How to Make an Outlier? Studying the Effect of Presentational Features on the Outlierness of Items in Product Search Results

In two-sided marketplaces, items compete for attention from users since attention translates to revenue for suppliers. Item exposure is an indication of the amount of attention that items receive from users in a ranking. It can be influenced by factors like position bias. Recent work suggests that another phenomenon related to inter-item dependencies may also affect item exposure, viz. outlier items in the ranking. Hence, a deeper understanding of outlier items is crucial to determining an item's exposure distribution. In this work, we study the impact of different presentational e-commerce features on users' perception of outlierness of an item in a search result page. Informed by visual search literature, we design a set of crowdsourcing tasks where we compare the observability of three main features, viz. price, star rating, and discount tag. We find that various factors affect item outlierness, namely, visual complexity (e.g., shape, color), discriminative item features, and value range. In particular, we observe that a distinctive visual feature such as a colored discount tag can attract users' attention much easier than a high price difference, simply because of visual characteristics that are easier to spot. Moreover, we see that the magnitude of deviations in all features affects the task complexity, such that when the similarity between outlier and non-outlier items increases, the task becomes more difficult.</p

On the Impact of Outlier Bias on User Clicks

User interaction data is an important source of supervision in counterfactual learning to rank (CLTR). Such data suffers from presentation bias. Much work in unbiased learning to rank (ULTR) focuses on position bias, i.e., items at higher ranks are more likely to be examined and clicked. Inter-item dependencies also influence examination probabilities, with outlier items in a ranking as an important example. Outliers are defined as items that observably deviate from the rest and therefore stand out in the ranking. In this paper, we identify and introduce the bias brought about by outlier items: users tend to click more on outlier items and their close neighbors. To this end, we first conduct a controlled experiment to study the effect of outliers on user clicks. Next, to examine whether the findings from our controlled experiment generalize to naturalistic situations, we explore real-world click logs from an e-commerce platform. We show that, in both scenarios, users tend to click significantly more on outlier items than on non-outlier items in the same rankings. We show that this tendency holds for all positions, i.e., for any specific position, an item receives more interactions when presented as an outlier as opposed to a non-outlier item. We conclude from our analysis that the effect of outliers on clicks is a type of bias that should be addressed in ULTR. We therefore propose an outlier-aware click model that accounts for both outlier and position bias, called outlier-aware position-based model (OPBM). We estimate click propensities based on OPBM; through extensive experiments performed on both real-world e-commerce data and semi-synthetic data, we verify the effectiveness of our outlier-aware click model. Our results show the superiority of OPBM against baselines in terms of ranking performance and true relevance estimation.</p

Functionally dissociating ventro-dorsal components within the rostro-caudal hierarchical organization of the human prefrontal cortex

This work was supported by a grant of the BrainLinks-BrainTools Cluster of Excellence funded by the German Research Foundation (DFG, grant number EXC 1086).Peer reviewedPostprin

The impact of physiological noise on hemodynamic-derived estimates of directed functional connectivity

This work was supported by a grant of the BrainLinks-BrainTools Cluster of Excellence funded by the German Research Foundation (DFG, Grant Number EXC 1086).Peer reviewedPostprintPostprin

Vamsa: Automated Provenance Tracking in Data Science Scripts

There has recently been a lot of ongoing research in the areas of fairness, bias and explainability of machine learning (ML) models due to the self-evident or regulatory requirements of various ML applications. We make the following observation: All of these approaches require a robust understanding of the relationship between ML models and the data used to train them. In this work, we introduce the ML provenance tracking problem: the fundamental idea is to automatically track which columns in a dataset have been used to derive the features/labels of an ML model. We discuss the challenges in capturing such information in the context of Python, the most common language used by data scientists. We then present Vamsa, a modular system that extracts provenance from Python scripts without requiring any changes to the users' code. Using 26K real data science scripts, we verify the effectiveness of Vamsa in terms of coverage, and performance. We also evaluate Vamsa's accuracy on a smaller subset of manually labeled data. Our analysis shows that Vamsa's precision and recall range from 90.4% to 99.1% and its latency is in the order of milliseconds for average size scripts. Drawing from our experience in deploying ML models in production, we also present an example in which Vamsa helps automatically identify models that are affected by data corruption issues

Tumor cell-derived Timp-1 is necessary for maintaining metastasis-promoting Met-signaling via inhibition of Adam-10.

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Geometric Approach to Quantum Statistical Mechanics and Application to Casimir Energy and Friction Properties

A geometric approach to general quantum statistical systems (including the harmonic oscillator) is presented. It is applied to Casimir energy and the dissipative system with friction. We regard the (N+1)-dimensional Euclidean {\it coordinate} system (X$^i$,$\tau$) as the quantum statistical system of N quantum (statistical) variables (X$^i$) and one {\it Euclidean time} variable ($\tau$). Introducing paths (lines or hypersurfaces) in this space (X$^i$,$\tau$), we adopt the path-integral method to quantize the mechanical system. This is a new view of (statistical) quantization of the {\it mechanical} system. The system Hamiltonian appears as the {\it area}. We show quantization is realized by the {\it minimal area principle} in the present geometric approach. When we take a {\it line} as the path, the path-integral expressions of the free energy are shown to be the ordinary ones (such as N harmonic oscillators) or their simple variation. When we take a {\it hyper-surface} as the path, the system Hamiltonian is given by the {\it area} of the {\it hyper-surface} which is defined as a {\it closed-string configuration} in the bulk space. In this case, the system becomes a O(N) non-linear model. We show the recently-proposed 5 dimensional Casimir energy (ArXiv:0801.3064,0812.1263) is valid. We apply this approach to the visco-elastic system, and present a new method using the path-integral for the calculation of the dissipative properties.Comment: 20 pages, 8 figures, Proceedings of ICFS2010 (2010.9.13-18, Ise-Shima, Mie, Japan

Moment inversion problem for piecewise D-finite functions

We consider the problem of exact reconstruction of univariate functions with jump discontinuities at unknown positions from their moments. These functions are assumed to satisfy an a priori unknown linear homogeneous differential equation with polynomial coefficients on each continuity interval. Therefore, they may be specified by a finite amount of information. This reconstruction problem has practical importance in Signal Processing and other applications. It is somewhat of a ``folklore'' that the sequence of the moments of such ``piecewise D-finite''functions satisfies a linear recurrence relation of bounded order and degree. We derive this recurrence relation explicitly. It turns out that the coefficients of the differential operator which annihilates every piece of the function, as well as the locations of the discontinuities, appear in this recurrence in a precisely controlled manner. This leads to the formulation of a generic algorithm for reconstructing a piecewise D-finite function from its moments. We investigate the conditions for solvability of the resulting linear systems in the general case, as well as analyze a few particular examples. We provide results of numerical simulations for several types of signals, which test the sensitivity of the proposed algorithm to noise

The rostro-caudal gradient in the prefrontal cortex and its modulation by subthalamic deep brain stimulation in Parkinson’s disease

Acknowledgements The authors thank Benjamin Rahm (University of Freiburg) and Michael Fox (Harvard Medical School) for valuable comments on a previous version of this manuscript. This work was supported by a grant of the BrainLinks-BrainTools Cluster of Excellence funded by the German Research Foundation (DFG, grant number EXC 1086) to C.P.K., F.A., T.P., B.O.S., C.W, and V.A.C.; A.H. was supported by the German Research Foundation (Deutsche Forschungsgemeinschaft, Emmy Noether Stipend 410169619 and 424778381 – TRR 295) as well as Deutsches Zentrum für Luft- und Raumfahrt (DynaSti grant within the EU Joint Programme Neurodegenerative Disease Research, JPND). Funding Open Access funding enabled and organized by Projekt DEAL.Peer reviewedPublisher PD