Exploiting coarse grained parallelism in conceptual data mining: finding a needle in a haystack as a distributed effort

A parallel implementation of Ganter’s algorithm to calculate concept lattices for Formal Concept Analysis is presented. A benchmark was executed to experimentally determine the algorithm’s performance, including an AMD Athlon64, Intel dual Xeon, and UltraSPARC T1, with respectively 1, 4, and 24 threads in parallel. Two subsets of Cranfield’s collection were chosen as document set. In addition, the theoretically maximum performance was determined. Due to scheduling problems, the performance of the UltraSPARC was disappointing. Two alternate schedulers are proposed to tackle this problem. It is shown that, given a good scheduler, the algorithm can massively exploit multi-threading architectures and so, substantially reduce the computational burden of Formal Concept Analysis

Deep Analogical Inference as the Origin of Hypotheses

The ability to generate novel hypotheses is an important problem-solving capacity of humans. This ability is vital for making sense of the complex and unfamiliar world we live in. Often, this capacity is characterized as an inference to the best explanation—selecting the “best” explanation from a given set of candidate hypotheses. However, it remains unclear where these candidate hypotheses originate from. In this paper we contribute to computationally explaining these origins by providing the contours of the computational problem solved when humans generate hypotheses. The origin of hypotheses, otherwise known as abduction proper, is hallmarked by seven properties: (1) isotropy, (2) open-endedness, (3) novelty, (4) groundedness, (5) sensibility, (6) psychological realism, and (7) computational tractability. In this paper we provide a computational-level theory of abduction proper that unifies the first six of these properties and lays the groundwork for the seventh property of computational tractability. We conjecture that abduction proper is best seen as a process of deep analogical inference

Intentional Communication: Computationally Easy or Difficult?

Human intentional communication is marked by its flexibility and context sensitivity. Hypothesized brain mechanisms can provide convincing and complete explanations of the human capacity for intentional communication only insofar as they can match the computational power required for displaying that capacity. It is thus of importance for cognitive neuroscience to know how computationally complex intentional communication actually is. Though the subject of considerable debate, the computational complexity of communication remains so far unknown. In this paper we defend the position that the computational complexity of communication is not a constant, as some views of communication seem to hold, but rather a function of situational factors. We present a methodology for studying and characterizing the computational complexity of communication under different situational constraints. We illustrate our methodology for a model of the problems solved by receivers and senders during a communicative exchange. This approach opens the way to a principled identification of putative model parameters that control cognitive processes supporting intentional communication

Highly versatile cell-penetrating peptide loaded scaffold for efficient and localised gene delivery to multiple cell types: From development to application in tissue engineering

Gene therapy has recently come of age with seven viral vector-based therapies gaining regulatory approval in recent years. In tissue engineering, non-viral vectors are preferred over viral vectors, however, lower transfection efficiencies and difficulties with delivery remain major limitations hampering clinical translation. This study describes the development of a novel multi-domain cell-penetrating peptide, GET, designed to enhance cell interaction and intracellular translocation of nucleic acids; combined with a series of porous collagen-based scaffolds with proven regenerative potential for different indications. GET was capable of transfecting cell types from all three germ layers, including stem cells, with an efficiency comparable to Lipofectamine® 3000, without inducing cytotoxicity. When implanted in vivo, GET gene-activated scaffolds allowed for host cell infiltration, transfection localized to the implantation site and sustained, but transient, changes in gene expression – demonstrating both the efficacy and safety of the approach. Finally, GET carrying osteogenic (pBMP-2) and angiogenic (pVEGF) genes were incorporated into collagen-hydroxyapatite scaffolds and with a single 2μg dose of therapeutic pDNA, induced complete repair of critical-sized bone defects within 4 weeks. GET represents an exciting development in gene therapy and by combining it with a scaffold-based delivery system offers tissue engineering solutions for a myriad of regenerative indications

Computational challenges in explaining communication: How deep the rabbit hole goes

When people are unsure of the intended meaning of a word, they often ask for clarification. One way of doing so-often assumed in models of communication-is to point at a potential target: "Do you mean [points at the rabbit]?'' However, what if the target is unavailable? Then the only recourse is language itself, which seems equivalent to pulling oneself up from a swamp by one's hair. We created two computational models of communication, one able to point and one not. The latter incorporates inference to resolve the meaning of non-pointing signals. Simulations show agents in both models reach perceived understanding equally quickly. While this means agents think they are successfully communicating, non-pointing agents understand each other only at chance level. This shows that state-of-the-art computational explanations have difficulty explaining how people solve the puzzle of underdetermination, and that doing so will require a fundamental leap forward

Compositionality, modularity, and the architecture of the language faculty

It is often assumed that language is strongly compositional, i.e., that the meaning of complex expressions is uniquely determined by the meanings of their constituents and their mode of composition (Fodor, 1987). Compositionality naturally connects to a broadly modular architecture of the language faculty, according to which our capacity for assigning meaning relies exclusively on lexical and syntactic knowledge (Baggio et al., 2015). Here, we discuss several arguments against strong compositionality. One such argument focuses on novel experimental data on the interpretation of privative adjectives (e.g., ‘fake’) (Partee, 2007). These data show that the interpretation of these adjectives is inexorably connected to the conceptual structure of the modified noun. We argue that lexical and syntactic information serve as important cues for, but do not uniquely determine, the process of meaning assignment (Martin, 2016). We discuss consequences for semantic theorising and the cognitive architecture of the language faculty

Computational challenges in explaining communication: How deep the rabbit hole goes

When people are unsure of the intended meaning of a word, they often ask for clarification. One way of doing so-often assumed in models of communication-is to point at a potential target: "Do you mean [points at the rabbit]?'' However, what if the target is unavailable? Then the only recourse is language itself, which seems equivalent to pulling oneself up from a swamp by one's hair. We created two computational models of communication, one able to point and one not. The latter incorporates inference to resolve the meaning of non-pointing signals. Simulations show agents in both models reach perceived understanding equally quickly. While this means agents think they are successfully communicating, non-pointing agents understand each other only at chance level. This shows that state-of-the-art computational explanations have difficulty explaining how people solve the puzzle of underdetermination, and that doing so will require a fundamental leap forward

Intractability of Bayesian belief-updating during communication

Imagine a friend asks: "could you pass me the dragonfruit''. You have no idea what they mean and inquire, "do you mean the orange fruit in the bowl?'', but they respond: "no, the pink one''. Now you know which fruit they want and in future contexts you will likely also understand their request. People seamlessly update beliefs about speakers' (word) meanings. Through interaction they somehow infer what "dragonfruit'' means: are all dragonfruits "pink'', or just this one? Is its shape more diagnostic, or some combination of the two? The number of possible belief updates is vast, growing exponentially with the number of features. Using formal complexity analysis, we prove that state-of-the-art models of communication are computationally intractable. Hence, they cannot yet explain how people can navigate this search space efficiently and communicate seamlessly. The intractability result holds for different model variants, suggesting a fundamental computational challenge for explaining communicative interaction