Weighted logics for artificial intelligence : an introductory discussion

International audienceBefore presenting the contents of the special issue, we propose a structured introductory overview of a landscape of the weighted logics (in a general sense) that can be found in the Artificial Intelligence literature, highlighting their fundamental differences and their application areas

Understanding complex constructions: a quantitative corpus-linguistic approach to the processing of english relative clauses

Die vorliegende Arbeit präsentiert einen korpusbasierten Ansatz an die kognitive Verarbeitung komplexer linguistische Konstruktionen am Beispiel englischer Relativsatzkonstruktionen (RCC). Im theoretischen Teil wird für eine konstruktionsgrammatische Perspektive auf sprachliches Wissen argumentiert, welche erlaubt, RCCs als schematische Konstruktionen zu charakterisieren. Diese Perspektive wird mit Konzeptionen exemplarbasierter Modelle menschlicher Sprachverarbeitung zusammengeführt, welche die Verarbeitung einer linguistischen Struktur als Funktion von der Häufigkeit vergangener Verarbeitungen typidentischer Vorkommnisse begreift. Häufige Strukturen gelangen demnach zu einem priviligierten Status im kognitiven System eines Sprechers, welcher in konstruktionsgrammatischen Theorien als entrenchment bezeichnet wird. Während der jeweilge entrenchment-Wert einer gegebenen Konstruktion für konkrete Zeichen vergleichsweise einfach zu bestimmen ist, wird die Einschätzung mit ansteigender Komplexität und Schematizität der Zielkonstruktion zunehmend schwieriger. Für höherstufige N-gramme, welche durch eine grosse Anzahl an variablen Positionen ausgezeichnet sind, ist das Feld noch vergleichweise unerforscht. Die vorliegende Arbeit ist bemüht, diese Lücke zu schließen entwickelt eine korpusbasierte mehrstufige Messprozedur, um den entrenchment-Wert komplexer schematischer Konstruktionen zu erfassen. Da linguistisches Wissen hochstrukturiert ist und menschliche Sprachverarbeitungsprozesse struktursensitiv sind, wird ein clusteranalytisches Verfahren angewendet, welches die salienten RCC hinsichtlich ihrer strukturellen Ähnlichlichkeit organisiert. Aus der Position einer RCC im konstruktionalen Netzwerk sowie dessen entrenchment-Wert kann nun der Grad der erwarteteten Verarbeitungsschwierigkeit abgeleitet werden. Der abschliessende Teil der Arbeit interpretiert die Ergebnisse vor dem Hintergrung psycholinguistischer Befunde zur Relativsatzverarbeitung

Stochastic Ion Channel Gating in Dendritic Neurons: Morphology Dependence and Probabilistic Synaptic Activation of Dendritic Spikes

Neuronal activity is mediated through changes in the probability of stochastic transitions between open and closed states of ion channels. While differences in morphology define neuronal cell types and may underlie neurological disorders, very little is known about influences of stochastic ion channel gating in neurons with complex morphology. We introduce and validate new computational tools that enable efficient generation and simulation of models containing stochastic ion channels distributed across dendritic and axonal membranes. Comparison of five morphologically distinct neuronal cell types reveals that when all simulated neurons contain identical densities of stochastic ion channels, the amplitude of stochastic membrane potential fluctuations differs between cell types and depends on sub-cellular location. For typical neurons, the amplitude of membrane potential fluctuations depends on channel kinetics as well as open probability. Using a detailed model of a hippocampal CA1 pyramidal neuron, we show that when intrinsic ion channels gate stochastically, the probability of initiation of dendritic or somatic spikes by dendritic synaptic input varies continuously between zero and one, whereas when ion channels gate deterministically, the probability is either zero or one. At physiological firing rates, stochastic gating of dendritic ion channels almost completely accounts for probabilistic somatic and dendritic spikes generated by the fully stochastic model. These results suggest that the consequences of stochastic ion channel gating differ globally between neuronal cell-types and locally between neuronal compartments. Whereas dendritic neurons are often assumed to behave deterministically, our simulations suggest that a direct consequence of stochastic gating of intrinsic ion channels is that spike output may instead be a probabilistic function of patterns of synaptic input to dendrites

Learning from data: Plant breeding applications of machine learning

Increasingly, new sources of data are being incorporated into plant breeding pipelines. Enormous amounts of data from field phenomics and genotyping technologies places data mining and analysis into a completely different level that is challenging from practical and theoretical standpoints. Intelligent decision-making relies on our capability of extracting from data useful information that may help us to achieve our goals more efficiently. Many plant breeders, agronomists and geneticists perform analyses without knowing relevant underlying assumptions, strengths or pitfalls of the employed methods. The study endeavors to assess statistical learning properties and plant breeding applications of supervised and unsupervised machine learning techniques. A soybean nested association panel (aka. SoyNAM) was the base-population for experiments designed in situ and in silico. We used mixed models and Markov random fields to evaluate phenotypic-genotypic-environmental associations among traits and learning properties of genome-wide prediction methods. Alternative methods for analyses were proposed

Context-awareness to increase inclusion of people with DS in society

Assistive technologies have the potential to enhance the quality of life of citizens. Most especially of interest are those cases where a person is affected by some physical or cognitive impairment. Whilst most work in this area have been focused on assisting people indoors to support their independence, the POSEIDON project is focused on empowering citizens with Down’s Syndrome to support their independence outdoors. This paper explains the POSEIDON module which we are in the process of developing to make the system context-aware,reactive and adaptive

A chemokine network of T cell exhaustion and metabolic reprogramming in renal cell carcinoma

Renal cell carcinoma (RCC) is frequently infiltrated by immune cells, a process which is governed by chemokines. CD8+ T cells in the RCC tumor microenvironment (TME) may be exhausted which most likely influence therapy response and survival. The aim of this study was to evaluate chemokine-driven T cell recruitment, T cell exhaustion in the RCC TME, as well as metabolic processes leading to their functional anergy in RCC. Eight publicly available bulk RCC transcriptome collectives (n=1819) and a single cell RNAseq dataset (n=12) were analyzed. Immunodeconvolution, semi-supervised clustering, gene set variation analysis and Monte Carlo-based modeling of metabolic reaction activity were employed. Among 28 chemokine genes available, CXCL9/10/11/CXCR3, CXCL13/CXCR5 and XCL1/XCR1 mRNA expression were significantly increased in RCC compared to normal kidney tissue and also strongly associated with tumor-infiltrating effector memory and central memory CD8+ T cells in all investigated collectives. M1 TAMs, T cells, NK cells as well as tumor cells were identified as the major sources of these chemokines, whereas T cells, B cells and dendritic cells were found to predominantly express the cognate receptors. The cluster of RCCs characterized by high chemokine expression and high CD8+ T cell infiltration displayed a strong activation of IFN/JAK/STAT signaling with elevated expression of multiple T cell exhaustion-associated transcripts. Chemokinehigh RCCs were characterized by metabolic reprogramming, in particular by downregulated OXPHOS and increased IDO1-mediated tryptophan degradation. None of the investigated chemokine genes was significantly associated with survival or response to immunotherapy. We propose a chemokine network that mediates CD8+ T cell recruitment and identify T cell exhaustion, altered energy metabolism and high IDO1 activity as key mechanisms of their suppression. Concomitant targeting of exhaustion pathways and metabolism may pose an effective approach to RCC therapy

Lifecycle cost optimization of tuned mass dampers for the seismic improvement of inelastic structures

The seismic performance of tuned mass dampers (TMDs) on structures undergoing inelastic deformations may largely depend on the ground motion intensity. By estimating the impact of each seismic intensity on the overall cost of future seismic damages, lifecycle cost (LCC) proves a rational metric for evaluating the benefits of TMDs on inelastic structures. However, no incorporation of this metric into an optimization framework is reported yet. This paper presents a methodology for the LCC‐optimal design of TMDs on inelastic structures, which minimizes the total seismic LCC of the combined building‐TMD system. Its distinctive features are the assumption of a mass‐proportional TMD cost model, the adoption of an iterative suboptimization procedure, and the initialization of the TMD frequency and damping ratios according to a conventional linear TMD design technique. The methodology is applied to the seismic improvement of the SAC‐LA benchmark buildings, taken as representative of standard steel moment‐resisting frame office buildings in LA, California. Results show that, despite their limited performance at the highest intensity levels, LCC‐optimal TMDs considerably reduce the total LCC, to an extent that depends on both the building vulnerability and the TMD unit cost. They systematically present large mass ratios (around 10%) and frequency and damping ratios close to their respective linearly designed optima. Simulations reveal the effectiveness of the proposed design methodology and the importance of adopting a nonlinear model to correctly evaluate the cost‐effectiveness of TMDs on ordinary structures in highly seismic areas