Harms and wrongs in epistemic practice

This volume has its roots in two recent developments within mainstream analytic epistemology: a growing recognition over the past two or three decades of the active and social nature of our epistemic lives; and, more recently still, the increasing appreciation of the various ways in which the epistemic practices of individuals and societies can, and often do, go wrong. The theoretical analysis of these breakdowns in epistemic practice, along with the various harms and wrongs that follow as a consequence, constitutes an approach to epistemology that we refer to as non-ideal epistemology. In this introductory chapter we introduce and contextualise the ten essays that comprise this volume, situating them within four broad sub-fields: vice epistemology, epistemic injustice, inter-personal epistemic practices, and applied epistemology. We also provide a brief overview of several other important growth areas in non-ideal epistemology

fixation free inguinal hernia repair with the 3d dynamic responsive prosthesis proflor features procedural steps and long term results

Abstract Background Static and fixated meshes to repair mobile structures like the groin may seem a procedural incongruence. Inguinal hernia is described as a degenerative disease. Therefore, the objective of disease treatment should be the regeneration of wasted tissue. The fibrotic scar plate, a typical biologic response of conventional static meshes, does not represent tissue regeneration but rather a foreign body reaction. These contrasting aspects seem to be related to high complication rates of inguinal herniorrhaphy. Recent studies concerning the pathophysiology of the groin have led to the development of new concepts for repairing inguinal protrusions. A proprietary designed 3D dynamic responsive implant showing regenerative biologic response is the result of this studies. Materials and methods A cohort of 389 individuals underwent open inguinal hernia repair with the 3D dynamic responsive implant following a specific surgical technique. Thanks to the inherent dynamic properties, all procedures were performed without need for fixation of the 3D prosthesis. Results The outcomes of the dynamic hernia repair procedure were reduced postoperative pain and minimized overall complication rates, also long term. Moreover, no patient discomfort or chronic pain was reported. Conclusions Inguinal hernia repair with the 3D dynamic responsive implant ProFlor seems to represent an effective concept change for the treatment of this widespread degenerative disease. Moving in synchrony with the groin, implanted without need of fixation and acting as a regenerative scaffold, ProFlor™ appears to possess all that is needed for a physiologic and pathogenetical consequent treatment of inguinal protrusions leading to a dramatic lessening of intra- and postoperative complications

Att-TasNet: attending to encodings in time-domain audio speech separation of noisy, reverberant speech mixtures

Separation of speech mixtures in noisy and reverberant environments remains a challenging task for state-of-the-art speech separation systems. Time-domain audio speech separation networks (TasNets) are among the most commonly used network architectures for this task. TasNet models have demonstrated strong performance on typical speech separation baselines where speech is not contaminated with noise. When additive or convolutive noise is present, performance of speech separation degrades significantly. TasNets are typically constructed of an encoder network, a mask estimation network and a decoder network. The design of these networks puts the majority of the onus for enhancing the signal on the mask estimation network when used without any pre-processing of the input data or post processing of the separation network output data. Use of multihead attention (MHA) is proposed in this work as an additional layer in the encoder and decoder to help the separation network attend to encoded features that are relevant to the target speakers and conversely suppress noisy disturbances in the encoded features. As shown in this work, incorporating MHA mechanisms into the encoder network in particular leads to a consistent performance improvement across numerous quality and intelligibility metrics on a variety of acoustic conditions using the WHAMR corpus, a data-set of noisy reverberant speech mixtures. The use of MHA is also investigated in the decoder network where it is demonstrated that smaller performance improvements are consistently gained within specific model configurations. The best performing MHA models yield a mean 0.6 dB scale invariant signal-to-distortion (SISDR) improvement on noisy reverberant mixtures over a baseline 1D convolution encoder. A mean 1 dB SISDR improvement is observed on clean speech mixtures

The University of Sheffield CHiME-7 UDASE challenge speech enhancement system

The CHiME-7 unsupervised domain adaptation speech enhancement (UDASE) challenge targets domain adaptation to unlabelled speech data. This paper describes the University of Sheffield team’s system submitted to the challenge. A generative adversarial network (GAN) methodology based on a conformer-based metric GAN (CMGAN) is employed as opposed to the unsupervised RemixIT strategy used in the CHiME-7 baseline system. The discriminator of the GAN is trained to predict the output score of a Deep Noise Suppression Mean Opinion Score (DNSMOS) metric. Additional data augmentation strategies are employed which provide the discriminator with historical training data outputs as well as more diverse training examples from an additional pseudo-generator. The proposed approach, denoted as CMGAN+/+, achieves significant improvement in DNSMOS evaluation metrics with the best proposed system achieving 3.51 OVR-MOS, a 24% improvement over the baseline

MetricGAN+/- : increasing robustness of noise reduction on unseen data

Training of speech enhancement systems often does not incorporate knowledge of human perception and thus can lead to unnatural sounding results. Incorporating psychoacoustically motivated speech perception metrics as part of model training via a predictor network has recently gained interest. However, the performance of such predictors is limited by the distribution of metric scores that appear in the training data. In this work, we propose MetricGAN+/- (an extension of MetricGAN+, one such metric-motivated system) which introduces an additional network - a "de-generator" which attempts to improve the robustness of the prediction network (and by extension of the generator) by ensuring observation of a wider range of metric scores in training. Experimental results on the VoiceBank-DEMAND dataset show relative improvement in PESQ score of 3.8% (3.05 vs 3.22 PESQ score), as well as better generalisation to unseen noise and speech