Amritesh Banerjee

Metal artifacts in postoperative musculoskeletal CT can obscure bone–implant and adjacent soft-tissue interfaces, limiting assessment of implant position, fracture fixation, loosening, infection, and periprosthetic complications. Many metal artifact reduction (MAR) methods either require raw projection access that is unavailable in retrospective reconstructed CT studies or rely on learned models whose behavior may shift across scanner protocols and implant appearances. We present VoxelSynth3D, a training-free 3D image-domain MAR framework for reconstructed CT. The method localizes correction to an implant-adaptive artifact support and uses inspectable support masks, normalized tissue priors, deviation-gated blending weights, and restricted edge-guided refinement to suppress artifacts while limiting changes to unaffected anatomy. We also construct a paired synthetic CLINIC-Metal benchmark from nometal CTPelvic1K pelvic CT volumes with clean targets, metal and artifact masks, reproducible implant insertion, and patientlevel splits. Because real metal CT lacks paired clean targets, 75 real CLINIC-metal cases are evaluated only with qualitative and no-reference metrics. The reported configuration was fixed as a conservative operating point inside a near-flat validation basin. On 40 synthetic test cases, VoxelSynth3D achieved lower artifactROI RMSE than the main comparators (785.43 HU versus 811.86 HU for input, 802.21 HU for pseudo LI-MAR, and 803.10 HU for InDuDoNet) and the highest ROI MSE reduction among them (6.33%). Paired ROI-RMSE testing favored VoxelSynth3D over each comparator after Holm correction, with the largest gains in high-artifact-size cases. These results support VoxelSynth3D as an interpretable reconstructed-volume MAR approach and provide a reproducible paired benchmark for future studies.

Agent evaluation relies almost entirely on outcome metrics such as success rate, which capture whether an agent succeeds but not how consistently it behaves. We argue that behavioral consistency across tasks is a distinct and measurable property, and we introduce the Behavioral Consistency Metric (BCM) to quantify it. BCM trains a model to predict task success from behavioral features of agent execution traces, derives a per-trajectory feature-attribution vector, and measures the mean pairwise similarity of these vectors within an agent system. Across roughly 9,000 trajectories from six language model agents on software engineering tasks, our central finding is that cross-task and within-task consistency are distinct axes that can diverge: some systems are locally reproducible, behaving similarly on repeated attempts at one task, yet globally fragmented, with no stable strategy across different tasks, while others are consistent at both scales. Prior work measures only same-task reproducibility and so cannot observe this separation. We further find that consistency is not reducible to success rate, since systems with comparable success can differ sharply in consistency, and that the frontier-versus-open-source consistency gap persists under a within-task control that holds task difficulty constant. We position BCM as a process-level reliability signal that complements outcome metrics, and we are explicit about the conditions under which it is meaningful.

The integration of iterative LLMs within multi-agent diagnostic frameworks requires a rigorous quantitative reevaluation of underlying communication topologies. Frequently used architectural paradigms depend on scale-free or small-world networks, assuming optimal communication efficiency. Our study mathematically dismantles that assumption for semantic data. By mapping multi-agent communication uncertainty trajectories onto a 768-dimensional Bio_ClinicalBERT embedding space via an analytical isotropic variance proxy using Barabási–Albert (BA) and Watts–Strogatz (WS) networks, we prove that structural bottlenecks compromise diagnostic safety. Our phase transition matrices illustrate that localized dense cliques confine hallucinated data, preventing global consensus and forcing the system toward a permanent entropy saturation threshold of H_∞ ≈ 5.947. As a result, we measure a severe terminal cosine similarity degradation of 53.29%, completely overwriting the original ground-truth. Moreover, the terminal semantic drift reveals a catastrophic variance amplification of 51.81% (ρ = 1.5181) in highly clustered architectures, proving total system unpredictability when compared to Erdős–Rényi configurations (ρ = 1.0766). Instead of reducing errors, hub-centric systems autonomously compound localized hallucinations. By introducing dynamic spectral monitoring operating at an O(N³) time complexity and imposing a strict lower bound on algebraic connectivity (λ_{2 min}) via the continuous eigen-decomposition of the graph Laplacian, we present a mathematically rigorous technique to ensure global state diffusion. Securing the reliability of autonomous medical diagnostics necessitates treating topological stability as a non-negotiable quantitative imperative.

This research investigates the evolution of U.S. political rhetoric over the past eighty years, focusing on how thematic priorities and emotional framing have influenced partisan discourse. The study applies computational methods, including semantic similarity and sentiment analysis, to presidential nomination speeches from 1944 to 2024, with the aim of understanding patterns of ideological alignment, divergence, and rhetorical strategy. Beyond identifying these patterns in party messaging, the research examines how differences in thematic emphasis and emotional tone shape voter perception, engagement and political polarization. By combining quantitative linguistic analysis with historical and political context, the study provides insight into the mechanisms through which political language both mirrors and reinforces broader social and ideological divisions in the United States.

This study investigates the application of Artificial Intelligence (AI) in Computed Topography (CT) scans to enhance the image quality; this paper will also address the limitations in traditional methods and human interpretation. Utilizing Python, this research uses common AI-driven reconstruction techniques to reduce noise and remove artifacts from image scans. The methodology’s primary focus is to develop algorithms that leverage machine learning to identify and evaluate image patterns and optimize the reconstruction parameters. The results indicate that AI-powered reconstruction techniques significantly boost the image quality, yielding sharper images and a considerable reduction in noise and artifacts when compared with conventional methods. This facilitates improved pattern recognition and could aid in the detection of subtle features. This study highlights the potential of Python-based AI solutions to overcome current obstacles in image refinement and offers a promising avenue for more reliable computer-aided diagnostics.