Multiple instance learning (MIL) is a variation of supervised learning where a single class label is assigned to a bag of instances. In this paper, we state the MIL problem as learning the Bernoulli distribution of the bag label where the bag label probability is fully parameterized by neural networks. Furthermore, we propose a neural network-based permutation-invariant aggregation operator tha...

We extend a previous work on a multithreaded typed assembly language (MIL) targeted at shared memory multiprocessors, and describe the design of a type-preserving compiler from the π-calculus into MIL. The language enforces a policy on lock usage through a typing system that also ensures race-freedom for typable programs, while allowing for typing various important concurrency patterns. Our tra...

In this paper, the effectiveness of a corrective learning algorithm MIL (Mirror Image Learning) [1] [2] is comparatively studied with that of ALSM (Average Learning Subspace Method) [3]. Both MIL and ALSM were proposed to improve the learning effectiveness of class conditional distributions. While the ALSM modifies the basis vectors of a subspace by subtracting the autocorrelation matrix for co...

We extend a previous work on a multithreaded typed assembly language (MIL) targeted at shared memory multiprocessors, and describe the design of a type-preserving compiler from the π-calculus into MIL. The language enforces a policy on lock usage through a typing system that also ensures race-freedom for typable programs, while allowing for typing various important concurrency patterns. Our tra...

Introduction This is the last of three articles dealing with statistics and its applications to the data analysis of materials and components, as used in Metallic Materials and Elements for Aerospace Vehicle Structures (MIL HDBK 5) and the Composite Materials Handbook (MIL HDBK 17) [1,2]. The objective of this series is to discuss some ideas and philosophies underlying the use of statistical pr...

In the supervised learning setting termed Multiple-Instance Learning (MIL), the examples are bags of instances, and the bag label is a function of the labels of its instances. Typically, this function is the Boolean OR. The learner observes a sample of bags and the bag labels, but not the instance labels that determine the bag labels. The learner is then required to emit a classification rule f...

BACKGROUND Miltefosine (MIL), the only oral drug for visceral leishmaniasis (VL), is currently the first-line therapy in the VL elimination program of the Indian subcontinent. Given the paucity of anti-VL drugs and the looming threat of resistance, there is an obvious need for close monitoring of clinical efficacy of MIL. METHODS In a cohort study of 120 VL patients treated with MIL in Nepal,...

A hydrophilic, hydrostable porous metal organic framework (MOF) material-MIL-101 (Cr) was successfully doped into the dense selective polyamide (PA) layer on the polysulfone (PS) ultrafiltration (UF) support to prepare a new thin film nanocomposite (TFN) membrane for water desalination. The TFN-MIL-101 (Cr) membranes were characterized by SEM, AFM, XPS, wettability measurement and reverse osmos...

A cationic porous framework with mobile anions (MIL-101(Cr)-Cl) was easily and successfully synthesized by utilizing the stronger affinity of F- to Al3+ than Cr3+ in the charge-balanced framework of MIL-101(Cr). The structure, morphology and porosity of MIL-101(Cr)-Cl were characterized. The obtained new materials retain the high surface area, good thermostability, and structure topology of MIL...

Multiple Instance Learning (MIL) has been widely used in various applications including image classification. However, existing MIL methods do not explicitly address the multi-target problem where the distributions of positive instances are likely to be multi-modal. This strongly limits the performance of multiple instance learning in many real world applications. To address this problem, this ...