Market Segmentation Trees

We seek to provide an interpretable framework for segmenting users in a population for personalized decision-making. The standard approach is to perform market segmentation by clustering users according to similarities in their contextual features, after which a "response model" is fit to each segment to model how users respond to personalized decisions. However, this methodology is not ideal for personalization, since two users could in theory have similar features but different response behaviors. We propose a general methodology, Market Segmentation Trees (MSTs), for learning interpretable market segmentations explicitly driven by identifying differences in user response patterns. To demonstrate the versatility of our methodology, we design two new, specialized MST algorithms: (i) Choice Model Trees (CMTs) which can be used to predict a user's choice amongst multiple options, and (ii) Isotonic Regression Trees (IRTs) which can be used to solve the bid landscape forecasting problem. We provide a customizable, open-source code base for training MSTs in Python which employs several strategies for scalability, including parallel processing and warm starts. We provide a theoretical analysis of the asymptotic running time of our training method validating its computational tractability on large datasets. We assess the practical performance of MSTs on several synthetic and real world datasets, showing our method reliably finds market segmentations which accurately model response behavior. Further, when applying MSTs to historical bidding data from a leading demand-side platform (DSP), we show that MSTs consistently achieve a 5-29% improvement in bid landscape forecasting accuracy over the DSP's current model. Our findings indicate that integrating market segmentation with response modeling consistently leads to improvements in response prediction accuracy, thereby aiding personalization

Synthesis and characterization of some novel 1,2,4-triazoles, 1,3,4-thiadiazoles and Schiff\u27s bases incorporating imidazole moiety as potential antimicrobial agents

(1,4,5-Triphenylimidazol-2-yl-thio)butyric acid hydrazide (3) was obtained via alkylation of 1,4,5-triphenylimidazol-2-thiol (1) with ethylbromobutyrate, followed by addition of hydrazine hydrate. Treatment of acid hydrazide 3 with carbon disulfide in an ethanolic potassium hydroxide solution gave the intermediate potassium dithiocarbazinate salt, which was cyclized to 4-amino-5-[(1,4,5-triphenylimidazol-2-yl)thiopropyl]-2H-1,2,4-triazole-3-thione (4) in the presence of hydrazine hydrate. Condensation of compound 3 with alkyl/arylisothiocyanate afforded the corresponding 1-[4-(1,4,5-triphenylimidazol-2-ylthio)butanoyl]-4-alkyl/arylthiosemicarbazides (5-7), which upon refluxing with sodium hydroxide, yielded the corresponding 1,2,4-triazole-3-thiols (8-10). Under acidic conditions, compounds 4-6 were converted to aminothiadiazoles 11-13. Moreover, the series of Schiff bases 14-18 were synthesized from the condensation of compound 3 with different aromatic aldehydes. The newly synthesized compounds were characterized by IR, 1H NMR, 13C NMR and mass spectral analyses. They were also preliminarily screened for their antimicrobial activity

Characterization of Acute Lymphoblastic Leukemia Subtypes in Moroccan Children

We present the incidence and the immunologic characteristics of acute lymphoblastic leukemia (ALL) subsets in Moroccan children. We studied 279 unselected patients below the age of 18 years with newly diagnosed ALL. Cases were classified according to immunophenotype: 216 (77.42%) precursor B-cell phenotype (pB-cell), mature B-cell in 4 (1.43%), and T-cell in 59 (21.15%) cases. The subclassification using the CD10 antibody revealed 197 cases pB-ALL CD10+ (91.2%) and 9 cases T-ALL CD10+ (19.2%). The age distribution showed a peak in incidence between 3 and 5 years among the pB-cell ALLs subtype. There was a significantly higher frequency of males in the T-ALL subset (M/F ratio: 2.93 : 1) and more females in the T-ALL CD10+ subset when compared with the T-ALL CD10– subset. All tested pB-cell-lineage ALLs expressed CD19, CD79a, and surface CD22, terminal deoxynucleotidyl transferase (TdT) was detectable in 89.9% of cases, and cells in 74.1% of cases express CD34. All tested T-lineage ALL cells have surface CD7 and cytoplasmic CD3 (cCD3) antigens, CD5 was found in 98.2% cases, and 70.5% express TdT. CD1a, surface CD3 (sCD3), and CD4 are detected in more than 80% of cases; this frequency is higher than the 45% generally observed. Myeloid antigens occur more frequently and were expressed in 124 (57.4%) of pB-cell-ALL cases and 20 (33.9%) of T-cell ALL cases. Our results show that the distribution of ALLs in Moroccan children is similar with the general distribution pattern in developed countries except for the high frequency of T-ALL phenotype. The phenotypic profiles of our patients are close to those reported in literature for B-lineage ALLs; for the T-cell ALL subgroup, the blast cells express more CD1a, surface CD3, and CD4 while expressing less TdT. The high frequency of CD1a expression resulted in an excess of the common thymocyte subtype

Epithelial-mesenchymal plasticity determines estrogen receptor positive breast cancer dormancy and epithelial reconversion drives recurrence

More than 70% of human breast cancers (BCs) are estrogen receptor α-positive (ER+). A clinical challenge of ER+ BC is that they can recur decades after initial treatments. Mechanisms governing latent disease remain elusive due to lack of adequate in vivo models. We compare intraductal xenografts of ER+ and triple-negative (TN) BC cells and demonstrate that disseminated TNBC cells proliferate similarly as TNBC cells at the primary site whereas disseminated ER+ BC cells proliferate slower, they decrease CDH1 and increase ZEB1,2 expressions, and exhibit characteristics of epithelial-mesenchymal plasticity (EMP) and dormancy. Forced E-cadherin expression overcomes ER+ BC dormancy. Cytokine signalings are enriched in more active versus inactive disseminated tumour cells, suggesting microenvironmental triggers for awakening. We conclude that intraductal xenografts model ER + BC dormancy and reveal that EMP is essential for the generation of a dormant cell state and that targeting exit from EMP has therapeutic potential

From predictive choice models to near-optimal algorithms

Thesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2017.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (pages 251-256).Finding optimal product offerings is a fundamental operational issue in modern retailing, exemplified by the development of recommendation systems and decision support tools. The challenge is that designing an accurate predictive choice model generally comes at the detriment of efficient algorithms, which can prescribe near-optimal decisions. This thesis attempts to resolve this disconnect in the context of assortment and inventory optimization, through theoretical and empirical investigation. First, we tightly characterize the complexity of general nonparametric assortment optimization problems. We reveal connections to maximum independent set and combinatorial pricing problems, allowing to derive strong inapproximability bounds. We devise simple algorithms that achieve essentially best-possible factors with respect to the price ratio, size of customers' consideration sets, etc. Second, we develop a novel tractable approach to choice modeling, in the vein of nonparametric models, by leveraging documented assumptions on the customers' consider-then-choose behavior. We show that the assortment optimization problem can be cast as a dynamic program, that exploits the properties of a bi-partite graph representation to perform a state space collapse. Surprisingly, this exact algorithm is provably and practically efficient under common consider-then-choose assumptions. On the estimation front, we show that a critical step of standard nonparametric estimation methods (rank aggregation) can be solved in polynomial time in settings of interest, contrary to general nonparametric models. Predictive experiments on a large purchase panel dataset show significant improvements against common benchmarks. Third, we turn our attention to joint assortment optimization and inventory management problems under dynamic customer choice substitution. Prior to our work, little was known about these optimization models, which are intractable using modern discrete optimization solvers. Using probabilistic analysis, we unravel hidden structural properties, such as weak notions of submodularity. Building on these findings, we develop efficient and yet conceptually-simple approximation algorithms for common parametric and nonparametric choice models. Among notable results, we provide best-possible approximations under general nonparametric choice models (up to lower-order terms), and develop the first constant-factor approximation under the popular Multinomial Logit model. In synthetic experiments vis-a-vis existing heuristics, our approach is an order of magnitude faster in several cases and increases revenue by 6% to 16%.by Ali Aouad.Ph. D

Understanding the business process of reactive maintenance projects

This paper presents the analysis of the existing business process in reactive maintenance projects including the information and communication technology that supports the process. All information reported in this research is mainly primary data gathered from interviews with the parties that are involved in the process. The analysis consists of identification of the parties involved, understanding the business process, understanding the information and communication technology used in the process and identification of problems within the process. The research reveals several existing deficiencies with RM projects which require some measures of improvement i.e. poor communication between different parties; lack of knowledge sharing; and poor quality of information, which often lead to longer time taken to fix a problem and incurs higher cost

Approximation Algorithms for Dynamic Assortment Optimization Models

© 2018 INFORMS We consider the single-period joint assortment and inventory planning problem with stochastic demand and dynamic substitution across products, motivated by applications in highly differentiated markets, such as online retailing and airlines. This class of problems is known to be notoriously hard to deal with from a computational standpoint. In fact, prior to the present paper, only a handful of modeling approaches were shown to admit provably good algorithms, at the cost of strong restrictions on customers’ choice outcomes. Our main contribution is to provide the first efficient algorithms with provable performance guarantees for a broad class of dynamic assortment optimization models. Under general rank-based choice models, our approximation algorithm is best possible with respect to the price parameters, up to lower-order terms. In particular, we obtain a constant-factor approximation under horizontal differentiation, where product prices are uniform. In more structured settings, where the customers’ ranking behavior is motivated by price and quality cues, we derive improved guarantees through tailor-made algorithms. In extensive computational experiments, our approach dominates existing heuristics in terms of revenue performance, as well as in terms of speed, given the myopic nature of our methods. From a technical perspective, we introduce a number of novel algorithmic ideas of independent interest, and unravel hidden relations to submodular maximization

Brucella osteomyelitis of the pubic bones

Brucellosis is a worldwide zoonotic disease that represents a serious public health problem in developing countries. Clinical manifestations are diverse and can affect any organ or body system. Osteoarticular disease is the most common localized form and has been reported in up to 80%. Pubic osteomyelitis is a very rare localization for Brucella infection and only 4 cases have been previously published. The clinical manifestations, laboratory and radiologic findings are non-specific. High index of suspicion is required to make an early diagnosis. Herein we report a case of Brucella osteomyelitis of the symphysis pubis with its radiologic features