379 research outputs found
Image-based Recommendations on Styles and Substitutes
Humans inevitably develop a sense of the relationships between objects, some
of which are based on their appearance. Some pairs of objects might be seen as
being alternatives to each other (such as two pairs of jeans), while others may
be seen as being complementary (such as a pair of jeans and a matching shirt).
This information guides many of the choices that people make, from buying
clothes to their interactions with each other. We seek here to model this human
sense of the relationships between objects based on their appearance. Our
approach is not based on fine-grained modeling of user annotations but rather
on capturing the largest dataset possible and developing a scalable method for
uncovering human notions of the visual relationships within. We cast this as a
network inference problem defined on graphs of related images, and provide a
large-scale dataset for the training and evaluation of the same. The system we
develop is capable of recommending which clothes and accessories will go well
together (and which will not), amongst a host of other applications.Comment: 11 pages, 10 figures, SIGIR 201
CNN Architectures for Large-Scale Audio Classification
Convolutional Neural Networks (CNNs) have proven very effective in image
classification and show promise for audio. We use various CNN architectures to
classify the soundtracks of a dataset of 70M training videos (5.24 million
hours) with 30,871 video-level labels. We examine fully connected Deep Neural
Networks (DNNs), AlexNet [1], VGG [2], Inception [3], and ResNet [4]. We
investigate varying the size of both training set and label vocabulary, finding
that analogs of the CNNs used in image classification do well on our audio
classification task, and larger training and label sets help up to a point. A
model using embeddings from these classifiers does much better than raw
features on the Audio Set [5] Acoustic Event Detection (AED) classification
task.Comment: Accepted for publication at ICASSP 2017 Changes: Added definitions of
mAP, AUC, and d-prime. Updated mAP/AUC/d-prime numbers for Audio Set based on
changes of latest Audio Set revision. Changed wording to fit 4 page limit
with new addition
On the Expressivity and Applicability of Model Representation Formalisms
A number of first-order calculi employ an explicit model representation
formalism for automated reasoning and for detecting satisfiability. Many of
these formalisms can represent infinite Herbrand models. The first-order
fragment of monadic, shallow, linear, Horn (MSLH) clauses, is such a formalism
used in the approximation refinement calculus. Our first result is a finite
model property for MSLH clause sets. Therefore, MSLH clause sets cannot
represent models of clause sets with inherently infinite models. Through a
translation to tree automata, we further show that this limitation also applies
to the linear fragments of implicit generalizations, which is the formalism
used in the model-evolution calculus, to atoms with disequality constraints,
the formalisms used in the non-redundant clause learning calculus (NRCL), and
to atoms with membership constraints, a formalism used for example in decision
procedures for algebraic data types. Although these formalisms cannot represent
models of clause sets with inherently infinite models, through an additional
approximation step they can. This is our second main result. For clause sets
including the definition of an equivalence relation with the help of an
additional, novel approximation, called reflexive relation splitting, the
approximation refinement calculus can automatically show satisfiability through
the MSLH clause set formalism.Comment: 15 page
Network conduciveness with application to the graph-coloring and independent-set optimization transitions
We introduce the notion of a network's conduciveness, a probabilistically
interpretable measure of how the network's structure allows it to be conducive
to roaming agents, in certain conditions, from one portion of the network to
another. We exemplify its use through an application to the two problems in
combinatorial optimization that, given an undirected graph, ask that its
so-called chromatic and independence numbers be found. Though NP-hard, when
solved on sequences of expanding random graphs there appear marked transitions
at which optimal solutions can be obtained substantially more easily than right
before them. We demonstrate that these phenomena can be understood by resorting
to the network that represents the solution space of the problems for each
graph and examining its conduciveness between the non-optimal solutions and the
optimal ones. At the said transitions, this network becomes strikingly more
conducive in the direction of the optimal solutions than it was just before
them, while at the same time becoming less conducive in the opposite direction.
We believe that, besides becoming useful also in other areas in which network
theory has a role to play, network conduciveness may become instrumental in
helping clarify further issues related to NP-hardness that remain poorly
understood
Tilt order parameters, polarity and inversion phenomena in smectic liquid crystals
The order parameters for the phenomenological description of the smectic-{\it
A} to smectic-{\it C} phase transition are formulated on the basis of molecular
symmetry and structure. It is shown that, unless the long molecular axis is an
axis of two-fold or higher rotational symmetry, the ordering of the molecules
in the smectic-{\it C} phase gives rise to more than one tilt order parameter
and to one or more polar order parameters. The latter describe the indigenous
polarity of the smectic-{\it C} phase, which is not related to molecular
chirality but underlies the appearance of spontaneous polarisation in chiral
smectics. A phenomenological theory of the phase transition is formulated by
means of a Landau expansion in two tilt order parameters (primary and
secondary) and an indigenous polarity order parameter. The coupling among these
order parameters determines the possibility of sign inversions in the
temperature dependence of the spontaneous polarisation and of the helical pitch
observed experimentally for some chiral smectic-{\it } materials. The
molecular interpretation of the inversion phenomena is examined in the light of
the new formulation.Comment: 12 pages, 5 figures, RevTe
Dispositional optimism as a correlate of decision-making styles in adolescence
Despite the numerous psychological areas in which optimism has been
studied, including career planning, only a small amount of research has been done to
investigate the relationship between optimism and decision-making styles. Consequently,
we have investigated the role of dispositional optimism as a correlate of different
decision-making styles, in particular, positive for effective styles and negative for
ineffective ones (doubtfulness, procrastination, and delegation). Data were gathered
through questionnaires administered to 803 Italian adolescents in their last 2 years of
high schools with different fields of study, each at the beginning stages of planning
for their professional future. A paper questionnaire was completed containing measures
of dispositional optimism and career-related decision styles, during a vocational
guidance intervention conducted at school. Data were analyzed using stepwise multiple
regression. Results supported the proposed model by showing optimism to be a strong
correlate of decision-making styles, thereby offering important intervention guidelines
aimed at modifying unrealistically negative expectations regarding their future and
helping students learn adaptive decision-making skills
Effects of experimentally added salmon subsidies on resident fishes via direct and indirect pathways
Artificial additions of nutrients of differing forms such as salmon carcasses and analog pellets (i.e. pasteurized fishmeal) have been proposed as a means of stimulating aquatic productivity and enhancing populations of anadromous and resident fishes. Nutrient mitigation to enhance fish production in stream ecosystems assumes that the central pathway by which effects occur is bottom-up, through aquatic primary and secondary production, with little consideration of reciprocal aquatic-terrestrial pathways. The net outcome (i.e. bottom-up vs. top-down) of adding salmon-derived materials to streams depend on whether or not these subsidies indirectly intensify predation on in situ prey via increases in a shared predator or alleviate such predation pressure. We conducted a 3-year experiment across nine tributaries of the N. Fork Boise River, Idaho, USA, consisting of 500-m stream reaches treated with salmon carcasses (n = 3), salmon carcass analog (n = 3), and untreated control reaches (n = 3). We observed 2–8 fold increases in streambed biofilms in the 2–6 weeks following additions of both salmon subsidy treatments in years 1 and 2 and a 1.5-fold increase in standing crop biomass of aquatic invertebrates to carcass additions in the second year of our experiment. The consumption of benthic invertebrates by stream fishes increased 110–140% and 44–66% in carcass and analog streams in the same time frame, which may have masked invertebrate standing crop responses in years 3 and 4. Resident trout directly consumed 10.0–24.0 g·m-2·yr-1 of salmon carcass and \u3c1–11.0 g·m-2·yr-1 of analog material, which resulted in 1.2–2.9 g·m-2·yr-1 and 0.03–1.4 g·m-2·yr-1 of tissue produced. In addition, a feedback flux of terrestrial maggots to streams contributed 0.0–2.0 g·m-2·yr-1 to trout production. Overall, treatments increased annual trout production by 2–3 fold, though density and biomass were unaffected. Our results indicate the strength of bottom-up and top-down responses to subsidy additions was asymmetrical, with top-down forces masking bottom-up effects that required multiple years to manifest. The findings also highlight the need for nutrient mitigation programs to consider multiple pathways of energy and nutrient flow to account for the complex effects of salmon subsidies in stream-riparian ecosystems
A statistical learning strategy for closed-loop control of fluid flows
This work discusses a closed-loop control strategy for complex systems utilizing scarce and streaming data. A discrete embedding space is first built using hash functions applied to the sensor measurements from which a Markov process model is derived, approximating the complex system’s dynamics. A control strategy is then learned using reinforcement learning once rewards relevant with respect to the control objective are identified. This method is designed for experimental configurations, requiring no computations nor prior knowledge of the system, and enjoys intrinsic robustness. It is illustrated on two systems: the control of the transitions of a Lorenz’63 dynamical system, and the control of the drag of a cylinder flow. The method is shown to perform well
The immunomodulatory anticancer agent, RRx-001, induces an interferon response through epigenetic induction of viral mimicry
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