On Similarities between Inference in Game Theory and Machine Learning

In this paper, we elucidate the equivalence between inference in game theory and machine learning. Our aim in so doing is to establish an equivalent vocabulary between the two domains so as to facilitate developments at the intersection of both fields, and as proof of the usefulness of this approach, we use recent developments in each field to make useful improvements to the other. More specifically, we consider the analogies between smooth best responses in fictitious play and Bayesian inference methods. Initially, we use these insights to develop and demonstrate an improved algorithm for learning in games based on probabilistic moderation. That is, by integrating over the distribution of opponent strategies (a Bayesian approach within machine learning) rather than taking a simple empirical average (the approach used in standard fictitious play) we derive a novel moderated fictitious play algorithm and show that it is more likely than standard fictitious play to converge to a payoff-dominant but risk-dominated Nash equilibrium in a simple coordination game. Furthermore we consider the converse case, and show how insights from game theory can be used to derive two improved mean field variational learning algorithms. We first show that the standard update rule of mean field variational learning is analogous to a Cournot adjustment within game theory. By analogy with fictitious play, we then suggest an improved update rule, and show that this results in fictitious variational play, an improved mean field variational learning algorithm that exhibits better convergence in highly or strongly connected graphical models. Second, we use a recent advance in fictitious play, namely dynamic fictitious play, to derive a derivative action variational learning algorithm, that exhibits superior convergence properties on a canonical machine learning problem (clustering a mixture distribution)

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Feasibility of an Outpatient Training Program after COVID-19

Long-term physical consequences of coronavirus disease 2019 (COVID-19) are currently being reported. As a result, the focus is turning towards interventions that support recovery after hospitalization. To date, the feasibility of an outpatient program for people recovering from COVID-19 has not been investigated. This study presents data for a physiotherapy-led, comprehensive outpatient pulmonary rehabilitation (PR) program. Patients were recruited after hospital discharge. Training consisted of twice weekly, interval-based aerobic cycle endurance (ACE) training, followed by resistance training (RT); 60–90 min per session at intensities of 50% peak work rate; education and physical activity coaching were also provided. Feasibility outcomes included: recruitment and dropout rates, number of training sessions undertaken, and tolerability for dose and training mode. Of the 65 patients discharged home during the study period, 12 were successfully enrolled onto the program. Three dropouts (25%) were reported after 11–19 sessions. Tolerability of interval-based training was 83% and 100% for exercise duration of ACE and RT, respectively; 92% for training intensity, 83% progressive increase of intensity, and 83% mode in ACE. We tentatively suggest from these preliminary findings that the PR protocol used may be both feasible, and confer benefits to a small subgroup of patients recovering from COVID-19

Transitionless quantum drivings for the harmonic oscillator

Two methods to change a quantum harmonic oscillator frequency without transitions in a finite time are described and compared. The first method, a transitionless-tracking algorithm, makes use of a generalized harmonic oscillator and a non-local potential. The second method, based on engineering an invariant of motion, only modifies the harmonic frequency in time, keeping the potential local at all times.Comment: 11 pages, 1 figure. Submitted for publicatio

One year follow-up of physical performance and quality of life in patients surviving COVID-19: a prospective cohort study

INTRODUCTION: The coronavirus disease (COVID-19) continues to affect many countries globally, with the long-term impact of the disease now being recognized. According to the latest research, some of the affected individuals continue to experience functional limitations, reduced physical performance and impaired health-related quality of life (HRQoL) even after eight months. This prospective cohort study aimed to describe the longer-term recovery of physical performance and HRQoL in COVID-19 survivors over one year. METHOD: A cohort (n = 43; 32-84 years old) hospitalized with COVID-19 between March and June 2020 was followed over one year and assessed at three time points: hospital discharge, 3 months and 12 months post-admission. Participants experienced mild (10/43) to critical (6/43) pneumonia and stayed in the hospital for a median of 10 days (IQR 9). Participants were assessed for physical performance (six-minute walk test), HRQoL (EQ-5D-5L), COVID-19 related limitations in functionality (PCFS), hospital-related anxiety and depression (HADS-A/-D), lung function (FEV1, FVC) and dyspnea during activity (mMRC). All assessments were conducted by physiotherapists trained in cardio-respiratory rehabilitation. RESULTS: After discharge, 8/34 showed reduced physical performance, 9/42 had lower HRQoL and 14/32 had COVID-19 induced limitations in functionality on the PCFS scale. Physical performance did not change significantly between discharge and 12-month follow-up, but 15/34 participants showed clinically relevant improvements in walking distance (>30 m). However, 16/34 had a decreased walking distance >30 m when comparing 3-month to 12-month follow-up. At 12 months, 12/41 of participants still perceived COVID-19 related limitations in daily life on the PCFS scale. For HRQoL, 12/41 participants still perceived moderate-to-severe symptoms of pain and discomfort and 13/41 slight-to-severe symptoms of anxiety and depression. CONCLUSION: This cohort of adult patients hospitalized for mild to severe COVID-19 in Switzerland was generally mildly affected but still reported some limitations after one year. These results offer preliminary indications for ongoing support after hospitalization and point towards the need for specific, individualized follow-up to support their recovery. ClinicalTrials.gov (NCT04375709

Aggregates, Formational Emergence, and the Focus on Practice in Stone Artifact Archaeology

The stone artifact record has been one of the major grounds for investigating our evolution. With the predominant focus on their morphological attributes and technological aspects of manufacture, stone artifacts and their assemblages have been analyzed as explicit measures of past behaviors, adaptations, and population histories. This analytical focus on technological andmorphological appearance is one of the characteristics of the conventional approach for constructing inferences from this record. An equally persistent routine involves ascribing the emerged patterns and variability within the archaeological deposits directly to long-term central tendencies in human actions and cultural transmission. Here we re-evaluate this conventional approach. By invoking some of the known concerns and concepts about the formation of archaeological record, we introduce notions of aggregates and formational emergence to expand on the understanding of how artifacts accumulate, what these accumulations represent, and how the patterns and variability among them emerge. To infer behavior that could inform on past lifeways, we further promote a shift in the focus of analysis from the technological and morphological appearance of artifacts and assemblages to the practice of stone use. We argue for a more rigorous and multi-level inferential procedure in modeling behavioral adaptation and evolution

Anesthesia assessment based on ICA permutation entropy analysis of two-channel EEG signals

Inaccurate assessment may lead to inaccurate levels of dosage given to the patients that may lead to intraoperative awareness that is caused by under dosage during surgery or prolonged recovery in patients that is caused by over dosage after the surgery is done. Previous research and evidence show that assessing anesthetic levels with the help of electroencephalography (EEG) signals gives an overall better aspect of the patient’s anesthetic state. This paper presents a new method to assess the depth of anesthesia (DoA) using Independent Component Analysis (ICA) and permutation entropy analysis. ICA is performed on two-channel EEG to reduce the noise then Wavelet and permutation entropy are applied on these channels to extract the features. A linear regression model was used to build the new DoA index using the selected features. The new index designed by proposed methods performs well under low signal quality and it was overall consistent in most of the cases where Bispectral index (BIS) may fail to provide any valid value

Wavelet analysis of epileptic spikes

Interictal spikes and sharp waves in human EEG are characteristic signatures of epilepsy. These potentials originate as a result of synchronous, pathological discharge of many neurons. The reliable detection of such potentials has been the long standing problem in EEG analysis, especially after long-term monitoring became common in investigation of epileptic patients. The traditional definition of a spike is based on its amplitude, duration, sharpness, and emergence from its background. However, spike detection systems built solely around this definition are not reliable due to the presence of numerous transients and artifacts. We use wavelet transform to analyze the properties of EEG manifestations of epilepsy. We demonstrate that the behavior of wavelet transform of epileptic spikes across scales can constitute the foundation of a relatively simple yet effective detection algorithm.Comment: 4 pages, 3 figure

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