Using Posters to Recommend Anime and Mangas in a Cold-Start Scenario

Item cold-start is a classical issue in recommender systems that affects anime and manga recommendations as well. This problem can be framed as follows: how to predict whether a user will like a manga that received few ratings from the community? Content-based techniques can alleviate this issue but require extra information, that is usually expensive to gather. In this paper, we use a deep learning technique, Illustration2Vec, to easily extract tag information from the manga and anime posters (e.g., sword, or ponytail). We propose BALSE (Blended Alternate Least Squares with Explanation), a new model for collaborative filtering, that benefits from this extra information to recommend mangas. We show, using real data from an online manga recommender system called Mangaki, that our model improves substantially the quality of recommendations, especially for less-known manga, and is able to provide an interpretation of the taste of the users.Comment: 6 pages, 3 figures, 1 table, accepted at the MANPU 2017 workshop, co-located with ICDAR 2017 in Kyoto on November 10, 201

The Quality-Diversity Transformer: Generating Behavior-Conditioned Trajectories with Decision Transformers

In the context of neuroevolution, Quality-Diversity algorithms have proven effective in generating repertoires of diverse and efficient policies by relying on the definition of a behavior space. A natural goal induced by the creation of such a repertoire is trying to achieve behaviors on demand, which can be done by running the corresponding policy from the repertoire. However, in uncertain environments, two problems arise. First, policies can lack robustness and repeatability, meaning that multiple episodes under slightly different conditions often result in very different behaviors. Second, due to the discrete nature of the repertoire, solutions vary discontinuously. Here we present a new approach to achieve behavior-conditioned trajectory generation based on two mechanisms: First, MAP-Elites Low-Spread (ME-LS), which constrains the selection of solutions to those that are the most consistent in the behavior space. Second, the Quality-Diversity Transformer (QDT), a Transformer-based model conditioned on continuous behavior descriptors, which trains on a dataset generated by policies from a ME-LS repertoire and learns to autoregressively generate sequences of actions that achieve target behaviors. Results show that ME-LS produces consistent and robust policies, and that its combination with the QDT yields a single policy capable of achieving diverse behaviors on demand with high accuracy.Comment: 10+7 page

Assessing Quality-Diversity Neuro-Evolution Algorithms Performance in Hard Exploration Problems

A fascinating aspect of nature lies in its ability to produce a collection of organisms that are all high-performing in their niche. Quality-Diversity (QD) methods are evolutionary algorithms inspired by this observation, that obtained great results in many applications, from wing design to robot adaptation. Recently, several works demonstrated that these methods could be applied to perform neuro-evolution to solve control problems in large search spaces. In such problems, diversity can be a target in itself. Diversity can also be a way to enhance exploration in tasks exhibiting deceptive reward signals. While the first aspect has been studied in depth in the QD community, the latter remains scarcer in the literature. Exploration is at the heart of several domains trying to solve control problems such as Reinforcement Learning and QD methods are promising candidates to overcome the challenges associated. Therefore, we believe that standardized benchmarks exhibiting control problems in high dimension with exploration difficulties are of interest to the QD community. In this paper, we highlight three candidate benchmarks and explain why they appear relevant for systematic evaluation of QD algorithms. We also provide open-source implementations in Jax allowing practitioners to run fast and numerous experiments on few compute resources.Comment: GECCO 2022 Workshop on Quality Diversity Algorithm Benchmark

Neuroevolution is a Competitive Alternative to Reinforcement Learning for Skill Discovery

Deep Reinforcement Learning (RL) has emerged as a powerful paradigm for training neural policies to solve complex control tasks. However, these policies tend to be overfit to the exact specifications of the task and environment they were trained on, and thus do not perform well when conditions deviate slightly or when composed hierarchically to solve even more complex tasks. Recent work has shown that training a mixture of policies, as opposed to a single one, that are driven to explore different regions of the state-action space can address this shortcoming by generating a diverse set of behaviors, referred to as skills, that can be collectively used to great effect in adaptation tasks or for hierarchical planning. This is typically realized by including a diversity term - often derived from information theory - in the objective function optimized by RL. However these approaches often require careful hyperparameter tuning to be effective. In this work, we demonstrate that less widely-used neuroevolution methods, specifically Quality Diversity (QD), are a competitive alternative to information-theory-augmented RL for skill discovery. Through an extensive empirical evaluation comparing eight state-of-the-art methods on the basis of (i) metrics directly evaluating the skills' diversity, (ii) the skills' performance on adaptation tasks, and (iii) the skills' performance when used as primitives for hierarchical planning; QD methods are found to provide equal, and sometimes improved, performance whilst being less sensitive to hyperparameters and more scalable. As no single method is found to provide near-optimal performance across all environments, there is a rich scope for further research which we support by proposing future directions and providing optimized open-source implementations

Comparison of microbiological diagnosis of urinary tract infection in young children by routine health service laboratories and a research laboratory: Diagnostic cohort study

OBJECTIVES: To compare the validity of diagnosis of urinary tract infection (UTI) through urine culture between samples processed in routine health service laboratories and those processed in a research laboratory. POPULATION AND METHODS: We conducted a prospective diagnostic cohort study in 4808 acutely ill children aged <5 years attending UK primary health care. UTI, defined as pure/predominant growth ≥105 CFU/mL of a uropathogen (the reference standard), was diagnosed at routine health service laboratories and a central research laboratory by culture of urine samples. We calculated areas under the receiver-operator curve (AUC) for UTI predicted by pre-specified symptoms, signs and dipstick test results (the "index test"), separately according to whether samples were obtained by clean catch or nappy (diaper) pads. RESULTS: 251 (5.2%) and 88 (1.8%) children were classified as UTI positive by health service and research laboratories respectively. Agreement between laboratories was moderate (kappa = 0.36; 95% confidence interval [CI] 0.29, 0.43), and better for clean catch (0.54; 0.45, 0.63) than nappy pad samples (0.20; 0.12, 0.28). In clean catch samples, the AUC was lower for health service laboratories (AUC = 0.75; 95% CI 0.69, 0.80) than the research laboratory (0.86; 0.79, 0.92). Values of AUC were lower in nappy pad samples (0.65 [0.61, 0.70] and 0.79 [0.70, 0.88] for health service and research laboratory positivity, respectively) than clean catch samples. CONCLUSIONS: The agreement of microbiological diagnosis of UTI comparing routine health service laboratories with a research laboratory was moderate for clean catch samples and poor for nappy pad samples and reliability is lower for nappy pad than for clean catch samples. Positive results from the research laboratory appear more likely to reflect real UTIs than those from routine health service laboratories, many of which (particularly from nappy pad samples) could be due to contamination. Health service laboratories should consider adopting procedures used in the research laboratory for paediatric urine samples. Primary care clinicians should try to obtain clean catch samples, even in very young children

Identification of germline monoallelic mutations in IKZF2 in patients with immune dysregulation

Helios, encoded by IKZF2, is a member of the Ikaros family of transcription factors with pivotal roles in T-follicular helper, NK- and T-regulatory cell physiology. Somatic IKZF2 mutations are frequently found in lymphoid malignancies. Although germline mutations in IKZF1 and IKZF3 encoding Ikaros and Aiolos have recently been identified in patients with phenotypically similar immunodeficiency syndromes, the effect of germline mutations in IKZF2 on human hematopoiesis and immunity remains enigmatic. We identified germline IKZF2 mutations (one nonsense (p.R291X)- and 4 distinct missense variants) in six patients with systemic lupus erythematosus, immune thrombocytopenia or EBV-associated hemophagocytic lymphohistiocytosis. Patients exhibited hypogammaglobulinemia, decreased number of T-follicular helper and NK cells. Single-cell RNA sequencing of PBMCs from the patient carrying the R291X variant revealed upregulation of proinflammatory genes associated with T-cell receptor activation and T-cell exhaustion. Functional assays revealed the inability of HeliosR291X to homodimerize and bind target DNA as dimers. Moreover, proteomic analysis by proximity-dependent Biotin Identification revealed aberrant interaction of 3/5 Helios mutants with core components of the NuRD complex conveying HELIOS-mediated epigenetic and transcriptional dysregulation.Peer reviewe

Treat-to-target in systemic lupus erythematosus: recommendations from an international task force.

The principle of treating-to-target has been successfully applied to many diseases outside rheumatology and more recently to rheumatoid arthritis. Identifying appropriate therapeutic targets and pursuing these systematically has led to improved care for patients with these diseases and useful guidance for healthcare providers and administrators. Thus, an initiative to evaluate possible therapeutic targets and develop treat-to-target guidance was believed to be highly appropriate in the management of systemic lupus erythematosus (SLE) patients as well. Specialists in rheumatology, nephrology, dermatology, internal medicine and clinical immunology, and a patient representative, contributed to this initiative. The majority convened on three occasions in 2012-2013. Twelve topics of critical importance were identified and a systematic literature review was performed. The results were condensed and reformulated as recommendations, discussed, modified and voted upon. The finalised bullet points were analysed for degree of agreement among the task force. The Oxford Centre level of evidence (LoE, corresponding to the research questions) and grade of recommendation (GoR) were determined for each recommendation. The 12 systematic literature searches and their summaries led to 11 recommendations. Prominent features of these recommendations are targeting remission, preventing damage and improving quality of life. LoE and GoR of the recommendations were variable but agreement was >0.9 in each case. An extensive research agenda was identified, and four overarching principles were also agreed upon. Treat-to-target-in-SLE (T2T/SLE) recommendations were developed by a large task force of multispecialty experts and a patient representative. It is anticipated that 'treating-to-target' can and will be applicable to the care of patients with SLE

A framework for remission in SLE: consensus findings from a large international task force on definitions of remission in SLE (DORIS)

Objectives Treat-to-target recommendations have identified 'remission' as a target in systemic lupus erythematosus (SLE), but recognise that there is no universally accepted definition for this. Therefore, we initiated a process to achieve consensus on potential definitions for remission in SLE. Methods An international task force of 60 specialists and patient representatives participated in preparatory exercises, a face-to-face meeting and follow-up electronic voting. The level for agreement was set at 90%. Results The task force agreed on eight key statements regarding remission in SLE and three principles to guide the further development of remission definitions: 1. Definitions of remission will be worded as follows: remission in SLE is a durable state characterised by . (reference to symptoms, signs, routine labs). 2. For defining remission, a validated index must be used, for example, clinical systemic lupus erythematosus disease activity index (SLEDAI)=0, British Isles lupus assessment group (BILAG) 2004 D/E only, clinical European consensus lupus outcome measure (ECLAM)=0; with routine laboratory assessments included, and supplemented with physician's global assessment. 3. Distinction is made between remission off and on therapy: remission off therapy requires the patient to be on no other treatment for SLE than maintenance antimalarials; and remission on therapy allows patients to be on stable maintenance antimalarials, low-dose corticosteroids (prednisone ≤5 mg/day), maintenance immunosuppressives and/or maintenance biologics. The task force also agreed that the most appropriate outcomes (dependent variables) for testing the prognostic value (construct validity) of potential remission definitions are: death, damage, flares and measures of health-related quality of life. Conclusions The work of this international task force provides a framework for testing different definitions of remission against long-term outcomes