Who talks to whom about what? How interdisciplinary communication and knowledge of expertise distribution improve in integrated R&D labs

Although several studies have examined the impact of open workspaces, there is still an on-going debate about its advantages and disadvantages. Our paper contributes to this debate by shedding light on three issues: the effect of open workspaces on (1) the flow of communication along and across hierarchical lines; (2) the content of communication; and (3) the specificities of open integrated laboratories. Our findings derive from a longitudinal case in a large pharmaceutical company that has relocated some R&D teams from enclosed to multi-space offices and labs. The relocation has resulted in (a) increased interdisciplinary communication, particularly at lower hierarchical levels, (b) a shift of the location of discussions and the content of conversations and (c) an improved knowledge about expertise distribution.

Practitioner Summary: Communication is essential in knowledge-driven organisations. This article examines the impact of a relocation of R&D employees from enclosed to multi-space offices and labs on communication patterns. We explain how the new environment fosters interdisciplinary communication, shifts the location of discussions and increases the knowledge of expertise distribution.     

Is computational complexity a barrier to manipulation?

When agents are acting together, they may need a simple mechanism to decide on joint actions. One possibility is to have the agents express their preferences in the form of a ballot and use a voting rule to decide the winning action(s). Unfortunately, agents may try to manipulate such an election by mis-reporting their preferences. Fortunately, it has been shown that it is NP-hard to compute how to manipulate a number of different voting rules. However, NP-hardness only bounds the worst-case complexity. In this survey article, we summarize the evidence for and against computational complexity being a barrier to manipulation. We look both at techniques identified to increase complexity (for example, hybridizing together two or more voting rules), as well as other features that may change the computational complexity of computing a manipulation (for example, if votes are restricted to be single peaked then some of the complexity barriers fall away). We discuss recent theoretical results that consider the average case, as well as simple greedy and approximate methods. We also describe how computational ??phase transitions??, which have been fruitful in identifying hard instances of propositional satisfiability and other NP-hard problems, have provided insight into the hardness of manipulating voting rules in practice. Finally, we consider manipulation of other related problems like stable marriage and tournament problems.     

How can proteomics elucidate the complexity of multiple sclerosis?

Multiple sclerosis affects more than 2.5 million people worldwide. Although multiple sclerosis was described almost 150 years ago, there are many knowledge gaps regarding its etiology, diagnosis, prognosis, and pathogenesis. Multiple sclerosis is an inflammatory, demyelinating, neurodegenerative disease of the CNS. During the last several decades, experimental models of multiple sclerosis have contributed to our understanding of the inflammatory disease mechanisms and have aided drug testing and development. However, little is known about the neurodegenerative mechanisms that operate during the evolution of the disease. Currently, all therapeutic approaches are primarily based on the inflammatory aspect of the disease. During the last decade, proteomics has emerged as a promising tool for revealing molecular pathways as well as identifying and quantifying differentially expressed proteins. Therefore, proteomics may be used for the discovery of biomarkers, potential drug targets, and new regulatory mechanisms. To date, a considerable number of proteomics studies have been conducted on samples from experimental models and patients with multiple sclerosis. These data form a solid base for further careful analysis and validation.     

How to Guard a Graph?

We initiate the study of the algorithmic foundations of games in which a set of cops has to guard a region in a graph (or digraph) against a robber. The robber and the cops are placed on vertices of the graph; they take turns in moving to adjacent vertices (or staying). The goal of the robber is to enter the guarded region at a vertex with no cop on it. The problem is to find the minimum number of cops needed to prevent the robber from entering the guarded region. The problem is highly non-trivial even if the robber’s or the cops’ regions are restricted to very simple graphs. The computational complexity of the problem depends heavily on the chosen restriction. In particular, if the robber’s region is only a path, then the problem can be solved in polynomial time. When the robber moves in a tree (or even in a star), then the decision version of the problem is NP-complete. Furthermore, if the robber is moving in a directed acyclic graph, the problem becomes PSPACE-complete.     

The “log rank” conjecture for modular communication complexity

The “log rank” conjecture involves the question of how precisely the deterministic communication complexity of a problem can be described in terms of algebraic invariants of the communication matrix of this problem. We answer this question in the context of modular communication complexity. We show that the modular communication complexity can be exactly characterised in terms of the logarithm of a certain rigidity function of the communication matrix. Thus, we are able to exactly determine the modular communication complexity of several problems, such as, e.g., set disjointness, comparability, and undirected graph connectivity. From the bounds obtained for the modular communication complexity we deduce exponential lower bounds on the size of depth-two circuits having arbitrary symmetric gates at the bottom level and a MOD _m -gate at the top. Received: April 14, 2000.     

How can multimodal cues from child-directed interaction reduce learning complexity in robots?

Robots have to deal with an enormous amount of sensory stimuli. One solution in making sense of them is to enable a robot system to actively search for cues that help structuring the information. Studies with infants reveal that parents support the learning-process by modifying their interaction style, dependent on their child's developmental age. In our study, in which parents demonstrated everyday actions to their preverbal children (8–11 months old), our aim was to identify objective parameters for multimodal action modification. Our results reveal two action parameters being modified in adult–child interaction: roundness and pace. Furthermore, we found that language has the power to help children structuring actions sequences by synchrony and emphasis. These insights are discussed with respect to the built-in attention architecture of a socially interactive robot, which enables it to understand demonstrated actions. Our algorithmic approach towards automatically detecting the task structure in child-designed input demonstrates the potential impact of insights from developmental learning on robotics. The presented findings pave the way to automatically detect when to imitate in a demonstration task.     

Frameworks: How to Build One?

Frameworks are useful guides to the thought processes of information security professionals for building their solutions. These frameworks are not solutions, only guides. They ensure that nothing is left out and that the work is done thoroughly and well. The quality of frameworks unfortunately is not consistent. Following a framework that is not fitting to the business requirements can create false assurance. A methodology is discussed in this paper about building a fitting framework. Asking pertinent questions forms the basis for such framework. The questions and the process of asking those questions determine the quality of the solution. A set of questions are described as examples, and explained how they define the areas that are necessary to enable a sound solution development. Some common errors and misconceptions are highlighted together with pointers to how they can be avoided or overcome. The methodology for developing those areas identified by the questions completes the paper.     

Time–space complexity of quantum search algorithms in symmetric cryptanalysis: applying to AES and SHA-2

Performance of cryptanalytic quantum search algorithms is mainly inferred from query complexity which hides overhead induced by an implementation. To shed light on quantitative complexity analysis removing hidden factors, we provide a framework for estimating time–space complexity, with carefully accounting for characteristics of target cryptographic functions. Processor and circuit parallelization methods are taken into account, resulting in the time–space trade-off curves in terms of depth and qubit. The method guides how to rank different circuit designs in order of their efficiency. The framework is applied to representative cryptosystems NIST referred to as a guideline for security parameters, reassessing the security strengths of AES and SHA-2.     

How to Wear Beautifully? Clothing Pair Recommendation

In this paper, we present a practical system to automatically suggest the most pairing clothing items, given the reference clothing (upper-body or low-body). This has been a challenge, due to clothes having a variety of categories. Clothing is one of the most informative cues for human appearance. In our daily life, people need to wear properly and beautifully to show their confidence, politeness and social status in various occasions. However, it is not easy to decide what to wear and how to coordinate their own clothes. To address this problem, we propose a recommendation approach that includes clothing region detection, clothing pair recommendation and distance fusion. Clothing region detection based on Faster R-CNN is used to detect clothing region. Clothing pair recommendation consists of a quadruple network architecture, where one dual network of the architecture adopts Siamese convolution neural network architecture. Training examples are pairs of upper-body and low-body clothing items that are either compatible or incompatible. The other dual network is used to learn clothing style features of the input image. This framework is designed to learn a feature transformation from the images of clothing items into two latent spaces, which we call them compatible space and style space respectively. After training the two dual networks, we use a distance fusion method to fuse the features extracted from the compatible and style dual networks. To acquire an optimized model and verify our proposed method, we expand an existing large clothing dataset WoG (Weather-to-Garment), and the resulted dataset is called “How to Wear Beautifully” (H2WB). Experiments on the H2WB dataset demonstrate that our approach is effective with clothing region detection and clothing pair recommendation as well as distance fusion.     

How to improve collaborative learning with video tools in the classroom? Social vs. cognitive guidance for student teams

Digital video technologies offer a variety of functions for supporting collaborative learning in classrooms. Yet, for novice learners, such as school students, positive learning outcomes also depend centrally on effective social interactions. We present empirical evidence for the positive effects of instructive guidance on performance and on learning of students who use web-based video tools during a short collaborative-design task in their history lesson. In an experiment with 16-year old learners (N?=?148) working on a history topic, we compared two contrasting types of guidance for student teams?? collaboration processes (social-interaction-related vs. cognitive-task-related guidance). We also compared two types of advanced video tools. Both types of guidance and tools were aimed at supporting students?? active, meaningful learning and critical analysis of a historical newsreel. Results indicated that social-interaction-related guidance was more effective in terms of learning outcomes (e.g., the students?? history skills) than cognitive-task-related guidance. The different tools did not yield consistent results. The implications of these findings are discussed.     

How do we measure and improve the quality of a hierarchical ontology?

Hierarchical ontologies enable organising information in a human-machine understandable form, but constructing them for reuse and maintainability remains difficult. Often supporting tools available lack formal methodological underpinning and their developers are not supported by any concomitant metrics. The paper presents a formal underpinning to provide quality metrics of a taxonomy hierarchical ontology and proposes a methodology for semi-automatic building of maintainable taxonomies. Users provide terms to be used to describe different ontological elements as well as their attributes and their ranges of values. The methodology uses the formalised metrics to assess the quality of the users input and proposes changes according to given quality constraints. The paper illustrates the metrics and the methodology in constructing and repairing two medium size well-known taxonomies.     

How to shoot yourself right with a smartphone?

Virtual Reality - This article presents a new use case of using handheld augmented reality to set up a smartphone camera for taking a self-photograph needed for evaluating the user’s sports...     

How does business analytics contribute to business value?

This paper presents a model, synthesized from the literature, of factors that explain how business analytics contributes to business value. It also reports results from a preliminary assessment of that model. The model consists of two parts: a process and a variance model. The process model depicts the analyze‐insight‐decision‐action process through which use of an organization's business analytic capabilities is intended to create business value. The variance model proposes that the five factors in Davenport et al.'s DELTA model of business analytics success factors, six from Watson & Wixom and three from Seddon et al.'s model of organizational benefits from enterprise systems, assist a firm to gain business value from business analytics. A preliminary assessment of the model was conducted using data from 100 customer success stories from vendors such as IBM, SAP and Teradata. Our conclusion is that the business analytics success model is likely to be a useful basis for future research.     

How hard is it to control a group?

We consider group identification models in which the aggregation of individual opinions concerning who is qualified in a given society determines the set of socially qualified persons. In this setting, we study the extent to which social qualification can be changed when societies expand, shrink, or partition themselves. The answers we provide are with respect to the computational complexity of the corresponding control problems and fully cover the class of consent aggregation rules introduced by Samet and Schmeidler (J Econ Theory, 110(2):213–233, 2003) as well as procedural rules for group identification. We obtain both polynomial-time solvability results and NP-hardness results. In addition, we also study these problems from the parameterized complexity perspective, and obtain some fixed-parameter tractability results.     

How to localize humanoids with a single camera?

In this paper, we propose a real-time vision-based localization approach for humanoid robots using a single camera as the only sensor. In order to obtain an accurate localization of the robot, we first build an accurate 3D map of the environment. In the map computation process, we use stereo visual SLAM techniques based on non-linear least squares optimization methods (bundle adjustment). Once we have computed a 3D reconstruction of the environment, which comprises of a set of camera poses (keyframes) and a list of 3D points, we learn the visibility of the 3D points by exploiting all the geometric relationships between the camera poses and 3D map points involved in the reconstruction. Finally, we use the prior 3D map and the learned visibility prediction for monocular vision-based localization. Our algorithm is very efficient, easy to implement and more robust and accurate than existing approaches. By means of visibility prediction we predict for a query pose only the highly visible 3D points, thus, speeding up tremendously the data association between 3D map points and perceived 2D features in the image. In this way, we can solve very efficiently the Perspective-n-Point (PnP) problem providing robust and fast vision-based localization. We demonstrate the robustness and accuracy of our approach by showing several vision-based localization experiments with the HRP-2 humanoid robot.     

How close are we to understanding v1?

A wide variety of papers have reviewed what is known about the function of primary visual cortex. In this review, rather than stating what is known, we attempt to estimate how much is still unknown about V1 function. In particular, we identify five problems with the current view of V1 that stem largely from experimental and theoretical biases, in addition to the contributions of nonlinearities in the cortex that are not well understood. Our purpose is to open the door to new theories, a number of which we describe, along with some proposals for testing them.     

A reinforcement learning approach to fail-safe design for multiple space robots—cooperation mechanism without communication and negotiation schemes

This paper explores a fail-safe design for multiple space robots, which enables robots to complete given tasks even when they can no longer be controlled due to a communication accident or negotiation problem. As the first step towards this goal, we propose new reinforcement learning methods that help robots avoid deadlock situations in addition to improving the degree of task completion without communications via ground stations or negotiations with other robots. Through intensive simulations on a truss construction task, we found that our reinforcement learning methods have great potential to contribute towards fail-safe design for multiple space robots in the above case. Furthermore, the simulations revealed the following detailed implications: (i) the first several planned behaviors must not be reinforced with negative rewards even in deadlock situations in order to derive cooperation among multiple robots, (ii) a certain amount of positive rewards added into negative rewards in deadlock situations contributes to reducing the computational cost of finding behavior plans for task completion, and (iii) an appropriate balance between positive and negative rewards in deadlock situations is indispensable for finding good behavior plans at a small computational cost.