Unsupervised Feature Learning through Divergent Discriminative Feature Accumulation

Unlike unsupervised approaches such as autoencoders that learn to reconstruct their inputs, this paper introduces an alternative approach to unsupervised feature learning called divergent discriminative feature accumulation (DDFA) that instead continually accumulates features that make novel discriminations among the training set. Thus DDFA features are inherently discriminative from the start even though they are trained without knowledge of the ultimate classification problem. Interestingly, DDFA also continues to add new features indefinitely (so it does not depend on a hidden layer size), is not based on minimizing error, and is inherently divergent instead of convergent, thereby providing a unique direction of research for unsupervised feature learning. In this paper the quality of its learned features is demonstrated on the MNIST dataset, where its performance confirms that indeed DDFA is a viable technique for learning useful features.Comment: Corrected citation formattin

Clinical Relevance of Baseline TCP in Transcatheter Aortic Valve Replacement

AIMS: To investigate the influence of baseline thrombocytopenia (TCP) on short-term and long-term outcomes after transcatheter aortic valve replacement (TAVR). METHODS AND RESULTS: A total of 732 consecutive patients with severe, symptomatic aortic stenosis undergoing TAVR from January 2012 to December 2015 were included. Primary outcomes of interest were the relationship of baseline TCP with 30-day and 1-year all-cause mortality. Secondary outcomes of interest were procedural complications and in-hospital mortality in the same subgroups. The prevalence of TCP (defined as platelet count <150 × 109/L) at baseline was 21.9%, of whom 4.0% had moderate/severe TCP (defined as platelet count <100 × 109/L). Compared to no or mild TCP, moderate/severe TCP at baseline was associated with a significantly higher 30-day mortality (23.3% vs 2.3% and 3.1%, respectively; P<.001) and 1-year mortality (40.0% vs 8.3% and 13.4%, respectively; P<.001). In Cox regression analysis, moderate/severe baseline TCP was an independent predictor of 30-day and 1-year mortality (hazard ratio [HR], 13.18; 95% confidence interval [CI], 4.49-38.64; P<.001 and HR, 5.90; 95% CI, 2.68-13.02; P<.001, respectively). CONCLUSIONS: In conclusion, baseline TCP is a strong predictor of mortality in TAVR patients, possibly identifying a specific subgroup of frail patients; therefore, it should be taken into account when addressing TAVR risk

Design and baseline characteristics of the finerenone in reducing cardiovascular mortality and morbidity in diabetic kidney disease trial

Background: Among people with diabetes, those with kidney disease have exceptionally high rates of cardiovascular (CV) morbidity and mortality and progression of their underlying kidney disease. Finerenone is a novel, nonsteroidal, selective mineralocorticoid receptor antagonist that has shown to reduce albuminuria in type 2 diabetes (T2D) patients with chronic kidney disease (CKD) while revealing only a low risk of hyperkalemia. However, the effect of finerenone on CV and renal outcomes has not yet been investigated in long-term trials. Patients and Methods: The Finerenone in Reducing CV Mortality and Morbidity in Diabetic Kidney Disease (FIGARO-DKD) trial aims to assess the efficacy and safety of finerenone compared to placebo at reducing clinically important CV and renal outcomes in T2D patients with CKD. FIGARO-DKD is a randomized, double-blind, placebo-controlled, parallel-group, event-driven trial running in 47 countries with an expected duration of approximately 6 years. FIGARO-DKD randomized 7,437 patients with an estimated glomerular filtration rate >= 25 mL/min/1.73 m(2) and albuminuria (urinary albumin-to-creatinine ratio >= 30 to <= 5,000 mg/g). The study has at least 90% power to detect a 20% reduction in the risk of the primary outcome (overall two-sided significance level alpha = 0.05), the composite of time to first occurrence of CV death, nonfatal myocardial infarction, nonfatal stroke, or hospitalization for heart failure. Conclusions: FIGARO-DKD will determine whether an optimally treated cohort of T2D patients with CKD at high risk of CV and renal events will experience cardiorenal benefits with the addition of finerenone to their treatment regimen. Trial Registration: EudraCT number: 2015-000950-39; ClinicalTrials.gov identifier: NCT02545049

Atrasentan and renal events in patients with type 2 diabetes and chronic kidney disease (SONAR): a double-blind, randomised, placebo-controlled trial

Background: Short-term treatment for people with type 2 diabetes using a low dose of the selective endothelin A receptor antagonist atrasentan reduces albuminuria without causing significant sodium retention. We report the long-term effects of treatment with atrasentan on major renal outcomes. Methods: We did this double-blind, randomised, placebo-controlled trial at 689 sites in 41 countries. We enrolled adults aged 18–85 years with type 2 diabetes, estimated glomerular filtration rate (eGFR)25–75 mL/min per 1·73 m 2 of body surface area, and a urine albumin-to-creatinine ratio (UACR)of 300–5000 mg/g who had received maximum labelled or tolerated renin–angiotensin system inhibition for at least 4 weeks. Participants were given atrasentan 0·75 mg orally daily during an enrichment period before random group assignment. Those with a UACR decrease of at least 30% with no substantial fluid retention during the enrichment period (responders)were included in the double-blind treatment period. Responders were randomly assigned to receive either atrasentan 0·75 mg orally daily or placebo. All patients and investigators were masked to treatment assignment. The primary endpoint was a composite of doubling of serum creatinine (sustained for ≥30 days)or end-stage kidney disease (eGFR <15 mL/min per 1·73 m 2 sustained for ≥90 days, chronic dialysis for ≥90 days, kidney transplantation, or death from kidney failure)in the intention-to-treat population of all responders. Safety was assessed in all patients who received at least one dose of their assigned study treatment. The study is registered with ClinicalTrials.gov, number NCT01858532. Findings: Between May 17, 2013, and July 13, 2017, 11 087 patients were screened; 5117 entered the enrichment period, and 4711 completed the enrichment period. Of these, 2648 patients were responders and were randomly assigned to the atrasentan group (n=1325)or placebo group (n=1323). Median follow-up was 2·2 years (IQR 1·4–2·9). 79 (6·0%)of 1325 patients in the atrasentan group and 105 (7·9%)of 1323 in the placebo group had a primary composite renal endpoint event (hazard ratio [HR]0·65 [95% CI 0·49–0·88]; p=0·0047). Fluid retention and anaemia adverse events, which have been previously attributed to endothelin receptor antagonists, were more frequent in the atrasentan group than in the placebo group. Hospital admission for heart failure occurred in 47 (3·5%)of 1325 patients in the atrasentan group and 34 (2·6%)of 1323 patients in the placebo group (HR 1·33 [95% CI 0·85–2·07]; p=0·208). 58 (4·4%)patients in the atrasentan group and 52 (3·9%)in the placebo group died (HR 1·09 [95% CI 0·75–1·59]; p=0·65). Interpretation: Atrasentan reduced the risk of renal events in patients with diabetes and chronic kidney disease who were selected to optimise efficacy and safety. These data support a potential role for selective endothelin receptor antagonists in protecting renal function in patients with type 2 diabetes at high risk of developing end-stage kidney disease. Funding: AbbVie

Maestrogenesis: Computer-Assisted Musical Accompaniment Generation

There has been research conducted which focused on the topic of juvenile transfer to adult court. However, less research has been done focusing on the effectiveness of juvenile transfer in reducing recidivism for transferred offenders. Of the research that has been done, most may not have adequately addressed selection bias. The current study investigates the impact of juvenile transfer on subsequent recidivism, using an eight-year follow-up period. The research is based on 308 violent youth legislatively waived to adult court in Pennsylvania. Propensity score matching is used to minimize the impact of selection bias, and sensitivity analysis is performed to determine the effect of hidden bias on the results. The findings indicate that youth processed in juvenile court have a higher recidivism rate than youth processed in adult court. Implications for subsequent research and policy are discussed. © 2012, Taylor & Francis Group, LLC

Functional Scaffolding For Composing Additional Musical Voices

Many tools for computer-assisted composition contain built-in music-theoretical assumptions that may constrain the output to particular styles. In contrast, this article presents a new musical representation that contains almost no built-in knowledge, but that allows even musically untrained users to generate polyphonic textures that are derived from the user\u27s own initial compositions. This representation, called functional scaffolding for musical composition (FSMC), exploits a simple yet powerful property of multipart compositions: The pattern of notes and rhythms in different instrumental parts of the same song are functionally related. That is, in principle, one part can be expressed as a function of another. Music in FSMC is represented accordingly as a functional relationship between an existing human composition, or scaffold, and a generated set of one or more additional musical voices. A human user without any musical expertise can then explore how the generated voice (or voices) should relate to the scaffold through an interactive evolutionary process akin to animal breeding. By inheriting from the intrinsic style and texture of the piece provided by the user, this approach can generate additional voices for potentially any style of music without the need for extensive musical expertise

Generating Musical Accompaniment Through Functional Scaffolding

A popular approach to music generation in recent years is to extract rules and statistical relationships by analyzing a large corpus of musical data. The aim of this paper is to present an alternative to such data-intensive techniques. The main idea, called functional scaffolding for musical composition (FSMC), exploits a simple yet powerful property of multipart compositions: The pattern of notes and rhythms in different instrumental parts of the same song are functionally related. That is, in principle, one part can be expressed as a function of another. The utility of this insight is validated by an application that assists the user in exploring the space of possible accompaniments to preexisting parts through a process called interactive evolutionary computation. In effect, without the need for musical expertise, the user explores transforming functions that yield plausible accompaniments derived from preexisting parts. In fact, a survey of listeners shows that participants cannot distinguish songs with computer-generated parts from those that are entirely human composed. Thus this one simple mathematical relationship yields surprisingly convincing results even without any real musical knowledge programmed into the system. With future refinement, FSMC might lead to practical aids for novices aiming to fulfill incomplete visions. © 2011 Amy K. Hoover et al

Interactively evolving harmonies through functional scaffolding

While the real-time focus of today’s automated accompaniment generators can benefit instrumentalists and vocalists in their practice, improvisation, or performance, an opportunity remains specifically to assist novice composers. This paper introduces a novel such approach based on evolutionary computation called functional scaffolding for musical composition (FSMC), which helps the user explore potential accompaniments for existing musical pieces, or scaffolds. The key idea is to produce accompaniment as a function of the scaffold, thereby inheriting from its inherent style and texture. To implement this idea, accompaniments are represented by a special type of neural network called a compositional pattern producing network (CPPN), which produces harmonies by elaborating on and exploiting regularities in pitches and rhythms found in the scaffold. This paper focuses on how inexperienced composers can personalize accompaniments by first choosing any MIDI scaffold, then selecting which parts (e.g. the piano, guitar, or bass guitar) the CPPN can hear, and finally customizing and refining the computer-generated accompaniment through an interactive process of selection and mutation of CPPNs called interactive evolutionary computation (IEC). The potential of this approach is demonstrated by following the evolution of a specific accompaniment and studying whether listeners appreciate the results

Implications From Music Generation For Music Appreciation

This position paper argues that fundamental principles that are exploited to achieve effective music generation can also shed light on the elusive question of why humans appreciate music, and which music is easiest to appreciate. In particular, we highlight the key principle behind an existing approach to assisted accompaniment generation called functional scaffolding for musical composition (FSMC). In this approach, accompaniment is generated as a function of the preexisting parts. The success of this idea at generating plausible accompaniment according to studies with human participants suggests that perceiving a functional relationship among parts in a composition may be essential to the appreciation of music in general. This insight is intriguing because it can help to explain without any appeal to traditional music theory why humans with no knowledge or training in music can nevertheless find satisfaction in coherent musical structure

Demo: A Computer-Assisted Approach To Composing With Maestrogenesis

This demonstration presents MaestroGenesis, a program that helps users create complete polyphonic musical pieces from as little as a simple, human composed monophonic melody. MaestroGenesis creates music by exploiting two key ideas behind the functional scaffolding for musical composition (FSMC) approach: (1) that music a function of time and (2) that functional transformations of initial human starting melodies, or scaffolds, inherit some of the essential human qualities contained in the scaffold. Music in FSMC is represented as a functional relationship between the scaffold and a generated accompaniment. The GUI helps users evolve these functions by importing and developing their music through a breeding process akin to animal breeding, called interactive evolutionary computation. Some resulting pieces are indistinguishable from completely human-composed pieces. Copyright © 2012, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved