The Passive Voice in Artificial Intelligence Language: Algorithmic Neutrality and the Disappearance of Agency by Agustin V. Startari (21179732)

“…Abstract This article explores the use of passive voice in texts generated by artificial intelligence systems, examining how algorithmic language reproduces the illusion of neutrality by structurally erasing agency. …”

Two protein-ligand complexes shown with their persistence fingerprint. by Yuxi Long (11024307)

10-fold cross-validation evaluation of accuracy of animal tracking algorithms. by Catalin Mitelut (5587493)

“…<p>We labeled 1,000 frames per cohort under both day and night conditions (total 6,000 human labeled frames across three cohorts), and studied the accuracy of the animal tracking algorithm (SLEAP) as a function of the number of frames (<i>x</i>-axis) trained. …”

Comparative analysis of WordNet replacement impact on data distribution. by Enock Niyonkuru (21246879)

Power quality control algorithms for small scale power intergration systems by Advocate Mlamla (20170815)

“…The load is protected from such and in the case where the DC link storage is not enough solar PV will support the DVR. …”

Filled contour plot based on histograms of the occurence-frequency of binned values of and as a function of rate ratio . by Paul Kinsler (3975620)

Evolution of porosity (<i>ϕ</i>) as a function of rescaled CBL (relative age; ) measured in seal skulls. by Jonathan E Kings (20818367)

LBQANA python code + Merged Gene Expression Dataset from GSE10810, GSE17907, GSE20711, GSE42568, GSE45827, and GSE61304 for Breast Cancer Biomarker Discovery by M RN (9866504)

Dataset of networks used in assessing the Troika algorithm for clique partitioning and community detection by Samin Aref (4683934)

“…Each network is provided in .gml format or .pkl format which can be read into a networkX graph object using standard functions from the networkX library in Python. For accessing other networks used in the study, please refer to the article for references to the primary sources of those network data.…”

Nash equilibrium payoffs (_<i>i</i>) with the sensory–motor algorithm between a single predator and a single prey. by Hiroyuki Ichijo (13891153)

Synthetic Realness: Authenticity as Algorithm (Reality Drift Working Paper Series, 2025) by Reality Drift Working Papers Series 2020-2025 (22445446)

“…<p dir="ltr">This paper explores the concept of synthetic realness: how authenticity is increasingly engineered by algorithms until the line between real and fake becomes functionally irrelevant. …”

Table 1 - Fusing multisensory signals across channels and time by Swathi Anil (17382903)

Fig 2 - Fusing multisensory signals across channels and time by Swathi Anil (17382903)

Fig 1 - Fusing multisensory signals across channels and time by Swathi Anil (17382903)

“…In classical multisensory algorithms the information from independent sensory channels (<i>Ch</i>₀, <i>Ch</i>₁) is combined via a linear sum (<b>C</b>) or nonlinear function (<b>D</b>) then summed over time. …”

AIC values for an ARMA(p, q) model fit to Lake Michigan-Huron depths. by Jesse Wheeler (18462457)

Software: Order-flow and long-memory in a simulated financial market by Shane Silverman (22497770)

“…Key scripts apply custom metaorder generation algorithms to the empirical data to estimate and compare the $\alpha$ and $\gamma$ exponents.…”

ABIDE dataset subject demographics. by Alexandria M. Jensen (6494957)

“…By incorporating notions of similarity between network community structures into a kernel distance function, the high-dimensional feature space of brain networks, defined on input pairs, can be generalized to non-linear spaces, allowing for a wider class of distance-based algorithms. …”

Active Control of Laminar and Turbulent Flows Using Adjoint-Based Machine Learning by Xuemin Liu (20372739)

“…This dissertation extends and applies an adjoint-based machine learning method, the deep learning PDE augmentation method (DPM), for closed-loop active control on both laminar and turbulent flows. The end-to-end sensitivities for optimization are computed using adjoints of the governing equations without restriction on the terms that may appear in the objective function, which we construct using algorithmic differentiation applied to the flow solver. …”

Fig 12 - by Nicholas H. Vitale (20469289)

Predicted SNR normalized to the acquisition time of 1s versus input excitation power for CW and TG systems. by Nicholas H. Vitale (20469289)