Does the Geometry of Word Embeddings Help Document Classification? A Case Study on Persistent Homology Based Representations

We investigate the pertinence of methods from algebraic topology for text data analysis. These methods enable the development of mathematically-principled isometric-invariant mappings from a set of vectors to a document embedding, which is stable with respect to the geometry of the document in the selected metric space. In this work, we evaluate the utility of these topology-based document representations in traditional NLP tasks, specifically document clustering and sentiment classification. We find that the embeddings do not benefit text analysis. In fact, performance is worse than simple techniques like $\textit{tf-idf}$, indicating that the geometry of the document does not provide enough variability for classification on the basis of topic or sentiment in the chosen datasets.Comment: 5 pages, 3 figures. Rep4NLP workshop at ACL 201

Low Power 6-Transistor Latch Design for Portable Devices

The latest advances in mobile battery-powered devices such as the Personal Digital Assistant (PDA) and mobile phones have set new goals in digital VLSI design. The portable devices require high speed and low power consumption. Even low power consumption is the dominant requirement and to do so speed can be compromised. In this paper a novel area efficient latch design is proposed. The simulation results show that the proposed design with less transistor count is better choice for low power and high speed portable applications. Keywords: Latch, Low power, Portable, 8T, 6T, Power consumption, Delay

Intent-Aware Contextual Recommendation System

Recommender systems take inputs from user history, use an internal ranking algorithm to generate results and possibly optimize this ranking based on feedback. However, often the recommender system is unaware of the actual intent of the user and simply provides recommendations dynamically without properly understanding the thought process of the user. An intelligent recommender system is not only useful for the user but also for businesses which want to learn the tendencies of their users. Finding out tendencies or intents of a user is a difficult problem to solve. Keeping this in mind, we sought out to create an intelligent system which will keep track of the user's activity on a web-application as well as determine the intent of the user in each session. We devised a way to encode the user's activity through the sessions. Then, we have represented the information seen by the user in a high dimensional format which is reduced to lower dimensions using tensor factorization techniques. The aspect of intent awareness (or scoring) is dealt with at this stage. Finally, combining the user activity data with the contextual information gives the recommendation score. The final recommendations are then ranked using filtering and collaborative recommendation techniques to show the top-k recommendations to the user. A provision for feedback is also envisioned in the current system which informs the model to update the various weights in the recommender system. Our overall model aims to combine both frequency-based and context-based recommendation systems and quantify the intent of a user to provide better recommendations. We ran experiments on real-world timestamped user activity data, in the setting of recommending reports to the users of a business analytics tool and the results are better than the baselines. We also tuned certain aspects of our model to arrive at optimized results.Comment: Presented at the 5th International Workshop on Data Science and Big Data Analytics (DSBDA), 17th IEEE International Conference on Data Mining (ICDM) 2017; 8 pages; 4 figures; Due to the limitation "The abstract field cannot be longer than 1,920 characters," the abstract appearing here is slightly shorter than the one in the PDF fil

The Role of CXCR2 in Pancreatic Cancer Development and Progression

This dissertation examines the role of CXCR2, a seven transmembrane G- protein coupled receptor, in mediating autocrine as well as paracrine mechanisms during pancreatic cancer progression. Data presented in the initial section demonstrates the aberrant expression of the CXCR2 biological axis in human pancreatic cancer tissue specimens. A study performed within the first section of this dissertation investigates the contribution of CXCR2 signaling in pancreatic cancer initiation. These studies have identified a novel role of CXCR2 in mediating KRAS(G12D) -induced autocrine growth transformation of pancreatic cancer cells. The upregulation of the CXCR2 biological axis was found to be directly regulated by the KRAS(G12D) mutation using in vitro and in vivo model systems. Furthermore, the inhibition of CXCR2 by genetic and pharmacological tools was able to downregulate the protein level of KRAS. The tumor microenvironment in pancreatic cancer is composed of heterogeneous populations of cells including endothelial, fibroblast and immune cells. CXCR2 is known to be expressed by a majority of these cell types. Besides, CXCR2 is also known to mediate immune responses in various diseases including cancer. The studies in the later section of this dissertation investigate the role of CXCR2 in altering local and systemic host-mediated responses in pancreatic cancer. Two experimental strategies were used: 1) Evaluating the impact of host CXCR2 depletion on tumor growth in subcutaneous versus orthotopic tumor cell implants. 2) Examining the effect of host CXCR2 deletion on the infiltration of immune cells in orthotopic pancreatic tumors. The first approach identified a pancreatic-parenchyma specific role of CXCR2 in inhibiting fibrosis in pancreatic cancer. The second strategy unraveled an important role of CXCR2 in causing local immunosuppression where CXCR2 mediates the infiltration of myeloid-derived suppressor cells (MDSCs) in pancreatic cancer. However, CXCR2 was found to be important for inhibiting extramedullary hematopoiesis and expansion of MDSCs in the spleen. Overall, the results presented in this dissertation suggest that CXCR2 signaling functions as a double-edged sword in pancreatic cancer by mediating both tumor-promoting and -inhibitory effects

Baselining carbon dioxide emissions of Las Vegas Metropolitan Area residential building sector

This research aims to baseline the annual energy consumption and resulting carbon dioxide emissions from the residential sector of Las Vegas Metropolitan Area. The study institutes an operative database of individual energy consumption patterns and also derives the correlation between age of dwellings and their consumption. Finally, it evaluates the reductions needed in energy consumption and carbon dioxide emissions to meet the Kyoto Protocol targets for 2012; The first part of the research synthesizes electricity and gas consumption data for 2005 obtained from the respective utility providers. Using this, the latest consumption trends are consolidated to create a baseline against which reduction targets can be formulated. The average consumption from electricity, gas and total energy use for Incorporated Clark County and the three cities---City of Las Vegas, City of North Las Vegas and City of Henderson---is established, and compared to national and state averages. Using Geographic Information Systems, ready-reference maps are constructed for the 54 zip codes that form the study area, highlighting consumption patterns and CO2 emissions of each street within these zip codes and comparing them to national and state averages. It is found that 74% of the streets in Las Vegas Metro Area have higher electricity consumption than the national average of 10,656 KWH; The research then explores the effect of dwelling age and size on energy consumption. Consumption patterns are studied across a series of \u27Age ranges\u27 represented by the year of construction of dwellings. The entire Las Vegas Metro Area together, as well as each city within the study area, is individually analyzed for these patterns. Finally, the average consumption of each Age range is compared to the national average for that range. The results of this analysis show that dwellings built between 1980-84 consume the largest amount of electricity for the least number of dwellings as compared to all other Age ranges. It is also found that pre-1980 dwellings in the Las Vegas Metro Area on an average consume 49% more electricity as compared to pre-1980 dwellings in the rest of the country; The second part of the research estimates the target reductions needed from Las Vegas Metro Area\u27s residential sector so as to meet the Kyoto Protocol targets of 7% reductions of 1990 CO2 levels by 2012. Findings indicate that even if Supply-Side Management initiatives assist in reducing state carbon emissions, conservation measures would be needed to reduce present average residential consumption by 60% in the next four years in order to keep at par with the projected population growth of the valley; The research presents a useful database for policy makers, designers and home owners by identifying energy-intensive hotspots in the valley and establishing concrete reduction targets needed in light of the global energy crisis

Awareness in PG students and Research Scholar in Mysore University about open access Institutional Repository

The study has been conducted as a pilot study to know the users awareness and feedback towards the existing Institutional repository of the University of Mysore. The study focuses mainly on student’s awareness of the Institutional Repository and the open access repositories and software. The study is an attempt to know how students are familiar with IR and whether they are interested in submitting their intellectual output and using the Institutional Repository

Growth and Development of Institutional Repository: A literature review

This study has been conducted to review the literature on Institutional Repository. The study mainly focuses on the growth and development of IR around the world. This paper will explain how the IR started in different countries and what is the current status of institutional repositories

Design and development of drug delivery systems for immediate and sustained release utilizing hot melt extrusion

Polymers have indispensable role in pharmaceutical formulation development. Polymer choice is a critical factor to obtain the desired drug-release profile during formulation development for HME (Hot melt extrusion). Many commercially available, pharmaceutical-grade polymers can be used in HME formulations. The suitable polymer choice facilitates processing in the extruder. When choosing a polymer to use in a formulation, processing conditions and processing attributes of the active pharmaceutical ingredients (APIs) should be considered. The physicochemical and the mechanisms of drug release from drug delivery systems prepared by utilizing HME with various polymeric carriers were investigated. Amorphous forms of API can have as much as 10-1600 fold higher solubility than their crystalline forms. HME technology is extensively been used to convert crystalline form to amorphous form of drug with increased solubility with polymeric matrices as carriers. Efavirenz (EFZ) and Carbamazepine (CBZ) are crystalline lipophilic model drugs used in the studies. These are class II drugs (low solubility, high permeability) according to the BCS guidance by the FDA. Various polymers for example cellulose ethers (HEC, HPC and HPMC), hypermellose ester derivatives (HPMCAS and HPMCP) and acrylic polymer (Eudragit® EPO) with pH dependant solubility were examined for suitability as solubility enhancers for EFZ and processability in melt extrusion processes. To determine suitable polymeric carrier, different tools like solubility parameter calculation, Thermogravimetric analysis (TGA), Differential scanning calorimetry (DSC) and Dissolution were employed. The physicochemical characteristics of the extruded formulations were compared to the respective physical mixtures to examine the effect of the extrusion process. Furthermore, HME formulations were evaluated for drug polymer interaction utilizing Fourier transform infrared spectroscopy (FTIR). Sugar alcohols were used as carriers in solid dispersions, since it is known that glass formation is comin many polyhydroxy substances, presumably due to their strong hydrogen bonding which may prevent re-crystallization of the amorphous form of drug molecules. Furthermore, they possess the advantage of high thermal stability and absence of browning reactions. The sugar alcohols (Mannitol, Sorbitol, and Xylitol) investigated in this study proved to be very effective in forming solid dispersions and enhancing solubility of CBZ form III. Xylitol exhibited good processability. Chlorpheniramine Maleate (CLPM) and Diltiazem Hydrochloride (DTZ) were used as a model API to design a sustained release pellet formulations utilizing EthocelTM (EC). EC is also studied as matrix former with lipophillic processing aids (Stearic acid, Tristearin and Trimyristin) for HME sustained release pellets. The purpose of this project was to study the effect various levels of processing aid with Ethyl cellulose matrices utilizing melt extrusion techniques. All of the processing aids decreased the Tg of EthocelTM, which facilitated the extrusion process. With addition of Stearic acid (10%w/w), the Tg of the EC matrix decreased from 132.6±2.5°C to 125.4±1.7°C. FTIR spectra of extruded pellets of EC with lipophillic processing aids indicated band shift when compared to the spectra of pure EC suggesting intermolecular interaction between EC and lipophillic processing aids

Intra-Day Solar Irradiance Forecasting for Remote Microgrids Using Hidden Markov Model

Accurate solar irradiance forecasting is the key to accurate estimation of solar power output at any given time. The accuracy of this information is especially crucial in diesel-PV based remote microgrids with batteries to determine the set points of the batteries and generators for their optimal dispatch. This, in turn, is related directly to the overall operating cost because both an overestimation and an underestimation of the irradiance means additional operating costs for either suddenly ramping up the backup resources or causing under-utilization of the available PV power output. Accurately predicting the solar irradiance is not an easy task because of the sporadic nature of the irradiance that is received at the solar panel surfaces. Handling the dynamic nature of the irradiance pattern requires a strong and flexible model that can precisely capture the irradiance trend in any given location at a given time. Usually, such a robust model requires a lot of input variables like weather data including humidity, temperature, pressure, wind speed, wind direction, etc. and/or large inventory of satellite images of clouds over a long period of time. The expensive sensors and database tools for collecting and storing such huge information may not be installed in remote locations. Therefore, this thesis prioritizes on developing a simple method requiring a minimum input to accurately forecast the solar irradiance for remote microgrids