Forming Maximally Diverse Workgroups: An Empirical Study

This work addresses two related important themes in business and business schools today: expanding diversity in the workplace and the increasing reliance on teams as an organizational structure. The paper describes an approach for creating student work groups where the objective is to maximize within group diversity based upon multiple criteria. This approach is an extension of a heuristic-based multiple-criteria decision support system (MCADSS) developed in earlier work (Weitz and Jelassi [1992]); that system was successfully implemented, and is currently in use, at the European Institute of Business Administration (INSEAD) in Fontainebleau, France. The heuristic has been modified here to incorporate a different set of criteria, and to allow for students placing out of core courses. This paper discusses the modified system, its implementation at the Stern School of Business at New York University (NYU), and an empirical experiment evaluating the performance of the system

Kernel-Based Just-In-Time Learning for Passing Expectation Propagation Messages

We propose an efficient nonparametric strategy for learning a message operator in expectation propagation (EP), which takes as input the set of incoming messages to a factor node, and produces an outgoing message as output. This learned operator replaces the multivariate integral required in classical EP, which may not have an analytic expression. We use kernel-based regression, which is trained on a set of probability distributions representing the incoming messages, and the associated outgoing messages. The kernel approach has two main advantages: first, it is fast, as it is implemented using a novel two-layer random feature representation of the input message distributions; second, it has principled uncertainty estimates, and can be cheaply updated online, meaning it can request and incorporate new training data when it encounters inputs on which it is uncertain. In experiments, our approach is able to solve learning problems where a single message operator is required for multiple, substantially different data sets (logistic regression for a variety of classification problems), where it is essential to accurately assess uncertainty and to efficiently and robustly update the message operator.Comment: accepted to UAI 2015. Correct typos. Add more content to the appendix. Main results unchange

Structural studies on encapsulation of tetrahedral and octahedral anions by a protonated octaaminocryptand cage

Structural aspects of the binding of inorganic anions such as perchlorate, hydrogen sulfate, and hexafluorosilicate with the proton cage of octaaminocryptand L1, N(CH2CH2NHCH2-p-xylyl-CH2NHCH2CH2)3N), are examined thoroughly. Crystallographic results for a hexaprotonated perchlorate complex of L1, [(H6L1)6+(ClO4-)]5(ClO4-)·11H2O·CH3CN (1), an octaprotonated hydrogen sulfate complex of L1, [(H8L1)8+(HSO4-)]7(HSO4-)·3H2O·CH3OH (2) and an octaprotonated fluorosilicate complex of L1,[(H8L1)8+(HSiF6-)]3(SiF62-)·(HSiF6-)·15H2O (3), show encapsulation of one perchlorate, hydrogen sulfate and hexafluorosilicate, respectively inside the cage of L1 in their protonated states. Further, detailed structural analysis on complex 1 reveals that the hexaprotonated L1 encapsulates a perchlorate via two N–H···O and five O–H···O hydrogen bonds from protonated secondary nitrogen atoms of L1 and lattice water molecules, respectively. Encapsulated hydrogen sulfate in complex 2 is “glued” inside the octaprotonated cage of L1 via four N–H···O and six C–H···O hydrogen bonds whereas encapsulated HSiF6− in complex 3 has short contacts via six N–H···F and three C–H···F hydrogen bonds with [H8L1]8+. In the cases of complexes 2 and 3, the cryptand L1 in octaprotonated state shows monotopic encapsulation of the guest and the final conformation of these receptors is spherical in nature compared to the elongated shape of hexaprotonated state of L1 in complex 1

Diabetic Retinopathy Analysis

Diabetic retinopathy is one of the common complications of diabetes. Unfortunately, in many cases the patient is not aware of any symptoms until it is too late for effective treatment. Through analysis of evoked potential response of the retina, the optical nerve, and the optical brain center, a way will be paved for early diagnosis of diabetic retinopathy and prognosis during the treatment process. In this paper, we present an artificial-neural-network-based method to classify diabetic retinopathy subjects according to changes in visual evoked potential spectral components and an anatomically realistic computer model of the human eye under normal and retinopathy conditions in a virtual environment using 3D Max Studio and Windows Movie Maker

Evolution of Spur-Length Diversity in Aquilegia Petals Is Achieved Solely Through Cell-Shape Anisotropy

The role of petal spurs and specialized pollinator interactions has been studied since Darwin. Aquilegia petal spurs exhibit striking size and shape diversity, correlated with specialized pollinators ranging from bees to hawkmoths in a textbook example of adaptive radiation. Despite the evolutionary significance of spur length, remarkably little is known about Aquilegia spur morphogenesis and its evolution. Using experimental measurements, both at tissue and cellular levels, combined with numerical modelling, we have investigated the relative roles of cell divisions and cell shape in determining the morphology of the Aquilegia petal spur. Contrary to decades-old hypotheses implicating a discrete meristematic zone as the driver of spur growth, we find that Aquilegia petal spurs develop via anisotropic cell expansion. Furthermore, changes in cell anisotropy account for 99 per cent of the spur-length variation in the genus, suggesting that the true evolutionary innovation underlying the rapid radiation of Aquilegia was the mechanism of tuning cell shape.Engineering and Applied SciencesOrganismic and Evolutionary Biolog

AFMB-Net: DeepFake Detection Network Using Heart Rate Analysis

With advances in deepfake generating technology, it is getting increasingly difficult to detect deepfakes. Deepfakes can be used for many malpractices such as blackmail, politics, social media, etc. These can lead to widespread misinformation and can be harmful to an individual or an institution’s reputation. It has become important to be able to identify deepfakes effectively, while there exist many machine learning techniques to identify them, these methods are not able to cope up with the rapidly improving GAN technology which is used to generate deepfakes. Our project aims to identify deepfakes successfully using machine learning along with Heart Rate Analysis. The heart rate identified by our model is unique to each individual and cannot be spoofed or imitated by a GAN and is thus susceptible to improving GAN technology. To solve the deepfake detection problem we employ various machine learning models along with heart rate analysis to detect deepfakes

Strategies in Translating the Therapeutic Potentials of Host Defense Peptides

The golden era of antibiotics, heralded by the discovery of penicillin, has long been challenged by the emergence of antimicrobial resistance (AMR). Host defense peptides (HDPs), previously known as antimicrobial peptides, are emerging as a group of promising antimicrobial candidates for combatting AMR due to their rapid and unique antimicrobial action. Decades of research have advanced our understanding of the relationship between the physicochemical properties of HDPs and their underlying antimicrobial and non-antimicrobial functions, including immunomodulatory, anti-biofilm, and wound healing properties. However, the mission of translating novel HDP-derived molecules from bench to bedside has yet to be fully accomplished, primarily attributed to their intricate structure-activity relationship, toxicity, instability in host and microbial environment, lack of correlation between in vitro and in vivo efficacies, and dwindling interest from large pharmaceutical companies. Based on our previous experience and the expanding knowledge gleaned from the literature, this review aims to summarize the novel strategies that have been employed to enhance the antimicrobial efficacy, proteolytic stability, and cell selectivity, which are all crucial factors for bench-to-bedside translation of HDP-based treatment. Strategies such as residues substitution with natural and/or unnatural amino acids, hybridization, L-to-D heterochiral isomerization, C- and N-terminal modification, cyclization, incorporation with nanoparticles, and “smart design” using artificial intelligence technology, will be discussed. We also provide an overview of HDP-based treatment that are currently in the development pipeline

Validating secure and reliable IP/MPLS communications for current differential protection

Current differential protection has stringent real-time communications requirements and it is critical that protection traffic is transmitted securely, i.e., by using appropriate data authentication and encryption methods. This paper demonstrates that real-time encryption of protection traffic in IP/MPLS-based communications networks is possible with negligible impact on performance and system operation. It is also shown how the impact of jitter and asymmetrical delay in real communications networks can be eliminated. These results will provide confidence to power utilities that modern IP/MPLS infrastructure can securely and reliably cater for even the most demanding applications