Miracle-dz/UniFAR-FTUs huggingface dataset:
Design of Bio-inspired Fault-tolerant Adaptive Routing Based on Enzymatic Feedback Control in the Cell: Towards Averaging Load Balance in the Network

The routing algorithm of SPF is widely distributed in the Internet. Since this routing algorithm is designed in order to improve throughput of each packet, it is not suitable for averaging load balance in the network. On the contrary, metabolic networks in the cell can realize load balance and achieve fault-tolerance by using enzymatic feedback mechanism. That is, a metabolic pathway in the cell is composed of a lot of enzymatic reaction steps in which biochemical reactant (substrate) is converted to the product by unique enzyme, and the product of a late step frequently acts as an inhibitor of the first committed step in this pathway (feedback control). This way, the end product of a pathway controls its own synthesis and prevents useless accumulation of intermediates and of end product. Recently, by mimicking enzymatic feedback mechanism in the cell, we have designed a fault-tolerant adaptive routing algorithm to avoid the partial and time-variant congestions in the network. We evaluated and compared the proposed algorithm with SPF and ECMP (Equal Cost Multi-path Protocol) by using the simulation of test data. The simulation results show that the proposed algorithm can remarkably improve both latency, load balance and fault tolerance. Since there are enormous numbers of nodes in the Internet, however, it is difficult to replace all existing nodes to the proposed nodes. In this paper, we shall propose an efficient method for the allocation of adaptive nodes in random and a scale-free network composed of 100 nodes. Examined the time-variant traffic at each node, and only focused on around 10% top ranked heavy-traffic nodes, we replace such nodes to our proposed adaptive nodes. By doing this, we could design a fault-tolerant adaptive routing, which can dynamically average load-balance within the network.
The agreement process: an empirical investigation of human-human computer-mediated collaborative dialogs

In this paper, we investigate the empirical correlates of the agreement process. Informally, the agreement process is the dialog process by which collaborators achieve joint commitment on a joint action. We propose a specific instantiation of the agreement process, derived from our theoretical model, that integrates the IRMA framework for rational problem solving (Bratman, Israel & Pollack, 1988) with Clark's (1992, 1996) work on language as a collaborative activity; and from the characteristics of our task, a simple design problem (furnishing a two-room apartment) in which knowledge is equally distributed among agents, and needs to be shared. The main contribution of our paper is an empirical study of some of the components of the agreement process. We first discuss why we believe the findings from our corpus of computer-mediated dialogs are applicable to human? human collaborative dialogs in general. We then present our theoretical model, and apply it to make predictions about the components of the agreement process. We focus on how information is exchanged in order to arrive at a proposal, and on what constitutes a proposal and its acceptance/rejection. Our corpus study makes use of features of both the dialog and the domain reasoning situation, and led us to discover that the notion of commitment is more useful to model the agreement process than that of acceptance/rejection, as it more closely relates to the unfolding of negotiation.
Optimal Risk Control for The Excess of Loss Reinsurance Policies

Abstract The primary objective of the paper is to explore using reinsurance as a risk management tool for an insurance company. We consider an insurance company whose surplus can be modeled by a Brownian motion with drift and that the surplus can be invested in a risky or riskless asset. Under the above Black-Scholes type framework and using the objective of minimizing the ruin probability of the insurer, we formally establish that the excess-of-loss reinsurance treaty is optimal among the class of plausible reinsurance treaties. We also obtain the optimal level of retention as well as provide an explicit expression of the minimal probability of ruin.
Prediction of Domain Interactions in C. elegans

High throughput experiments and computational methods allow the elucidation of networks of protein-protein interactions (PPI) in several organisms. Since about 80% of all proteins consist of multiple self-folding units called domains, it is desirable to determine which domains are responsible for a given interaction. We use an integrative computational and experimental approach to predict domain-domain interactions (DDI) from the known PPI for the metazoan C. elegans.We use Maximum Likelihood Estimation and Maximum Specificity Set Cover algorithms to predict the DDI. We validate the results using a small and accurate yeast 2 hybrid (Y2H) screen on 8 protein-protein interactions involving 13 proteins in C. elegans . The computational predictions are consistent with Y2H in 7 of the 13 interaction domains, including the prediction of domain fusions. These results illustrate the predictive power of a combined computational and experimental approach to DDI determination.
Adaptive optimal control for linear discrete time-varying systems

In this paper, adaptive optimal control is proposed for linear discrete time-varying (LDTV) systems subject to unknown system dynamics. The idea of the method is a direct application of the Q-learning adaptive dynamic programming for time-varying systems. In order to derive the optimal control policy, an actor-critic structure is constructed and the time-varying least square method is adopted for parameter adaptation. The derived control policy robustly stabilizes the time-varying system and guarantees an optimal control performance. As no particular system information is required throughout the process, the proposed method provides a feasible solution to a large variety of applications. The validity of the proposed method is verified through simulation studies.
Global optimization using local information with applications to flow control

Flow control in high speed networks requires distributed routers to make fast decisions based only on local information in allocating bandwidth to connections. While most previous work on this problem focuses on achieving local objective functions, in many cases it may be necessary to achieve global objectives such as maximizing the total flow. This problem illustrates one of the basic aspects of distributed computing: achieving global objectives using local information. Papadimitriou and Yannakakis (1993) initiated the study of such problems in a framework of solving positive linear programs by distributed agents. We take their model further, by allowing the distributed agents to acquire more information over time. We therefore turn attention to the tradeoff between the running time and the quality of the solution to the linear program. We give a distributed algorithm that obtains a (1+/spl epsiv/) approximation to the global optimum solution and runs in a polylogarithmic number of distributed rounds. While comparable in running time, our results exhibit a significant improvement on the logarithmic ratio previously obtained by Awerbuch and Azar (1994). Our algorithm, which draws from techniques developed by Luby and Nisan (1993) is considerably simpler than previous approximation algorithms for positive linear programs, and thus may have practical value in both centralized and distributed settings.
Recruiting for Ideas: How Firms Exploit the Prior Inventions of New Hires

When firms recruit inventors, they acquire not only the use of their skills but also enhanced access to their stock of ideas. But do hiring firms actually increase their use of new recruits' prior inventions? Our estimates suggest they do, quite significantly in fact, by approximately 219% on average. However, this does not necessarily reflect widespread “learning by hiring. ” In fact, we estimate that a recruit's exploitation of her own prior ideas accounts for almost half of the above effect, with much of the diffusion to others being limited to the recruit's immediate collaborative network. Furthermore, although one might expect the recruit's role to diminish rapidly as her tacit knowledge diffuses across her new firm, our estimates indicate that her importance is surprisingly persistent over time. We base these findings on an empirical strategy that exploits the variation over time in hiring firms' citations to the recruits' premove patents. Specifically, we employ a difference-in-differences approach to compare premove versus postmove citation rates for the recruits' prior patents and corresponding matched-pair control patents. Our methodology has three benefits compared to previous studies that also examine the link between labor mobility and knowledge flow: (1) it does not suffer from the upward bias inherent in the conventional cross-sectional comparison, (2) it generates results that are robust to a more stringently matched control sample, and (3) it enables a temporal examination of knowledge flow patterns. This paper was accepted by Kamalini Ramdas, entrepreneurship and innovation.
Dynamic Coreference-Based Summarization

\Ve ha.ve developed a query-sensitive text summarization technology \Vell suited for the task of determining whether a. document is relevant to <'-" query. Enoug;h of the docurnent is displayed for the user to determine whether the document should l:H~ read in its entirety. Evaluations indicate that sununarics are classif-ied for relevauce uearly as well as full documents. This approach i.s based on the concept that a good SltJnrnary will repn-)sent each of the topics in the query and is n'alized by ~electing smltcnc<-! S from the document until all the phrases in the query which are represented in the sumiHa.ry are (covered. ' A phrase in th<:; docu. ' Jllcnt is considered to cover a phraf:le in the qu(~r:Y if it is cmeferent \Vith it. This approach maxirnizes the space of <! ntities reta.incd in th(:: summ<Jxy with minimal rednnda.ncy. 'rhe software is built upon the CAMP NLP spt.cm [2].
3D Object Reconstruction with Heterogeneous Sensor Data

In this paper, we reconstruct 3D objects with a heterogeneous sensor network of Range Imaging(RIM) sensors and high-res camcorders. With this setup, we first carry out simple but effective depth calibration for the RIM cameras. We then combine the camcorder silhouette cues and RIM camera depth information, for the reconstruction. Our main contribution is the proposal of a sensor fusion framework so that the computation is general, simple and scalable. Although we only discuss the camcorders and RIM cameras in this paper, the proposed framework can be applied to any type of vision sensors. It uses a space occupancy grid as a probabilistic 3D representation of scene contents. After defining sensing models for each type of sensors, the reconstruction is simply a Bayesian inference problem, and can be solved robustly. The experiments show that the recover full 3D closed shapes substantially improved the quality of the noisy RIM sensor measurement.
A novel adaptive structure of the energy detector applied to cognitive radio networks

The energy detector (ED) is one of the spectrum sensing techniques that is used in the cognitive radio networks to detect the presence of the primary user (PU) in a certain spectrum band. This technique assumes that the PU is either present or absent during the sensing time. However, the ED's performance degrades in a scenario when the PU is absent and then appears during the sensing time. The ED's performance is evaluated for this scenario and a novel adaptive ED structure is proposed to improve the detector performance. Analytical performance evaluation of the adaptive ED along with simulation results show that the proposed ED structure improves the probability of detection compared to the conventional ED.
Platinum metallization for MEMS application

The adherence of Platinum thin film on Si/SiO2 wafer was studies using Chromium, Titanium or Alumina (Cr, Ti, Al2O3) as interlayer. The adhesion of Pt is a fundamental property in different areas, for example in MEMS devices, which operate at high temperature conditions, as well as in biomedical applications, where the problem of adhesion of a Pt film to the substrate is known as a major challenge in several industrial applications health and in biomedical devices, such as for example in the stents.1-4 We investigated the properties of Chromium, Titanium, and Alumina (Cr, Ti, and Al2O3) used as adhesion layers of Platinum (Pt) electrode. Thin films of Chromium, Titanium and Alumina were deposited on Silicon/Silicon dioxide (Si/SiO2) wafer by electron beam. We introduced Al2O3 as a new adhesion layer to test the behavior of the Pt film at higher temperature using a ceramic adhesion thin film. Electric behaviors were measured for different annealing temperatures to know the performance for Cr/Pt, Ti/Pt, and Al2O3/Pt metallic film in the gas sensor application. All these metal layers showed a good adhesion onto Si/SiO2 and also good Au wire bondability at room temperature, but for higher temperature than 400 °C the thin Cr/Pt and Ti/Pt films showed poor adhesion due to the atomic inter-diffusion between Platinum and the metal adhesion layers.5 The proposed Al2O3/Pt ceramic-metal layers confirmed a better adherence for the higher temperatures tested.
Incremental session based collaborative filtering with forgetting mechanisms

Most of research works in music recommendation systems use Collaborative Filtering (CF) for generating personalized recommendations based on user's previous song ratings or static usage history data. But those researches adapting CF do not consider behavior of listening to songs and are not able to maintain the systems to sensitive to recent user's preferences. Behavior of music listening is continuous and repetitive process, especially, the latest song listening can infer to the favorite song at that moment. In this work, we present Incremental Session based Collaborative Filtering with forgetting mechanism or ISSCF by adapting Session-based Collaborative Filtering (SSCF), which considers music listened continuously and maintains the recent session. In order to avoid unnecessary memory usage and processing time, we use forgetting mechanism: sliding windows and fading factors incorporating with SSCF. We evaluate our purposed framework by measuring the HitRatio. From experimental results, it shows that performance of our purposed approach increases the accuracy of recommendation and low computational time and space when comparing with than SSCF.
Workplace Chronotype Bias, Flexible Scheduling, and Performance Beliefs

Author: Gilmer, Lauren, O. MS Institution: Purdue University Degree Received: August 2018 Title: Workplace Chronotype Bias, Flexible Scheduling, and Performance Beliefs Major Professor: Margaret Stockdale Workers who request a flexible schedule to accommodate their biologically-determined sleep-wake cycle (chronotype) may face prejudice if supervisors perceive them, particularly “night owls”, as lazy or unconscientious. Such bias may be exacerbated in organizational cultures characterized by stability and control. Thus, chronotype bias was examined in a 2 (rigid vs. flexible organizational norms) X 3 (morningness chronotype, eveningness chronotype, educational pursuit/control as reason for a flexible schedule request) online scenario study. Participants were recruited through Amazon Mechanical Turk (N=398) and were instructed to act as managers to decide whether to approve a fictitious employee’s request for a flexible schedule. Organizational culture and reason for schedule request were orthogonally manipulated in the scenarios. Ps completed measures of schedule approval (including an open-ended justification item), beliefs about the employee’s performance (job-specific task performance, contextual performance, personal discipline, and conscientiousness), and manipulation checks, as well as measures of their own chronotype. Ps were less likely to approve a flexible schedule request for employees with chronotype-based requests (both morningness and eveningness) compared to control (educational pursuit/control request). Task performance beliefs mediated the effect. Organizational norms had both a direct and moderating effect on schedule approval, such that approval was higher and chronotype bias was weakened in the flexible norm condition compared to the rigid norm condition. Ps’ own chronotype had no direct or moderating effect on
A Prospective Pharmacokinetic Study of Docetaxel in Breast Cancer Patients in Relation to CYP3A4 Activity

Abstract Purpose: To study the correlation between pharmacokinetics & pharmacodynamic of docetaxel and CYP3A4 activity in Egyptian cancer patients. Patients and methods: Fourteen Egyptian female Patients with metastatic breast cancer, World Health Organization (WHO) performance status 0 to 2, had received prior chemotherapy regimen, were treated with single-agent docetaxel (100 mg/m2), given every 21 days. Hydrocortisone 300 mg IV was administered 2 days before docetaxel treatment and Cytochrome 3A4 activity was determined by measuring the level of urinary metabolites of 6β-hydroxy cortisol (6β-OHF) and cortisol (FC). For the pharmacokinetic study, Blood samples were taken before and after IV infusion for 1 hr of 100 mg/m2 docetaxel. The level of the drug was determined using HPLC and the correlation between pharmacokinetics and CYP3A4 activity were determined. Results: After cortisol administration, the total amount of 24-hour urinary 6β-OHF and FC were19.97 ± 10.43 and 16.84 ± 10.36 mg/24 h (mean ± SD) respectively. On the other hand, the 6β-OHF/FC ratio after cortisol administration was 1.86 ± 1.933. The pharmacokinetic parameters of docetaxel were clearance 19.9 ± 4.5 L/hr, the volume of distribution 65.6 ± 28.6 L (mean ± SD) and AUC 7.2 μg/ml.hr (range 5-8.8 μg/ml.hr) ± SD). A significant correlation was found between 6β-OHF/FC ratio and neutropenia (p=0.04) in addition correlation between 6β-OHF and Cmax (p=0.04). Conclusion: The interpatient variability of CYP3A4 activity in each patient could be predicted by measuring the total amount of 24 hour urinary 63-OHF after cortisol administration. Individualized dosing to optimize drug exposure for each patient could be performed based on this method. A farther study of fixed versus individualized dosing of docetaxel is needed to determine whether individualized chemotherapy with the application of this method can reduce PK and toxicity variability.
Collective Biobjective Optimization Algorithm for Parallel Test Paper Generation

Parallel Test Paper Generation (k-TPG) is a biobjective distributed resource allocation problem, which aims to generate multiple similarly optimal test papers automatically according to multiple user-specified criteria. Generating high-quality parallel test papers is challenging due to its NP-hardness in maximizing the collective objective functions. In this paper, we propose a Collective Biobjective Optimization (CBO) algorithm for solving k-TPG. CBO is a multi-step greedy-based approximation algorithm, which exploits the submodular property for biobjective optimization of k-TPG. Experiment results have shown that CBO has drastically outperformed the current techniques in terms of paper quality and runtime efficiency.
Diabetes Medication Use and Blood Lactate Level among Participants with Type 2 Diabetes: The Atherosclerosis Risk in Communities Carotid MRI Study

Background The objective of this study is to compare lactate levels between users and non-users of diabetes medications under the hypothesis that the level of lactate is a marker of oxidative capacity. Methods The cross-sectional data of 493 participants aged 61–84 with type 2 diabetes who participated in the Atherosclerosis Risk in Communities Carotid MRI study were analyzed using survey weighted linear regression. Results Median plasma lactate level was 8.58 (95% CI: 8.23, 8.87) mg/dl. Comparing users of diabetic medications with non-users, thiazolidinedione use was significantly associated with lower lactate level (7.57 (6.95–8.25) mg/dL vs. 8.78 (8.43–9.14) mg/dL), metformin use with a slightly higher lactate level (9.02 (8.51–9.58) mg/dL vs. 8.36 (7.96–8.77) mg/dL), and sulfonylurea and insulin use were not associated with lactate level. After adjustment for demographic and lifestyle factors, the plasma lactate level for thiazolidinedione users was 15.78% lower than that for non-users (p<0.001). Considering use of each medication separately and in combination did not change the results. Conclusion In conclusion, thiazolidinedione use was associated with lower plasma lactate level compared to non-use and metformin use was only marginally associated with a slightly higher lactate level. These results are consistent with the previously demonstrated effects of diabetes medications on oxidative metabolism. Further investigation of the role that diabetes medications play in improvement of oxidative metabolism is warranted.
The Missing Link – Dynamic Components for ML ( Extended )

Despite its powerful module system, ML has not yet evolved for the modern world of dynamic and open modular programming, to which more primitive languages have adapted better so far. We present the design and semantics of a simple yet expressive firstclass component system for ML. It provides dynamic linking in a type-safe and type-flexible manner, and allows selective execution in sandboxes. The system is defined solely by reduction to higherorder modules plus an extension with simple module-level dynamics, which we call packages. To represent components outside processes we employ generic pickling. We give a module calculus formalising the semantics of packages and pickling.
A ug 2 00 1 Testing m u = 0 on the Lattice

A massless up quark is an intriguing possible solution to the strong CP problem. We discuss how lattice computations can be used in conjunction with chiral perturbation theory to address the consistency of mu = 0 with the observed hadron spectrum and interactions. It is not necessary to simulate very light quarks—three flavor partially quenched computations with comparable sea and valence quark masses on the order of the strange quark mass could suffice. cohen@bu.edu, dbkaplan@phys.washington.edu, anelson@fermi.phys.washington.edu
Appearance-based eye control system by manifold learning

Eye-movements are increasingly employed to study usability issues in HCI (Human-Computer Interacetion) contexts. In this paper we introduce our appearance-based eye control system which utilizes 5 specific eye movements, such as closed-eye movement and eye movements with gaze fixation at the positions (up, down, right, left) for HCI applications. In order to measure these eye movements, we employ a fast appeance-based gaze tracking method with manifold learning technique. First we propose to concatenate local eye appearance Center-Symmetric Local Binary Pattern(CS-LBP) descriptor for each subregion of eye image to form an eye appearance feature vector. The calibration phase is then introduced to construct a trainning samples by spectral clustering. After that, Laplacian Eigenmaps will be applied to the trainning set and unseen input together to get the structure of eye manifolds. Finally we can infer the eye movement of the new input by its distances with the clusters in the trainning set. Experimental results demonstrate that our system with quick 4-points calibration not only can reduce the run-time cost, but also provide another way to mesure eye movements without mesuring gaze coordinates to a HCI application such as our eye control system.