Single-machine scheduling with a time-dependent learning effect

Author name used in this publication: J.-B. WangAuthor name used in this publication: C. T. NgAuthor name used in this publication: T. C. E. Cheng2007-2008 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

Face-voice association towards multimodal-based authentication using modulated spike-time dependent learning

We propose a reward based learning to associate face and voice stimuli. In particular, we implement learning in a spiking neural network paradigm using modulated spike-time dependent plasticity (STDP).The face and voice stimuli are paired with a temporal delay, and the network is trained to associate the paired face-voice with a target response.The learning rule is dependent on a reward policy in which the network is given a positive reward for a correct response to a face-voice stimulus pair, or the network receives a negative reward for an incorrect response. Despite a stochastic environment, the learning result of real images and sound indicates a good performance with 77.33% accuracy.The result demonstrates that a machine can be trained to associate a pair of biometric inputs to a target response

Dynamically generated cyclic dominance in spatial prisoner's dilemma games

We have studied the impact of time-dependent learning capacities of players in the framework of spatial prisoner's dilemma game. In our model, this capacity of players may decrease or increase in time after strategy adoption according to a step-like function. We investigated both possibilities separately and observed significantly different mechanisms that form the stationary pattern of the system. The time decreasing learning activity helps cooperator domains to recover the possible intrude of defectors hence supports cooperation. In the other case the temporary restrained learning activity generates a cyclic dominance between defector and cooperator strategies, which helps to maintain the diversity of strategies via propagating waves. The results are robust and remain valid by changing payoff values, interaction graphs or functions characterizing time-dependence of learning activity. Our observations suggest that dynamically generated mechanisms may offer alternative ways to keep cooperators alive even at very larger temptation to defect.Comment: 7 pages, 6 figures, accepted for publication in Physical Review

Single machine scheduling with exponential time-dependent learning effect and past-sequence-dependent setup times

AbstractIn this paper we consider the single machine scheduling problem with exponential time-dependent learning effect and past-sequence-dependent (p-s-d) setup times. By the exponential time-dependent learning effect, we mean that the processing time of a job is defined by an exponent function of the total normal processing time of the already processed jobs. The setup times are proportional to the length of the already processed jobs. We consider the following objective functions: the makespan, the total completion time, the sum of the quadratic job completion times, the total weighted completion time and the maximum lateness. We show that the makespan minimization problem, the total completion time minimization problem and the sum of the quadratic job completion times minimization problem can be solved by the smallest (normal) processing time first (SPT) rule, respectively. We also show that the total weighted completion time minimization problem and the maximum lateness minimization problem can be solved in polynomial time under certain conditions

Minimizing Total Weighted Completion Time on Single Machine with Past-Sequence-Dependent Setup Times and Exponential Time-Dependent and Position-Dependent Learning Effects

This paper addresses a single-machine problem in which the past-sequence-dependent (p-s-d) setup times and exponential time-dependent and position-dependent learning effects are considered. By the exponential time-dependent learning effect, it means that the processing time of a job is defined by an exponent function of the total actual processing time of the already processed jobs. The setup times are proportional to the length of the already processed jobs. The aim is to minimize the total weighted completion time, this is an NP-hard problem. Under certain conditions, it is shown that the classical WSPT rule is optimal for the problem

Amount of Time-Dependent Learning: Learning Difficulty (The Example of Problem Solving with Fractional)

Bu çalışmanın amacı altıncı sınıf öğrencilerinin “kesirlerle işlem yapmayı gerektiren problemleri çözer” kazanımına göre zamana bağlı öğrenme güçlüklerini belirlemektir. Tarama modelinin benimsendiği çalışma altıncı sınıf öğrenim düzeyinde 80 kız ve 68 erkek olmak üzere toplam 148 öğrenci ile yürütülmüştür. Veri toplama aracı olarak açık uçlu 12 sorudan oluşan ölçme aracı kullanılmıştır. Elde edilen bulgular, 148 kişilik öğrenme grubunun tam öğrenme seviyesinden 0.013’lük miktar gerisinde kaldığını göstermektedir. Tam öğrenme miktarı için belirlenen 0.987 alt sınır seviyesine K1 öğrencisi 1.8, K58 öğrencisi 2.49 ve K148 öğrencisi ise 11.17 ders saati zaman diliminde ulaşmıştır. Tam öğrenme miktarına en yakın değer olan 0.999 öğrenme miktarına öğrenme grubu 6.4, K1 öğrencisi 3.53, K58 öğrencisi 5.81 ve K148 öğrencisi 37.70 ders saati zaman diliminde ulaşmıştır. Bunun yanı sıra elde edilen veriler, zaman ile öğrenme miktarına ait eğrinin altında kalan alan arttığında öğrencilerin daha fazla öğrenme güçlüğü yaşadığını göstermiştir. Çalışma sonucunda, bir öğretim planına sahip tüm eğitim kademelerinde tam öğrenme düzeyine en yakın ders saati sürelerinin belirlenebileceği öneri olarak sunulmuştur.The purpose of this study is a time dependent analysis for learning difficulties of sixth grade students in solving problems that require calculating of swith fractions. The study, adopting the screening model, consisted of a total of 148 students, including 80 female and 68 male students at the sixth grade. Data was collected using an assessment tool consisting of 12 open-ended questions. The findings show that the learning groups of 148 students were behind the value closest to the full learning level by a score of 0.013. K1 student reached the lower limit of 0.987 specified for the full learning level in a period of 1.8 course hours, K58 student reached this limit in 2.49 course hours and K148 student reached this limit in 11.17 course hours. The learning amount of 0.999, which is the closest value to the full learning level, was reached by the learning group in a period of 6.4, K1 student in 3.53, K58 student in 5.81 and the K148 students in 37.70 course hours. Moreover, as there was a decrease in the area under the curve belonging to the learning level-time graphic, there was also a decrement in the number of learning difficulties that the learning group encounters before. As a result of the study, it was recommended that it is possible to determine the closest course periods for the full learning level for each of the gains found in all levels of education and all curricula

The early bee catches the flower - circadian rhythmicity influences learning performance in honey bees, Apis mellifera

Circadian rhythmicity plays an important role for many aspects of honey bees’ lives. However, the question whether it also affects learning and memory remained unanswered. To address this question, we studied the effect of circadian timing on olfactory learning and memory in honey bees Apis mellifera using the olfactory conditioning of the proboscis extension reflex paradigm. Bees were differentially conditioned to odours and tested for their odour learning at four different “Zeitgeber” time points. We show that learning behaviour is influenced by circadian timing. Honey bees perform best in the morning compared to the other times of day. Additionally, we found influences of the light condition bees were trained at on the olfactory learning. This circadian-mediated learning is independent from feeding times bees were entrained to, indicating an inherited and not acquired mechanism. We hypothesise that a co-evolutionary mechanism between the honey bee as a pollinator and plants might be the driving force for the evolution of the time-dependent learning abilities of bees