Unique branching pattern of the internal iliac artery accompanied by an supernumerary internal iliac vein

Understanding the complex vascular anatomy of the lesser pelvis is vital in diagnostics and management of numerous pathologies in gynaecology, urology, orthopaedics and general surgery. The following case reports describes an unusual, undescribed branching pattern of the internal iliac artery with additional specific branches, as well as an unprecedented supernumerary internal iliac vein. Both clinical significance and embryology of the case are discussed

Non-invasive transcutaneous Supraorbital Neurostimulation (tSNS) using Cefaly® device in prevention of primary headaches

Headaches are one of the most common pain syndromes experienced by adult patients. International Classification of Headache Disorders identifies about 300 different entities. Primary headaches (migraine, tension-type headache, trigeminal autonomic cephalalgias, other primary headaches) has the common occurrence. Although effective treatment of these disorders is possible, it is inefficient or poorly tolerated in some patients. Neuromodulation methods, being element of multimodal treatment, provide an additional treatment option in pharmacotherapy-refractory patients. Both invasive and non-invasive stimulation methods are used. The non-invasive techniques is transcutaneous nerve stimulation using Cefaly® device. In this study, Cefaly® was used as prevention treatment in patients with pharmacotherapy-refractory headaches. This device is indicated for the prophylactic treatment of episodic primary headaches. A total of 91-patients (30 without and 61 with tSNS) were enrolled in the study, including 60-patients with migraine and 31-patients with other primary headaches. Ten courses of non-invasive peripheral (supraorbitral/supratrochlear) nerves stimulation were delivered to 57-patients; in the remaining 4 patients, the treatment was abandoned due to poor tolerance. Patients were observed for 30 days after stimulation treatment. Compared to the pre-treatment period, the reduction in the intensity of pain was observed in both the migraine group and patients with other types of headaches; this included the number of pain episodes being reduced by half, with simultaneous reduction in average pain intensity and duration of individual pain episodes. The subjective assessment of pain reduction was in the range of 40–47%. Based on our data we recommend tSNS as useful tool in the prophylaxis of primary headaches, including migraine

Hiformer: Heterogeneous Feature Interactions Learning with Transformers for Recommender Systems

Learning feature interaction is the critical backbone to building recommender systems. In web-scale applications, learning feature interaction is extremely challenging due to the sparse and large input feature space; meanwhile, manually crafting effective feature interactions is infeasible because of the exponential solution space. We propose to leverage a Transformer-based architecture with attention layers to automatically capture feature interactions. Transformer architectures have witnessed great success in many domains, such as natural language processing and computer vision. However, there has not been much adoption of Transformer architecture for feature interaction modeling in industry. We aim at closing the gap. We identify two key challenges for applying the vanilla Transformer architecture to web-scale recommender systems: (1) Transformer architecture fails to capture the heterogeneous feature interactions in the self-attention layer; (2) The serving latency of Transformer architecture might be too high to be deployed in web-scale recommender systems. We first propose a heterogeneous self-attention layer, which is a simple yet effective modification to the self-attention layer in Transformer, to take into account the heterogeneity of feature interactions. We then introduce \textsc{Hiformer} (\textbf{H}eterogeneous \textbf{I}nteraction Trans\textbf{former}) to further improve the model expressiveness. With low-rank approximation and model pruning, \hiformer enjoys fast inference for online deployment. Extensive offline experiment results corroborates the effectiveness and efficiency of the \textsc{Hiformer} model. We have successfully deployed the \textsc{Hiformer} model to a real world large scale App ranking model at Google Play, with significant improvement in key engagement metrics (up to +2.66\%)

Branching pattern of the internal iliac artery accompanied by a venous anastomosis: rare vascular variations

The ability to navigate the complex and often deceptive branching patterns of the internal iliac artery can be decisive in planning and performing surgeries within the lesser pelvis. The following case report presents a peculiar quadruple division of the internal iliac artery, accompanied by a venous anastomotic structure. Apart from the posterior and anterior trunks, the superior vesicle and iliolumbar arteries arose independently from the internal iliac artery. The division was surrounded by a venous oval, compressing certain branches and potentially complicating surgical access. Due to the uncommon course of the internal iliac artery and the presence of the anastomosis, a possible nerve root compression has been identified. Both clinical significance and classification method of the case are discussed. Knowledge of this anatomical variation is valuable for both diagnosis and surgery, especially within the specialties of urology, gynecology and general surgery

Towards Multi-Language Recipe Personalisation and Recommendation

Multi-language recipe personalisation and recommendation is an under-explored field of information retrieval in academic and production systems. The existing gaps in our current understanding are numerous, even on fundamental questions such as whether consistent and high-quality recipe recommendation can be delivered across languages. In this paper, we introduce the multi-language recipe recommendation setting and present grounding results that will help to establish the potential and absolute value of future work in this area. Our work draws on several billion events from millions of recipes and users from Arabic, English, Indonesian, Russian, and Spanish. We represent recipes using a combination of normalised ingredients, standardised skills and image embeddings obtained without human intervention. In modelling, we take a classical approach based on optimising an embedded bi-linear user-item metric space towards the interactions that most strongly elicit cooking intent. For users without interaction histories, a bespoke content-based cold-start model that predicts context and recipe affinity is introduced. We show that our approach to personalisation is stable and easily scales to new languages. A robust cross-validation campaign is employed and consistently rejects baseline models and representations, strongly favouring those we propose. Our results are presented in a language-oriented (as opposed to model-oriented) fashion to emphasise the language-based goals of this work. We believe that this is the first large-scale work that comprehensively considers the value and potential of multi-language recipe recommendation and personalisation as well as delivering scalable and reliable models.Comment: 5 table

Recommender Systems with Generative Retrieval

Modern recommender systems perform large-scale retrieval by first embedding queries and item candidates in the same unified space, followed by approximate nearest neighbor search to select top candidates given a query embedding. In this paper, we propose a novel generative retrieval approach, where the retrieval model autoregressively decodes the identifiers of the target candidates. To that end, we create semantically meaningful tuple of codewords to serve as a Semantic ID for each item. Given Semantic IDs for items in a user session, a Transformer-based sequence-to-sequence model is trained to predict the Semantic ID of the next item that the user will interact with. To the best of our knowledge, this is the first Semantic ID-based generative model for recommendation tasks. We show that recommender systems trained with the proposed paradigm significantly outperform the current SOTA models on various datasets. In addition, we show that incorporating Semantic IDs into the sequence-to-sequence model enhances its ability to generalize, as evidenced by the improved retrieval performance observed for items with no prior interaction history.Comment: Preprint versio