98,562 research outputs found
Heart Beat Characterization from Ballistocardiogram Signals using Extended Functions of Multiple Instances
A multiple instance learning (MIL) method, extended Function of Multiple
Instances (FUMI), is applied to ballistocardiogram (BCG) signals produced by
a hydraulic bed sensor. The goal of this approach is to learn a personalized
heartbeat "concept" for an individual. This heartbeat concept is a prototype
(or "signature") that characterizes the heartbeat pattern for an individual in
ballistocardiogram data. The FUMI method models the problem of learning a
heartbeat concept from a BCG signal as a MIL problem. This approach elegantly
addresses the uncertainty inherent in a BCG signal e. g., misalignment between
training data and ground truth, mis-collection of heartbeat by some
transducers, etc. Given a BCG training signal coupled with a ground truth
signal (e.g., a pulse finger sensor), training "bags" labeled with only binary
labels denoting if a training bag contains a heartbeat signal or not can be
generated. Then, using these bags, FUMI learns a personalized concept of
heartbeat for a subject as well as several non-heartbeat background concepts.
After learning the heartbeat concept, heartbeat detection and heart rate
estimation can be applied to test data. Experimental results show that the
estimated heartbeat concept found by FUMI is more representative and a more
discriminative prototype of the heartbeat signals than those found by
comparison MIL methods in the literature.Comment: IEEE EMBC 2016, pp. 1-
Mapping-equivalence and oid-equivalence of single-function object-creating conjunctive queries
Conjunctive database queries have been extended with a mechanism for object
creation to capture important applications such as data exchange, data
integration, and ontology-based data access. Object creation generates new
object identifiers in the result, that do not belong to the set of constants in
the source database. The new object identifiers can be also seen as Skolem
terms. Hence, object-creating conjunctive queries can also be regarded as
restricted second-order tuple-generating dependencies (SO tgds), considered in
the data exchange literature.
In this paper, we focus on the class of single-function object-creating
conjunctive queries, or sifo CQs for short. We give a new characterization for
oid-equivalence of sifo CQs that is simpler than the one given by Hull and
Yoshikawa and places the problem in the complexity class NP. Our
characterization is based on Cohen's equivalence notions for conjunctive
queries with multiplicities. We also solve the logical entailment problem for
sifo CQs, showing that also this problem belongs to NP. Results by Pichler et
al. have shown that logical equivalence for more general classes of SO tgds is
either undecidable or decidable with as yet unknown complexity upper bounds.Comment: This revised version has been accepted on 11 January 2016 for
publication in The VLDB Journa
Memory and Parallelism Analysis Using a Platform-Independent Approach
Emerging computing architectures such as near-memory computing (NMC) promise
improved performance for applications by reducing the data movement between CPU
and memory. However, detecting such applications is not a trivial task. In this
ongoing work, we extend the state-of-the-art platform-independent software
analysis tool with NMC related metrics such as memory entropy, spatial
locality, data-level, and basic-block-level parallelism. These metrics help to
identify the applications more suitable for NMC architectures.Comment: 22nd ACM International Workshop on Software and Compilers for
Embedded Systems (SCOPES '19), May 201
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