TTC: A Tensor Transposition Compiler for Multiple Architectures

We consider the problem of transposing tensors of arbitrary dimension and describe TTC, an open source domain-specific parallel compiler. TTC generates optimized parallel C++/CUDA C code that achieves a significant fraction of the system's peak memory bandwidth. TTC exhibits high performance across multiple architectures, including modern AVX-based systems (e.g.,~Intel Haswell, AMD Steamroller), Intel's Knights Corner as well as different CUDA-based GPUs such as NVIDIA's Kepler and Maxwell architectures. We report speedups of TTC over a meaningful baseline implementation generated by external C++ compilers; the results suggest that a domain-specific compiler can outperform its general purpose counterpart significantly: For instance, comparing with Intel's latest C++ compiler on the Haswell and Knights Corner architecture, TTC yields speedups of up to $8\times$ and $32\times$, respectively. We also showcase TTC's support for multiple leading dimensions, making it a suitable candidate for the generation of performance-critical packing functions that are at the core of the ubiquitous BLAS 3 routines

Exacerbating Mindless Compliance: The Danger of Justifications during Privacy Decision Making in the Context of Facebook Applications

Online companies exploit mindless compliance during users’ privacy decision making to avoid liability while not impairing users’ willingness to use their services. These manipulations can play against users since they subversively influence their decisions by nudging them to mindlessly comply with disclosure requests rather than enabling them to make deliberate choices. In this paper, we demonstrate the compliance-inducing effects of defaults and framing in the context of a Facebook application that nudges people to be automatically publicly tagged in their friends’ photos and/or to tag their friends in their own photos. By studying these effects in a Facebook application, we overcome a common criticism of privacy research, which often relies on hypothetical scenarios. Our results concur with previous findings on framing and default effects. Specifically, we found a reduction in privacy-preserving behaviors (i.e., a higher tagging rate in our case) in positively framed and accept-by-default decision scenarios. Moreover, we tested the effect that two types of justifications—information that implies what other people do (normative) or what the user ought to do (rationale based)— have on framing- and default-induced compliance. Existing work suggests that justifications may increase compliance in a positive (agree-by-) default scenario even when the justification does not relate to the decision. In this study, we expand this finding and show that even a justification that is opposite to the default action (e.g., a justification suggesting that one should not use the application) can increase mindless compliance with the default. Thus, when companies abide by policy makers’ requirements to obtain informed user consent through explaining the privacy settings, they will paradoxically induce mindless compliance and further threaten user privacy

Reducing Default and Framing Effects in Privacy Decision-Making

Framing and default effects have been studied for more than a decade in different disciplines. A common criticism of these studies is that they use hypothetical scenarios. In this study, we developed a real decision environment: a Facebook application in which users had to decide whether or not they wanted to be automatically publicly tagged in their friends’ pictures and/or tag their friends in their own pictures. To ensure ecological validity, participants had to log in to their Facebook account. Our results confirmed previous studies indicating a higher tagging rate in positively framed and accept-by-default conditions. Furthermore, we introduced a manipulation that we assumed would overshadow and thereby reduce the effects of default and framing: a justification highlighting a positive or negative descriptive social norm or giving a rationale for or against tagging. We found that such justifications may at times increase tagging rates

Explaining recommendations in an interactive hybrid social recommender

Hybrid social recommender systems use social relevance from multiple sources to recommend relevant items or people to users. To make hybrid recommendations more transparent and controllable, several researchers have explored interactive hybrid recommender interfaces, which allow for a user-driven fusion of recommendation sources. In this field of work, the intelligent user interface has been investigated as an approach to increase transparency and improve the user experience. In this paper, we attempt to further promote the transparency of recommendations by augmenting an interactive hybrid recommender interface with several types of explanations. We evaluate user behavior patterns and subjective feedback by a within-subject study (N=33). Results from the evaluation show the effectiveness of the proposed explanation models. The result of post-treatment survey indicates a significant improvement in the perception of explainability, but such improvement comes with a lower degree of perceived controllability

Using latent features diversification to reduce choice difficulty in recommendation lists

Ail important side effect of using recoinmender systems is a phenomenon called "choice overload"; the negative feeling incurred by the increased difficulty to choose from large sets of high quality recommendations. Choice overload has traditionally been related to the size of the item set, but recent work suggests that the diversity of the item set is an important moderator. Using the latent feanires of a matrix factorization algorithm, we were able to manipulate the diversity of the items, while controlling the overall attractiveness of the list of recommendations. In a user study, participants evaluated personalized item lists (varying in level of diversity) on perceived diversity and attractiveness, and their experienced choice difficulty and tradeoff difficulty. The results suggest that diversifying the recommendations might be an effective way to reduce choice overload, as perceived diversity and attractiveness increase with item set diversity, subsequently resulting in participants experiencing less tradeoff difficulty and choice difficulty.</p

On the Delivery of Recommendations in Social Software: a User's Perspective

Recommendation is a popular feature of social software. Recommendations could be made by the software autonomously or by social contacts who are often aided by the software on what to recommend. A great deal of emphasis in the literature has been given to the algorithmic solution to infer relevant and interesting recommendations. Yet, the delivery method of recommendation is still a widely unexplored research topic. This paper advocates that the success in deducing recommendations is not the sole factor for “recommendees” to consider. Users have their own requirements on the way a recommendation is made and delivered. Failure in meeting user expectations would often lead to the rejection of the recommendations as well as the violation of user experience. In this paper, we conduct an empirical research to explore such user’s perspective. We start with qualitative phase, based on interviews, and confirm and enhance the results in a quantitative phase through surveying a large sample of users. We report on the results and conclude with a set of guidelines on how recommendations delivery should be designed from a user’s perspective

A regression model approach to enable cell morphology correction in high-throughput flow cytometry

Large variations in cell size and shape can undermine traditional gating methods for analyzing flow cytometry data. Correcting for these effects enables analysis of high-throughput data sets, including >5000 yeast samples with diverse cell morphologies

What am I not seeing? An Interactive Approach to Social Content Discovery in Microblogs

In this paper, we focus on the informational and user experience benefits of user-driven topic exploration in microblog communities, such as Twitter, in an inspectable, controllable and personalized manner. To this end, we introduce ``HopTopics'' -- a novel interactive tool for exploring content that is popular just beyond a user's typical information horizon in a microblog, as defined by the network of individuals that they are connected to. We present results of a user study (N=122) to evaluate HopTopics with varying complexity against a typical microblog feed in both personalized and non-personalized conditions. Results show that the HopTopics system, leveraging content from both the direct and extended network of a user, succeeds in giving users a better sense of control and transparency. Moreover, participants had a poor mental model for the degree of novel content discovered when presented with non-personalized data in the Inspectable interface

Evaluation of the antigen-experienced B-cell receptor repertoire in healthy children and adults

Upon antigen recognition via their B cell receptor (BR), B cells migrate to the germinal center where they undergo somatic hypermutation (SHM) to increase their affinity for the antigen, and class switch recombination (CSR) to change the effector function of the secreted antibodies. These steps are essential to create an antigen-experienced BR repertoire that efficiently protects the body against pathogens. At the same time, the BR repertoire should be selected to protect against responses to self-antigen or harmless antigens. Insights into the processes of SHM, selection, and CSR can be obtained by studying the antigen-experienced BR repertoire. Currently, a large reference data set of healthy children and adults, which ranges from neonates to the elderly, is not available. In this study, we analyzed the antigen-experienced repertoire of 38 healthy donors (HD), ranging from cord blood to 74 years old, by sequencing IGA and IGG transcripts using next generation sequencing. This resulted in a large, freely available reference data set containing 412,890 IGA and IGG transcripts. We used this data set to study mutation levels, SHM patterns, antigenic selection, and CSR from birth to elderly HD. Only small differences were observed in SHM patterns, while the mutation levels increase in early childhood and stabilize at 6 years of age at around 7%. Furthermore, comparison of the antigen-experienced repertoire with sequences from the naive immune repertoire showed that features associated with autoimmunity such as long CDR3 length and IGHV4-34 usage are reduced in the antigen-experienced repertoire. Moreover, IGA2 and IGG2 usage was increased in HD in higher age categories, while IGG1 usage was decreased. In addition, we studied clonal relationship in the different samples. Clonally related sequences were found with different subclasses. Interestingly, we found transcripts with the same CDR1-CDR3 sequence, but different subclasses. Together, these data suggest that a single antigen can provoke a B-cell response with BR of different subclasses and that, during the course of an immune response, some B cells change their isotype without acquiring additional SHM or can directly switch to different isotypes