Fast and accurate sentiment classification using an enhanced Naive Bayes model

We have explored different methods of improving the accuracy of a Naive Bayes classifier for sentiment analysis. We observed that a combination of methods like negation handling, word n-grams and feature selection by mutual information results in a significant improvement in accuracy. This implies that a highly accurate and fast sentiment classifier can be built using a simple Naive Bayes model that has linear training and testing time complexities. We achieved an accuracy of 88.80% on the popular IMDB movie reviews dataset.Comment: 8 pages, 2 figure

Phosphorylation of the Arp2 subunit relieves auto-inhibitory interactions for Arp2/3 complex activation.

Actin filament assembly by the actin-related protein (Arp) 2/3 complex is necessary to build many cellular structures, including lamellipodia at the leading edge of motile cells and phagocytic cups, and to move endosomes and intracellular pathogens. The crucial role of the Arp2/3 complex in cellular processes requires precise spatiotemporal regulation of its activity. While binding of nucleation-promoting factors (NPFs) has long been considered essential to Arp2/3 complex activity, we recently showed that phosphorylation of the Arp2 subunit is also necessary for Arp2/3 complex activation. Using molecular dynamics simulations and biochemical assays with recombinant Arp2/3 complex, we now show how phosphorylation of Arp2 induces conformational changes permitting activation. The simulations suggest that phosphorylation causes reorientation of Arp2 relative to Arp3 by destabilizing a network of salt-bridge interactions at the interface of the Arp2, Arp3, and ARPC4 subunits. Simulations also suggest a gain-of-function ARPC4 mutant that we show experimentally to have substantial activity in the absence of NPFs. We propose a model in which a network of auto-inhibitory salt-bridge interactions holds the Arp2 subunit in an inactive orientation. These auto-inhibitory interactions are destabilized upon phosphorylation of Arp2, allowing Arp2 to reorient to an activation-competent state

Magnetotransport properties of individual InAs nanowires

We probe the magnetotransport properties of individual InAs nanowires in a field effect transistor geometry. In the low magnetic field regime we observe magnetoresistance that is well described by the weak localization (WL) description in diffusive conductors. The weak localization correction is modified to weak anti-localization (WAL) as the gate voltage is increased. We show that the gate voltage can be used to tune the phase coherence length ($l_\phi$) and spin-orbit length ($l_{so}$) by a factor of $\sim$ 2. In the high field and low temperature regime we observe the mobility of devices can be modified significantly as a function of magnetic field. We argue that the role of skipping orbits and the nature of surface scattering is essential in understanding high field magnetotransport in nanowires

Hsp70–Bag3 complex is a hub for proteotoxicity-induced signaling that controls protein aggregation

Protein abnormalities in cells are the cause of major pathologies, and a number of adaptive responses have evolved to relieve the toxicity of misfolded polypeptides. To trigger these responses, cells must detect the buildup of aberrant proteins which often associate with proteasome failure, but the sensing mechanism is poorly understood. Here we demonstrate that this mechanism involves the heat shock protein 70–Bcl-2–associated athanogene 3 (Hsp70–Bag3) complex, which upon proteasome suppression responds to the accumulation of defective ribosomal products, preferentially recognizing the stalled polypeptides. Components of the ribosome quality control system LTN1 and VCP and the ribosome-associated chaperone NAC are necessary for the interaction of these species with the Hsp70–Bag3 complex. This complex regulates important signaling pathways, including the Hippo pathway effectors LATS1/2 and the p38 and JNK stress kinases. Furthermore, under proteotoxic stress Hsp70–Bag3–LATS1/2 signaling regulates protein aggregation. We established that the regulated step was the emergence and growth of abnormal protein oligo-mers containing only a few molecules, indicating that aggregation is regulated at very early stages. The Hsp70–Bag3 complex therefore functions as an important signaling node that senses proteo-toxicity and triggers multiple pathways that control cell physiology, including activation of protein aggregation

Machine Learning Methods to Optimize the Geometry and Topology of Meshes

Meshes are used ubiquitously in engineering for representing geometries, performing computational simulations, and generating computer graphics renderings. Automatically generating suitable meshes for downstream applications remains a key bottleneck in many workflows and often requires significant manual intervention. It is challenging to optimize mesh data-structures because they can be highly unstructured in their most general form.A mesh has two fundamental attributes — geometry and topology. Geometry deals with the position and shape of objects in space. Topology is concerned with the connectivity of mesh elements. It is essential to optimize both of these attributes to generate a desirable mesh for a target application such as simulation. This dissertation explores machine learning methods to optimize both of these attributes.The first part of this dissertation is concerned with mesh topology. We will describe a deep reinforcement learning framework to optimize the topology of 2D meshes using elementary mesh editing operations. The framework is trained purely in self-play reinforcement learning to optimize a given user defined objective function. We describe a novel neural network architecture that is able to encode the local topology of a mesh around a given mesh neighborhood. Subsequently, the neural network is trained to predict a probability distribution over the local action space in order to maximize the cumulative reward as prescribed by the given objective function. The agent is trained on randomly generated 2D polygonal shapes. We demonstrate generalization to inputs that were never seen during training. The proposed framework is particularly effective at coarse block decomposition of polygonal shapes where the aim is to minimize the number of irregular vertices in the mesh.We will then tackle the problem of geometry. We describe a deep learning method to automatically generate patient-specific, simulation ready 3D surface meshes of the human heart directly from clinical imaging. The proposed method is a two-stage mesh deformation process that transforms a given template mesh to match the underlying target geometry in the image data. The first stage consists of a learned affine transformation conditioned on the input image. This stage is trained to roughly align the template in terms of scale and orientation to the image data. The second stage consists of a learned local diffeomorphic deformation field conditioned on the image and the current location of the template. This stage improves the accuracy of the prediction by capturing finer details of the target geometry. We describe a novel loss function derived from the kinematics of motion of continuous bodies that penalizes undesirable phenomenon such as surface interpenetration resulting in anatomically accurate, physically realistic, simulation ready meshes. The proposed framework is validated against a large held-out test dataset and compared with prior state-of-the-art along a variety of accuracy and quality metrics

Learning topological operations on meshes with application to block decomposition of polygons

We present a learning based framework for mesh quality improvement on unstructured triangular and quadrilateral meshes. Our model learns to improve mesh quality according to a prescribed objective function purely via self-play reinforcement learning with no prior heuristics. The actions performed on the mesh are standard local and global element operations. The goal is to minimize the deviation of the node degrees from their ideal values, which in the case of interior vertices leads to a minimization of irregular nodes.Comment: Submitted to Computer-Aided Design Journal. Presented at 17th US National Conference on Computational Mechanics, Albuquerque, N

RecXplainer: Post-Hoc Attribute-Based Explanations for Recommender Systems

Recommender systems are ubiquitous in most of our interactions in the current digital world. Whether shopping for clothes, scrolling YouTube for exciting videos, or searching for restaurants in a new city, the recommender systems at the back-end power these services. Most large-scale recommender systems are huge models trained on extensive datasets and are black-boxes to both their developers and end-users. Prior research has shown that providing recommendations along with their reason enhances trust, scrutability, and persuasiveness of the recommender systems. Recent literature in explainability has been inundated with works proposing several algorithms to this end. Most of these works provide item-style explanations, i.e., `We recommend item A because you bought item B.' We propose a novel approach, RecXplainer, to generate more fine-grained explanations based on the user's preference over the attributes of the recommended items. We perform experiments using real-world datasets and demonstrate the efficacy of RecXplainer in capturing users' preferences and using them to explain recommendations. We also propose ten new evaluation metrics and compare RecXplainer to six baseline methods.Comment: Awarded the Best Student Paper at TEA Workshop at NeurIPS 2022. 13 page

Accomplishments of Endwall Contouring on Heat Transfer in a Passage of a Turbine Blade

The study explores axisymmetric endwall contouring with emphasis on the design of novel endwalls capable of heat load reduction. Optimizations with parameterization numerically determined by the endwall of flat shape led to the endwall of the contoured shape with substantial depletion of heat transfer in the passage of the vane. Heat transfer attributes for the generated contoured endwalls were analyzed for the exit Reynolds number of 2 × 106 . Endwall three-dimensional contouring resulted in remarkable changes in secondary flow vortices, jet-to-secondary flow interaction, and film cooling effectiveness on the flat endwall. The results pointed out that the axisymmetric convergent contouring causes a significant increase in endwall film cooling, especially for the hard-to-cooled regions throughout the vane, but the level of benefit is significantly affected by the blowing ratios. The obtained efficacy demonstrated the flow impact of the cross-passage on the proliferation of the coolant on top of the flat endwall and the amenability for jet lift-off at elevated blowing ratios. The optimal mass flow rate selection of the current work could identify the passage of the endwall, contoured with superior axial turbine efficiency and durability than that of the flat endwall

A Physical Model for z~2 Dust Obscured Galaxies

We present a physical model for the origin of z~2 Dust-Obscured Galaxies (DOGs), a class of high-redshift ULIRGs selected at 24 micron which are particularly optically faint (24/R>1000). By combining N-body/SPH simulations of high redshift galaxy evolution with 3D polychromatic dust radiative transfer models, we find that luminous DOGs (with F24 > 0.3 mJy at z~2 are well-modeled as extreme gas-rich mergers in massive (~5x10^12-10^13 Msun) halos, with elevated star formation rates (~500-1000 Msun/yr) and/or significant AGN growth (Mdot > 0.5 Msun/yr), whereas less luminous DOGs are more diverse in nature. At final coalescence, merger-driven DOGs transition from being starburst dominated to AGN dominated, evolving from a "bump" to a power-law shaped mid-IR (IRAC) spectral energy distribution (SED). After the DOG phase, the galaxy settles back to exhibiting a "bump" SED with bluer colors and lower star formation rates. While canonically power-law galaxies are associated with being AGN-dominated, we find that the power-law mid-IR SED can owe both to direct AGN contribution, as well as to a heavily dust obscured stellar bump at times that the galaxy is starburst dominated. Thus power-law galaxies can be either starburst or AGN dominated. Less luminous DOGs can be well-represented either by mergers, or by massive ($M_{\rm baryon} ~5x10^11 Msun) secularly evolving gas-rich disc galaxies (with SFR > 50 Msun/yr). By utilising similar models as those employed in the SMG formation study of Narayanan et al. (2010), we investigate the connection between DOGs and SMGs. We find that the most heavily star-forming merger driven DOGs can be selected as Submillimetre Galaxies (SMGs), while both merger-driven and secularly evolving DOGs typically satisfy the BzK selection criteria.Comment: Accepted by MNRAS; major changes include better description of dependency on ISM specification and updated models allowing dust to evolve with metallicity