Low-level Interactions Research Articles

Emergence of Social Networks via Direct and Indirect Reciprocity

Many models of social network formation implicitly assume that network properties are static in steady-state. In contrast, actual social networks are highly dynamic: allegiances and collaborations expire and may or may not be renewed at a later date. Moreover, empirical studies show that human social networks are dynamic at the individual level but static at the global level: individuals’ degree rankings change considerably over time, whereas network-level metrics such as network diameter and clustering coefficient are relatively stable. There have been some attempts to explain these properties of empirical social networks using agent-based models in which agents play social dilemma games with their immediate neighbours, but can also manipulate their network connections to strategic advantage. However, such models cannot straightforwardly account for reciprocal behaviour based on reputation scores (“indirect reciprocity”), which is known to play an important role in many economic interactions. In order to account for indirect reciprocity, we model the network in a bottom-up fashion: the network emerges from the low-level interactions between agents. By so doing we are able to simultaneously account for the effect of both direct reciprocity (e.g. “tit-for-tat”) as well as indirect reciprocity (helping strangers in order to increase one’s reputation). This leads to a strategic equilibrium in the frequencies with which strategies are adopted in the population as a whole, but intermittent cycling over different strategies at the level of individual agents, which in turn gives rise to social networks which are dynamic at the individual level but stable at the network level.

Heterogeneous auditory–visual integration: Effects of pitch, band-width and visual eccentricity

The identification of monosynaptic connections between primary cortices in non-human primates has recently been complemented by observations of early-latency and low-level non-linear interactions in brain responses in humans as well as observations of facilitative effects of multisensory stimuli on behavior/performance in both humans and monkeys. While there is some evidence in favor of causal links between early–latency interactions within low-level cortices and behavioral facilitation, it remains unknown if such effects are subserved by direct anatomical connections between primary cortices. In non-human primates, the above monosynaptic projections from primary auditory cortex terminate within peripheral visual field representations within primary visual cortex, suggestive of there being a potential bias for the integration of eccentric visual stimuli and pure tone (vs. broad-band) sounds. To date, behavioral effects in humans (and monkeys) have been observed after presenting (para)foveal stimuli with any of a range of auditory stimuli from pure tones to noise bursts. The present study aimed to identify any heterogeneity in the integration of auditory–visual stimuli. To this end, we employed a 3 × 3 within subject design that varied the visual eccentricity of an annulus (2.5°, 5.7°, 8.9°) and auditory pitch (250, 1000, 4000 Hz) of multisensory stimuli while subjects completed a simple detection task. We also varied the auditory bandwidth (pure tone vs. pink noise) across blocks of trials that a subject completed. To ensure attention to both modalities, multisensory stimuli were equi-probable with both unisensory visual and unisensory auditory trials that themselves varied along the abovementioned dimensions. Median reaction times for each stimulus condition as well as the percentage gain/loss of each multisensory condition vs. the best constituent unisensory condition were measured. The preliminary results reveal that multisensory interactions (as measured from simple reaction times) are indeed heterogeneous across the tested dimensions and may provide a means for delimiting the anatomo-functional substrates of behaviorally-relevant early–latency neural response interactions. Interestingly, preliminary results suggest selective interactions for visual stimuli when presented with broadband stimuli but not when presented with pure tones. More precisely, centrally presented visual stimuli show the greatest index of multisensory facilitation when coupled to a high pitch tone embedded in pink noise, while visual stimuli presented at approximately 5.7° of visual angle show the greatest slowing of reaction times.

Interactions between Senses: Updating on Neural Mechanisms and Behavioral Evidence

Interactions between Senses: Updating on Neural Mechanisms and Behavioral Evidence

Towards Implicitly Thwarting Stealth Malware with a Feather-Weight Hypervisor

Presently, stealth malware has become a fatal threat to the PC platforms. Existing host-based security solutions suffer the difficulty to thwart the privileged malware, while previous hypervisor-based systems cannot protect preinstalled OSes because they have to introduce a heavyweight hypervisor layer and reinstall the OSes. In this paper, we propose a novel feather-weight hardware-assisted hypervisor, called ScalerVisor, to detect and clear the stealth malware. ScalerVisor offers four distinct advantages: preinstalled commodity OS compatibility, implicit detection of stealth malware, hypervisor-layer malware clearing and non-bypassable self-protection. Unlike typical hypervisors, deploying ScalerVisor does not require OS reinstallation. By intercepting the low-level interactions between the OS and the hardware, ScalerVisor is decoupled with the explicit OS implementation information which is subvertable for the privileged malware. To the best of our knowledge, ScalerVisor is the first approach that can manipulate various OS resources at a feather-weight hypervisor layer. ScalerVisor also can remove the malicious files without the help of OS. Besides, ScalerVisor provides a capability of protecting itself in a non-bypassable way. Our functionality evaluation shows ScalerVisor can impede more stealth malware than existing detectors while the performance evaluation shows desktop-oriented workloads achieve 93.28% of native speed on average.

An analytical study of the interaction of low levels of formaldehyde with active pharmaceuticals

An analytical study of the interaction of low levels of formaldehyde with active pharmaceuticals

The Genetics of Conflict: Low Level Interaction between Conflict Events

Conflict observations are not independent of each other, therefore studies are often statistically weak or simply invalid. With military dataset from Iraq, Afghanistan and Vietnam, I analysis the interaction between events utilizing the dependence as an advantage. For Viet Cong & Taliban activity, I find that the local interaction between events is the essential factor. On that basis I conclude that in conventional insurgencies, attacks are mainly coordinated at a local level.

Synthesis and redox properties of triarylmethane dyecation salts of anions [M6O19]2−(M = Mo, W)

Four salts have been isolated combining the triarylmethane dye cations pararosaniline (PR(+)) and crystal violet (CV(+)) with the hexametalates [M(6)O(19)](2-) (M = Mo, W). A new hexatungstic acid H(2)[W(6)O(19)]·4dma (dma = dimethylacetamide) was isolated and is a useful synthon for hexatungstate salts. Single-crystal X-ray diffraction confirmed the presence of PR(+) and [Mo(6)O(19)](2-) ions in [PR](2)[Mo(6)O(19)]·6dmf (dmf = dimethylformamide). A number of charge-assisted hydrogen bonds N-H···O exist between the cation -NH(2) functions and the anion oxygen atoms. Comparative cyclic voltammetry of salts [A]Cl (A = PR, CV), [Bu(4)N](2)[M(6)O(19)](2-) and A(2)[M(6)O(19)] was established in MeCN and Me(2)SO solutions and of solids in contact with the ionic liquid 1-ethyl-3-methylimidazolium bis(trifluoromethanesulfonyl)amide [emim][tfsa]. In the molecular solvents, the reversible potential for the process [Mo(6)O(19)](2-/3-) is less negative than the first reduction processes of the dye cations. In contrast, that for [W(6)O(19)](2-/3-) is more negative. Spectro-electrochemistry and bulk electrolysis experiments reveal significantly different pathways in the two cases. In contrast, in the [emim][tfsa] medium, a positive shift in reduction potential of at least 400 mV is seen for the anion processes but relatively little change for the dye cation processes. This means that initial reduction of the anions always precedes that of the dyes, providing significant simplification of the complex voltammetric data. Chemically modified electrodes can be used in the ionic liquid because of slow dissolution kinetics. However, reduced anion salts dissolve rapidly, allowing dissolved phase electrochemistry to be examined. The electrochemistries of the oxidized salts A(2)[M(6)O(19)] are essentially those of the individual ions, although low level interaction of A(+) with reduced anions [M(6)O(19)](3-,4-) is evident. The work establishes protocols for synthesis and handling of intensely absorbing and relatively insoluble salts which can now be applied to systems containing more complex polyoxometalate anions.

The role of students' motivation in peer-moderated asynchronous online discussions

This study conducted a content analysis of online discussions to understand the nature of computer-supported collaborative learning and discover how students' motivation, which is a crucial factor to the success of collaborative learning, relates with their interaction and knowledge construction in peer-moderated online discussions. Discussion contents from 23 students in an online class were analysed. The results indicated that perceived value, competence and autonomy were critical factors that influenced lower level interactions; intrinsic motivation was the critical factor that influenced the individualistic elaboration interactions, whereas relatedness was the critical factor that influenced the collaborative elaboration interactions. The results also indicated that autonomy and relatedness were the critical factors that influenced the moderation behaviours. The findings suggest that teachers in online classes should promote students' motivation, and more importantly, scaffold student moderators in meaningful learning during peer-moderated online discussions.

Biological Validation of Increased Schizophrenia Risk With NRG1, ERBB4, and AKT1 Epistasis via Functional Neuroimaging in Healthy Controls

NRG1 is a schizophrenia candidate gene and plays an important role in brain development and neural function. Schizophrenia is a complex disorder, with etiology likely due to epistasis. To examine epistasis between NRG1 and selected N-methyl-d-aspartate-glutamate pathway partners implicated in its effects, including ERBB4, AKT1, DLG4, NOS1, and NOS1AP. Schizophrenia case-control sample analyzed using machine learning algorithms and logistic regression with follow-up using neuroimaging on an independent sample of healthy controls. A referred sample of schizophrenic patients (n = 296) meeting DSM-IV criteria for schizophrenia spectrum disorder and a volunteer sample of controls for case-control comparison (n = 365) and a separate volunteer sample of controls for neuroimaging (n = 172). Epistatic association between single-nucleotide polymorphisms (SNPs) and case-control status; epistatic association between SNPs and the blood oxygen level-dependent physiological response during working memory measured by functional magnetic resonance imaging. We observed interaction between NRG1 5' and 3' SNPs rs4560751 and rs3802160 (likelihood ratio test P = .00020) and schizophrenia, which was validated using functional magnetic resonance imaging of working memory in healthy controls; carriers of risk-associated genotypes showed inefficient processing in the dorsolateral prefrontal cortex (P = .015, familywise error corrected). We observed epistasis between NRG1 (rs10503929; Thr286/289/294Met) and its receptor ERBB4 (rs1026882; likelihood ratio test P = .035); a 3-way interaction with these 2 SNPs and AKT1 (rs2494734) was also observed (odds ratio, 27.13; 95% confidence interval, 3.30-223.03; likelihood ratio test P = .042). These same 2- and 3-way interactions were further biologically validated via functional magnetic resonance imaging: healthy individuals carrying risk genotypes for NRG1 and ERBB4, or these 2 together with AKT1, were disproportionately less efficient in dorsolateral prefrontal cortex processing. Lower-level interactions were not observed between NRG1 /ERBB4 and AKT1 in association or neuroimaging, consistent with biological evidence that NRG1 × ERBB4 interaction modulates downstream AKT1 signaling. Our data suggest complex epistatic effects implicating an NRG1 molecular pathway in cognitive brain function and the pathogenesis of schizophrenia.

The mechanism of word crowding

Word reading speed in peripheral vision is slower when words are in close proximity of other words (Chung, 2004). This word crowding effect could arise as a consequence of interaction of low-level letter features between words, or the interaction between high-level holistic representations of words. We evaluated these two hypotheses by examining how word crowding changes for five configurations of flanking words: the control condition - flanking words were oriented upright; scrambled - letters in each flanking word were scrambled in order; horizontal-flip - each flanking word was the left-right mirror-image of the original; letter-flip - each letter of the flanking word was the left-right mirror-image of the original; and vertical-flip - each flanking word was the up-down mirror-image of the original. The low-level letter feature interaction hypothesis predicts similar word crowding effect for all the different flanker configurations, while the high-level holistic representation hypothesis predicts less word crowding effect for all the alternative flanker conditions, compared with the control condition. We found that oral reading speed for words flanked above and below by other words, measured at 10° eccentricity in the nasal field, showed the same dependence on the vertical separation between the target and its flanking words, for the various flanker configurations. The result was also similar when we rotated the flanking words by 90° to disrupt the periodic vertical pattern, which presumably is the main structure in words. The remarkably similar word crowding effect irrespective of the flanker configurations suggests that word crowding arises as a consequence of interactions of low-level letter features.

Lack of Assertion, Peer Victimization, and Risk for Depression in Girls: Testing a Diathesis–Stress Model

To apply a diathesis × stress model to testing the association between peer victimization and depression in a sample of preadolescent girls. DSM-IV symptoms of depression symptoms were measured at ages 9 and 11, assertiveness and peer victimization were assessed by youth report at age 9. The interaction of low levels of assertiveness and high peer victimization at age 9 was predictive of depression symptoms at age 11, controlling for earlier depression symptoms. The results extend the published data on peer relations and depression by identifying a group of girls who may be particularly vulnerable to the stress of negative peer interactions.

Multimodal and mobile conversational Health and Fitness Companions

Multimodal conversational spoken dialogues using physical and virtual agents provide a potential interface to motivate and support users in the domain of health and fitness. This paper describes how such multimodal conversational Companions can be implemented to support their owners in various pervasive and mobile settings. We present concrete system architectures, virtual, physical and mobile multimodal interfaces, and interaction management techniques for such Companions. In particular how knowledge representation and separation of low-level interaction modelling from high-level reasoning at the domain level makes it possible to implement distributed, but still coherent, interaction with Companions. The distribution is enabled by using a dialogue plan to communicate information from domain level planner to dialogue management and from there to a separate mobile interface. The model enables each part of the system to handle the same information from its own perspective without containing overlapping logic, and makes it possible to separate task-specific and conversational dialogue management from each other. In addition to technical descriptions, results from the first evaluations of the Companions interfaces are presented.

Bigraphical models for protein and membrane interactions

We present a bigraphical framework suited for modeling biological systems both at protein level and at membrane level. We characterize formally bigraphs corresponding to biologically meaningful systems, and bigraphic rewriting rules representing biologically admissible interactions. At the protein level, these bigraphic reactive systems correspond exactly to systems of kappa-calculus. Membrane-level interactions are represented by just two general rules, whose application can be triggered by protein-level interactions in a well-de\"ined and precise way. This framework can be used to compare and merge models at different abstraction levels; in particular, higher-level (e.g. mobility) activities can be given a formal biological justification in terms of low-level (i.e., protein) interactions. As examples, we formalize in our framework the vesiculation and the phagocytosis processes.

A framework for protein and membrane interactions

We introduce the BioBeta Framework, a meta-model for both protein-level and membrane-level interactions of living cells. This formalism aims to provide a formal setting where to encode, compare and merge models at different abstraction levels; in particular, higher-level (e.g. membrane) activities can be given a formal biological justification in terms of low-level (i.e., protein) interactions. A BioBeta specification provides a protein signature together a set of protein reactions, in the spirit of the kappa-calculus. Moreover, the specification describes when a protein configuration triggers one of the only two membrane interaction allowed, that is pinch and fuse. In this paper we define the syntax and semantics of BioBeta, analyse its properties, give it an interpretation as biobigraphical reactive systems, and discuss its expressivity by comparing with kappa-calculus and modelling significant examples. Notably, BioBeta has been designed after a bigraphical metamodel for the same purposes. Hence, each instance of the calculus corresponds to a bigraphical reactive system, and vice versa (almost). Therefore, we can inherith the rich theory of bigraphs, such as the automatic construction of labelled transition systems and behavioural congruences.

Automated verification of practical garbage collectors

Garbage collectors are notoriously hard to verify, due to their low-level interaction with the underlying system and the general difficulty in reasoning about reachability in graphs. Several papers have presented verified collectors, but either the proofs were hand-written or the collectors were too simplistic to use on practical applications. In this work, we present two mechanically verified garbage collectors, both practical enough to use for real-world C# benchmarks. The collectors and their associated allocators consist of x86 assembly language instructions and macro instructions, annotated with preconditions, postconditions, invariants, and assertions. We used the Boogie verification generator and the Z3 automated theorem prover to verify this assembly language code mechanically. We provide measurements comparing the performance of the verified collector with that of the standard Bartok collectors on off-the-shelf C# benchmarks, demonstrating their competitiveness.

Communicating neurons: A connectionist spiking neuron implementation of stochastic diffusion search

An information processing paradigm in the brain is proposed, instantiated in an artificial neural network using biologically motivated temporal encoding. The network will locate within the external world stimulus, the target memory, defined by a specific pattern of micro-features. The proposed network is robust and efficient. Akin in operation to the swarm intelligence paradigm, stochastic diffusion search, it will find the best-fit to the memory with linear time complexity. Information multiplexing enables neurons to process knowledge as ‘tokens’ rather than ‘types’. The network illustrates possible emergence of cognitive processing from low level interactions such as memory retrieval based on partial matching.

Interaction Between Forex and Stock Markets in India: An Entropy Approach

Interactions between the foreign exchange market and the stock market of a country are considered to be an important internal force of the markets in a financially liberalized environment. If causal relationship from a market to the other is not detected, then informational efficiency exists in the other whereas existence of causality implies that hedging of exposure to one market by taking position in the other market will be effective. The temporal relationship between the forex market and the stock market of developing and developed countries has been studied, especially after the East Asian financial crisis of 1997–98, using various methods like cross-correlation, cross-spectrum, and error correction model, but these methods identify only linear relations. A statistically rigorous approach to the detection of interdependence, including non-linear dynamic relationships, between time series is provided by tools defined using the information theoretic concept of entropy. Entropy is the amount of disorder in the system and also is the amount of information needed to predict the next measurement with a certain precision. The mutual information between two random variables X and Y with a joint probability mass function p(x,y) and marginal mass functions p(x) and p(y), is defined as the relative entropy between the joint distribution p(x,y) and the product distribution p(x)*p(y). Mutual information is the reduction in the uncertainty of X due to the knowledge of Y and vice versa. Since mutual information measures the deviation from independence of the variables, it has been proposed as a tool to measure the relationship between financial market segments. However, mutual information is a symmetric measure and does not contain either dynamic information or directional sense. Even time delayed mutual information does not distinguish information actually exchanged from shared information due to a common input signal or history and therefore does not quantify the actual overlap of the information content of two variables. Another information theoretic measure called transfer entropy has been introduced by Thomas Schreiber (2000) to study the relationship between dynamic systems; the concept has also been applied by some authors to study the causal structure between financial time series. In this paper, an attempt has been made to study the interaction between the stock and the forex markets in India by computing transfer entropy between daily data series of the 50 stock index of the National Stock Exchange of India Limited, viz., Nifty and the exchange rate of Indian Rupee vis- à- vis US Dollar, viz., Reserve Bank of India reference rate. The entire period–November 1995 to March 2007–selected for the study, has been divided into three sub-periods for the purpose of analysis, considering the developments that took place during these sub-periods. The results obtained reveal that: there exist only low level interactions between the stock and the forex markets of India at a time scale of a day or less, although theory suggests interactive relationship between the two markets the flow from the stock market to the forex market is more pronounced than the flow in the reverse direction.

AppMonitor: A tool for recording user actions in unmodified Windows applications

This article describes AppMonitor, a Microsoft Windows-based client-side logging tool that records user actions in unmodified Windows applications. AppMonitor allows researchers to gain insights into many facets of interface interaction such as command use frequency, behavioral patterns prior to or following command use, and methods of navigating through systems and data sets. AppMonitor uses the Windows SDK libraries to monitor both low-level interactions, such as "left mouse button pressed" and "Ctrl-F pressed," as well as high-level "logical" actions, such as menu selections and scrollbar manipulations. The events recorded are configurable, allowing researchers to perform broad or targeted studies. No user input is required to manage logging, allowing participants to seamlessly conduct everyday work while their actions are monitored. The system currently supports logging in Microsoft Word and Adobe Reader; however, it could be extended for use with any Microsoft Windows-based application. To support other researchers wishing to create multilevel event loggers, we describe AppMonitor's underlying architecture and implementation, and provide a brief example of the data generated during our 4-month trial with six users.

Toward a Deeper Understanding of the Role of Interaction in Information Visualization

Even though interaction is an important part of information visualization (Infovis), it has garnered a relatively low level of attention from the Infovis community. A few frameworks and taxonomies of Infovis interaction techniques exist, but they typically focus on low-level operations and do not address the variety of benefits interaction provides. After conducting an extensive review of Infovis systems and their interactive capabilities, we propose seven general categories of interaction techniques widely used in Infovis: 1) Select, 2) Explore, 3) Reconfigure, 4) Encode, 5) Abstract/Elaborate, 6) Filter, and 7) Connect. These categories are organized around a user's intent while interacting with a system rather than the low-level interaction techniques provided by a system. The categories can act as a framework to help discuss and evaluate interaction techniques and hopefully lay an initial foundation toward a deeper understanding and a science of interaction.

Toward a Deeper Understanding of the Role of Interaction in Information Visualization

Even though interaction is an important part of information visualization (Infovis), it has garnered a relatively low level of attention from the Infovis community. A few frameworks and taxonomies of Infovis interaction techniques exist, but they typically focus on low-level operations and do not address the variety of benefits interaction provides. After conducting an extensive review of Infovis systems and their interactive capabilities, we propose seven general categories of interaction techniques widely used in Infovis: 1) Select, 2) Explore, 3) Reconfigure, 4) Encode, 5) Abstract/Elaborate, 6) Filter, and 7) Connect. These categories are organized around a user's intent while interacting with a system rather than the low-level interaction techniques provided by a system. The categories can act as a framework to help discuss and evaluate interaction techniques and hopefully lay an initial foundation toward a deeper understanding and a science of interaction.