gebhart/patrepeval huggingface dataset:
<strong>Deep Learning Microscope</strong> (2017)

Label-free cell analysis is essential to personalized genomics, cancer diagnostics, and drug development as it avoids adverse effects of staining reagents on cellular viability and cell signaling. However, currently available label-free cell assays mostly rely only on a single feature and lack accuracy. Also, the sample size analyzed by these assays is limited due to their low throughput. We have integrated feature extraction and deep learning with high-throughput quantitative phase imaging enabled by photonic time stretch, achieving record high accuracy in label-free blood cell classification. Our system captures quantitative phase and intensity images and extracts 16 biophysical features from each cell. These biophysical measurements form a hyperdimensional feature space in which supervised learning is performed for cell classification. We show classification of white blood T-cells against colon cancer cells, as well as lipid accumulating algal strains for biofuel production. This system opens up a new path to data-driven phenotypic diagnosis and better understanding of the heterogeneous gene expressions in cells.
Big Area Additive Manufacturing of High Performance Bonded NdFeB Magnets (2016)

Additive manufacturing allows for the production of complex parts with minimum material waste, offering an effective technique for fabricating permanent magnets which frequently involve critical rare earth elements. In this report, we demonstrate a novel method - Big Area Additive Manufacturing (BAAM) - to fabricate isotropic near-net-shape NdFeB bonded magnets with magnetic and mechanical properties comparable or better than those of traditional injection molded magnets. The starting polymer magnet composite pellets consist of 65 vol% isotropic NdFeB powder and 35 vol% polyamide (Nylon-12). The density of the final BAAM magnet product reached 4.8 g/cm<sup>3</sup>, and the room temperature magnetic properties are: intrinsic coercivity H<sub>ci</sub> = 688.4 kA/m, remanence B<sub>r</sub> = 0.51 T, and energy product (BH)<sub>max</sub> = 43.49 kJ/m<sup>3</sup> (5.47 MGOe). In addition, tensile tests performed on four dog-bone shaped specimens yielded an average ultimate tensile strength of 6.60 MPa and an average failure strain of 4.18%. Scanning electron microscopy images of the fracture surfaces indicate that the failure is primarily related to the debonding of the magnetic particles from the polymer binder. The present method significantly simplifies manufacturing of near-net-shape bonded magnets, enables efficient use of rare earth elements thus contributing towards enriching the supply of critical materials.
LiveMaps (2017)

Image search is a popular application on web search engines. Issuing a location-related query in image search engines often returns multiple images of maps among the top ranked results. Traditionally, clicking on such images either opens the image in a new browser tab or takes users to a web page containing the image. However, finding the area of intent on an interactive web map is a manual process. In this paper, we describe a novel system, LiveMaps, for analyzing and retrieving an appropriate map viewport for a given image of a map. This allows annotation of images of maps returned by image search engines, allowing users to directly open a link to an interactive map centered on the location of interest.
Tissue Engineering of Arteries by Directed Remodeling of Intact Arterial Segments (2003)

Traditional approaches to generating tissue-engineered arteries in vitro rely on expansion of cells in culture to seed appropriate scaffolds. In most envisioned applications, small autologous blood vessels would be harvested and used as a source for these cells. We propose that small autologous arteries, not the cells derived from them, may be an attractive starting point for engineered arteries. This approach capitalizes on the ability of intact arteries to grow and remodel in response to chronic changes in their mechanical environment. Carotid arteries from juvenile (approximately 30-kg) pigs were stretched longitudinally in an ex vivo perfusion system over 9 days. This resulted in a 40% increase in artery length at physiological longitudinal stress and a 20 +/- 3% increase when unstressed. Control arteries were perfused for 9 days ex vivo at their physiological loaded length. Control and elongated arteries displayed native appearance (macroscopic and histological), excellent viability (cellularity and mitochondrial activity), normal vasoactivity, and similar mechanical properties (ultimate stress and ultimate strain) as compared with freshly harvested arteries. Growth, as opposed to just redistribution of existing mass, contributed to elongation as evidenced by an increase in artery weight. Results on elongation of arteries from neonatal and adolescent pigs are also presented and discussed.
Leveraging Communication Topologies Between Learning Agents in Deep\n Reinforcement Learning (2019)

A common technique to improve learning performance in deep reinforcement\nlearning (DRL) and many other machine learning algorithms is to run multiple\nlearning agents in parallel. A neglected component in the development of these\nalgorithms has been how best to arrange the learning agents involved to improve\ndistributed search. Here we draw upon results from the networked optimization\nliteratures suggesting that arranging learning agents in communication networks\nother than fully connected topologies (the implicit way agents are commonly\narranged in) can improve learning. We explore the relative performance of four\npopular families of graphs and observe that one such family (Erdos-Renyi random\ngraphs) empirically outperforms the de facto fully-connected communication\ntopology across several DRL benchmark tasks. Additionally, we observe that 1000\nlearning agents arranged in an Erdos-Renyi graph can perform as well as 3000\nagents arranged in the standard fully-connected topology, showing the large\nlearning improvement possible when carefully designing the topology over which\nagents communicate. We complement these empirical results with a theoretical\ninvestigation of why our alternate topologies perform better. Overall, our work\nsuggests that distributed machine learning algorithms could be made more\neffective if the communication topology between learning agents was optimized.\n
New Air Cleaning Strategies for Reduced Commercial Building Ventilation Energy ? FY11 Final Report (2011)

The research carried out in this project focuses on developing novel volatile organic compounds (VOCs) air cleaning technologies needed to enable energy-saving reductions in ventilation rates. we targeted a VOC air cleaning system that could enable a 50% reduction in ventilation rates. In a typical commercial HVAC system that provides a mixture of recirculated and outdoor air, a VOC air cleaner in the supply airstream must have a 15% to 20% VOC removal efficiency to counteract a 50% reduction in outdoor air supply.
Polydimethylsiloxane‐Based Self‐Healing Materials (2006)

Polydimethylsiloxane-based self-healing is achieved through the tin-catalyzed polycondensation of phase-separated droplets. The catalyst is released into the crack plane when microcapsules containing a tin compound are ruptured by the advancing crack (see figure). This system greatly extends the capability of self-healing polymers by introducing a new, stable healing chemistry into a structural polymer matrix and may provide a route to self-healing in harsh and corrosive environments.
Highly parallel HEVC motion estimation based on multiple temporal predictors and nested diamond search (2017)

Rate-constrained motion estimation (RCME) is the most computationally intensive task of H.265/HEVC encoding. Massively parallel architectures, such as graphics processing units (GPUs), used in combination with a multi-core central processing unit (CPU), provide a promising computing platform to achieve fast encoding. However, the dependencies in deriving motion vector predictors (MVPs) prevent the parallelization of prediction units (PUs) processing at a frame level. Moreover, the conditional execution structure of typical fast search algorithms is not suitable for GPUs designed for data-intensive parallel problems. In this paper, we propose a novel highly parallel RCME method based on multiple temporal motion vector (MV) predictors and a new fast nested diamond search (NDS) algorithm well-suited for a GPU. The proposed framework provides fine-grained encoding parallelism. Experimental results show that our approach provides reduced GPU load with better BD-Rate compared to prior full search parallel methods based on a single MV predictor.
Widely Tunable External Cavity Interband Cascade Laser for Spectroscopic Applications (2010)

We performed spectroscopy of methane using a broadly tunable external cavity interband cascade laser (EC-ICL). A miniaturized EC-ICL design for field applications, with improved anti-reflection and high-reflection coatings and new EC tuning mechanism, is presented.
Depth resolution and displayable depth of a scene in three-dimensional images (2004)

In evaluating 3 dimensional images, the depth resolution which the image can provide, is an important parameter defining the image quality. The depth resolution obtainable with 3 dimensional images is mainly determined by the parameters of cameras used for multiview image acquisitions and viewer's eye resolution. The depth resolution and the possible image depth range, obtainable with parallel, toed-in and sliding aperture camera configurations for multiview image acquisition in the 3-dimensional imaging systems are calculated by assuming that the focusing beam is diffraction limited and that a pixel pitch of the imaging sensor is a limiting image spot size. The calculation reveals that these parameters are essentially the same for the configurations considered. For the depth resolution comparison with a hologram with a size corresponding to the aperture size of camera objective, it is shown that the hologram provides better depth resolved image than the multiview systems. The depth resolution of the images in the the 3-D imaging systems is further reduced by viewer's eye resolution. The amount of the reduction is proportional to the number of picture elements in the eye resolution spot.
Microwave synthesis of graphene sheets supporting metal nanocrystals in aqueous and organic media (2009)

We have developed a facile and scalable chemical reduction method assisted by microwave irradiation for the synthesis of chemically converted graphene sheets and metal nanoparticles dispersed on the graphene sheets. The method allows rapid chemical reduction of exfoliated graphite oxide (GO) using a variety of reducing agents in either aqueous or organic media. It also allows the simultaneous reduction of GO and a variety of metal salts thus resulting in the dispersion of metallic and bimetallic nanoparticles supported on the large surface area of the thermally stable 2D graphene sheets.
V4D:4D Convolutional Neural Networks for Video-level Representation Learning (2020)

Most existing 3D CNNs for video representation learning are clip-based methods, and thus do not consider video-level temporal evolution of spatio-temporal features. In this paper, we propose Video-level 4D Convolutional Neural Networks, referred as V4D, to model the evolution of long-range spatio-temporal representation with 4D convolutions, and at the same time, to preserve strong 3D spatio-temporal representation with residual connections. Specifically, we design a new 4D residual block able to capture inter-clip interactions, which could enhance the representation power of the original clip-level 3D CNNs. The 4D residual blocks can be easily integrated into the existing 3D CNNs to perform long-range modeling hierarchically. We further introduce the training and inference methods for the proposed V4D. Extensive experiments are conducted on three video recognition benchmarks, where V4D achieves excellent results, surpassing recent 3D CNNs by a large margin.
Establishment of a Chimeric, Replication-Deficient Influenza A Virus Vector by Modulation of Splicing Efficiency (2010)

Segment 8 of the influenza A virus codes for two proteins (NS1 and NS2/NEP) via splicing. Here, we developed a viral vector expressing a cytokine or chemokine instead of the interferon antagonist NS1. To achieve both the desired genetic stability and high transgene expression levels, NS2/NEP mRNA splicing efficacy had to be fine-tuned by modification of splicing elements. Expression levels of secreted foreign proteins could be further enhanced by fusing the N-terminal 13 amino acids of NS1 with an IgK-derived secretion signal peptide. Thus, the first start codon was used for translation initiation of both NS2/NEP and the foreign protein.
Chip-interleaved block-spread CDMA for the downlink with inter-cell interference and soft hand-off (2003)

Recently, a so-termed chip-interleaved block-spread (CIBS) CDMA system has been introduced for cellular applications in the presence of frequency selective multipath channels. In both uplink and downlink operations, CIBS-CDMA achieves multiuser-interference (MUI) free reception within each cell. This paper focuses on the cellular downlink configuration, and analyzes the performance in the presence of inter-cell interference and soft handoff. Performance results favor the proposed CIBS-CDMA over the conventional DS-CDMA with chip equalizers.
Potential therapeutic applications of muscle-derived mesenchymal stem and progenitor cells (2010)

There is potential for stem and progenitor cells derived from skeletal muscle to be incorporated in clinical interventions, either as a cellular therapy to modify the natural history of disease or as a component of engineered tissue constructs that can replace diseased or damaged tissues.
Fast Dynamic Multiple-Set Membership Testing Using Combinatorial Bloom Filters (2011)

In this paper, we consider the problem of designing a data structure that can perform fast multiple-set membership testing in deterministic time. Our primary goal is to develop a hardware implementation of the data structure that uses only embedded memory blocks. Prior efforts to solve this problem involve hashing into multiple Bloom filters. Such approach needs a priori knowledge of the number of elements in each set in order to size the Bloom filter. We use a single-Bloom-filter-based approach and use multiple sets of hash functions to code for the set (group) id. Since a single Bloom filter is used, it does not need a priori knowledge of the distribution of the elements across the different sets. We show how to improve the performance of the data structure by using constant-weight error-correcting codes for coding the group id. Using error-correcting codes improves the performance of these data structures especially when there are a large number of sets. We also outline an efficient hardware-based approach to generate the large number of hash functions that we need for this data structure. The resulting data structure, COMB, is amenable to a variety of time-critical network applications.
A probabilistic model for retrospective news event detection (2005)

Retrospective news event detection (RED) is defined as the discovery of previously unidentified events in historical news corpus. Although both the contents and time information of news articles are helpful to RED, most researches focus on the utilization of the contents of news articles. Few research works have been carried out on finding better usages of time information. In this paper, we do some explorations on both directions based on the following two characteristics of news articles. On the one hand, news articles are always aroused by events; on the other hand, similar articles reporting the same event often redundantly appear on many news sources. The former hints a generative model of news articles, and the latter provides data enriched environments to perform RED. With consideration of these characteristics, we propose a probabilistic model to incorporate both content and time information in a unified framework. This model gives new representations of both news articles and news events. Furthermore, based on this approach, we build an interactive RED system, HISCOVERY, which provides additional functions to present events, Photo Story and Chronicle.
Two-Photon Fluorescent Turn-On Probe for Lipid Rafts in Live Cell and Tissue (2008)

We report a new two-photon fluorescence turn-on probe 6-[(E)-3-oxo-1-dodecenyl]-2-[N-methyl-N-(carboxymethyl)amino]naphthalene (CL2) that is designed specifically for visualizing lipid rafts in living cells and tissues. This probe emits much brighter two-photon excited fluorescence in lipid rafts than in non-raft domains and allows direct visualization of the lipid rafts in the live cells and pyramidal neuron layer of the CA1 region at a depth of 100-250 mum in live tissues using two-photon microscopy.
Mechanically Reconfigurable, Dual-Band Slot Dipole Antennas (2015)

Mechanically reconfigurable dual-band slot dipole antennas with wide tuning ranges are developed in this communication. The dual-band operation is achieved by inserting parallel slots with different lengths and the tuning is realized by incorporating a dual rack and pinion mechanism to slide parasitic patches to vary the slot lengths and the frequency of operation of each band. To demonstrate the proposed approach, two frequency-tunable antennas are prototyped. The first design uses two motors to tune the frequency band ratio between 1 and 2.6. The second design uses one motor to maintain a constant frequency band ratio of 2 over the tuning range of 2-3 GHz for the lower band and 4-6 GHz for the upper band. Both designs show minimal variation in gain, polarization, and radiation pattern over the tuning range.
The HEROIC Framework: Encrypted Computation Without Shared Keys (2015)

Outsourcing computation to the cloud has recently become a very attractive option for enterprises and consumers, due to mostly reduced cost and extensive scalability. At the same time, however, concerns about the privacy of the data entrusted to cloud providers keeps rising. To address these concerns and thwart potential attackers, cloud providers today resort to numerous security controls as well as data encryption. Since the actual computation is still unencrypted inside cloud microprocessor chips, it is only a matter of time until new attacks and side channels are devised to leak sensitive information. To address the challenge of securing general-purpose computation inside microprocessor chips, we propose a novel computer architecture, and present a complete framework for general-purpose encrypted computation without shared keys, enabling secure data processing. This new architecture, called homomophically encrypted one instruction computation, contrary to the previous work in the area does not require a secret key installed inside the microprocessor chip. Instead, it leverages the powerful properties of homomorphic encryption combined with the simplicity of one instruction set computing. The proposed framework introduces: 1) a RTL implementation for reconfigurable hardware and 2) a ready-to-deploy virtual machine, which can be readily ported to existing server processor architectures.
Statistical Reconstruction for Cosmic (2007)

Highly penetrating cosmic ray muons constantly shower the earth at a rate of about 1 muon per cm per minute. We have developed a technique which exploits the multiple Coulomb scattering of these particles to perform nondestructive inspection without the use of artificial radiation. In prior work (1)-(3), we have described heuristic methods for processing muon data to create reconstructed images. In this paper, we present a maximum likelihood/expectation maximization tomographic reconstruction algorithm designed for the technique. This algo- rithm borrows much from techniques used in medical imaging, particularly emission tomography, but the statistics of muon scattering dictates differences. We describe the statistical model for multiple scattering, derive the reconstruction algorithm, and present simulated examples. We also propose methods to improve the robustness of the algorithm to experimental errors and events departing from the statistical model.
Development and Evaluation of a Sensitive and Specific Assay for Diagnosis of Human Toxocariasis by Use of Three Recombinant Antigens (TES-26, TES-30USM, and TES-120) (2009)

Diagnosis of human toxocariasis currently relies on serologic tests that use Toxocara excretory-secretory (TES) antigen to detect immunoglobulin G (IgG) antibodies to the larvae. In general, however, these assays do not have adequate specificity for use in countries in which other soil-transmitted helminths are endemic. The use of recombinant antigens in these assays, however, is promising for improving the specificity of the diagnosis of toxocariasis. Toward this goal, we developed an IgG4 enzyme-linked immunosorbent assay (ELISA) involving three recombinant antigens: rTES-30USM (previously produced), rTES-26, and rTES-120. The latter two antigens were produced by reverse transcription-PCR cloning; subcloned into glutathione S-transferase (GST)-tagged and His-tagged prokaryotic expression vectors, respectively; and expressed in Escherichia coli. The recombinant proteins were subsequently purified by affinity chromatography using GST and His-Trap resins. The diagnostic potential of each purified recombinant antigen was tested with various immunoglobulin classes (IgG, IgM, and IgE) and IgG subclasses. The IgG4 ELISA was determined to have the highest specificity and was further evaluated using a panel of serum samples. The rTES-26 IgG4 ELISA showed 80.0% (24/30 samples positive) sensitivity, and both the rTES-30USM IgG4 ELISA and rTES-120 IgG4 ELISA had 93.0% (28/30) sensitivity. Combined use of rTES-120 and rTES-30 IgG4 ELISA for the diagnosis of toxocariasis provided 100% sensitivity. The specificities of rTES-26, rTES-30USM, and rTES-120 antigens were 96.2%, 93.9%, and 92.0%, respectively. These results indicate that the development of a diagnostic test using the three recombinant antigens will allow for more-accurate detection of toxocariasis.
Biofunctional Magnetic Nanoparticles for Protein Separation and Pathogen Detection (2006)

Abstract ChemInform is a weekly Abstracting Service, delivering concise information at a glance that was extracted from about 200 leading journals. To access a ChemInform Abstract, please click on HTML or PDF.
Automated Physics Based Animation of Fonts (2020)

We present a new method for automatic font animation, where the outlines of one or more glyphs are automatically segmented into sets of curves that can be efficiently controlled over time. This segmentation takes into account the directionality of similar segments, and allows arbitrary levels of subdivision. We showcase different examples of physics-based effects that can be driven by motion sensors in mobile devices to achieve novel and engaging text experiences. Our method works over existing off-the-shelf fonts, enabling new automatic text-based effects previously requiring lots of manual work.