arXiv subjects

Joseph L. Natale

Publications and source records attributed to Joseph L. Natale.

4 recordsLinked to original sources

arXiv2026

Improving Punching "Power": Reconsidering the roles of initial strike velocity and effective mass

We review the principal features of one-dimensional, elastic collisions pertinent to modeling strike-mediated impacts in martial arts and combat sports contexts. Arguing for the post-collision velocity of a stationary, target mass as a useful proxy for determining overall punch efficacy, we demonstrate that pre-collision velocities for real strikes are bounded, such that speed prior to impact has a more limited effect on the impulse experienced by said target than suggested by supporting literature. We find that the value of incoming, "effective" mass -- despite eluding uniform establishment criteria or measurement protocols across the same body of literature -- exerts an independent influence on the target velocity which can, in principle, match or exceed that of the strike speed. We perform explicit numerical analyses to supplement these theoretical considerations, in dynamical regimes relevant to real human combat, and then briefly discuss the potential for a given fighter to attain a fold-increase in effective mass (from a reasonable baseline value based on bodyweight, and supported by previous work) sufficient to realize limits for upper-limb strikes to the body, in practice, as a trainable goal.

physics.pop-ph↗

arXiv2021

Dynamical clustering of U.S. states reveals four distinct infection patterns that predict SARS-CoV-2 pandemic behavior

The SARS-CoV-2 pandemic has so far unfolded diversely across the fifty United States of America, reflected both in different time progressions of infection "waves" and in magnitudes of local infection rates. Despite a marked diversity of presentations, most U.S. states experienced their single greatest surge in daily new cases during the transition from Fall 2020 to Winter 2021. Popular media also cite additional similarities between states -- often despite disparities in governmental policies, reported mask-wearing compliance rates, and vaccination percentages. Here, we identify a set of robust, low-dimensional clusters that 1) summarize the timings and relative heights of four historical COVID-19 "wave opportunities" accessible to all 50 U.S. states, 2) correlate with geographical and intervention patterns associated with those groups of states they encompass, and 3) predict aspects of the "fifth wave" of new infections in the late Summer of 2021. In particular, we argue that clustering elucidates a negative relationship between vaccination rates and subsequent case-load variabilities within state groups. We advance the hypothesis that vaccination acts as a ``seat belt," in effect constraining the likely range of new-case upticks, even in the context of the Summer 2021, variant-driven surge.

physics.soc-ph↗

arXiv2019

Precise Spatial Memory in Local Random Networks

Self-sustained, elevated neuronal activity persisting on time scales of ten seconds or longer is thought to be vital for aspects of working memory, including brain representations of real space. Continuous-attractor neural networks, one of the most well-known modeling frameworks for persistent activity, have been able to model crucial aspects of such spatial memory. These models tend to require highly structured or regular synaptic architectures. In contrast, we elaborate a geometrically-embedded model with a local but otherwise random connectivity profile which, combined with a global regulation of the mean firing rate, produces localized, finely spaced discrete attractors that effectively span a 2D manifold. We demonstrate how the set of attracting states can reliably encode a representation of the spatial locations at which the system receives external input, thereby accomplishing spatial memory via attractor dynamics without synaptic fine-tuning or regular structure. We measure the network's storage capacity and find that the statistics of retrievable positions are also equivalent to a full tiling of the plane, something hitherto achievable only with (approximately) translationally invariant synapses, and which may be of interest in modeling such biological phenomena as visuospatial working memory in two dimensions.

q-bio.NC↗

arXiv2017

Reverse-engineering biological networks from large data sets

Much of contemporary systems biology owes its success to the abstraction of a network, the idea that diverse kinds of molecular, cellular, and organismal species and interactions can be modeled as relational nodes and edges in a graph of dependencies. Since the advent of high-throughput data acquisition technologies in fields such as genomics, metabolomics, and neuroscience, the automated inference and reconstruction of such interaction networks directly from large sets of activation data, commonly known as reverse-engineering, has become a routine procedure. Whereas early attempts at network reverse-engineering focused predominantly on producing maps of system architectures with minimal predictive modeling, reconstructions now play instrumental roles in answering questions about the statistics and dynamics of the underlying systems they represent. Many of these predictions have clinical relevance, suggesting novel paradigms for drug discovery and disease treatment. While other reviews focus predominantly on the details and effectiveness of individual network inference algorithms, here we examine the emerging field as a whole. We first summarize several key application areas in which inferred networks have made successful predictions. We then outline the two major classes of reverse-engineering methodologies, emphasizing that the type of prediction that one aims to make dictates the algorithms one should employ. We conclude by discussing whether recent breakthroughs justify the computational costs of large-scale reverse-engineering sufficiently to admit it as a mainstay in the quantitative analysis of living systems.

q-bio.MN↗