Local Communication Protocols for Learning Complex Swarm Behaviors with Deep Reinforcement Learning

Swarm systems constitute a challenging problem for reinforcement learning (RL) as the algorithm needs to learn decentralized control policies that can cope with limited local sensing and communication abilities of the agents. While it is often difficult to directly define the behavior of the agents, simple communication protocols can be defined more easily using prior knowledge about the given task. In this paper, we propose a number of simple communication protocols that can be exploited by deep reinforcement learning to find decentralized control policies in a multi-robot swarm environment. The protocols are based on histograms that encode the local neighborhood relations of the agents and can also transmit task-specific information, such as the shortest distance and direction to a desired target. In our framework, we use an adaptation of Trust Region Policy Optimization to learn complex collaborative tasks, such as formation building and building a communication link. We evaluate our findings in a simulated 2D-physics environment, and compare the implications of different communication protocols.Comment: 13 pages, 4 figures, version 2, accepted at ANTS 201

Role of social media and the Internet in pathways to care for adolescents and young adults with psychotic disorders and non-psychotic mood disorders

AIMS: Although psychosis often occurs during adolescence, there has been little research on how the ubiquitously used Internet and social media could impact pathways to care. We examined how youth with psychotic spectrum disorders (PSD) versus non-psychotic mood disorders (NPMD) use online resources in the early illness stages. METHODS: Social media use and pathways to care data were collected using a semi-structured interview from 80 youth (PSD = 40 and NPMD = 40) aged 12-21 years within 2 years of symptom onset. RESULTS: A total of 97.5% of participants (mean age = 18.3 years) regularly used social media, spending approximately 2.6 +/- 2.5 h per day online. There were 22.4% of our sample (PSD = 19.4%, NPMD = 25.0%, P = 0.56) who reported waiting to reach out for help believing that symptoms would disappear. A total of 76.5% (PSD = 67.5%, NPMD = 85.0%, P = 0.06) noticed social media habit changes during symptom emergence. Thirty per cent reported discussing their symptoms on social media (PSD = 22.5%, NPMD = 37.5%, P = 0.14). NPMD patients sought information most on how to stop symptoms (40.0% vs. 13.5%, P = 0.01), while PSD youth were more commonly interested in what caused their symptoms (21.6% vs. 15.0%, P = 0.45). More PSD patients (42.9% vs. 25.0%, P = 0.10) would prefer to receive mental health information via the Internet. Altogether, 63.6% (PSD = 64.9%, NPMD = 62.5%, P = 0.83) were amenable to clinicians proactively approaching them via social media during symptom emergence. A total of 74.3% (PSD = 78.4%, NPMD = 70.0%, P = 0.40) liked the idea of obtaining help/advice from professionals via social media. CONCLUSIONS: The Internet and social media provide an unparalleled opportunity to supplement and potentially transform early intervention services, and acceptance of this approach appears to be high

Nonpoint pollution of surface waters with phosphorus and nitrogen

Agriculture and urban activities are major sources of phosphorus and nitrogen to aquatic ecosystems. Atmospheric deposition further contributes as a source of N. These nonpoint inputs of nutrients are difficult to measure and regulate because they derive from activities dispersed over wide areas of land and are variable in time due to effects of weather. In aquatic ecosystems, these nutrients cause diverse problems such as toxic algal blooms, loss of oxygen, fish kills, loss of biodiversity (including species important for commerce and recreation), loss of aquatic plant beds and coral reefs, and other problems. Nutrient enrichment seriously degrades aquatic ecosystems and impairs the use of water for drinking, industry, agriculture, recreation, and other purposes. Based on our review of the scientific literature, we are certain that (1) eutrophication is a widespread problem in rivers, lakes, estuaries, and coastal oceans, caused by overenrichment with P and N; (2) nonpoint pollution, a major source of P and N to surface waters of the United States, results primarily from agriculture and urban activity, including industry; (3) inputs of P and N to agriculture in the form of fertilizers exceed outputs in produce in the United States and many other nations; (4) nutrient flows to aquatic ecosystems are directly related to animal stocking densities, and under high livestock densities, manure production exceeds the needs of crops to which the manure is applied; (5) excess fertilization and manure production cause a P surplus to accumulate in soil, some of which is transported to aquatic ecosystems; and (6) excess fertilization and manure production on agricultural lands create surplus N, which is mobile in many soils and often leaches to downstream aquatic ecosystems, and which can also volatilize to the atmosphere, redepositing elsewhere and eventually reaching aquatic ecosystems. If current practices continue, nonpoint pollution of surface waters is virtually certain to increase in the future. Such an outcome is not inevitable, however, because a number of technologies, land use practices, and conservation measures are capable of decreasing the flow of nonpoint P and N into surface waters. From our review of the available scientific information, we are confident that: (1) nonpoint pollution of surface waters with P and N could be reduced by reducing surplus nutrient flows in agricultural systems and processes, reducing agricultural and urban runoff by diverse methods, and reducing N emissions from fossil fuel burning; and (2) eutrophication can be reversed by decreasing input rates of P and N to aquatic ecosystems, but rates of recovery are highly variable among water bodies. Often, the eutrophic state is persistent, and recovery is slow

Abnormal Involuntary Movement Scale in Tardive Dyskinesia:Minimal Clinically Important Difference

Background A minimal clinically important difference has not been established for the Abnormal Involuntary Movement Scale in patients with tardive dyskinesia. Valbenazine is a vesicular monoamine transporter 2 inhibitor approved for the treatment of tardive dyskinesia in adults. Efficacy in randomized, double�blind, placebo�controlled trials was defined as the change from baseline in Abnormal Involuntary Movement Scale total score (sum of items 1�7). Objectives To estimate an minimal clinically important difference for the Abnormal Involuntary Movement Scale using valbenazine trial data and an anchor�based method. Methods Data were pooled from three 6�week double�blind, placebo�controlled trials: KINECT (NCT01688037), KINECT 2 (NCT01733121), and KINECT 3 (NCT02274558). Valbenazine doses were pooled for analyses as follows: “low dose,� which includes 40 or 50 mg/day; and “high dose,� which includes 75 or 80 mg/day. Mean changes from baseline in Abnormal Involuntary Movement Scale total score were analyzed in all participants (valbenazine� and placebo�treated) with a Clinical Global Impression of Change�Tardive Dyskinesia or Patient Global Impression of Change score of 1 (very much improved) to 3 (minimally improved). Results The least squares mean improvement from baseline to week 6 in Abnormal Involuntary Movement Scale total score was significantly greater with valbenazine (low dose: –2.4; high dose: –3.2; both, P < 0.001) versus placebo (–0.7). An minimal clinically important difference of 2 points was estimated based on least squares mean changes in Abnormal Involuntary Movement Scale total score in participants with a Clinical Global Impression of Change�Tardive Dyskinesia score ≤3 at week 6 (mean change: –2.2; median change: –2) or Patient Global Impression of Change score ≤3 at week 6 (mean change: –2.0; median change: –2). Conclusions Results from an anchor�based method indicate that a 2�point decrease in Abnormal Involuntary Movement Scale total score may be considered clinically important. © 2019 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society

How can we improve antidepressant adherence in the management of depression? A targeted review and 10 clinical recommendations.

Adherence to antidepressants is crucial for optimal treatment outcomes when treating depressive disorders. However, poor adherence is common among patients prescribed antidepressants. This targeted review summarizes the main factors associated with poor adherence, interventions that promote antidepressant adherence, pharmacological aspects related to antidepressant adherence, and formulates 10 clinical recommendations to optimize antidepressant adherence. Patient-related factors associated with antidepressant non-adherence include younger age, psychiatric and medical comorbidities, cognitive impairment, and substance use disorders. Prescriber behavior-related factors include neglecting medical and family histories, selecting poorly tolerated antidepressants, or complex antidepressant regimens. Multi-disciplinary interventions targeting both patient and prescriber, aimed at improving antidepressant adherence, include psychoeducation and providing the patient with clear behavioral interventions to prevent/minimize poor adherence. Regarding antidepressant choice, agents with individually tailored tolerability profile should be chosen. Ten clinical recommendations include four points focusing on the patient (therapeutic alliance, adequate history taking, measurement of depressive symptoms, and adverse effects improved access to clinical care), three focusing on prescribing practice (psychoeducation, individually tailored antidepressant choice, simplified regimen), two focusing on mental health services (improved access to mental health care, incentivized adherence promotion and monitoring), and one relating to adherence measurement (adherence measurement with scales and/or therapeutic drug monitoring)

Mortality after the First Diagnosis of Schizophrenia-Spectrum Disorders: A Population-based Retrospective Cohort Study

There is emerging evidence of high mortality rates after the first diagnosis of psychotic disorder. The objective of this study was to estimate the standardized mortality ratio (SMR) in a population-based cohort of individuals with a first diagnosis of schizophrenia-spectrum psychotic disorder (SSD). The cohort included a population-based sample of individuals with a first diagnosis of SSD based on the first diagnosis occurring during hospitalization or in an outpatient setting between 2007 and 2010 in Ontario, Canada. All patients were followed for 5 years after the first diagnosis. The primary outcome was SMR, including all-cause, suicide-related, accidental, and other causes. Between 2007 and 2010, there were 2382 patients in the hospitalization cohort and 11 003 patients in the outpatient cohort. Over the 5-year observation period, 97 (4.1%) of the hospitalization cohort and 292 (2.7%) of the outpatient cohort died, resulting in an SMR of 13.6 and 9.1, respectively. In both cohorts, suicide was the most common cause of death. Approximately 1 in 25 patients with a first diagnosis of SSD during hospitalization, and 1 in 40 patients with a first diagnosis of SSD in an outpatient setting, died within 5 years of first diagnosis in Ontario, Canada. This mortality rate is between 9 and 13 times higher than would be expected in the age-matched general population. Based on these data, timely access to services should be a public health priority to reduce mortality following a first diagnosis of an SSD