SIGIR 2021 E-Commerce Workshop Data Challenge

The 2021 SIGIR workshop on eCommerce is hosting the Coveo Data Challenge for "In-session prediction for purchase intent and recommendations". The challenge addresses the growing need for reliable predictions within the boundaries of a shopping session, as customer intentions can be different depending on the occasion. The need for efficient procedures for personalization is even clearer if we consider the e-commerce landscape more broadly: outside of giant digital retailers, the constraints of the problem are stricter, due to smaller user bases and the realization that most users are not frequently returning customers. We release a new session-based dataset including more than 30M fine-grained browsing events (product detail, add, purchase), enriched by linguistic behavior (queries made by shoppers, with items clicked and items not clicked after the query) and catalog meta-data (images, text, pricing information). On this dataset, we ask participants to showcase innovative solutions for two open problems: a recommendation task (where a model is shown some events at the start of a session, and it is asked to predict future product interactions); an intent prediction task, where a model is shown a session containing an add-to-cart event, and it is asked to predict whether the item will be bought before the end of the session.Comment: SIGIR eCOM 2021 Data Challeng

EvalRS 2023. Well-Rounded Recommender Systems For Real-World Deployments

EvalRS aims to bring together practitioners from industry and academia to foster a debate on rounded evaluation of recommender systems, with a focus on real-world impact across a multitude of deployment scenarios. Recommender systems are often evaluated only through accuracy metrics, which fall short of fully characterizing their generalization capabilities and miss important aspects, such as fairness, bias, usefulness, informativeness. This workshop builds on the success of last year's workshop at CIKM, but with a broader scope and an interactive format.Comment: EvalRS 2023 will be a workshop hosted at KDD2

FashionCLIP: Connecting Language and Images for Product Representations

The steady rise of online shopping goes hand in hand with the development of increasingly complex ML and NLP models. While most use cases are cast as specialized supervised learning problems, we argue that practitioners would greatly benefit from more transferable representations of products. In this work, we build on recent developments in contrastive learning to train FashionCLIP, a CLIP-like model for the fashion industry. We showcase its capabilities for retrieval, classification and grounding, and release our model and code to the community.Comment: Code will soon be available at https://github.com/patrickjohncyh, dataset at https://github.com/Farfetc

E Pluribus Unum: Guidelines on Multi-Objective Evaluation of Recommender Systems

Recommender Systems today are still mostly evaluated in terms of accuracy, with other aspects beyond the immediate relevance of recommendations, such as diversity, long-term user retention and fairness, often taking a back seat. Moreover, reconciling multiple performance perspectives is by definition indeterminate, presenting a stumbling block to those in the pursuit of rounded evaluation of Recommender Systems. EvalRS 2022 -- a data challenge designed around Multi-Objective Evaluation -- was a first practical endeavour, providing many insights into the requirements and challenges of balancing multiple objectives in evaluation. In this work, we reflect on EvalRS 2022 and expound upon crucial learnings to formulate a first-principles approach toward Multi-Objective model selection, and outline a set of guidelines for carrying out a Multi-Objective Evaluation challenge, with potential applicability to the problem of rounded evaluation of competing models in real-world deployments.Comment: 15 pages, under submissio

Cervical vagal nerve stimulation activates the stellate ganglion in ambulatory dogs

BACKGROUND AND OBJECTIVES: Recent studies showed that, in addition to parasympathetic nerves, cervical vagal nerves contained significant sympathetic nerves. We hypothesized that cervical vagal nerve stimulation (VNS) may capture the sympathetic nerves within the vagal nerve and activate the stellate ganglion. MATERIALS AND METHODS: We recorded left stellate ganglion nerve activity (SGNA), left thoracic vagal nerve activity (VNA), and subcutaneous electrocardiogram in seven dogs during left cervical VNS with 30 seconds on-time and 30 seconds off time. We then compared the SGNA between VNS on and off times. RESULTS: Cervical VNS at moderate (0.75 mA) output induced large SGNA, elevated heart rate (HR), and reduced HR variability, suggesting sympathetic activation. Further increase of the VNS output to >1.5 mA increased SGNA but did not significantly increase the HR, suggesting simultaneous sympathetic and parasympathetic activation. The differences of integrated SGNA and integrated VNA between VNS on and off times (ΔSGNA) increased progressively from 5.2 mV-s {95% confidence interval (CI): 1.25-9.06, p=0.018, n=7} at 1.0 mA to 13.7 mV-s (CI: 5.97-21.43, p=0.005, n=7) at 1.5 mA. The difference in HR (ΔHR, bpm) between on and off times was 5.8 bpm (CI: 0.28-11.29, p=0.042, n=7) at 1.0 mA and 5.3 bpm (CI 1.92 to 12.61, p=0.122, n=7) at 1.5 mA. CONCLUSION: Intermittent cervical VNS may selectively capture the sympathetic components of the vagal nerve and excite the stellate ganglion at moderate output. Increasing the output may result in simultaneously sympathetic and parasympathetic capture

Classifying aerosol type using in situ surface spectral aerosol optical properties

Knowledge of aerosol size and composition is important for determining radiative forcing effects of aerosols, identifying aerosol sources and improving aerosol satellite retrieval algorithms. The ability to extrapolate aerosol size and composition, or type, from intensive aerosol optical properties can help expand the current knowledge of spatiotemporal variability in aerosol type globally, particularly where chemical composition measurements do not exist concurrently with optical property measurements. This study uses medians of the scattering Ångström exponent (SAE), absorption Ångström exponent (AAE) and single scattering albedo (SSA) from 24 stations within the NOAA/ESRL Federated Aerosol Monitoring Network to infer aerosol type using previously published aerosol classification schemes. Three methods are implemented to obtain a best estimate of dominant aerosol type at each station using aerosol optical properties. The first method plots station medians into an AAE vs. SAE plot space, so that a unique combination of intensive properties corresponds with an aerosol type. The second typing method expands on the first by introducing a multivariate cluster analysis, which aims to group stations with similar optical characteristics and thus similar dominant aerosol type. The third and final classification method pairs 3-day backward air mass trajectories with median aerosol optical properties to explore the relationship between trajectory origin (proxy for likely aerosol type) and aerosol intensive parameters, while allowing for multiple dominant aerosol types at each station. The three aerosol classification methods have some common, and thus robust, results. In general, estimating dominant aerosol type using optical properties is best suited for site locations with a stable and homogenous aerosol population, particularly continental polluted (carbonaceous aerosol), marine polluted (carbonaceous aerosol mixed with sea salt) and continental dust/biomass sites (dust and carbonaceous aerosol); however, current classification schemes perform poorly when predicting dominant aerosol type at remote marine and Arctic sites and at stations with more complex locations and topography where variable aerosol populations are not well represented by median optical properties. Although the aerosol classification methods presented here provide new ways to reduce ambiguity in typing schemes, there is more work needed to find aerosol typing methods that are useful for a larger range of geographic locations and aerosol populations

The clustering of galaxies in the SDSS-III Baryon Oscillation Spectroscopic Survey : baryon acoustic oscillations in the Data Releases 10 and 11 Galaxy samples

We present a one per cent measurement of the cosmic distance scale from the detections of the baryon acoustic oscillations (BAO) in the clustering of galaxies from the Baryon Oscillation Spectroscopic Survey, which is part of the Sloan Digital Sky Survey III. Our results come from the Data Release 11 (DR11) sample, containing nearly one million galaxies and covering approximately 8500 square degrees and the redshift range 0.2 < z < 0.7. We also compare these results with those from the publicly released DR9 and DR10 samples. Assuming a concordance Λ cold dark matter (ΛCDM) cosmological model, the DR11 sample covers a volume of 13 Gpc3 and is the largest region of the Universe ever surveyed at this density. We measure the correlation function and power spectrum, including density-field reconstruction of the BAO feature. The acoustic features are detected at a significance of over 7σ in both the correlation function and power spectrum. Fitting for the position of the acoustic features measures the distance relative to the sound horizon at the drag epoch, rd, which has a value of rd,fid = 149.28 Mpc in our fiducial cosmology. We find DV = (1264 ± 25 Mpc)(rd/rd,fid) at z = 0.32 and DV = (2056 ± 20 Mpc)(rd/rd,fid) at z = 0.57. At 1.0 per cent, this latter measure is the most precise distance constraint ever obtained from a galaxy survey. Separating the clustering along and transverse to the line of sight yields measurements at z = 0.57 of DA = (1421 ± 20 Mpc)(rd/rd,fid) and H = (96.8 ± 3.4 km s−1 Mpc−1)(rd,fid/rd). Our measurements of the distance scale are in good agreement with previous BAO measurements and with the predictions from cosmic microwave background data for a spatially flat CDM model with a cosmological constant.Publisher PDFPeer reviewe

A Phase 2, Multicenter, Open-Label Study of Anti-Lag-3 Ieramilimab in Combination With Anti-Pd-1 Spartalizumab in Patients With Advanced Solid Malignancies

Ieramilimab, a humanized anti-LAG-3 monoclonal antibody, was well tolerated in combination with the anti-PD-1 antibody spartalizumab in a phase 1 study. This phase 2 study aimed to further investigate the efficacy and safety of combination treatment in patients with selected advanced (locally advanced or metastatic) solid malignancies. Eligible patients with non-small cell lung cancer (NSCLC), melanoma, renal cell carcinoma (RCC), mesothelioma, and triple-negative breast cancer (TNBC) were grouped depending on prior anti-PD-1/L1 therapy (anti-PD-1/L1 naive or anti-PD-1/L1 pretreated). Patients received ieramilimab (400 mg) followed by spartalizumab (300 mg) every 3 weeks. The primary endpoint was objective response rate (ORR), along with safety, pharmacokinetics, and biomarker assessments. Of 235 patients, 142 were naive to anti-PD-1/L1 and 93 were pretreated with anti-PD-1/L1 antibodies. Durable responses (\u3e24 months) were seen across all indications for patients naive to anti-PD-1/L1 and in melanoma and RCC patients pretreated with anti-PD1/L1. The most frequent study drug-related AEs were pruritus (15.5%), fatigue (10.6%), and rash (10.6%) in patients naive to anti-PD-1/L1 and fatigue (18.3%), rash (14.0%), and nausea (10.8%) in anti-PD-1/L1 pretreated patients. Biomarker assessment indicated higher expression of T-cell-inflamed gene signature at baseline among responding patients. Response to treatment was durable (\u3e24 months) in some patients across all enrolled indications, and safety findings were in accordance with previous and current studies exploring LAG-3/PD-1 blockade