Incorporating Boltzmann Machine Priors for Semantic Labeling in Images and Videos

Semantic labeling is the task of assigning category labels to regions in an image. For example, a scene may consist of regions corresponding to categories such as sky, water, and ground, or parts of a face such as eyes, nose, and mouth. Semantic labeling is an important mid-level vision task for grouping and organizing image regions into coherent parts. Labeling these regions allows us to better understand the scene itself as well as properties of the objects in the scene, such as their parts, location, and interaction within the scene. Typical approaches for this task include the conditional random field (CRF), which is well-suited to modeling local interactions among adjacent image regions. However the CRF is limited in dealing with complex, global (long-range) interactions between regions in an image, and between frames in a video. This thesis presents approaches to modeling long-range interactions within images and videos, for use in semantic labeling. In order to model these long-range interactions, we incorporate priors based on the restricted Boltzmann machine (RBM). The RBM is a generative model which has demonstrated the ability to learn the shape of an object and the CRBM is a temporal extension which can learn the motion of an object. Although the CRF is a good baseline labeler, we show how the RBM and CRBM can be added to the architecture to model both the global object shape within an image and the temporal dependencies of the object from previous frames in a video. We demonstrate the labeling performance of our models for the parts of complex face images from the Labeled Faces in the Wild database (for images) and the YouTube Faces Database (for videos). Our hybrid models produce results that are both quantitatively and qualitatively better than the baseline CRF alone for both images and videos

Bounding the Probability of Error for High Precision Recognition

We consider models for which it is important, early in processing, to estimate some variables with high precision, but perhaps at relatively low rates of recall. If some variables can be identified with near certainty, then they can be conditioned upon, allowing further inference to be done efficiently. Specifically, we consider optical character recognition (OCR) systems that can be bootstrapped by identifying a subset of correctly translated document words with very high precision. This "clean set" is subsequently used as document-specific training data. While many current OCR systems produce measures of confidence for the identity of each letter or word, thresholding these confidence values, even at very high values, still produces some errors. We introduce a novel technique for identifying a set of correct words with very high precision. Rather than estimating posterior probabilities, we bound the probability that any given word is incorrect under very general assumptions, using an approximate worst case analysis. As a result, the parameters of the model are nearly irrelevant, and we are able to identify a subset of words, even in noisy documents, of which we are highly confident. On our set of 10 documents, we are able to identify about 6% of the words on average without making a single error. This ability to produce word lists with very high precision allows us to use a family of models which depends upon such clean word lists

Investigating the Role of O-GlcNAc Glycosylation in Neurodegeneration

O-GlcNAc glycosylation of nuclear and cytosolic proteins is an essential post-translational modification implicated in many diseases, from cancer to diabetes. Importantly, many important neuronal proteins are also O-GlcNAc modified, and aberrant O-GlcNAcylation of these proteins may contribute to the pathology of neurodegenerative diseases although these mechanisms have not been well defined. Here we investigated the role of O-GlcNAc glycosylation in the brain, utilizing both chemistry and molecular biology to study O-GlcNAc transferase (OGT), the enzyme that adds the sugar modification. To evaluate the role of OGT in adult neurons, we generated a forebrain-specific conditional knockout of OGT (OGT cKO) in mice. Although indistinguishable from wild-type littermates at birth, after three weeks we observe progressive neurodegeneration in OGT cKO mice. Hallmarks of Alzheimer’s disease, including neuronal loss, neuroinflammation, behavioral deficits, hyperphosphorylated tau, and amyloid beta peptide accumulation, are observed. Furthermore, decreases in OGT protein levels were found in human AD brain tissue, suggesting that altered O-GlcNAcylation likely contributes to neurodegenerative diseases in humans. This model is one of a few mouse models that recapitulate AD phenotypes without mutating and overexpressing human tau, amyloid precursor protein, or presenilin, highlighting the essential role of OGT in neurodegenerative pathways. Given the importance of OGT in the brain, we further investigated the regulation of the OGT enzyme by phosphorylation. We found that phosphorylation of OGT near its C-terminus reduces its activity in cancer cells, and have developed phosphorylation-specific antibodies to aid mechanistic studies. Furthermore, mutation of this phosphorylation site on OGT, followed by overexpression in neurons was shown to enhance neurite outgrowth, demonstrating a functional consequence for this site. Thus phosphorylation of OGT inhibits its activity and enhances neurite outgrowth, and current studies aim to characterize the signaling pathway that regulates OGT phosphorylation in neurons.</p

Acoustic Wave Separation – A non-filtration approach for continuous clarification of perfusion cell culture prior to capture chromatography

Advances in perfusion cell culture have led to cell densities in excess of 100 million cells/mL with product titers similar to those obtained in fed batch (3-5 g/L). This performance has necessitated improvements in the yield and efficiency of the cell harvest and clarification stage to generate a stream of Harvested Cell Culture Fluid (HCCF) for capture chromatography and subsequent downstream processing. This is further driven by the evolution of continuous processes where there is a preference for a continuous feed of HCCF available for direct load to the continuous multicolumn capture chromatography step. In the present work we report on a novel disruptive and scalable single-use technology for cell retention during perfusion cell culture based on an acoustophoretic separation. Acoustic Wave Separation (AWS) technology exploits the use of low frequency acoustic forces to generate a three-dimensional standing wave across a flow channel. Recirculating cell culture from a perfusion bioreactor enters the flow channel and passes below the acoustic zone. The product-containing stream of HCCF is removed from the recirculating cell culture by passage through the acoustic zone. This yields a well clarified HCCF that can be polished using a small area filter. We report the continuous cell retention during a perfusion culture of a CHO cell line expressing a mAb. At process development (PD) scale we demonstrate the ability to continuously process CHO cell culture and retain cells at densities of up to 100 million cells/mL, at flow rates of up to 2 bioreactor volumes per day. Since the clarification technology does not involve the use of hollow fiber tangential flow filtration (TFF) we ensure 100% transmission of the mAb through the AWS device. The closed system remains operational for up to 60 days enabling this scalable technology to be suitable for use in clinical manufacture. The post-AWS HCCF is 99% clarified and any residual cellular material can be removed using a small gamma stable membrane filter or directly loaded onto a 0.2 micron filter prior to chromatography. Additionally, no demonstrable adverse effects have been identified for the quality of the HCCF, the product itself, or the viability of the returning perfusion cell culture following cell retention using AWS technology. AWS technology enables the continuous cell retention from recirculating cell culture withdrawn from perfusion bioreactors in a single-use operation. AWS technology has been shown to perform well at cell densities of up to 100 million cells/mL, so is well positioned to meet the cell retention requirements of emerging higher cell density perfusion processes that are gaining momentum in the biotech space. This novel cell retention approach offers economic benefits in terms of yield improvement as well as eliminating the hollow fiber TFF operation. This offers the advantage of a stable mAb concentration in the HCCF stream during the perfusion process. This facilitates improved process control since the volume of HCCF to load on to the capture columns remains constant which is especially important during continuous multicolumn chromatography. By comparison with hollow fiber TFF, the mAb concentration varies during the cell retention process making an integrated process more complex to control

Pulse shaping by coupled-cavities: Single photons and qudits

Dynamic coupling of cavities to a quantum network is of major interest to distributed quantum information processing schemes based on cavity quantum electrodynamics. This can be achieved by active tuning a mediating atom-cavity system. In particular, we consider the dynamic coupling between two coupled cavities, each interacting with a two-level atom, realized by tuning one of the atoms. One atom-field system can be controlled to become maximally and minimally coupled with its counterpart, allowing high fidelity excitation confinement, Q-switching and reversible state transport. As an application, we first show that simple tuning can lead to emission of near-Gaussian single-photon pulses that is significantly different from the usual exponential decay in a passive cavity-based system. The influences of cavity loss and atomic spontaneous emission are studied in detailed numerical simulations, showing the practicality of these schemes within the reach of current experimental technology in solid-state environment. We then show that when the technique is employed to an extended coupled-cavity scheme involving a multi-level atom, arbitrary temporal superposition of single photons can be engineered in a deterministic way.Comment: 11 pages, 11 figures, minor revision

High speed quantum gates with cavity quantum electrodynamics

Cavity quantum electrodynamic schemes for quantum gates are amongst the earliest quantum computing proposals. Despite continued progress, and the dramatic recent demonstration of photon blockade, there are still issues with optimal coupling and gate operation involving high-quality cavities. Here we show dynamic control techniques that allow scalable cavity-QED based quantum gates, that use the full bandwidth of the cavities. When applied to quantum gates, these techniques allow an order of magnitude increase in operating speed, and two orders of magnitude reduction in cavity Q, over passive cavity-QED architectures. Our methods exploit Stark shift based Q-switching, and are ideally suited to solid-state integrated optical approaches to quantum computing.Comment: 4 pages, 3 figures, minor revision

Solubilities of Ethylene and Carbon Dioxide Gases in Lithium-Ion Battery Electrolyte

During Li-ion battery operation, (electro)chemical side reactions occur within the cell that can promote or degrade performance. These complex reactions produce byproducts in the solid, liquid, and gas phases. Studying byproducts in these three phases can help optimize battery lifetimes. To relate the measured gas-phase byproducts to species dissolved in the liquid-phase, equilibrium proprieties such as the Henry's law constants are required. The present work implements a pressure decay experiment to determine the thermodynamic equilibrium concentrations between the gas and liquid phases for ethylene (C2H4) and carbon dioxide (CO2), which are two gases commonly produced in Li-ion batteries, with an electrolyte of 1.2 M LiPF6 in 3:7 wt/wt ethylene carbonate/ethyl methyl carbonate and 3 wt % fluoroethylene carbonate (15:25:57:3 wt % total composition). The experimentally measured pressure decay curve is fit to an analytical dissolution model and extrapolated to predict the final pressure at equilibrium. The relationship between the partial pressures and concentration of dissolved gas in electrolyte at equilibrium is then used to determine Henry's law constants of 2.0 × 104 kPa for C2H4 and k CO2 = 1.1 × 104 kPa for CO2. These values are compared to Henry's law constants predicted from density functional theory and show good agreement within a factor of 3