arXiv subjects

Jared Smith

Publications and source records attributed to Jared Smith.

4 recordsLinked to original sources

arXiv2026

Semiclassical Wave Packet Dynamics For Nonlinear Schrödinger Equations with Harmonic Potentials

We propose an approximate Hamiltonian dynamics for the Gross--Pitaevskii equation $i\hbarψ_t = -\frac{\hbar^2}{2m}Δψ+ V(x)ψ+ \hbar^2 λ|ψ|^2ψ$ with harmonic potential $V(x)$ and $x\in\mathbb{R}^d$. We do this by extending the Gaussian ansatz of Perez-Garcia et al.~(Phys.~Rev.~A 56, 1424 (1997)) to the semiclassical wave packets of Hagedorn. We derive the evolution equations for the wave packets from the Hamiltonian/symplectic perspective, rather than the Lagrangian/variational one taken by Perez-Garcia et al. We first show that these two perspectives yield identical evolution equations in the Gaussian case. We then derive the evolution equations for an arbitrary element of the semiclassical wave packets. Finally, we demonstrate the efficacy of the approximate dynamics with numerical experiments.

math-ph↗

arXiv2026

Vision-language models see more about agriculture than they report, and test-time scaling remains bounded by parametric knowledge

Vision-language models (VLMs) show promise for agricultural diagnosis but fail unpredictably in zero-shot settings. We test whether this stems from poor visual features or an inability to verbalize what the model already perceives, using a benchmark spanning classification and detection drawn from 118 AgML classification and 54 detection datasets. Linear probing shows agricultural features are linearly separable in frozen VLM vision towers, with Gemma-4-31B's encoder reaching F1-macro of 0.60--0.78 across tasks and approaching the vision-only DINOv3 backbone, indicating the bottleneck lies downstream of perception. We then test whether test-time scaling, an iterative in-context skill-memory loop, can close this gap without updating weights. Gains are modest and inconsistent. Skill-memory improves classification accuracy by up to 3.1 points on three of four tasks while regressing on species, with gains concentrated on classes seen during optimization and unseen classes regressing. Detection F1 is effectively unchanged (0.412 to 0.421), though an oracle filtering condition reaches 0.432 F1, revealing real headroom unconstrained prompting fails to surface. These results indicate VLMs see more agricultural features than they report, but test-time scaling alone fails to surface that hidden parametric knowledge, motivating agentic approaches grounded in external, retrievable knowledge.

cs.CV↗

arXiv2020

An Assessment of the Usability of Machine Learning Based Tools for the Security Operations Center

Gartner, a large research and advisory company, anticipates that by 2024 80% of security operation centers (SOCs) will use machine learning (ML) based solutions to enhance their operations. In light of such widespread adoption, it is vital for the research community to identify and address usability concerns. This work presents the results of the first in situ usability assessment of ML-based tools. With the support of the US Navy, we leveraged the national cyber range, a large, air-gapped cyber testbed equipped with state-of-the-art network and user emulation capabilities, to study six US Naval SOC analysts' usage of two tools. Our analysis identified several serious usability issues, including multiple violations of established usability heuristics form user interface design. We also discovered that analysts lacked a clear mental model of how these tools generate scores, resulting in mistrust and/or misuse of the tools themselves. Surprisingly, we found no correlation between analysts' level of education or years of experience and their performance with either tool, suggesting that other factors such as prior background knowledge or personality play a significant role in ML-based tool usage. Our findings demonstrate that ML-based security tool vendors must put a renewed focus on working with analysts, both experienced and inexperienced, to ensure that their systems are usable and useful in real-world security operations settings.

cs.HC↗

arXiv2019

Measuring Irregular Geographic Exposure on the Internet

We examine the extent of needless traffic exposure by the routing infrastructure to nations geographically irrelevant to packet transmission. We quantify what countries are geographically logical to observe on a network path traveling between two nations through the use of convex hulls circumscribing major population centers. We then compare that to the nation states observed in over 2.5 billion measured paths. We examine both the entire geographic topology of the Internet and a subset of the topology that a Tor user would typically interact with. We find that 44% of paths across the entire geographic topology of the Internet and 33% of paths in the user experience subset unnecessarily expose traffic to one or more nations. Finally, we consider the scenario where countries exercise both legal and physical control over autonomous systems, gaining access to traffic outside of their geographic borders, but carried by organizations that fall under the AS's registered country's legal jurisdiction. At least 49% of paths in both measurements expose traffic to a geographically irrelevant country when considering both the physical and legal countries that a path traverses.

cs.NI↗