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Regge bootstrap
No full textWe present a numerical linear programming bootstrap to construct dual model scattering amplitudes. Dual models describe tree-level exchanges of higher spin resonances in theories like string theory and large N gauge theories. Despite being very simple objects, their numerical bootstrap has proven challenging due to slow convergence of the infinite sums over resonances. Our bootstrap succeeds thanks to an efficient parametrization of the amplitude in terms of Mandelstam-Regge poles and the use of combined regions that make crossing symmetry constraining. Along the way, we discover and conjecture a property of “superunitarity” of the Veneziano amplitude, which we use to keep a linear problem. As results, we present first the study of a class of stringlike amplitudes with linear trajectories, for which we observe that the Veneziano amplitude lies at a preferred location, at the bottom of a pit, which minimizes crossing. Then, we introduce a toy-model deformation to nonlinear trajectories, mimicking some features of QCD, for which our algorithm also detects a clear pit. This gives compelling evidence that our bootstrap is able to produce amplitudes that can exhibit nontrivial phenomenological features
On subprojectivity of Goldie torsion modules
No full textRecently, the concept of subprojectivity domains for modules has been introduced as a means of quantifying the level of projectivity exhibited by a module. In this research article, we focus on the subprojectivity domain of Goldie torsion modules. In particular, we establish that a ring denoted as R is classified as right nonsingular if and only if the subprojectivity domain of each Goldie torsion module is closed under submodules. In addition, we demonstrate that a right C-ring is a right nonsingular ring if and only if every module possesses an epic ecf-flat envelope, which is further equivalent to each Goldie torsion module having an epic projective envelope
Representation learning for fast radio burst dynamic spectra
No full textFast radio bursts (FRBs) are millisecond-duration radio transients of extragalactic origin, with diverse time-frequency patterns and emission properties that require explanation. With one possible exception, FRBs are detected only in the radio, analysing their dynamic spectra is therefore crucial to disentangling the physical processes governing their generation and propagation. Furthermore, comparing FRB morphologies provides insights into possible differences among their progenitors and environments. This study applies unsupervized learning and deep-learning techniques to investigate FRB dynamic spectra, focusing on two approaches: principal component analysis (PCA) and a convolutional auto-encoder (CAE) enhanced by an information-ordered bottleneck (IOB) layer. PCA served as a computationally efficient baseline, capturing broad trends, identifying outliers, and providing valuable insights into large data sets. However, its linear nature limited its ability to reconstruct complex FRB structures. In contrast, the IOB-augmented CAE excelled at capturing intricate features, with high reconstruction accuracy and effective denoizing at modest signal-to-noise ratios. The IOB layer’s ability to prioritize relevant features enabled efficient data compression, preserving key morphological characteristics with minimal latent variables. When applied to real FRBs from Canadian Hydrogen Intensity Mapping Experiment (CHIME), the IOB–CAE generalized effectively, revealing a latent space that highlighted the continuum of FRB morphologies and the potential for distinguishing intrinsic differences between burst types. This framework demonstrates that while FRBs may not naturally cluster into discrete groups, advanced representation learning techniques can uncover meaningful structures, offering new insights into the diversity and origins of these bursts
Distance and Projectivity as Predictors of Sentence Acceptability in FreeWord Order Languages
No full textThis study investigates how two core metrics rooted in Dependency Grammar, Mean Dependency Distance (MDD) and projectivity, predict sentence acceptability in Russian and Serbo-Croatian. Using exhaustive word order permutations in controlled five-word sentences, we model how these metrics relate to acceptability judgments in two psycholinguistic experiments. While MDD has been widely studied as a processing constraint, projectivity violations have received less attention in experiments, and particularly in acceptability modeling. We demonstrate that both metrics have a significant independent impact on judgments, with projectivity playing a surprisingly strong role. In addition, Serbo-Croatian’s rigid clitic placement provides a natural test case for disentangling grammatical from processing constraints. Our findings offer a computationally precise, dependency-based model of acceptability that advances cognitively grounded language modeling for free word order languages
Dekonstrukcija
No full textSince starting my bachelor’s degree, I’ve struggled to come up with ideas for an animated project that would actually feel personal. For the first two years of studies, I’ve been discovering animation as a concept and figuring out my place in it. Art was, and still to an extent is, a direction my brain isn’t fully developed in yet, and I haven’t managed to use animation as a form of expression I could actually say something with. Leading up to the first pitch of our graduate films, I thought up a concept for a storyline that would portray a personal experience I would be excited to work on. However, it was quickly made clear to me that I’m not a good narrative storyteller and that this concept might take me much longer to develop coherently than I had previously anticipated. Abstract and experimental films weren’t something I imagined for myself, but I had to pick a direction.Že od začetka dodiplomskega študija se mučim z idejami za animirani projekt, ki bi se dejansko zdel oseben. Prvi dve leti študija sem odkrivala animacijo kot koncept in iskala svoje mesto v njej. Umetnost je bila in do neke mere še vedno je smer, v kateri moji možgani še niso povsem razviti, in animacije nisem uspela uporabiti kot oblike izražanja, s katero bi lahko dejansko kaj povedala. Pred prvo predstavitvijo naših diplomskih filmov sem si zamislila koncept za zgodbo, ki bi prikazala osebno izkušnjo, na kateri bi z veseljem delala. Vendar mi je hitro postalo jasno, da nisem dobra pripovedovalka zgodb in da bi mi ta koncept morda vzelo veliko več časa, da ga bom koherentno razvila, kot sem prej pričakovala. Abstraktnih in eksperimentalnih filmov si nisem predstavljala, vendar sem morala izbrati smer