Modeling Cell-to-Cell Communication Networks Using Response-Time Distributions.

Cell-to-cell communication networks have critical roles in coordinating diverse organismal processes, such as tissue development or immune cell response. However, compared with intracellular signal transduction networks, the function and engineering principles of cell-to-cell communication networks are far less understood. Major complications include: cells are themselves regulated by complex intracellular signaling networks; individual cells are heterogeneous; and output of any one cell can recursively become an additional input signal to other cells. Here, we make use of a framework that treats intracellular signal transduction networks as "black boxes" with characterized input-to-output response relationships. We study simple cell-to-cell communication circuit motifs and find conditions that generate bimodal responses in time, as well as mechanisms for independently controlling synchronization and delay of cell-population responses. We apply our modeling approach to explain otherwise puzzling data on cytokine secretion onset times in T cells. Our approach can be used to predict communication network structure using experimentally accessible input-to-output measurements and without detailed knowledge of intermediate steps

Disappearing Sounds: Fragility in the music of Jakob Ullmann

The music of Jakob Ullmann (b. 1958) is notable for its protracted structural stasis and delicacy; its fusion of rigorously engineered notational systems, abstract graphical elements and Byzantine iconography; and – above all – its unrelenting quietness. This article offers a rare view into Ullmann's compositional practices, with a specific focus upon the role of fragility in the work. Exploring this concept of fragility as a musical feature, this article considers a number of Ullmann's works from the perspectives of the compositions and their scores, the performance and the agency of performers and, finally, how audiences may listen to this fragility. The article concludes with a consideration of the importance of fragility to Ullmann's oeuvre, and of how it might help us to further understand his music

Computing hypergraph width measures exactly

Hypergraph width measures are a class of hypergraph invariants important in studying the complexity of constraint satisfaction problems (CSPs). We present a general exact exponential algorithm for a large variety of these measures. A connection between these and tree decompositions is established. This enables us to almost seamlessly adapt the combinatorial and algorithmic results known for tree decompositions of graphs to the case of hypergraphs and obtain fast exact algorithms. As a consequence, we provide algorithms which, given a hypergraph H on n vertices and m hyperedges, compute the generalized hypertree-width of H in time O*(2^n) and compute the fractional hypertree-width of H in time O(m*1.734601^n).Comment: 12 pages, 1 figur

Apartment House: Wolff, Cage, ‘Performing Indeterminacy’, University of Leeds, 1 July 2017

At the beginning of July, the University of Leeds played host to the ‘Performing Indeterminacy’ conference: a series of talks, panels and concerts that are part of a research project on John Cage's Concert for Piano and Orchestra (1957–58), led by Philip Thomas and Martin Iddon. In the middle of all this, Apartment House presented what many consider the pinnacle of Cage's indeterminate work alongside a new commission from Christian Wolff, the last surviving member of the New York School composers. Resistance (2016–17), Wolff's new work ‘for 10 or more players and a pianist’, was written in response to Cage's Concert, sharing elements of its instrumentation and schema. In Leeds’ Clothworkers Hall, Apartment House – led by Anton Lukoszevieze – premiered the new piece alongside its progenitor, composed some 59 years apart. At the heart of both pieces in this concert is Philip Thomas at the piano. The conscientiousness and exactitude that Thomas brings to the music of both Cage and Wolff (having worked closely with the latter over the past 15 years) make him, perhaps, the ideal soloist for this programme. Quite simply, it is a line-up that could not have come about through chance procedure

Learning to Discriminate Through Long-Term Changes of Dynamical Synaptic Transmission

Short-term synaptic plasticity is modulated by long-term synaptic changes. There is, however, no general agreement on the computational role of this interaction. Here, we derive a learning rule for the release probability and the maximal synaptic conductance in a circuit model with combined recurrent and feedforward connections that allows learning to discriminate among natural inputs. Short-term synaptic plasticity thereby provides a nonlinear expansion of the input space of a linear classifier, whereas the random recurrent network serves to decorrelate the expanded input space. Computer simulations reveal that the twofold increase in the number of input dimensions through short-term synaptic plasticity improves the performance of a standard perceptron up to 100%. The distributions of release probabilities and maximal synaptic conductances at the capacity limit strongly depend on the balance between excitation and inhibition. The model also suggests a new computational interpretation of spikes evoked by stimuli outside the classical receptive field. These neuronal activitiesmay reflect decorrelation of the expanded stimulus space by intracortical synaptic connections

Probabilistic Model Counting with Short XORs

The idea of counting the number of satisfying truth assignments (models) of a formula by adding random parity constraints can be traced back to the seminal work of Valiant and Vazirani, showing that NP is as easy as detecting unique solutions. While theoretically sound, the random parity constraints in that construction have the following drawback: each constraint, on average, involves half of all variables. As a result, the branching factor associated with searching for models that also satisfy the parity constraints quickly gets out of hand. In this work we prove that one can work with much shorter parity constraints and still get rigorous mathematical guarantees, especially when the number of models is large so that many constraints need to be added. Our work is based on the realization that the essential feature for random systems of parity constraints to be useful in probabilistic model counting is that the geometry of their set of solutions resembles an error-correcting code.Comment: To appear in SAT 1

Parameterized Compilation Lower Bounds for Restricted CNF-formulas

We show unconditional parameterized lower bounds in the area of knowledge compilation, more specifically on the size of circuits in decomposable negation normal form (DNNF) that encode CNF-formulas restricted by several graph width measures. In particular, we show that - there are CNF formulas of size $n$ and modular incidence treewidth $k$ whose smallest DNNF-encoding has size $n^{\Omega(k)}$, and - there are CNF formulas of size $n$ and incidence neighborhood diversity $k$ whose smallest DNNF-encoding has size $n^{\Omega(\sqrt{k})}$. These results complement recent upper bounds for compiling CNF into DNNF and strengthen---quantitatively and qualitatively---known conditional low\-er bounds for cliquewidth. Moreover, they show that, unlike for many graph problems, the parameters considered here behave significantly differently from treewidth