49,657 research outputs found
Learning semantic sentence representations from visually grounded language without lexical knowledge
Current approaches to learning semantic representations of sentences often
use prior word-level knowledge. The current study aims to leverage visual
information in order to capture sentence level semantics without the need for
word embeddings. We use a multimodal sentence encoder trained on a corpus of
images with matching text captions to produce visually grounded sentence
embeddings. Deep Neural Networks are trained to map the two modalities to a
common embedding space such that for an image the corresponding caption can be
retrieved and vice versa. We show that our model achieves results comparable to
the current state-of-the-art on two popular image-caption retrieval benchmark
data sets: MSCOCO and Flickr8k. We evaluate the semantic content of the
resulting sentence embeddings using the data from the Semantic Textual
Similarity benchmark task and show that the multimodal embeddings correlate
well with human semantic similarity judgements. The system achieves
state-of-the-art results on several of these benchmarks, which shows that a
system trained solely on multimodal data, without assuming any word
representations, is able to capture sentence level semantics. Importantly, this
result shows that we do not need prior knowledge of lexical level semantics in
order to model sentence level semantics. These findings demonstrate the
importance of visual information in semantics
The Lock-free -LSM Relaxed Priority Queue
Priority queues are data structures which store keys in an ordered fashion to
allow efficient access to the minimal (maximal) key. Priority queues are
essential for many applications, e.g., Dijkstra's single-source shortest path
algorithm, branch-and-bound algorithms, and prioritized schedulers.
Efficient multiprocessor computing requires implementations of basic data
structures that can be used concurrently and scale to large numbers of threads
and cores. Lock-free data structures promise superior scalability by avoiding
blocking synchronization primitives, but the \emph{delete-min} operation is an
inherent scalability bottleneck in concurrent priority queues. Recent work has
focused on alleviating this obstacle either by batching operations, or by
relaxing the requirements to the \emph{delete-min} operation.
We present a new, lock-free priority queue that relaxes the \emph{delete-min}
operation so that it is allowed to delete \emph{any} of the smallest
keys, where is a runtime configurable parameter. Additionally, the
behavior is identical to a non-relaxed priority queue for items added and
removed by the same thread. The priority queue is built from a logarithmic
number of sorted arrays in a way similar to log-structured merge-trees. We
experimentally compare our priority queue to recent state-of-the-art lock-free
priority queues, both with relaxed and non-relaxed semantics, showing high
performance and good scalability of our approach.Comment: Short version as ACM PPoPP'15 poste
Optimizing Abstract Abstract Machines
The technique of abstracting abstract machines (AAM) provides a systematic
approach for deriving computable approximations of evaluators that are easily
proved sound. This article contributes a complementary step-by-step process for
subsequently going from a naive analyzer derived under the AAM approach, to an
efficient and correct implementation. The end result of the process is a two to
three order-of-magnitude improvement over the systematically derived analyzer,
making it competitive with hand-optimized implementations that compute
fundamentally less precise results.Comment: Proceedings of the International Conference on Functional Programming
2013 (ICFP 2013). Boston, Massachusetts. September, 201
Exploiting Term Hiding to Reduce Run-time Checking Overhead
One of the most attractive features of untyped languages is the flexibility
in term creation and manipulation. However, with such power comes the
responsibility of ensuring the correctness of these operations. A solution is
adding run-time checks to the program via assertions, but this can introduce
overheads that are in many cases impractical. While static analysis can greatly
reduce such overheads, the gains depend strongly on the quality of the
information inferred. Reusable libraries, i.e., library modules that are
pre-compiled independently of the client, pose special challenges in this
context. We propose a technique which takes advantage of module systems which
can hide a selected set of functor symbols to significantly enrich the shape
information that can be inferred for reusable libraries, as well as an improved
run-time checking approach that leverages the proposed mechanisms to achieve
large reductions in overhead, closer to those of static languages, even in the
reusable-library context. While the approach is general and system-independent,
we present it for concreteness in the context of the Ciao assertion language
and combined static/dynamic checking framework. Our method maintains the full
expressiveness of the assertion language in this context. In contrast to other
approaches it does not introduce the need to switch the language to a (static)
type system, which is known to change the semantics in languages like Prolog.
We also study the approach experimentally and evaluate the overhead reduction
achieved in the run-time checks.Comment: 26 pages, 10 figures, 2 tables; an extension of the paper version
accepted to PADL'18 (includes proofs, extra figures and examples omitted due
to space reasons
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