arXiv subjects

John Smith

Publications and source records attributed to John Smith.

5 recordsLinked to original sources

arXiv2025

Dual Boost-Driven Graph-Level Clustering Network

Graph-level clustering remains a pivotal yet formidable challenge in graph learning. Recently, the integration of deep learning with representation learning has demonstrated notable advancements, yielding performance enhancements to a certain degree. However, existing methods suffer from at least one of the following issues: 1. the original graph structure has noise, and 2. during feature propagation and pooling processes, noise is gradually aggregated into the graph-level embeddings through information propagation. Consequently, these two limitations mask clustering-friendly information, leading to suboptimal graph-level clustering performance. To this end, we propose a novel Dual Boost-Driven Graph-Level Clustering Network (DBGCN) to alternately promote graph-level clustering and filtering out interference information in a unified framework. Specifically, in the pooling step, we evaluate the contribution of features at the global and optimize them using a learnable transformation matrix to obtain high-quality graph-level representation, such that the model's reasoning capability can be improved. Moreover, to enable reliable graph-level clustering, we first identify and suppress information detrimental to clustering by evaluating similarities between graph-level representations, providing more accurate guidance for multi-view fusion. Extensive experiments demonstrated that DBGCN outperforms the state-of-the-art graph-level clustering methods on six benchmark datasets.

cs.LG↗

arXiv2018

Segmentation of both Diseased and Healthy Skin from Clinical Photographs in a Primary Care Setting

This work presents the first segmentation study of both diseased and healthy skin in standard camera photographs from a clinical environment. Challenges arise from varied lighting conditions, skin types, backgrounds, and pathological states. For study, 400 clinical photographs (with skin segmentation masks) representing various pathological states of skin are retrospectively collected from a primary care network. 100 images are used for training and fine-tuning, and 300 are used for evaluation. This distribution between training and test partitions is chosen to reflect the difficulty in amassing large quantities of labeled data in this domain. A deep learning approach is used, and 3 public segmentation datasets of healthy skin are collected to study the potential benefits of pre-training. Two variants of U-Net are evaluated: U-Net and Dense Residual U-Net. We find that Dense Residual U-Nets have a 7.8% improvement in Jaccard, compared to classical U-Net architectures (0.55 vs. 0.51 Jaccard), for direct transfer, where fine-tuning data is not utilized. However, U-Net outperforms Dense Residual U-Net for both direct training (0.83 vs. 0.80) and fine-tuning (0.89 vs. 0.88). The stark performance improvement with fine-tuning compared to direct transfer and direct training emphasizes both the need for adequate representative data of diseased skin, and the utility of other publicly available data sources for this task.

cs.CV↗

arXiv2009

The kinetics and acoustics of fingering and note transitions on the flute

Motion of the keys was measured in a transverse flute while beginner, amateur and professional flutists played a range of exercises. The time taken for a key to open or close is typically 10 ms when pushed by a finger or 16 ms when moved by a spring. Delays between the motion of the fingers were typically tens of ms, with longer delays as more fingers are involved. Because the opening and closing of keys will never be exactly simultaneous, transitions between notes that involve the movement of multiple fingers can occur via several possible pathways with different intermediate fingerings. A transition is classified as `safe' if it is possible to be slurred from the initial to final note with little perceptible change in pitch or volume. Some transitions are `unsafe' and possibly involve a transient change in pitch or a decrease in volume. In transitions with multiple fingers, players, on average, used safe transitions more frequently than unsafe transitions. Professionals exhibited smaller average delays between the motion of fingers than did amateurs.

physics.soc-ph↗

arXiv2005

Rotationnal and translational waves in a bowed string

We measure and compare the rotational and transverse velocity of a bowed string. When bowed by an experienced player, the torsional motion is phase-locked to the transverse waves, producing highly periodic motion. The spectrum of the torsional motion includes the fundamental and harmonics of the transverse wave, with strong formants at the natural frequencies of the torsional standing waves in the whole string. Volunteers with no experience on bowed string instruments, however, often produced non-periodic motion. We present sound files of both the transverse and torsional velocity signals of well-bowed strings. The torsional signal has not only the pitch of the transverse signal, but it sounds recognisably like a bowed string, probably because of its rich harmonic structure and the transients and amplitude envelope produced by bowing.

physics.class-ph↗

arXiv2005

Torsional waves in a bowed string

Bowing a string with a non-zero radius exerts a torque, which excites torsional waves. In general, torsional standing waves have higher fundamental frequencies than do transverse standing waves, and there is generally no harmonic relationship between them. Although torsional waves have little direct acoustic effect, the motion of the bow-string contact depends on the sum of the transverse speed v of the string plus the radius times the angular velocity (rw) . Consequently, in some bowing regimes, torsional waves could introduce non-periodicity or jitter to the transverse wave. The ear is sensitive to jitter so, while quite small amounts of jitter are important in the sounds of (real) bowed strings, modest amounts of jitter can be perceived as unpleasant or unmusical. It follows that, for a well bowed string, aperiodicities produced in the transverse motion by torsional waves (and other effects) must be small. Is this because the torsional waves are of small amplitude or because of strong coupling between the torsional and transverse waves? We measure the torsional and transverse motion for a string bowed by an experienced player over a range of tunings. The peaks in (rw), which occur near the start and end of the stick phase in which the bow and string move together, are only several times smaller than v during this phase.

physics.class-ph↗