Just-in-time connectivity for large spiking networks

The scale of large neuronal network simulations is memory limited due to the need to store connectivity information: connectivity storage grows as the square of neuron number up to anatomically relevant limits. Using the NEURON simulator as a discrete-event simulator (no integration), we explored the consequences of avoiding the space costs of connectivity through regenerating connectivity parameters when needed: just in time after a presynaptic cell fires. We explored various strategies for automated generation of one or more of the basic static connectivity parameters: delays, postsynaptic cell identities, and weights, as well as run-time connectivity state: the event queue. Comparison of the JitCon implementation to NEURON's standard NetCon connectivity method showed substantial space savings, with associated run-time penalty. Although JitCon saved space by eliminating connectivity parameters, larger simulations were still memory limited due to growth of the synaptic event queue. We therefore designed a JitEvent algorithm that added items to the queue only when required: instead of alerting multiple postsynaptic cells, a spiking presynaptic cell posted a callback event at the shortest synaptic delay time. At the time of the callback, this same presynaptic cell directly notified the first postsynaptic cell and generated another self-callback for the next delay time. The JitEvent implementation yielded substantial additional time and space savings. We conclude that just-in-time strategies are necessary for very large network simulations but that a variety of alternative strategies should be considered whose optimality will depend on the characteristics of the simulation to be run.     

Auto-generation of IEC standard PLC code using t-MPSG

The objective of this paper is to reduce the development time of a PLC (Programmable Logic Controller) by automating the task of code generation. For this purpose, we applied t-MPSG (Timed-Message Based Part State Graph). The t-MPSG is an extended finite state automata used to model and generate an execution module for a real-time shop floor controller system. In our proposed method, t-MPSG is used to model the formal specification of the controller system that can be translated into textual structure. After the verification of the t-MPSG model, it can be used as an input to the plc-builder tool. The plc-builder tool is an extended version of a conventional MPSG simulator. It can be used to translate the textual structure of the t-MPSG into an IEC standard PLC code. Finally, the generated code can be downloaded to a PLC emulator or a PLC device for the purpose of simulation and execution. The similarity in the hierarchical structure of the t-MPSG and the IEC standard PLC program has made it convenient to transform from one form to another. Furthermore, an illustration of the methodology to auto-generate IEC standard PLC code using t-MPSG is explained with a suitable example. Recommended by Editorial Board member Young Soo Suh under the direction of Editor Jae Weon Choi. This work was partially supported by Defense Acquisition Program Administration and Agency for Defense Development under the contract (UD080042AD). Devinder Thapa is a Postdoc Research Fellow in the Department of Industrial & information systems at Ajou University, Korea. He completed his Ph.D. from Ajou University in Industrial and Information Systems Engineering. His area of research is related to manufacturing automation and intelligent decision support systems. Chang Mok Park is a Professor in the Department of Technology & Systems Management at Induk Institute of Technology. He completed his Ph.D. in 2002 from Ajou University in Industrial Engineering. His research interest is related to manufacturing optimization, discrete event system simulation and signal analysis. Sang C. Park is an Associate Professor in the Department of Industrial & Information Systems Engineering at Ajou University. He received his B.S., M.S., and Ph.D. degrees from KAIST in 1994, 1996, and 2000, respectively, all in Industrial Engineering. His research interests include geometric algorithms in CAD/CAM, process planning, engineering knowledge management, and discrete event system simulation. Gi-Nam Wang is the Head and a Professor in the Department of Industrial & Information Systems Engineering at Ajou University, Korea. He completed his Ph.D. in 1992 from Texas A&M University, in Industrial Engineering. He has worked as Visiting Professor at University of Texas at Austin during 2000–2001. His area of research is related to Intelligent Information & manufacturing systems, system integration & automation, e-Business solutions and image processing.     

Aberrant protein expression in plasma and kidney tissue during experimental obstructive nephropathy

Kidney failure is a major health problem worldwide. Patients with end-stage renal disease require intensive medical support by dialysis or kidney transplantation. Current methods for diagnosis of kidney disease are either invasive or insensitive, and renal function may decline by as much as 50% before it can be detected using current techniques. The goal of this study was, therefore, to identify biomarkers of kidney disease (associated with renal fibrosis) that can be used for the development of a non-invasive clinical test for early disease detection. We utilized two protein-profiling technologies (SELDI-TOF MS and 2-D) to screen the plasma and kidney proteome for aberrantly expressed proteins in an experimental mouse model of unilateral uretric obstruction, which mimics the pathology of human renal disease. Several differentially regulated proteins were detected at the plasma level of day-3-obstructed animals, which included serum amyloid A1, fibrinogen α, haptoglobin precursor protein, haptoglobin and major urinary proteins 11 and 8. Differentially expressed proteins detected at the tissue level included ras-like activator protein 2, haptoglobin precursor protein, malate dehydrogenase, α enolase and murine urinary protein (all p<0.05 versus controls). Immunohistochemistry was used to confirm the up-regulation of fibrinogen. Interestingly, these proteins are largely separated into four major classes: (i) acute-phase reactants (ii) cell-signaling molecules (iii) molecules involved in cell growth and metabolism and (iv) urinary proteins. These results provide new insights into the pathology of obstructive nephropathy and may facilitate the development of specific assay(s) to detect and monitor renal fibrosis.     

A change management framework for macroergonomic field research

With the proliferation of macroergonomic field research, it is time to carefully examine how such research should be managed and implemented. We argue that the importance of attending to high-quality implementation of field research is equal to that of methodological rigor. One way to systematically manage the implementation process is to adopt a change management framework, wherein the research project is conceptualized as an instance of organization-level change. Consequently, principles for successful organization-level change from the literature on change management can be used to guide successful field research implementation. This paper briefly reviews that literature, deriving 30 principles of successful change management, covering topics such as political awareness, assembling the change team, generating buy-in, and management support. For each principle, corresponding suggestions for macroergonomic field research practice are presented. We urge other researchers to further develop and adopt frameworks that guide the implementation of field research.     

Correlation of slurry reactor Fischer-Tropsch yield data

First stage slurry reactor Fischer-Tropsch (FT) yield data from Mobil's two-step pilot plant operations for the DOE have been correlated using a modified Schulz-Flory approach but allowing for changes in the probability of chain growth, , at key product molecular weights. Triple values of are invoked to explain (1) the high methane yield and (2) the very broad molecular weight distribution of the wax fraction, as compared to (3) moderate values of ₂ of between 0.79 and 0.85 for the C₂–C₂₀ hydrocarbons. Over this range, wax yields from 10 to 76 wt% are accommodated, at values of ₃ from 0.90 to 0.98. The second break in the yield-molecular weight curve occurs at the carbon number where most of the component remains in the reactor as liquid rather than leaving as vapor product. It is assumed that this is a function of vapor-liquid equilibrium. The correlation has proven useful in developing a computer model of the FT synthesis loop which is part of a baseline design and economic study for DOE/PETC.     

A Hybrid Strategy-Based Ultra-Narrow Stretchable Microelectrodes with Cell-Level Resolution

Stretchable ultra-narrow (e.g., 10 µm in width) microelectrodes are crucial for the electrophysiological monitoring of single cells providing the fundamental understanding to the working mechanism of neuro network or other electrically functional cells. Current fabrication strategies either focus on the preparation of normal stretchable electrodes with hundreds of micrometers or millimeters in width by using inorganic conductive materials or develop conductive organic polymer gel for ultra-narrow electrodes which suffer from low stretchability and instability for long-term implantation, therefore, it is still highly desirable to explore bio-interfacial ultra-narrow stretchable inorganic electrodes. Herein, a hybrid strategy is reported to prepare ultra-narrow multi-channel stretchable microelectrodes without using photolithography or laser-assisting etching. A 10 µm × 10 µm monitoring window is fabricated with enhanced interfacial impedance by the special rough surface. The stretchability achieves to 120% for this 10 µm-width stretchable electrode. Supported by these superior properties, it is demonstrated that the stretchable microelectrodes can detect electrophysiological signals of single cells in vitro and collect electrophysiological signals more precisely in vivo. The reported strategy will open up the accessible preparation of the fine-size stretchable microelectrode. It will significantly improve the resolution of monitoring and stimulation of inorganic stretchable electrodes.     

Targeted Nanoparticle Thermometry: A Method to Measure Local Temperature at the Nanoscale Point Where Water Vapor Nucleation Occurs

An optical nanothermometer technique based on laser trapping, moving and targeted attaching an erbium oxide nanoparticle cluster is developed to measure the local temperature. The authors apply this new nanoscale temperature measuring technique (limited by the size of the nanoparticles) to measure the temperature of vapor nucleation in water. Vapor nucleation is observed after superheating water above the boiling point for degassed and nondegassed water. The average nucleation temperature for water without gas is 560 K but this temperature is lowered by 100 K when gas is introduced into the water. The authors are able to measure the temperature inside the bubble during bubble formation and find that the temperature inside the bubble spikes to over 1000 K because the heat source (optically‐heated nanorods) is no longer connected to liquid water and heat dissipation is greatly reduced.     

Neural Machine Translation by Fusing Key Information of Text

When the Transformer proposed by Google in 2017, it was first used for machine translation tasks and achieved the state of the art at that time. Although the current neural machine translation model can generate high quality translation results, there are still mistranslations and omissions in the translation of key information of long sentences. On the other hand, the most important part in traditional translation tasks is the translation of key information. In the translation results, as long as the key information is translated accurately and completely, even if other parts of the results are translated incorrect, the final translation results’ quality can still be guaranteed. In order to solve the problem of mistranslation and missed translation effectively, and improve the accuracy and completeness of long sentence translation in machine translation, this paper proposes a key information fused neural machine translation model based on Transformer. The model proposed in this paper extracts the keywords of the source language text separately as the input of the encoder. After the same encoding as the source language text, it is fused with the output of the source language text encoded by the encoder, then the key information is processed and input into the decoder. With incorporating keyword information from the source language sentence, the model’s performance in the task of translating long sentences is very reliable. In order to verify the effectiveness of the method of fusion of key information proposed in this paper, a series of experiments were carried out on the verification set. The experimental results show that the Bilingual Evaluation Understudy (BLEU) score of the model proposed in this paper on the Workshop on Machine Translation (WMT) 2017 test dataset is higher than the BLEU score of Transformer proposed by Google on the WMT2017 test dataset. The experimental results show the advantages of the model proposed in this paper.     

Graph-based quadratic optimization: A fast evolutionary approach

Quadratic optimization lies at the very heart of many structural pattern recognition and computer vision problems, such as graph matching, object recognition, image segmentation, etc., and it is therefore of crucial importance to devise algorithmic solutions that are both efficient and effective. As it turns out, a large class of quadratic optimization problems can be formulated in terms of so-called “standard quadratic programs” (StQPs), which ask for finding the extrema of a quadratic polynomial over the standard simplex. Computationally, the standard approach for attacking this class of problems is to use replicator dynamics, a well-known family of algorithms from evolutionary game theory inspired by Darwinian selection processes. Despite their effectiveness in finding good solutions in a variety of applications, however, replicator dynamics suffer from being computationally expensive, as they require a number of operations per step which grows quadratically with the dimensionality of the problem being solved. In order to avoid this drawback, in this paper we propose a new population game dynamics (InImDyn) which is motivated by the analogy with infection and immunization processes within a population of “players.” We prove that the evolution of our dynamics is governed by a quadratic Lyapunov function, representing the average population payoff, which strictly increases along non-constant trajectories and that local solutions of StQPs are asymptotically stable (i.e., attractive) points. Each step of InImDyn is shown to have a linear time/space complexity, thereby allowing us to use it as a more efficient alternative to standard approaches for solving StQPs and related optimization problems. Indeed, we demonstrate experimentally that InImDyn is orders of magnitude faster than, and as accurate as, replicator dynamics on various applications ranging from tree matching to image registration, matching and segmentation.