TBBL: A Tree-Based Bidding Language for Iterative Combinatorial Exchanges

We present a novel tree-based logical bidding language, TBBL, for preference elicitation in combinatorial exchanges (CEs). TBBL provides new expressiveness for two-sided markets with agents that are both buying and selling goods. Moreover, the rich semantics of TBBL allow the language to capture new structure, making it exponentially more concise than OR* and LGB for preferences that are realistic in important domains for CEs. With simple extensions TBBL can subsume these earlier languages. TBBL can also explicitly represent partial information about valuations. The language is designed such that the structure in TBBL bids can be concisely captured directly in mixed-integer programs for the allocation problem. We illustrate TBBL through examples drawn from domains to which it can be (and is being) applied, and motivate further extensions we are currently pursuing.Engineering and Applied Science

Ulcerative colitis-risk loci on chromosomes 1p36 and 12q15 found by genome-wide association study.

Ulcerative colitis is a chronic inflammatory disease of the colon that presents as diarrhea and gastrointestinal bleeding. We performed a genome-wide association study using DNA samples from 1,052 individuals with ulcerative colitis and preexisting data from 2,571 controls, all of European ancestry. In an analysis that controlled for gender and population structure, ulcerative colitis loci attaining genome-wide significance and subsequent replication in two independent populations were identified on chromosomes 1p36 (rs6426833, combined P = 5.1 x 10(-13), combined odds ratio OR = 0.73) and 12q15 (rs1558744, combined P = 2.5 x 10(-12), combined OR = 1.35). In addition, combined genome-wide significant evidence for association was found in a region spanning BTNL2 to HLA-DQB1 on chromosome 6p21 (rs2395185, combined P = 1.0 x 10(-16), combined OR = 0.66) and at the IL23R locus on chromosome 1p31 (rs11209026, combined P = 1.3 x 10(-8), combined OR = 0.56; rs10889677, combined P = 1.3 x 10(-8), combined OR = 1.29)

The Partnership Co-Design Lab: Co-constructing a Patient Advisor Programme to increase adherence to rehabilitation after upper extremity replantation

A five-phase Patient Advisor Programme created by the Partnership Co-Design Lab led to higher rates of adherence to rehabilitation interventions for patients followed at the main rehabilitation centre compared to patients transferred to other, more remote, rehabilitation facilities

Visual Scan Paths and Recognition of Facial Identity in Autism Spectrum Disorder and Typical Development

Background: Previous research suggests that many individuals with autism spectrum disorder (ASD) have impaired facial identity recognition, and also exhibit abnormal visual scanning of faces. Here, two hypotheses accounting for an association between these observations were tested: i) better facial identity recognition is associated with increased gaze time on the Eye region; ii) better facial identity recognition is associated with increased eye-movements around the face. Methodology and Principal Findings: Eye-movements of 11 children with ASD and 11 age-matched typically developing (TD) controls were recorded whilst they viewed a series of faces, and then completed a two alternative forced-choice recognition memory test for the faces. Scores on the memory task were standardized according to age. In both groups, there was no evidence of an association between the proportion of time spent looking at the Eye region of faces and age-standardized recognition performance, thus the first hypothesis was rejected. However, the 'Dynamic Scanning Index' - which was incremented each time the participant saccaded into and out of one of the core-feature interest areas - was strongly asso ciated with age-standardized face recognition scores in both groups, even after controlling for various other potential predictors of performance. Conclusions and Significance: In support of the second hypothesis, results suggested that increased saccading between core-features was associated with more accurate face recognition ability, both in typical development and ASD. Causal directions of this relationship remain undetermined.10 page(s

Comparative performances of machine learning methods for classifying Crohn Disease patients using genome-wide genotyping data

Abstract: Crohn Disease (CD) is a complex genetic disorder for which more than 140 genes have been identified using genome wide association studies (GWAS). However, the genetic architecture of the trait remains largely unknown. The recent development of machine learning (ML) approaches incited us to apply them to classify healthy and diseased people according to their genomic information. The Immunochip dataset containing 18,227 CD patients and 34,050 healthy controls enrolled and genotyped by the international Inflammatory Bowel Disease genetic consortium (IIBDGC) has been re-analyzed using a set of ML methods: penalized logistic regression (LR), gradient boosted trees (GBT) and artificial neural networks (NN). The main score used to compare the methods was the Area Under the ROC Curve (AUC) statistics. The impact of quality control (QC), imputing and coding methods on LR results showed that QC methods and imputation of missing genotypes may artificially increase the scores. At the opposite, neither the patient/control ratio nor marker preselection or coding strategies significantly affected the results. LR methods, including Lasso, Ridge and ElasticNet provided similar results with a maximum AUC of 0.80. GBT methods like XGBoost, LightGBM and CatBoost, together with dense NN with one or more hidden layers, provided similar AUC values, suggesting limited epistatic effects in the genetic architecture of the trait. ML methods detected near all the genetic variants previously identified by GWAS among the best predictors plus additional predictors with lower effects. The robustness and complementarity of the different methods are also studied. Compared to LR, non-linear models such as GBT or NN may provide robust complementary approaches to identify and classify genetic markers

ICE: An expressive iterative combinatorial exchange

We present the design and analysis of the first fully expressive, iterative combinatorial exchange (ICE). The exchange incorporates a tree-based bidding language (TBBL) that is concise and expressive for CEs. Bidders specify lower and upper bounds in TBBL on their value for different trades and refine these bounds across rounds. These bounds allow price discovery and useful preference elicitation in early rounds, and allow termination with an efficient trade despite partial information on bidder valuations. All computation in the exchange is carefully optimized to exploit the structure of the bid-trees and to avoid enumerating trades. A proxied interpretation of a revealed-preference activity rule, coupled with simple linear prices, ensures progress across rounds. The exchange is fully implemented, and we give results demonstrating several aspects of its scalability and economic properties with simulated bidding strategies