4,329 research outputs found
Cost-saving or Cost-enhancing Mergers: the Impact of the Distribution of Roles in Oligopoly
Horizontal Merger, Efficiency gains, Efficiency losses, Stackelberg oligopoly, Market power
NPRF: A Neural Pseudo Relevance Feedback Framework for Ad-hoc Information Retrieval
Pseudo-relevance feedback (PRF) is commonly used to boost the performance of
traditional information retrieval (IR) models by using top-ranked documents to
identify and weight new query terms, thereby reducing the effect of
query-document vocabulary mismatches. While neural retrieval models have
recently demonstrated strong results for ad-hoc retrieval, combining them with
PRF is not straightforward due to incompatibilities between existing PRF
approaches and neural architectures. To bridge this gap, we propose an
end-to-end neural PRF framework that can be used with existing neural IR models
by embedding different neural models as building blocks. Extensive experiments
on two standard test collections confirm the effectiveness of the proposed NPRF
framework in improving the performance of two state-of-the-art neural IR
models.Comment: Full paper in EMNLP 201
Barriers to Financial Compensation for Artists in the Recording Industry in the Digital Age
For decades, consumers, due to frequent technological advances, have utilized a variety of music-listening processes that have each become obsolete as more easily accessible technologies emerged. This change in music consumption methods is often detrimental to parties in the recording industry. The digitalization of the recording industry has allowed consumers to obtain music through means other than physical purchase, leading to well-documented financial insecurity for artists (Eiriz & Leite, 2017). In 2018, the Music Industry Research Association (MIRA) conducted a survey of 1,227 musicians and found that 61% of the group agreed that their music-related income is not enough to cover their living expenses (MIRA, 2018). For this reason, frequent attempts to deter widespread copyright infringement have been made. However, the aggressive litigation strategy of the recording industry and the development of streaming services as a viable music consumption method have instead decreased sales and negatively impacted artists’ revenue from the recording industry (Fedock, 2005; Marshall, 2015)
Building high-level features using large scale unsupervised learning
We consider the problem of building high-level, class-specific feature
detectors from only unlabeled data. For example, is it possible to learn a face
detector using only unlabeled images? To answer this, we train a 9-layered
locally connected sparse autoencoder with pooling and local contrast
normalization on a large dataset of images (the model has 1 billion
connections, the dataset has 10 million 200x200 pixel images downloaded from
the Internet). We train this network using model parallelism and asynchronous
SGD on a cluster with 1,000 machines (16,000 cores) for three days. Contrary to
what appears to be a widely-held intuition, our experimental results reveal
that it is possible to train a face detector without having to label images as
containing a face or not. Control experiments show that this feature detector
is robust not only to translation but also to scaling and out-of-plane
rotation. We also find that the same network is sensitive to other high-level
concepts such as cat faces and human bodies. Starting with these learned
features, we trained our network to obtain 15.8% accuracy in recognizing 20,000
object categories from ImageNet, a leap of 70% relative improvement over the
previous state-of-the-art
Inference skipping for more efficient real-time speech enhancement with parallel RNNs
Deep neural network (DNN) based speech enhancement models have attracted
extensive attention due to their promising performance. However, it is
difficult to deploy a powerful DNN in real-time applications because of its
high computational cost. Typical compression methods such as pruning and
quantization do not make good use of the data characteristics. In this paper,
we introduce the Skip-RNN strategy into speech enhancement models with parallel
RNNs. The states of the RNNs update intermittently without interrupting the
update of the output mask, which leads to significant reduction of
computational load without evident audio artifacts. To better leverage the
difference between the voice and the noise, we further regularize the skipping
strategy with voice activity detection (VAD) guidance, saving more
computational load. Experiments on a high-performance speech enhancement model,
dual-path convolutional recurrent network (DPCRN), show the superiority of our
strategy over strategies like network pruning or directly training a smaller
model. We also validate the generalization of the proposed strategy on two
other competitive speech enhancement models.Comment: 11 pages, 8 figures, accepted by IEEE/ACM TASL
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An unprecedented 2D covalent organic framework with an htb net topology.
A 2D imine-linked COF with a hitherto unreported htb type topology was synthesized from a linear diamine linker and a judiciously designed tetra-aldehyde building block. This work opens the door to the development of COFs with unprecedented topologies and may broaden the scope of COF functional materials by pore size and pore surface engineering
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