Entity matching that finds records referring to the same entity is an important operation in data cleaning and integration. Existing studies usually use a given similarity function to quantify the similarity of records, and focus on devising index structures and algorithms for efficient entity matching. However it is a big challenge to define “how similar is similar” for real applications, since it is rather hard to automatically select appropriate similarity functions. In this paper we attempt to address this problem. As there are a large number of similarity functions, and even worse thresholds may have infinite values, it is rather expensive to find appropriate similarity functions and thresholds. Fortunately, we have an observation that different similarity functions and thresholds have redundancy, and we have an opportunity to prune inappropriate similarity functions. To this end, we propose effective optimization techniques to eliminate such redundancy, and devise efficient algorithms to find the best similarity functions. The experimental results on both real and synthetic datasets show that our method achieves high accuracy and outperforms the baseline algorithms.

On the Representation and Estimation of <=SEP=> This paper describes a general method for estimating the nominal relationship and expected error (covariance) between coordinate frames representing the relative locations of objects. The frames may be known only indirectly through a series of spatial relationships, each with its associated error, arisingfrom diverse causes, including positioning errors, measurement errors, or tolerances in part dimensions. This estimation method can be used to answer such questions as whether a camera attached, to a robot is likely to have a particular reference object in its field of view. The calculated estimates agree well with those/rom an independent Monte Carlo simulation. The method makes it possible to decide in advance whether an uncertain relationship is known accurately enough for some task and, i f not, how much of an improvement in locational knowledge a proposed sensor will provide. The method presented can be generalized to six degrees of freedom and provides a practical means of estimating the relationships (position and orientation) among objects, as well as estimating the uncertainty associated with the relationships.

Effect of Video Game Practice on Spatial Skills in Girls and Boys <=SEP=> A study of the effect of video game practice on spatial abilities in girls ond boys wos carried out. Spatial performance, measured using two subtests of a computerized spatial skills battery, was significantly better in boys than in girls during pretest assessment. Subjects then practiced on an action video game, Marble Madness, or a computerized word game, Conjecture. Video game practice wos significantly more effective than the word game in improving spatiol performance on the posttest assessment; there was no significant interaction of gender with experimental treotment. However, video game practice was more effective for children who started out with relatively poor spatial skills. The pattern of results suggests that video games may be useful in equalizing individual differences in spatial skill performance, including those associated with gender.

DDoS: Survey of Traceback Methods <=SEP=> The problem of identifying Distributed Denial of Service (DDoS) is one of the hardest threats in the internet security. It is important to protect the resource and trace from the Denial of Service (DoS) attack, but it is difficult to distinguish normal traffic and DoS attack traffic because the DoS generally hide their identities/origins. Especially the attackers often use incorrect or spoofed source IP address, so tracing the source of the denial of service is hardest in internet. Lot of techniques and methodologies are used to trace the DDoS attacks. This paper presents some of the mostly used predicting traceback techniques to solve the problem. The main goal of this paper is appraise the different traceback techniques of the DDoS. This paper evaluates the different traceback methods of the

Nonsuicidal self-injury as a gateway to suicide in young adults. <=SEP=> PURPOSE
To investigate the extent to which nonsuicidal self-injury (NSSI) contributes to later suicide thoughts and behaviors (STB) independent of shared risk factors.


METHODS
One thousand four hundred and sixty-six students at five U.S. colleges participated in a longitudinal study of the relationship between NSSI and suicide. NSSI, suicide history, and common risk/protective factors were assessed annually for three years. Analyses tested the hypotheses that the practice of NSSI prior to STB and suicide behavior (excluding ideation) reduced inhibition to later STB independent of shared risk factors. Analyses also examined factors that predicted subsequent STB among individuals with NSSI history.


RESULTS
History of NSSI did significantly predict concurrent or later STB (AOR 2.8, 95%, CI 1.9-4.1) independent of covariates common to both. Among those with prior or concurrent NSSI, risk of STB is predicted by > 20 lifetime NSSI incidents (AOR 3.8, 95% CI, 1.4-10.3) and history of mental health treatment (AOR 2.2, 95% CI, 1.9-4.6). Risk of moving from NSSI to STB is decreased by presence of meaning in life (AOR .6, 95% CI, .5-.7) and reporting parents as confidants (AOR, .3, 95% CI, .1-.9).


CONCLUSIONS
NSSI prior to suicide behavior serves as a "gateway" behavior for suicide and may reduce inhibition through habituation to self-injury. Treatments focusing on enhancing perceived meaning in life and building positive relationships with others, particularly parents, may be particularly effective in reducing suicide risk among youth with a history of NSSI.

A theory of multi-layer flat refractive geometry <=SEP=> Flat refractive geometry corresponds to a perspective camera looking through single/multiple parallel flat refractive mediums. We show that the underlying geometry of rays corresponds to an axial camera. This realization, while missing from previous works, leads us to develop a general theory of calibrating such systems using 2D-3D correspondences. The pose of 3D points is assumed to be unknown and is also recovered. Calibration can be done even using a single image of a plane. We show that the unknown orientation of the refracting layers corresponds to the underlying axis, and can be obtained independently of the number of layers, their distances from the camera and their refractive indices. Interestingly, the axis estimation can be mapped to the classical essential matrix computation and 5-point algorithm [15] can be used. After computing the axis, the thicknesses of layers can be obtained linearly when refractive indices are known, and we derive analytical solutions when they are unknown. We also derive the analytical forward projection (AFP) equations to compute the projection of a 3D point via multiple flat refractions, which allows non-linear refinement by minimizing the reprojection error. For two refractions, AFP is either 4th or 12th degree equation depending on the refractive indices. We analyze ambiguities due to small field of view, stability under noise, and show how a two layer system can be well approximated as a single layer system. Real experiments using a water tank validate our theory.

SPIKING NEURAL NETWORKS SPIKING NEURAL NETWORKS <=SEP=> Background: Infectious complications are the major cause of death in acute pancreatitis. Small bowel bacterial overgrowth and subsequent bacterial translocation are held responsible for the vast majority of these infections. Goal of this study is to determine whether selected probiotics are capable of preventing infectious complications without the disadvantages of antibiotic prophylaxis; antibiotic resistance and fungal overgrowth. Methods/design: PROPATRIA is a double-blind, placebo-controlled randomised multicenter trial in which 200 patients will be randomly allocated to a multispecies probiotic preparation (Ecologic 641) or placebo. The study is performed in all 8 Dutch University Hospitals and 7 non-University hospitals. The study-product is administered twice daily through a nasojejunal tube for 28 days or until discharge. Patients eligible for randomisation are adult patients with a first onset of predicted severe acute pancreatitis: Imrie criteria 3 or more, CRP 150 mg/L or more, APACHE II score 8 or more. Exclusion criteria are post-ERCP pancreatitis, malignancy, infection/sepsis caused by a second disease, intra-operative diagnosis of pancreatitis and use of probiotics during the study. Administration of the study product is started within 72 hours after onset of abdominal pain. The primary endpoint is the total number of infectious complications. Secondary endpoints are mortality, necrosectomy, antibiotic resistance, hospital stay and adverse events. To demonstrate that probiotic prophylaxis reduces the proportion of patients with infectious complications from 50% to 30%, with alpha 0,05 and power 80%, a total sample size of 200 patients was calculated. Conclusion: The PROPATRIA study is aimed to show a reduction in infectious complications due to early enteral use of multispecies probiotics in severe acute pancreatitis. Published: 29 September 2004 BMC Surgery 2004, 4:12 doi:10.1186/1471-2482-4-12 Received: 21 July 2004 Accepted: 29 September 2004 This article is available from: http://www.biomedcentral.com/1471-2482/4/12 © 2004 Besselink et al; licensee BioMed Central Ltd. This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Molecular assembly on two-dimensional materials. <=SEP=> Molecular self-assembly is a well-known technique to create highly functional nanostructures on surfaces. Self-assembly on two-dimensional (2D) materials is a developing field driven by the interest in functionalization of 2D materials in order to tune their electronic properties. This has resulted in the discovery of several rich and interesting phenomena. Here, we review this progress with an emphasis on the electronic properties of the adsorbates and the substrate in well-defined systems, as unveiled by scanning tunneling microscopy. The review covers three aspects of the self-assembly. The first one focuses on non-covalent self-assembly dealing with site-selectivity due to inherent moiré pattern present on 2D materials grown on substrates. We also see that modification of intermolecular interactions and molecule-substrate interactions influences the assembly drastically and that 2D materials can also be used as a platform to carry out covalent and metal-coordinated assembly. The second part deals with the electronic properties of molecules adsorbed on 2D materials. By virtue of being inert and possessing low density of states near the Fermi level, 2D materials decouple molecules electronically from the underlying metal substrate and allow high-resolution spectroscopy and imaging of molecular orbitals. The moiré pattern on the 2D materials causes site-selective gating and charging of molecules in some cases. The last section covers the effects of self-assembled, acceptor and donor type, organic molecules on the electronic properties of graphene as revealed by spectroscopy and electrical transport measurements. Non-covalent functionalization of 2D materials has already been applied for their application as catalysts and sensors. With the current surge of activity on building van der Waals heterostructures from atomically thin crystals, molecular self-assembly has the potential to add an extra level of flexibility and functionality for applications ranging from flexible electronics and OLEDs to novel electronic devices and spintronics.