This paper focuses on asymptotic properties of random monomial ideals through a statistical viewpoint. It extends the study of redundancy in monomial ideals by analyzing the poset density of the LCM-lattice. We explore how this density behaves across random algebraic models and structured networks. Experimental data reveal that the LCM-lattice exhibits sharp threshold behavior rather than changing smoothly. We observe a strong negative correlation between the number of generators and LCM-lattice density, abruptly separating three distinct regimes: a low-density Taylor-like regime, a high-density redundant regime, and a narrow transition window. We show that increasing the generator degree causes this density drop to occur at lower probability thresholds. We conclude by conjecturing that for equigenerated squarefree ideals, the LCM-lattice density undergoes a sharp phase transition, analogous to the emergence of giant components in hypergraphs. This suggests that the classical, ideal-by-ideal role of the LCM-lattice as a combinatorial invariant also admits a statistical/asymptotic counterpart: in natural random families, redundancy and resolution-complexity indicators concentrate into distinct typical regimes separated by a narrow transition window.

Manual data annotation is often slow, expensive, and difficult to scale-especially for tasks that are subjective and context-dependent, like detecting hate speech and stereotypes. With recent progress in Large Language Models (LLMs), there is growing potential to automate this process. In this study, first, we explore the use of a committee of LLMs (GPT-4omini, Gemini-1.5-flash, and DeepSeek-R1) to generate annotations for Italian social media content. Then, the quality of LLM-generated labels is evaluated by following a teacher-student approach, where we trained a smaller student model (phi3.5-miniinstruct) on them and testing its performance against a humanlabeled dataset. The results indicate that fine-tuning the student model over LLM-generated labels, with careful dataset balancing and hyperparameter tuning, can yield a significant improvement over the baseline (approximately $17 \%$ improvement), suggesting that committee-based LLM annotation can provide high-quality labels. Therefore, this work shows the proposed approach can be a reliable and scalable alternative to manual labeling by properly addressing challenges like class imbalance to be sure about the fairness and accuracy of the fine-tuned models.

We introduce the lcm-filtration and stepwise filtration, comparing their performance across various scenarios in terms of computational complexity, efficiency, and redundancy. The lcm-filtration often involves identical steps or ideals, leading to unnecessary computations. To address this, we analyse how stepwise filtration can effectively compute only the non-identical steps, offering a more efficient approach. We compare these filtrations in applications to networks, system signatures, and sensitivity analysis.

Loop invariants are properties of a program loop that hold before and after each iteration of the loop. They are often employed to verify programs and ensure that algorithms consistently produce correct results during execution. Consequently, the generation of invariants becomes a crucial task for loops. We specifically focus on polynomial loops, where both the loop conditions and assignments within the loop are expressed as polynomials. Although computing polynomial invariants for general loops is undecidable, efficient algorithms have been developed for certain classes of loops. For instance, when all assignments within a while loop involve linear polynomials, the loop becomes solvable. In this work, we study the more general case where the polynomials exhibit arbitrary degrees.

Abstract. We consider the global rigidity problem for bar-joint frameworks where each vertex is constrained to lie on a particular line in [Formula: see text]. In our setting, we allow multiple vertices to be constrained to the same line. We give a combinatorial characterization of generic rigidity in this setting for arbitrary line sets. Further, under a mild assumption on the given set of lines, we give a complete combinatorial characterization of graphs that are generically globally rigid. This gives a [Formula: see text]-dimensional extension of the well-known combinatorial characterization of two-dimensional global rigidity. In particular, our results imply that global rigidity is a generic property in this setting.

We use a monomial ideal I to model a discrete preference function on a set of n factors. We can measure the sensitivity of each point represented by a monomial m by calculating its formal partial derivatives with respect to each variable. These derivatives can be used to define the Koszul simplicial complex of the ideal I at m. We refer to points at which the homology of their Koszul complex is not null as sensitive corners. In the context of preference analysis, the ranks of the homology groups are not precise enough to distinguish between sensitive corners that have the same homology but correspond to different sensitivity behaviors. To address this issue, we propose using a filtration on the Koszul complexes of the sensitive corners based on the lcm-lattice of the ideal I. This filtration induces a persistent homology at each corner m. We then use unsupervised Machine Learning methods to classify the corners based on the distance between their persistence diagrams.

Template-based synthesis, also known as sketching, is a localized approach to program synthesis in which the programmer provides not only a specification, but also a high-level "sketch" of the program. The sketch is basically a partial program that models the general intuition of the programmer, while leaving the low-level details as unimplemented "holes". The role of the synthesis engine is then to fill in these holes such that the completed program satisfies the desired specification. In this work, we focus on template-based synthesis of polynomial imperative programs with real variables, i.e. imperative programs in which all expressions appearing in assignments, conditions and guards are polynomials over program variables. While this problem can be solved in a sound and complete manner by a reduction to the first-order theory of the reals, the resulting formulas will contain a quantifier alternation and are extremely hard for modern SMT solvers, even when considering toy programs with a handful of lines. Moreover, the classical algorithms for quantifier elimination are notoriously unscalable and not at all applicable to this use-case. In contrast, our main contribution is an algorithm, based on several well-known theorems in polyhedral and real algebraic geometry, namely Putinar's Positivstellensatz, the Real Nullstellensatz, Handelman's Theorem and Farkas' Lemma, which sidesteps the quantifier elimination difficulty and reduces the problem directly to Quadratic Programming (QP). Alternatively, one can view our algorithm as an efficient way of eliminating quantifiers in the particular formulas that appear in the synthesis problem. The resulting QP instances can then be handled quite easily by SMT solvers. Notably, our reduction to QP is sound and semi-complete, i.e. it is complete if polynomials of a sufficiently high degree are used in the templates. Thus, we provide the first method for sketching-based synthesis of polynomial programs that does not sacrifice completeness, while being scalable enough to handle meaningful programs. Finally, we provide experimental results over a variety of examples from the literature.

A graphical model encodes conditional independence relations via the Markov properties. For an undirected graph these conditional independence relations can be represented by a simple polytope known as the graph associahedron, which can be constructed as a Minkowski sum of standard simplices. There is an analogous polytope for conditional independence relations coming from a regular Gaussian model, and it can be defined using multiinformation or relative entropy. For directed acyclic graphical models and also for mixed graphical models containing undirected, directed, and bidirected edges, we give a construction of this polytope, up to equivalence of normal fans, as a Minkowski sum of matroid polytopes. Finally, we apply this geometric insight to construct a new ordering-based search algorithm for causal inference via directed acyclic graphical models.

We apply the methods of algebraic reliability to the study of percolation on trees. To a complete $k$-ary tree $T_{k,n}$ of depth $n$ we assign a monomial ideal $I_{k,n}$ on $\sum_{i=1}^n k^i$ variables and $k^n$ minimal monomial generators. We give explicit recursive formulae for the Betti numbers of $I_{k,n}$ and their Hilbert series, which allow us to study explicitly percolation on $T_{k,n}$. We study bounds on this percolation and study its asymptotical behavior with the mentioned commutative algebra techniques.