Fluorescent and photo-oxidizing TimeSTAMP tags track protein fates in light and electron microscopy.

Protein synthesis is highly regulated throughout nervous system development, plasticity and regeneration. However, tracking the distributions of specific new protein species has not been possible in living neurons or at the ultrastructural level. Previously we created TimeSTAMP epitope tags, drug-controlled tags for immunohistochemical detection of specific new proteins synthesized at defined times. Here we extend TimeSTAMP to label new protein copies by fluorescence or photo-oxidation. Live microscopy of a fluorescent TimeSTAMP tag reveals that copies of the synaptic protein PSD95 are synthesized in response to local activation of growth factor and neurotransmitter receptors, and preferentially localize to stimulated synapses in rat neurons. Electron microscopy of a photo-oxidizing TimeSTAMP tag reveals new PSD95 at developing dendritic structures of immature neurons and at synapses in differentiated neurons. These results demonstrate the versatility of the TimeSTAMP approach for visualizing newly synthesized proteins in neurons

Bounded Concurrent Timestamp Systems Using Vector Clocks

Shared registers are basic objects used as communication mediums in asynchronous concurrent computation. A concurrent timestamp system is a higher typed communication object, and has been shown to be a powerful tool to solve many concurrency control problems. It has turned out to be possible to construct such higher typed objects from primitive lower typed ones. The next step is to find efficient constructions. We propose a very efficient wait-free construction of bounded concurrent timestamp systems from 1-writer multireader registers. This finalizes, corrects, and extends, a preliminary bounded multiwriter construction proposed by the second author in 1986. That work partially initiated the current interest in wait-free concurrent objects, and introduced a notion of discrete vector clocks in distributed algorithms.Comment: LaTeX source, 35 pages; To apper in: J. Assoc. Comp. Mac

Cryptanalysis of Yang-Wang-Chang's Password Authentication Scheme with Smart Cards

In 2005, Yang, Wang, and Chang proposed an improved timestamp-based password authentication scheme in an attempt to overcome the flaws of Yang-Shieh_s legendary timestamp-based remote authentication scheme using smart cards. After analyzing the improved scheme proposed by Yang-Wang-Chang, we have found that their scheme is still insecure and vulnerable to four types of forgery attacks. Hence, in this paper, we prove that, their claim that their scheme is intractable is incorrect. Also, we show that even an attack based on Sun et al._s attack could be launched against their scheme which they claimed to resolve with their proposal.Comment: 3 Page

An Improved Timestamp-Based Password Authentication Scheme Using Smart Cards

With the recent proliferation of distributed systems and networking, remote authentication has become a crucial task in many networking applications. Various schemes have been proposed so far for the two-party remote authentication; however, some of them have been proved to be insecure. In this paper, we propose an efficient timestamp-based password authentication scheme using smart cards. We show various types of forgery attacks against a previously proposed timestamp-based password authentication scheme and improve that scheme to ensure robust security for the remote authentication process, keeping all the advantages that were present in that scheme. Our scheme successfully defends the attacks that could be launched against other related previous schemes. We present a detailed cryptanalysis of previously proposed Shen et. al scheme and an analysis of the improved scheme to show its improvements and efficiency.Comment: 6 page

Action Recognition from Single Timestamp Supervision in Untrimmed Videos

Recognising actions in videos relies on labelled supervision during training, typically the start and end times of each action instance. This supervision is not only subjective, but also expensive to acquire. Weak video-level supervision has been successfully exploited for recognition in untrimmed videos, however it is challenged when the number of different actions in training videos increases. We propose a method that is supervised by single timestamps located around each action instance, in untrimmed videos. We replace expensive action bounds with sampling distributions initialised from these timestamps. We then use the classifier's response to iteratively update the sampling distributions. We demonstrate that these distributions converge to the location and extent of discriminative action segments. We evaluate our method on three datasets for fine-grained recognition, with increasing number of different actions per video, and show that single timestamps offer a reasonable compromise between recognition performance and labelling effort, performing comparably to full temporal supervision. Our update method improves top-1 test accuracy by up to 5.4%. across the evaluated datasets.Comment: CVPR 201

Modeling The Intensity Function Of Point Process Via Recurrent Neural Networks

Event sequence, asynchronously generated with random timestamp, is ubiquitous among applications. The precise and arbitrary timestamp can carry important clues about the underlying dynamics, and has lent the event data fundamentally different from the time-series whereby series is indexed with fixed and equal time interval. One expressive mathematical tool for modeling event is point process. The intensity functions of many point processes involve two components: the background and the effect by the history. Due to its inherent spontaneousness, the background can be treated as a time series while the other need to handle the history events. In this paper, we model the background by a Recurrent Neural Network (RNN) with its units aligned with time series indexes while the history effect is modeled by another RNN whose units are aligned with asynchronous events to capture the long-range dynamics. The whole model with event type and timestamp prediction output layers can be trained end-to-end. Our approach takes an RNN perspective to point process, and models its background and history effect. For utility, our method allows a black-box treatment for modeling the intensity which is often a pre-defined parametric form in point processes. Meanwhile end-to-end training opens the venue for reusing existing rich techniques in deep network for point process modeling. We apply our model to the predictive maintenance problem using a log dataset by more than 1000 ATMs from a global bank headquartered in North America.Comment: Accepted at Thirty-First AAAI Conference on Artificial Intelligence (AAAI17