Multiple-Instance Learning of Real-Valued Geometric Patterns

Recently there has been significant research in multiple-instance learning, yet most of this work has only considered this model when there are Boolean labels. However, in many of the application areas for which the multiple-instance model fits, real-valued labels are more appropriate than Boolean labels. We define and study a real-valued multiple-instance model in which each multiple-instance example (bag) is given a real-valued label in [0, 1] that indicates the degree to which the bag satisfies the target concept. To provide additional structure to the learning problem, we associate a real-valued label with each point in the bag. These values are then combined using a real-valued aggregation operator to obtain the label for the bag. We then present on-line agnostic algorithms for learning real-valued multiple-instance geometric concepts defined by axis-aligned boxes in constant-dimensional space and describe several possible applications of these algorithms. We obtain our learning algorithms by reducing the problem to one in which the exponentiated gradient or gradient descent algorithm can be used. We also give a novel application of the virtual weights technique. In typical applications of the virtual weights technique, all of the concepts in a group have the same weight and prediction, allowing a single representative concept from each group to be tracked. However, in our application there are an exponential number of different weights and possible predictions. Hence, boxes in each group have different weights and predictions, making the computation of the contribution of a group significantly more involved. However, we are able to both keep the number of groups polynomial in the number of trials and efficiently compute the overall prediction.     

IRAG2 Interacts with IP3-Receptor Types 1, 2, and 3 and Regulates Intracellular Ca2+ in Murine Pancreatic Acinar Cells

The inositol 1,4,5-triphosphate receptor-associated 2 (IRAG2) is also known as Jaw1 or lymphoid-restricted membrane protein (LRMP) and shares homology with the inositol 1,4,5-triphosphate receptor-associated cGMP kinase substrate 1 (IRAG1). IRAG1 interacts with inositol trisphosphate receptors (IP₃ receptors /IP₃R) via its coiled-coil domain and modulates Ca²⁺ release from intracellular stores. Due to the homology of IRAG1 and IRAG2, especially in its coiled-coil domain, it is possible that IRAG2 has similar interaction partners like IRAG1 and that IRAG2 also modulates intracellular Ca²⁺ signaling. In our study, we localized IRAG2 in pancreatic acinar cells of the exocrine pancreas, and we investigated the interaction of IRAG2 with IP₃ receptors and its impact on intracellular Ca²⁺ signaling and exocrine pancreatic function, like amylase secretion. We detected the interaction of IRAG2 with different subtypes of IP₃R and altered Ca²⁺ release in pancreatic acinar cells from mice lacking IRAG2. IRAG2 deficiency decreased basal levels of intracellular Ca²⁺, suggesting that IRAG2 leads to activation of IP₃R under unstimulated basal conditions. Moreover, we observed that loss of IRAG2 impacts the secretion of amylase. Our data, therefore, suggest that IRAG2 modulates intracellular Ca²⁺ signaling, which regulates exocrine pancreatic function.     

Electromagnetic Design and Analysis of a Novel Axial-Transverse Flux Permanent Magnet Synchronous Machine

This paper analyzes a novel hybrid axial-transverse flux permanent magnet (PM) synchronous motor achieving high torque density. The stator pole pitch and the rotor pole pitch are the same. The motor is suitable for vehicle traction systems. The new axial-transverse machine has a single-sided or double-sided air gap. The machine has two adjacent quasi-U stator cores with ring-type winding, and the rotor has two PM groups, one outer and one inner. The shape of quasi-U stator core allows changing the flux path without changing power supply polarity. The analytical expressions of the no-load back-EMF and the torque are derived, after the development of 3-D magnetic equivalent circuit (MEC) model. The 3-D finite element method (FEM) is used to analyze the magnetic field, torque, and cogging torque for different skewings of the PMs.     

Rapid Synthesis and Antiproliferative Properties of Polyazamacrocycle-Based Bi- and Tetra-Gold(I) Phosphine Dithiocarbamate Complexes

A family of bi- and tetrametallic gold(I) phosphine dithiocarbamate complexes were synthesized, starting from cyclam and dimethylcyclam polyazamacrocycles, respectively, along with their monometallic gold(I) chloridophosphine precursors. Their antiproliferative properties were evaluated on two cancer cell lines (A549 and NSCLC-N6-L16). Most of the mono- and bimetallic complexes displayed strong activities and, in particular, one bimetallic derivative showed antiproliferative properties in the low micromolar range. Insights into the structure–activity relationships are given, along with determination of the thioredoxin reductase inhibition potential, two-photon imaging of the fluorescent derivatives, and evaluation of gold uptake.     

A multi-institutional, multinational study of programming concepts using card sort data

Abstract: This paper presents a case study of the use of a repeated single‐criterion card sort with an unusually large, diverse participant group. The study, whose goal was to elicit novice programmers' knowledge of programming concepts, involved over 20 researchers from four continents and 276 participants drawn from 20 different institutions. In this paper we present the design of the study and the unexpected result that there were few discernible systematic differences in the population. The study was one of the activities of the National Science Foundation funded Bootstrapping Research in Computer Science Education project (2003).     

Business Analytics-Based Enterprise Information Systems

Big data analytics and business analytics are a disruptive technology and innovative solution for enterprise development. However, what is the relationship between business analytics, big data analytics, and enterprise information systems (EIS)? How can business analytics enhance the development of EIS? How can analytics be incorporated into EIS? These are still big issues. This article addresses these three issues by proposing ontology of business analytics, presenting an analytics service-oriented architecture (ASOA) and applying ASOA to EIS, where our surveyed data analysis showed that the proposed ASOA is viable for developing EIS. This article then examines incorporation of business analytics into EIS through proposing a model for business analytics service-based EIS, or ASEIS for short. The proposed approach in this article might facilitate the research and development of EIS, business analytics, big data analytics, and business intelligence.     

Developing an Automated Enzyme Immunoassay for High-Throughput Screening of Bovine Serum Antibodies to Neospora Caninum

An enzyme-linked immunosorbent assay (ELISA) for Neospora caninum antibodies was automated with a robotic workstation, the Beckman Coulter Biomek 2000, to screen 200 bovine sera. Comparing these results with manually run ELISA data, a 95.92% agreement (K = 0.9592) between the two assays was obtained. The automated assay was specific and sensitive with excellent positive and negative predictive values. The results were repeatable and reproducible. The automation flexibility was high and the operation complexity was minimal. High-throughput screening (HTS) for bovine antibodies to Neospora caninum was achieved. The assay was developed according to the internationally recognized ISO17025 standard requirements.     

Learning from examples with unspecified attribute values

A challenging problem within machine learning is how to make good inferences from data sets in which pieces of information are missing. While it is valuable to have algorithms that perform well for specific domains, to gain a fundamental understanding of the problem, one needs a “theory” about how to learn with incomplete data. The important contribution of such a theory is not so much the specific algorithmic results, but rather that it provides good ways of thinking about the problem formally. In this paper we introduce the unspecified attribute value (UAV) learning model as a first step towards a theoretical framework for studying the problem of learning from incomplete data in the exact learning framework.In the UAV learning model, an example x is classified positive (resp., negative) if all possible assignments for the unspecified attributes result in a positive (resp., negative) classification. Otherwise the classification given to x is “?” (for unknown). Given an example x in which some attributes are unspecified, the oracle UAV-MQ responds with the classification of x. Given a hypothesis h, the oracle UAV-EQ returns an example x (that could have unspecified attributes) for which h(x) is incorrect.We show that any class of functions learnable in Angluin’s exact model using the MQ and EQ oracles is also learnable in the UAV model using the MQ and UAV-EQ oracles as long as the counterexamples provided by the UAV-EQ oracle have a logarithmic number of unspecified attributes. We also show that any class learnable in the exact model using the MQ and EQ oracles is also learnable in the UAV model using the UAV-MQ and UAV-EQ oracles as well as an oracle to evaluate a given boolean formula on an example with unspecified attributes. (For some hypothesis classes such as decision trees and unate formulas the evaluation can be done in polynomial time without an oracle.) We also study the learnability of a universal class of decision trees under the UAV model and of DNF formulas under a representation-dependent variation of the UAV model.     

Dual contextual module for neural machine translation

Machine Translation - Self-attention-based encoder-decoder frameworks have drawn increasing attention in recent years. The self-attention mechanism generates contextual representations by attending...     

Attachment style,social skills,and Facebook use amongst adults

Research suggests that online communication may benefit those high in social anxiety. The current study examined Facebook use from the perspective of adult attachment theory, exploring relationships between attachment anxiety and avoidance and Facebook use. Social skills have been found to be related to attachment style and internet use thus we also examined the role of these skills. A diverse sample of adult participants (N = 617; 50.1% female) completed a self report questionnaire measuring attachment dimensions, social skills, and Facebook use and experience. In line with predictions grounded in attachment theory, we found that individuals with high attachment anxiety used Facebook more frequently, were more likely to use it when feeling negative emotions and were more concerned about how others perceived them on Facebook. High attachment avoidance was related to less Facebook use, less openness and less positive attitudes towards Facebook. These relationships remained when social skills were controlled. These results suggest that Facebook may serve attachment functions and provide a basis for understanding how online communication may be related to attachment styles.