2,342 research outputs found
Exotic Charges, Multicomponent Dark Matter and Light Sterile Neutrinos
Generating small sterile neutrino masses via the same seesaw mechanism that
suppresses active neutrino masses requires a specific structure in the neutral
fermion mass matrix. We present a model where this structure is enforced by a
new U(1)' gauge symmetry, spontaneously broken at the TeV scale. In order not
to spoil the neutrino structure, the additional fermions necessary for anomaly
cancellations need to carry exotic charges, and turn out to form multicomponent
cold dark matter. The active-sterile mixing then connects the new particles and
the Standard Model---opening a new portal in addition to the usual Higgs- and
kinetic-mixing portals---which leads to dark matter annihilation almost
exclusively into neutrinos.Comment: 11 pages, 3 figures. More references, longer discussions. Matches
JHEP versio
VBPR: Visual Bayesian Personalized Ranking from Implicit Feedback
Modern recommender systems model people and items by discovering or `teasing
apart' the underlying dimensions that encode the properties of items and users'
preferences toward them. Critically, such dimensions are uncovered based on
user feedback, often in implicit form (such as purchase histories, browsing
logs, etc.); in addition, some recommender systems make use of side
information, such as product attributes, temporal information, or review text.
However one important feature that is typically ignored by existing
personalized recommendation and ranking methods is the visual appearance of the
items being considered. In this paper we propose a scalable factorization model
to incorporate visual signals into predictors of people's opinions, which we
apply to a selection of large, real-world datasets. We make use of visual
features extracted from product images using (pre-trained) deep networks, on
top of which we learn an additional layer that uncovers the visual dimensions
that best explain the variation in people's feedback. This not only leads to
significantly more accurate personalized ranking methods, but also helps to
alleviate cold start issues, and qualitatively to analyze the visual dimensions
that influence people's opinions.Comment: AAAI'1
Crowdsourcing Question-Answer Meaning Representations
We introduce Question-Answer Meaning Representations (QAMRs), which represent
the predicate-argument structure of a sentence as a set of question-answer
pairs. We also develop a crowdsourcing scheme to show that QAMRs can be labeled
with very little training, and gather a dataset with over 5,000 sentences and
100,000 questions. A detailed qualitative analysis demonstrates that the
crowd-generated question-answer pairs cover the vast majority of
predicate-argument relationships in existing datasets (including PropBank,
NomBank, QA-SRL, and AMR) along with many previously under-resourced ones,
including implicit arguments and relations. The QAMR data and annotation code
is made publicly available to enable future work on how best to model these
complex phenomena.Comment: 8 pages, 6 figures, 2 table
Deciphering Compatibility Relationships with Textual Descriptions via Extraction and Explanation
Understanding and accurately explaining compatibility relationships between
fashion items is a challenging problem in the burgeoning domain of AI-driven
outfit recommendations. Present models, while making strides in this area,
still occasionally fall short, offering explanations that can be elementary and
repetitive. This work aims to address these shortcomings by introducing the
Pair Fashion Explanation (PFE) dataset, a unique resource that has been curated
to illuminate these compatibility relationships. Furthermore, we propose an
innovative two-stage pipeline model that leverages this dataset. This
fine-tuning allows the model to generate explanations that convey the
compatibility relationships between items. Our experiments showcase the model's
potential in crafting descriptions that are knowledgeable, aligned with
ground-truth matching correlations, and that produce understandable and
informative descriptions, as assessed by both automatic metrics and human
evaluation. Our code and data are released at
https://github.com/wangyu-ustc/PairFashionExplanatio
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