Lost in Draft: Investigating Game Balance in Multiplayer Online Battle Arena Drafting

Master´s thesis in Information and Communication Technology (IKT590) University of Agder, GrimstadThis thesis explores modern machine learning solutions to turn-basedstrategy games. In particular, we explore the possibilities of equalizing the playing field for both teams in the draft phase of Defense of the Ancients 2 (Dota 2) and League of Legends (LoL), with both games being giants in the multi-million dollar esports industry. The thesis covers the Multiplayer Online Battle Arena video game genre and the draft phase the games use. We also discuss the tech-nology used to address the problem, as well as the basic concepts of modern machine learning that allowed this technology to arise. We then introduce the Win Rate Predictor, which is our implementation of the reward function in the Monte Carlo Tree Search algorithm used to predict the win rate of each team given different parameters in the draft phase. The results show clear and quantifiable differences in differentparts of the draft phase. This includes reordering the pick order, the impact of including banning in the draft phase, and the balance ofdifferent draft schemes. Specifically, first pick has a higher win rate than last pick for the majority of the draft schemes, suggesting that strong initial picks aremore valuable than reactive response picks. Additionally, bans can bea way to influence the balance of a draft phase. Our simulations also suggest that the southwestern locations on the map have a higher win rate in both Dota 2 and LoL. And finally, according to our simulations,the games’ respective implementation of a draft scheme is the most evenly balanced draft scheme for their game

Damage assessment in beam-like structures by correlation of spectrum using machine learning

Damage assessment in the actual operating process of the structure is a modern and exciting problem of construction engineering due to several practical knowledge about the current condition of the inspected structures. However, the problem faced is the difficulty in controlling the excitation in structures. Therefore, the output-based structural damage identification method is becoming attractive because of its potential to be applied to an actual application without being constrained by the collection of the information excitation source. An approach of damage assessment based on supervised Machine Learning is introduced in this study by using the correlation of spectral signal as an input feature for artificial neural network (ANN) and decision tree. The output of machine learning algorithms consists of the appearance of new cuts, the level of cutting and the cutting position. A supported beam model was constructed as an experiment to determine if the method is reasonable for engineering structures. Two machine learning algorithms have been applied to check the relevance of the proposed feature from vibration data. This study contributes a standard in the damage identification problem based on spectral correlation

Damage assessment in beam-like structures by correlation of spectrum using machine learning

Damage assessment in the actual operating process of the structure is a modern and exciting problem of construction engineering due to several practical knowledge about the current condition of the inspected structures. However, the problem faced is the difficulty in controlling the excitation in structures. Therefore, the output-based structural damage identification method is becoming attractive because of its potential to be applied to an actual application without being constrained by the collection of the information excitation source. An approach of damage assessment based on supervised Machine Learning is introduced in this study by using the correlation of spectral signal as an input feature for artificial neural network (ANN) and decision tree. The output of machine learning algorithms consists of the appearance of new cuts, the level of cutting and the cutting position. A supported beam model was constructed as an experiment to determine if the method is reasonable for engineering structures. Two machine learning algorithms have been applied to check the relevance of the proposed feature from vibration data. This study contributes a standard in the damage identification problem based on spectral correlation

Antibiotic use and prescription and its effects on Enterobacteriaceae in the gut in children with mild respiratory infections in Ho Chi Minh City, Vietnam. A prospective observational outpatient study.

BACKGROUND AND OBJECTIVES: Treatment guidelines do not recommend antibiotic use for acute respiratory infections (ARI), except for streptococcal pharyngitis/tonsillitis and pneumonia. However, antibiotics are prescribed frequently for children with ARI, often in absence of evidence for bacterial infection. The objectives of this study were 1) to assess the appropriateness of antibiotic prescriptions for mild ARI in paediatric outpatients in relation to available guidelines and detected pathogens, 2) to assess antibiotic use on presentation using questionnaires and detection in urine 3) to assess the carriage rates and proportions of resistant intestinal Enterobacteriaceae before, during and after consultation. MATERIALS AND METHODS: Patients were prospectively enrolled in Children's Hospital 1, Ho Chi Minh City, Vietnam and diagnoses, prescribed therapy and outcome were recorded on first visit and on follow-up after 7 days. Respiratory bacterial and viral pathogens were detected using molecular assays. Antibiotic use before presentation was assessed using questionnaires and urine HPLC. The impact of antibiotic usage on intestinal Enterobacteriaceae was assessed with semi-quantitative culture on agar with and without antibiotics on presentation and after 7 and 28 days. RESULTS: A total of 563 patients were enrolled between February 2009 and February 2010. Antibiotics were prescribed for all except 2 of 563 patients. The majority were 2nd and 3rd generation oral cephalosporins and amoxicillin with or without clavulanic acid. Respiratory viruses were detected in respiratory specimens of 72.5% of patients. Antibiotic use was considered inappropriate in 90.1% and 67.5%, based on guidelines and detected pathogens, respectively. On presentation parents reported antibiotic use for 22% of patients, 41% of parents did not know and 37% denied antibiotic use. Among these three groups, six commonly used antibiotics were detected with HPLC in patients' urine in 49%, 40% and 14%, respectively. Temporary selection of 3rd generation cephalosporin resistant intestinal Enterobacteriaceae during antibiotic use was observed, with co-selection of resistance to aminoglycosides and fluoroquinolones. CONCLUSIONS: We report overuse and overprescription of antibiotics for uncomplicated ARI with selection of resistant intestinal Enterobacteriaceae, posing a risk for community transmission and persistence in a setting of a highly granular healthcare system and unrestricted access to antibiotics through private pharmacies. REGISTRATION: This study was registered at the International Standard Randomised Controlled Trials Number registry under number ISRCTN32862422: http://www.isrctn.com/ISRCTN32862422

Safety and efficacy of fluoxetine on functional outcome after acute stroke (AFFINITY): a randomised, double-blind, placebo-controlled trial

Background Trials of fluoxetine for recovery after stroke report conflicting results. The Assessment oF FluoxetINe In sTroke recoverY (AFFINITY) trial aimed to show if daily oral fluoxetine for 6 months after stroke improves functional outcome in an ethnically diverse population. Methods AFFINITY was a randomised, parallel-group, double-blind, placebo-controlled trial done in 43 hospital stroke units in Australia (n=29), New Zealand (four), and Vietnam (ten). Eligible patients were adults (aged ≥18 years) with a clinical diagnosis of acute stroke in the previous 2–15 days, brain imaging consistent with ischaemic or haemorrhagic stroke, and a persisting neurological deficit that produced a modified Rankin Scale (mRS) score of 1 or more. Patients were randomly assigned 1:1 via a web-based system using a minimisation algorithm to once daily, oral fluoxetine 20 mg capsules or matching placebo for 6 months. Patients, carers, investigators, and outcome assessors were masked to the treatment allocation. The primary outcome was functional status, measured by the mRS, at 6 months. The primary analysis was an ordinal logistic regression of the mRS at 6 months, adjusted for minimisation variables. Primary and safety analyses were done according to the patient's treatment allocation. The trial is registered with the Australian New Zealand Clinical Trials Registry, ACTRN12611000774921. Findings Between Jan 11, 2013, and June 30, 2019, 1280 patients were recruited in Australia (n=532), New Zealand (n=42), and Vietnam (n=706), of whom 642 were randomly assigned to fluoxetine and 638 were randomly assigned to placebo. Mean duration of trial treatment was 167 days (SD 48·1). At 6 months, mRS data were available in 624 (97%) patients in the fluoxetine group and 632 (99%) in the placebo group. The distribution of mRS categories was similar in the fluoxetine and placebo groups (adjusted common odds ratio 0·94, 95% CI 0·76–1·15; p=0·53). Compared with patients in the placebo group, patients in the fluoxetine group had more falls (20 [3%] vs seven [1%]; p=0·018), bone fractures (19 [3%] vs six [1%]; p=0·014), and epileptic seizures (ten [2%] vs two [<1%]; p=0·038) at 6 months. Interpretation Oral fluoxetine 20 mg daily for 6 months after acute stroke did not improve functional outcome and increased the risk of falls, bone fractures, and epileptic seizures. These results do not support the use of fluoxetine to improve functional outcome after stroke

Lost in Draft: Investigating Game Balance in Multiplayer Online Battle Arena Drafting

This thesis explores modern machine learning solutions to turn-basedstrategy games. In particular, we explore the possibilities of equalizing the playing field for both teams in the draft phase of Defense of the Ancients 2 (Dota 2) and League of Legends (LoL), with both games being giants in the multi-million dollar esports industry. The thesis covers the Multiplayer Online Battle Arena video game genre and the draft phase the games use. We also discuss the tech-nology used to address the problem, as well as the basic concepts of modern machine learning that allowed this technology to arise. We then introduce the Win Rate Predictor, which is our implementation of the reward function in the Monte Carlo Tree Search algorithm used to predict the win rate of each team given different parameters in the draft phase. The results show clear and quantifiable differences in differentparts of the draft phase. This includes reordering the pick order, the impact of including banning in the draft phase, and the balance ofdifferent draft schemes. Specifically, first pick has a higher win rate than last pick for the majority of the draft schemes, suggesting that strong initial picks aremore valuable than reactive response picks. Additionally, bans can bea way to influence the balance of a draft phase. Our simulations also suggest that the southwestern locations on the map have a higher win rate in both Dota 2 and LoL. And finally, according to our simulations,the games’ respective implementation of a draft scheme is the most evenly balanced draft scheme for their game

Twelve-Month Outcomes of the AFFINITY Trial of Fluoxetine for Functional Recovery After Acute Stroke: AFFINITY Trial Steering Committee on Behalf of the AFFINITY Trial Collaboration

Background and Purpose: The AFFINITY trial (Assessment of Fluoxetine in Stroke Recovery) reported that oral fluoxetine 20 mg daily for 6 months after acute stroke did not improve functional outcome and increased the risk of falls, bone fractures, and seizures. After trial medication was ceased at 6 months, survivors were followed to 12 months post-randomization. This preplanned secondary analysis aimed to determine any sustained or delayed effects of fluoxetine at 12 months post-randomization. Methods: AFFINITY was a randomized, parallel-group, double-blind, placebo-controlled trial in adults (n=1280) with a clinical diagnosis of stroke in the previous 2 to 15 days and persisting neurological deficit who were recruited at 43 hospital stroke units in Australia (n=29), New Zealand (4), and Vietnam (10) between 2013 and 2019. Participants were randomized to oral fluoxetine 20 mg once daily (n=642) or matching placebo (n=638) for 6 months and followed until 12 months after randomization. The primary outcome was function, measured by the modified Rankin Scale, at 6 months. Secondary outcomes for these analyses included measures of the modified Rankin Scale, mood, cognition, overall health status, fatigue, health-related quality of life, and safety at 12 months. Results: Adherence to trial medication was for a mean 167 (SD 48) days and similar between randomized groups. At 12 months, the distribution of modified Rankin Scale categories was similar in the fluoxetine and placebo groups (adjusted common odds ratio, 0.93 [95% CI, 0.76–1.14]; P =0.46). Compared with placebo, patients allocated fluoxetine had fewer recurrent ischemic strokes (14 [2.18%] versus 29 [4.55%]; P =0.02), and no longer had significantly more falls (27 [4.21%] versus 15 [2.35%]; P =0.08), bone fractures (23 [3.58%] versus 11 [1.72%]; P =0.05), or seizures (11 [1.71%] versus 8 [1.25%]; P =0.64) at 12 months. Conclusions: Fluoxetine 20 mg daily for 6 months after acute stroke had no delayed or sustained effect on functional outcome, falls, bone fractures, or seizures at 12 months poststroke. The lower rate of recurrent ischemic stroke in the fluoxetine group is most likely a chance finding. REGISTRATION: URL: http://www.anzctr.org.au/ ; Unique identifier: ACTRN12611000774921