One of the key differences between the learning mechanism of humans and Artificial Neural Networks (ANNs) is the ability of humans to learn one task at a time. ANNs, on the other hand, can only learn multiple tasks simultaneously. Any attempts at learning new tasks incrementally cause them to completely forget about previous tasks. This lack of ability to learn incrementally, called Catastrophic Forgetting, is considered a major hurdle in building a true AI system. In this paper, our goal is to isolate the truly effective existing ideas for incremental learning from those that only work under certain conditions. To this end, we first thoroughly analyze the current state of the art (iCaRL) method for incremental learning and demonstrate that the good performance of the system is not because of the reasons presented in the existing literature. We conclude that the success of iCaRL is primarily due to knowledge distillation and recognize a key limitation of knowledge distillation, i.e, it often leads to bias in classifiers. Finally, we propose a dynamic threshold moving algorithm that is able to successfully remove this bias. We demonstrate the effectiveness of our algorithm on CIFAR100 and MNIST datasets showing near-optimal results. Our implementation is available at : https://github.com/Khurramjaved96/ incremental-learning.

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Accurate models of patient survival probabilities provide important information to clinicians prescribing care for life-threatening and terminal ailments. A recently developed class of modelsknown as individual survival distributions (ISDs) -produces patient-specific survival functions that offer greater descriptive power of patient outcomes than was previously possible. Unfortunately, at the time of writing, ISD models almost universally lack uncertainty quantification. In this paper we demonstrate that an existing method for estimating simultaneous prediction intervals from samples can easily be adapted for patient-specific survival curve analysis and yields accurate results. Furthermore, we introduce both a modification to the existing method and a novel method for estimating simultaneous prediction intervals and show that they offer competitive performance. It is worth emphasizing that these methods are not limited to survival analysis and can be applied in any context in which sampling the distribution of interest is tractable. Code is available at

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The IEEE 802.11 based wireless LAN (WLAN) is most widely used for the real time communication such as Voice over IP (VOIP) video conferencing and etc. As the wireless internet access is increasing day by day by the combination of different small regions which are served by one operator or one Access Terminal. So there is the problem of mobility that a Mobile Node (MN) may move from one network to other network, also there is the problem of connectivity. To maintain the connectivity of a established call between two different networks using mobility management we proposed an efficient algorithm which maintain the real time connection as well as preventing the data loss during transition. We experiment our proposed algorithm by using a redundant buffer which store the data in transition mode and vanish the alternate data bits when the connection is establish with the network. The alternate data bit is also send for the same time as the time taken by the redundant buffer to disappear the data. This process saves our data in the transition time.

Design of Voltage-Controlled Oscillator (VCO) inductors is a laborious and time-consuming task that is conventionally done manually by human experts. In this paper, we propose a framework for automating the design of VCO inductors, using Reinforcement Learning (RL). We formulate the problem as a sequential procedure, where wire segments are drawn one after another, until a complete inductor is created. We then employ an RL agent to learn to draw inductors that meet certain target specifications. In light of the need to tweak the target specifications throughout the circuit design cycle, we also develop a variant in which the agent can learn to quickly adapt to draw new inductors for moderately different target specifications. Our empirical results show that the proposed framework is successful at automatically generating VCO inductors that meet or exceed the target specification.

A new high-frequency power supply rejection (PSR) improvement technique is presented for a low-dropout (LDO) regulator. The proposed technique utilizes a negative capacitance at the gate of the power transistor to enhance the PSR at high frequencies by neutralizing the effect of parasitic capacitances. The simulation results show that the LDO is able to achieve a PSR of −67.9 dB at 10 MHz.

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