Castas: homología y analogía en la forma y función

English (original) version at doi:10.5281/zenodo.3066005International audienceAnt societies consist of reproductive and sterile adults that show tremendous diversity of phenotypes. These include permanently wingless queens and a soldier caste that evolved convergently in many genera. Myrmecologists describe ant castes with terminology based on form, or function, or both, moreover terms are used inconsistently in the literature. Because morphology changes less readily than behaviour, an emphasis on morphological definitions is recommended to facilitate comparative studies and understand the evolutionary origin of castes.Las sociedades de hormigas están compuestas por individuos reproductores y ayudantes estériles que presentan una gran diversidad de fenotipos. Entre ellos se encuentran reinas que nunca llegan a desarrollar alas, así como una casta de soldados; fenotipos que han evolucionado convergentemente en una gran cantidad de géneros. Esta variedad de castas en hormigas ha sido descrita en mirmecología usando términos basados en forma, en función o en una combinación de ambos. Esta terminología, además es usada de manera poco consistente en la literatura. Dado que la morfología es menos propensa al cambio en comparación al comportamiento, en este capítulo se recomienda el uso de la morfología como patrón base para establecer una clasificación unificada de castas que facilite los estudios comparativos y ayude a entender el origen y evolución de esta fascinante diversidad fenotípica presente en las hormigas

Independent colony foundation in Paraponera clavata (Hymenoptera, Formicidae): First workers lay trophic eggs to feed queen’s larvae

Paraponera clavata Smith is a large, notorious, and widely distributed ant, yet its colony founding behavior is poorly known. In the laboratory, a dealate queen collected from Peru reared a first generation of ten adult workers over 18 months; eight cocoons and several larvae failed. Food was obtained outside the nest and given to larvae. It took five and six months before the first two workers emerged, and they were smaller than average (i.e.‘nanitic’). At Q+4, trophic eggs were laid by workers and given directly to medium and mature larvae on three occasions. Six workers were dissected immediately after the queen’s death, and five had yolky oocytes in their ovaries. Queen foraging is known from anecdotal field observations, despite the prothorax (and corresponding neck muscles) being smaller than in other poneroid queens

Integral cycle bases for cyclic timetabling

AbstractCyclic railway timetables are typically modeled by a constraint graph G with a cycle period time T, in which a periodic tension x in G corresponds to a cyclic timetable. In this model, the periodic character of the tension x is guaranteed by requiring periodicity for each cycle in a strictly fundamental cycle basis, that is, the set of cycles generated by the chords of a spanning tree of G.We introduce the more general concept of integral cycle bases for characterizing periodic tensions. We characterize integral cycle bases using the determinant of a cycle basis, and investigate further properties of integral cycle bases.The periodicity of a single cycle is modeled by a so-called cycle integer variable. We exploit the wider class of integral cycle bases to find tighter bounds for these cycle integer variables, and provide various examples with tighter bounds. For cyclic railway timetabling in particular, we consider Minimum Cycle Bases for constructing integral cycle bases with tight bounds

Using ChatGPT for Entity Matching

Entity Matching is the task of deciding if two entity descriptions refer to the same real-world entity. State-of-the-art entity matching methods often rely on fine-tuning Transformer models such as BERT or RoBERTa. Two major drawbacks of using these models for entity matching are that (i) the models require significant amounts of fine-tuning data for reaching a good performance and (ii) the fine-tuned models are not robust concerning out-of-distribution entities. In this paper, we investigate using ChatGPT for entity matching as a more robust, training data-efficient alternative to traditional Transformer models. We perform experiments along three dimensions: (i) general prompt design, (ii) in-context learning, and (iii) provision of higher-level matching knowledge. We show that ChatGPT is competitive with a fine-tuned RoBERTa model, reaching an average zero-shot performance of 83% F1 on a challenging matching task on which RoBERTa requires 2000 training examples for reaching a similar performance. Adding in-context demonstrations to the prompts further improves the F1 by up to 5% even using only a small set of 20 handpicked examples. Finally, we show that guiding the zero-shot model by stating higher-level matching rules leads to similar gains as providing in-context examples

Practical Hidden Voice Attacks against Speech and Speaker Recognition Systems

Voice Processing Systems (VPSes), now widely deployed, have been made significantly more accurate through the application of recent advances in machine learning. However, adversarial machine learning has similarly advanced and has been used to demonstrate that VPSes are vulnerable to the injection of hidden commands - audio obscured by noise that is correctly recognized by a VPS but not by human beings. Such attacks, though, are often highly dependent on white-box knowledge of a specific machine learning model and limited to specific microphones and speakers, making their use across different acoustic hardware platforms (and thus their practicality) limited. In this paper, we break these dependencies and make hidden command attacks more practical through model-agnostic (blackbox) attacks, which exploit knowledge of the signal processing algorithms commonly used by VPSes to generate the data fed into machine learning systems. Specifically, we exploit the fact that multiple source audio samples have similar feature vectors when transformed by acoustic feature extraction algorithms (e.g., FFTs). We develop four classes of perturbations that create unintelligible audio and test them against 12 machine learning models, including 7 proprietary models (e.g., Google Speech API, Bing Speech API, IBM Speech API, Azure Speaker API, etc), and demonstrate successful attacks against all targets. Moreover, we successfully use our maliciously generated audio samples in multiple hardware configurations, demonstrating effectiveness across both models and real systems. In so doing, we demonstrate that domain-specific knowledge of audio signal processing represents a practical means of generating successful hidden voice command attacks

WDC Products: A Multi-Dimensional Entity Matching Benchmark

The difficulty of an entity matching task depends on a combination of multiple factors such as the amount of corner-case pairs, the fraction of entities in the test set that have not been seen during training, and the size of the development set. Current entity matching benchmarks usually represent single points in the space along such dimensions or they provide for the evaluation of matching methods along a single dimension, for instance the amount of training data. This paper presents WDC Products, an entity matching benchmark which provides for the systematic evaluation of matching systems along combinations of three dimensions while relying on real-word data. The three dimensions are (i) amount of corner-cases (ii) generalization to unseen entities, and (iii) development set size. Generalization to unseen entities is a dimension not covered by any of the existing benchmarks yet but is crucial for evaluating the robustness of entity matching systems. WDC Products is based on heterogeneous product data from thousands of e-shops which mark-up products offers using schema.org annotations. Instead of learning how to match entity pairs, entity matching can also be formulated as a multi-class classification task that requires the matcher to recognize individual entities. WDC Products is the first benchmark that provides a pair-wise and a multi-class formulation of the same tasks and thus allows to directly compare the two alternatives. We evaluate WDC Products using several state-of-the-art matching systems, including Ditto, HierGAT, and R-SupCon. The evaluation shows that all matching systems struggle with unseen entities to varying degrees. It also shows that some systems are more training data efficient than others

Differential regulation of MMP-13 (collagenase-3) and MMP-3 (stromelysin-1) in mouse calvariae1Dedicated to Professor Gilbert Vaes.1

AbstractBone resorption in mice involves the degradation of extracellular matrix. Whereas several proteases seem to be implicated in this process, it becomes increasingly clear that matrix metalloproteinases (MMPs), amongst them especially MMP-13 and MMP-3, play an essential role. We have purified MMP-13 and MMP-3 from mouse calvariae-conditioned media by differential fractionation and analyzed their collagenolytic, caseinolytic, gelatinolytic and proteoglycanolytic activities. It could be shown that in mouse calvariae-conditioned media most of the measured enzyme activities were due to MMP-13, although zymographies revealed that MMP-3, MMP-2, MMP-9 as well as TIMPs were present too. MMP-13 and MMP-3 proteins were detected and their enzyme activities were neutralized by specific polyclonal antisera. Furthermore, it was demonstrated that in cultures of mouse calvariae the production of MMP-13 was induced by the potent MMP-stimulator heparin and by parathyroid hormone (PTH), whereas the levels of MMP-3 remained unchanged. Although PTH-induced bone resorption was inhibited by calcitonin treatment, MMP-13 mRNA and protein expression were not significantly altered by this hormone. Together with previous observations, these results indicate that PTH regulates bone resorption through MMP-13, but not by MMP-3, and that its reversion by calcitonin involves neither of the two enzymes