On the exhaustiveness of truncation and dropping strategies in many-to-many matching markets

We consider two–sided many–to–many matching markets in which each worker may work for multiple firms and each firm may hire multiple workers. We study individual and group manipulations in centralized markets that employ (pairwise) stable mechanisms and that require participants to submit rank order lists of agents on the other side of the market. We are interested in simple preference manipulations that have been reported and studied in empirical and theoretical work: truncation strategies, which are the lists obtained by removing a tail of least preferred partners from a preference list, and the more general dropping strategies, which are the lists obtained by only removing partners from a preference list (i.e., no reshuffling). We study when truncation / dropping strategies are exhaustive for a group of agents on the same side of the market, i.e., when each match resulting from preference manipulations can be replicated or improved upon by some truncation / dropping strategies. We prove that for each stable mechanism, truncation strategies are exhaustive for each agent with quota 1 (Theorem 1). We show that this result cannot be extended neither to group manipulations (even when all quotas equal 1 – Example 1), nor to individual manipulations when the agent’s quota is larger than 1 (even when all other agents’ quotas equal 1 – Example 2). Finally, we prove that for each stable mechanism, dropping strategies are exhaustive for each group of agents on the same side of the market (Theorem 2), i.e., independently of the quotas.

Reverse engineering in many-body quantum physics: Correspondence between many-body systems and effective single-particle equations

The mapping, exact or approximate, of a many-body problem onto an effective single-body problem is one of the most widely used conceptual and computational tools of physics. Here, we propose and investigate the inverse map of effective approximate single-particle equations onto the corresponding many-particle system. This approach allows us to understand which interacting system a given single-particle approximation is actually describing, and how far this is from the original physical many-body system. We illustrate the resulting reverse engineering process by means of the Kohn-Sham equations of density-functional theory. In this application, our procedure sheds light on the nonlocality of the density-potential mapping of density-functional theory, and on the self-interaction error inherent in approximate density functionals.

Convex optimization framework for intermediate deadline assignment in soft and hard real-time distributed systems

It is generally challenging to determine end-to-end delays of applications for maximizing the aggregate system utility subject to timing constraints. Many practical approaches suggest the use of intermediate deadline of tasks in order to control and upper-bound their end-to-end delays. This paper proposes a unified framework for different time-sensitive, global optimization problems, and solves them in a distributed manner using Lagrangian duality. The framework uses global viewpoints to assign intermediate deadlines, taking resource contention among tasks into consideration. For soft real-time tasks, the proposed framework effectively addresses the deadline assignment problem while maximizing the aggregate quality of service. For hard real-time tasks, we show that existing heuristic solutions to the deadline assignment problem can be incorporated into the proposed framework, enriching their mathematical interpretation.

Lack of Discipline in Cape Verdean Secondary Schools: The Need to Better Define the Problem and Develop Strategies to Prevent or Manage the Situation

This research paper has been written with the intention to discuss the problem of discipline in Cape Verdean secondary schools. While many of us discuss the effects that student misbehavior has on the student misbehavior has on the student, school and society as a whole, very few of us seek solutions which would impact on the prevention and management of this problem that each day becomes more complicated and harder to handle. This paper will discuss the need to better define discipline at the school level; identify the causes and factors that aggravate the problem, in addition, to provide what I hope to be useful strategies to better manage the problem as we make the effort to reclaim our schools and better educate our students. My research included surveys completed by teachers and student alike as they baffled over the question: what is discipline and how can we better manage discipline problems at our schools?

Cost Models for Routing and Wavelength Assignment in Wavelength-Division Multiplexing Optical Net

WDM (Wavelength-Division Multiplexing) optiset verkot on tällä hetkellä suosituin tapa isojen määrän tietojen siirtämiseen. Jokaiselle liittymälle määrätään reitin ja aallonpituus joka linkin varten. Tarvittavan reitin ja aallon pituuden löytäminen kutsutaan RWA-ongelmaksi. Tämän työn kuvaa mahdollisia kustannuksen mallein ratkaisuja RWA-ongelmaan. Olemassa on paljon erilaisia optimoinnin tavoitteita. Edellä mainittuja kustannuksen malleja perustuu näillä tavoitteilla. Kustannuksen malleja antavat tehokkaita ratkaisuja ja algoritmeja. The multicommodity malli on käsitelty tässä työssä perusteena RV/A-kustannuksen mallille. Myöskin OB käsitelty heuristisia menetelmiä RWA-ongelman ratkaisuun. Työn loppuosassa käsitellään toteutuksia muutamalle mallille ja erilaisia mahdollisuuksia kustannuksen mallein parantamiseen.

Problem of assignment cells to switches in a cellular mobile network via beam search method

The problem of assigning cells to switches in a cellular mobile network is an NP-hard optimization problem. So, real size mobile networks could not be solved by using exact methods. The alternative is the use of the heuristic methods, because they allow us to find a good quality solution in a quite satisfactory computational time. This paper proposes a Beam Search method to solve the problem of assignment cell in cellular mobile networks. Some modifications in this algorithm are also presented, which allows its parallel application. Computational results obtained from several tests confirm the effectiveness of this approach to provide good solutions for medium- and large-sized cellular mobile network.

Spatio-temporal credit assignment in population learning

Learning by reinforcement is important in shaping animal behavior, and in particular in behavioral decision making. Such decision making is likely to involve the integration of many synaptic events in space and time. However, using a single reinforcement signal to modulate synaptic plasticity, as suggested in classical reinforcement learning algorithms, a twofold problem arises. Different synapses will have contributed differently to the behavioral decision, and even for one and the same synapse, releases at different times may have had different effects. Here we present a plasticity rule which solves this spatio-temporal credit assignment problem in a population of spiking neurons. The learning rule is spike-time dependent and maximizes the expected reward by following its stochastic gradient. Synaptic plasticity is modulated not only by the reward, but also by a population feedback signal. While this additional signal solves the spatial component of the problem, the temporal one is solved by means of synaptic eligibility traces. In contrast to temporal difference (TD) based approaches to reinforcement learning, our rule is explicit with regard to the assumed biophysical mechanisms. Neurotransmitter concentrations determine plasticity and learning occurs fully online. Further, it works even if the task to be learned is non-Markovian, i.e. when reinforcement is not determined by the current state of the system but may also depend on past events. The performance of the model is assessed by studying three non-Markovian tasks. In the first task, the reward is delayed beyond the last action with non-related stimuli and actions appearing in between. The second task involves an action sequence which is itself extended in time and reward is only delivered at the last action, as it is the case in any type of board-game. The third task is the inspection game that has been studied in neuroeconomics, where an inspector tries to prevent a worker from shirking. Applying our algorithm to this game yields a learning behavior which is consistent with behavioral data from humans and monkeys, revealing themselves properties of a mixed Nash equilibrium. The examples show that our neuronal implementation of reward based learning copes with delayed and stochastic reward delivery, and also with the learning of mixed strategies in two-opponent games.

Spatio-temporal credit assignment in population learning

Learning by reinforcement is important in shaping animal behavior. But behavioral decision making is likely to involve the integration of many synaptic events in space and time. So in using a single reinforcement signal to modulate synaptic plasticity a twofold problem arises. Different synapses will have contributed differently to the behavioral decision and, even for one and the same synapse, releases at different times may have had different effects. Here we present a plasticity rule which solves this spatio-temporal credit assignment problem in a population of spiking neurons. The learning rule is spike time dependent and maximizes the expected reward by following its stochastic gradient. Synaptic plasticity is modulated not only by the reward but by a population feedback signal as well. While this additional signal solves the spatial component of the problem, the temporal one is solved by means of synaptic eligibility traces. In contrast to temporal difference based approaches to reinforcement learning, our rule is explicit with regard to the assumed biophysical mechanisms. Neurotransmitter concentrations determine plasticity and learning occurs fully online. Further, it works even if the task to be learned is non-Markovian, i.e. when reinforcement is not determined by the current state of the system but may also depend on past events. The performance of the model is assessed by studying three non-Markovian tasks. In the first task the reward is delayed beyond the last action with non-related stimuli and actions appearing in between. The second one involves an action sequence which is itself extended in time and reward is only delivered at the last action, as is the case in any type of board-game. The third is the inspection game that has been studied in neuroeconomics. It only has a mixed Nash equilibrium and exemplifies that the model also copes with stochastic reward delivery and the learning of mixed strategies.

Spatio-Temporal Credit Assignment in Neuronal Population Learning

n learning from trial and error, animals need to relate behavioral decisions to environmental reinforcement even though it may be difficult to assign credit to a particular decision when outcomes are uncertain or subject to delays. When considering the biophysical basis of learning, the credit-assignment problem is compounded because the behavioral decisions themselves result from the spatio-temporal aggregation of many synaptic releases. We present a model of plasticity induction for reinforcement learning in a population of leaky integrate and fire neurons which is based on a cascade of synaptic memory traces. Each synaptic cascade correlates presynaptic input first with postsynaptic events, next with the behavioral decisions and finally with external reinforcement. For operant conditioning, learning succeeds even when reinforcement is delivered with a delay so large that temporal contiguity between decision and pertinent reward is lost due to intervening decisions which are themselves subject to delayed reinforcement. This shows that the model provides a viable mechanism for temporal credit assignment. Further, learning speeds up with increasing population size, so the plasticity cascade simultaneously addresses the spatial problem of assigning credit to synapses in different population neurons. Simulations on other tasks, such as sequential decision making, serve to contrast the performance of the proposed scheme to that of temporal difference-based learning. We argue that, due to their comparative robustness, synaptic plasticity cascades are attractive basic models of reinforcement learning in the brain.

Many-body quantum dynamics of polarization squeezing in optical fibers

We report new experiments that test quantum dynamical predictions of polarization squeezing for ultrashort photonic pulses in a birefringent fiber, including all relevant dissipative effects. This exponentially complex many-body problem is solved by means of a stochastic phase-space method. The squeezing is calculated and compared to experimental data, resulting in excellent quantitative agreement. From the simulations, we identify the physical limits to quantum noise reduction in optical fibers. The research represents a significant experimental test of first-principles time-domain quantum dynamics in a one-dimensional interacting Bose gas coupled to dissipative reservoirs.

Many Facet, Common Latent Trait Polytomous IRT Model and EM Algorithm

2000 Mathematics Subject Classification: 91E45.

Large change in the predicted number of small halos due to a small amplitude oscillating inflaton potential

A smooth inflaton potential is generally assumed when calculating the primordial power spectrum, implicitly assuming that a very small oscillation in the inflaton potential creates a negligible change in the predicted halo mass function. We show that this is not true. We find that a small oscillating perturbation in the inflaton potential in the slow-roll regime can alter significantly the predicted number of small halos. A class of models derived from supergravity theories gives rise to inflaton potentials with a large number of steps and many trans-Planckian effects may generate oscillations in the primordial power spectrum. The potentials we study are the simple quadratic (chaotic inflation) potential with superimposed small oscillations for small field values. Without leaving the slow-roll regime, we find that for a wide choice of parameters, the predicted number of halos change appreciably. For the oscillations beginning in the 10(7)-10(8) M(circle dot) range, for example, we find that only a 5% change in the amplitude of the chaotic potential causes a 50% suppression of the number of halos for masses between 10(7)-10(8) M(circle dot) and an increase in the number of halos for masses <10(6) M(circle dot) by factors similar to 15-50. We suggest that this might be a solution to the problem of the lack of observed dwarf galaxies in the range 10(7)-10(8) M(circle dot). This might also be a solution to the reionization problem where a very large number of Population III stars in low mass halos are required.

Multidimensional aspects related to shiftworkers' health and well-being

The impact of shift and night work on health shows a high inter- and intra-individual variability, both in terms of kind of troubles and temporal occurrence, related to various intervening factors dealing with individual characteristics, lifestyles, work demands, company organisation, family relations and social conditions. The way we define "health" and "well-being" can significantly influence appraisals, outcomes and interventions. As the goal is the optimisation of shiftworkers' health, it is necessary to go beyond the health protection and to act for health promotion. In this perspective, not only people related to medical sciences, but many other actors (ergonomists, psychologists, sociologists, educators, legislators), as well as shiftworkers themselves. Many models have been proposed aimed at describing the intervening variables mediating and/or moderating the effects; they try to define the interactions and the pathways connecting risk factors and outcomes through several human dimensions, which refer to physiology, psychology, pathology, sociology, ergonomics, economics, politics, and ethics. So, different criteria can be used to evaluate shiftworkers' health and well-being, starting from biological rhythms and ending in severe health disorders, passing through psychological strain, job dissatisfaction, family perturbation and social dis-adaptation, both in the short- and long-term. Consequently, it appears rather arbitrary to focus the problem of shiftworkers' health and tolerance only on specific aspects (e.g. individual characteristics), but a systemic approach appears more appropriate, able to match as many variables as possible, and aimed at defining which factors are the most relevant for those specific work and social conditions. This can support a more effective and profitable (for individuals, companies, and society) adoption of preventive and compensative measures, that must refer more to "countervalues" rather than to "counterweights".

A molecular contribution to the controversial taxonomical status of some freshwater snails (Caenogastropoda: Rissooidea, Cochliopidae) from the Central Andes desert to Patagonia

For over 40 years malacologists have been discussing the taxonomical status of Heleobia species, an enigmatic genus from Cochliopidae family (Caenogastropoda: Rissooidea). As with other rissooidean families, the considerable character convergence and the paucity of anatomical synapomorphies has proved to be a problem in resolving cochliopid phylogenetic relations and establishing the validity of several nominal cochliopid species. Here we present a molecular contribution to solve the taxonomical status of one of the most abundant Southern South America cochliopid genera which has many endemic species. We report molecular evidence that supports three of the four Heleobia groups described for this region, the "australis", "parchappii" and "piscium" groups. The fourth, the "hatcheri" group, belongs not to Heleobia but to a different genus which itself should not be considered as part of the family Cochliopidae but closely related to genus Potamolithus Pilsbry & Rush, 1896.

Using robots to understand social behaviour.

A major challenge in studying social behaviour stems from the need to disentangle the behaviour of each individual from the resulting collective. One way to overcome this problem is to construct a model of the behaviour of an individual, and observe whether combining many such individuals leads to the predicted outcome. This can be achieved by using robots. In this review we discuss the strengths and weaknesses of such an approach for studies of social behaviour. We find that robots-whether studied in groups of simulated or physical robots, or used to infiltrate and manipulate groups of living organisms-have important advantages over conventional individual-based models and have contributed greatly to the study of social behaviour. In particular, robots have increased our understanding of self-organization and the evolution of cooperative behaviour and communication. However, the resulting findings have not had the desired impact on the biological community. We suggest reasons for why this may be the case, and how the benefits of using robots can be maximized in future research on social behaviour.