676 research outputs found
Transport or Store? Synthesizing Flow-based Microfluidic Biochips using Distributed Channel Storage
Flow-based microfluidic biochips have attracted much atten- tion in the EDA
community due to their miniaturized size and execution efficiency. Previous
research, however, still follows the traditional computing model with a
dedicated storage unit, which actually becomes a bottleneck of the performance
of bio- chips. In this paper, we propose the first architectural synthe- sis
framework considering distributed storage constructed tem- porarily from
transportation channels to cache fluid samples. Since distributed storage can
be accessed more efficiently than a dedicated storage unit and channels can
switch between the roles of transportation and storage easily, biochips with
this dis- tributed computing architecture can achieve a higher execution
efficiency even with fewer resources. Experimental results con- firm that the
execution efficiency of a bioassay can be improved by up to 28% while the
number of valves in the biochip can be reduced effectively.Comment: ACM/IEEE Design Automation Conference (DAC), June 201
Multimedia Semantic Integrity Assessment Using Joint Embedding Of Images And Text
Real world multimedia data is often composed of multiple modalities such as
an image or a video with associated text (e.g. captions, user comments, etc.)
and metadata. Such multimodal data packages are prone to manipulations, where a
subset of these modalities can be altered to misrepresent or repurpose data
packages, with possible malicious intent. It is, therefore, important to
develop methods to assess or verify the integrity of these multimedia packages.
Using computer vision and natural language processing methods to directly
compare the image (or video) and the associated caption to verify the integrity
of a media package is only possible for a limited set of objects and scenes. In
this paper, we present a novel deep learning-based approach for assessing the
semantic integrity of multimedia packages containing images and captions, using
a reference set of multimedia packages. We construct a joint embedding of
images and captions with deep multimodal representation learning on the
reference dataset in a framework that also provides image-caption consistency
scores (ICCSs). The integrity of query media packages is assessed as the
inlierness of the query ICCSs with respect to the reference dataset. We present
the MultimodAl Information Manipulation dataset (MAIM), a new dataset of media
packages from Flickr, which we make available to the research community. We use
both the newly created dataset as well as Flickr30K and MS COCO datasets to
quantitatively evaluate our proposed approach. The reference dataset does not
contain unmanipulated versions of tampered query packages. Our method is able
to achieve F1 scores of 0.75, 0.89 and 0.94 on MAIM, Flickr30K and MS COCO,
respectively, for detecting semantically incoherent media packages.Comment: *Ayush Jaiswal and Ekraam Sabir contributed equally to the work in
this pape
Testing Microfluidic Fully Programmable Valve Arrays (FPVAs)
Fully Programmable Valve Array (FPVA) has emerged as a new architecture for
the next-generation flow-based microfluidic biochips. This 2D-array consists of
regularly-arranged valves, which can be dynamically configured by users to
realize microfluidic devices of different shapes and sizes as well as
interconnections. Additionally, the regularity of the underlying structure
renders FPVAs easier to integrate on a tiny chip. However, these arrays may
suffer from various manufacturing defects such as blockage and leakage in
control and flow channels. Unfortunately, no efficient method is yet known for
testing such a general-purpose architecture. In this paper, we present a novel
formulation using the concept of flow paths and cut-sets, and describe an
ILP-based hierarchical strategy for generating compact test sets that can
detect multiple faults in FPVAs. Simulation results demonstrate the efficacy of
the proposed method in detecting manufacturing faults with only a small number
of test vectors.Comment: Design, Automation and Test in Europe (DATE), March 201
Manganese-Catalyzed Cross-Coupling of Thiols with Aryl Iodides
Here we report the manganesecatalyzedcoupling reaction of thiols witharyl iodides, giving the aryl thioethers ingood to excellent yields; the system showsgood functional group tolerance and enablesthe sterically demanding aryl iodides tocouple with thiols
A General Procedure for the Regioselective Synthesis of Aryl Thioethers and Aryl Selenides Through C–H Activation of Arenes
A general procedure for the synthesis of aryl thioethers and aryl selenides in one-pot through sequential iridium-catalyzed C–H borylation and copper-promoted C–S and C–Se bond formation is described. Functional groups including chloro, nitro, fluoro, trifluoromethyl, and nitrogen-containing heterocycles were all tolerated under the reaction conditions. Importantly, not only aryl thiols and selenides but also their alkyl analogs were suitable coupling partners, and the products were obtained in good yields with high meta regioselectivity
Deep Regionlets for Object Detection
In this paper, we propose a novel object detection framework named "Deep
Regionlets" by establishing a bridge between deep neural networks and
conventional detection schema for accurate generic object detection. Motivated
by the abilities of regionlets for modeling object deformation and multiple
aspect ratios, we incorporate regionlets into an end-to-end trainable deep
learning framework. The deep regionlets framework consists of a region
selection network and a deep regionlet learning module. Specifically, given a
detection bounding box proposal, the region selection network provides guidance
on where to select regions to learn the features from. The regionlet learning
module focuses on local feature selection and transformation to alleviate local
variations. To this end, we first realize non-rectangular region selection
within the detection framework to accommodate variations in object appearance.
Moreover, we design a "gating network" within the regionlet leaning module to
enable soft regionlet selection and pooling. The Deep Regionlets framework is
trained end-to-end without additional efforts. We perform ablation studies and
conduct extensive experiments on the PASCAL VOC and Microsoft COCO datasets.
The proposed framework outperforms state-of-the-art algorithms, such as
RetinaNet and Mask R-CNN, even without additional segmentation labels.Comment: Accepted to ECCV 201
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