A stable matching model with an entrance criterion applied to the assignment of students to dormitories at the technion

This paper reports on a case study of the assignment of students to dormitories at the Technion–Israel Institute of Technology. Two criteria are used in considering applicants. The first criterion, determined by personal socio-economic characteristics, is used to make decisions about the privilege of getting on-campus housing. The second criterion is used for the actual assignment of the students who were found eligible for on-campus housing to specific dormitories—here the priority is determined by academic seniority and academic excellence. A modification of the classic stable matching model that allows for an “entrance criterion” is developed and analyzed. In particular, a new concept of quasi-stable outcomes is introduced and an algorithm that produces such an outcome with desirable properties is described. The algorithm was implemented successfully for the assignment of students to dormitories at the Technion toward the 2004/2005 academic year.     

Incentive compatibility for the stable matching model with an entrance criterion

A case study of matching students with dormitory-groups at the Technion lead recently to the study of a variant of the stable matching model with a “qualifying criterion” for the inclusion of a student among those getting an assignment. A notion of stability was introduced for the model and a (student-courting) algorithm which finds a matching that satisfied this criterion and has desired properties was described. Here, we show that students cannot benefit from misrepresenting preferences in an extension of the model that allows dormitory-groups to have different preferences over students and allows these preferences to be incomplete.     

Sulfonium cations as versatile strongly π-acidic ligands

More than a century old, sulfonium cations are still intriguing species in the landscape of organic chemistry. On one hand they have found broad applications in organic synthesis and materials science, but on the other hand, while isoelectronic to the ubiquitous tertiary phosphine ligands, their own coordination chemistry has been neglected for the last three decades. Here we report the synthesis and full characterization of the first Rh(i) and Pt(ii) complexes of sulfonium. Moreover, for the first time, coordination of an aromatic sulfonium has been established. A thorough computational analysis of the exceptionally short S–Rh bonds obtained attests to the strongly π-accepting nature of sulfonium cations and places them among the best π-acceptor ligands available today. Our calculations also show that embedding within a pincer framework enhances their π-acidity even further. Therefore, in addition to the stability and modularity that these frameworks offer, our pincer complexes might open the way for sulfonium cations to become powerful tools in π-acid catalysis.

Back to the scene: while isolobal to the ubiquitous tertiary phosphines, sulfonium cations as ligands were neglected for decades. This work revives the coordination chemistry of these species showing their potential as ligands for π-acid catalysis.     

Using a VOM model for reconstructing potential coding regions in EST sequences

This paper presents a method for annotating coding and noncoding DNA regions by using variable order Markov (VOM) models. A main advantage in using VOM models is that their order may vary for different sequences, depending on the sequences’ statistics. As a result, VOM models are more flexible with respect to model parameterization and can be trained on relatively short sequences and on low-quality datasets, such as expressed sequence tags (ESTs). The paper presents a modified VOM model for detecting and correcting insertion and deletion sequencing errors that are commonly found in ESTs. In a series of experiments the proposed method is found to be robust to random errors in these sequences.     

Detection of Static and Mobile Targets by an Autonomous Agent with Deep Q-Learning Abilities

This paper addresses the problem of detecting multiple static and mobile targets by an autonomous mobile agent acting under uncertainty. It is assumed that the agent is able to detect targets at different distances and that the detection includes errors of the first and second types. The goal of the agent is to plan and follow a trajectory that results in the detection of the targets in a minimal time. The suggested solution implements the approach of deep Q-learning applied to maximize the cumulative information gain regarding the targets’ locations and minimize the trajectory length on the map with a predefined detection probability. The Q-learning process is based on a neural network that receives the agent location and current probability map and results in the preferred move of the agent. The presented procedure is compared with the previously developed techniques of sequential decision making, and it is demonstrated that the suggested novel algorithm strongly outperforms the existing methods.