Miracle-dz/UniFAR-FTUs huggingface dataset:
Can we constrain the dark energy equation of state parameters using configuration entropy?

We propose a new scheme for constraining the dark energy equation of state parameter/parameters based on the study of the evolution of the configuration entropy. We analyse a set of one-parameter and two-parameter dynamical dark energy models and find that the derivative of the configuration entropy in all the dynamical dark energy models exhibits a minimum. The magnitude of the minimum of the entropy rate is decided by both the parametrization of the equation of state and the associated parameters. The location of the minimum of the entropy rate is less sensitive to the form of the parametrization but depends on the associated parameters. We determine the best-fitting equations for the location and magnitude of the minimum of the entropy rate in terms of the parameter/parameters of the dark energy equation of state. These relations would allow us to constrain the dark energy equation of state parameter/parameters for any given parametrization provided the evolution of the configuration entropy in the Universe is known from observations.
Chiral quantum optics in photonic sawtooth lattices

Chiral quantum optics has become a burgeoning field due to its potential applications in quantum networks or quantum simulation of many-body physics. Current implementations are based on the interplay between local polarization and propagation direction of light in nanophotonic structures. In this manuscript, we propose an alternative platform based on coupling quantum emitters to a photonic \emph{sawtooth} lattice, a one-dimensional model with an effective flux per plaquette introduced by complex tunnelings. We study the dynamics emerging from such structured photonic bath and find the conditions to obtain quasi-perfect directional emission when the emitters are resonant with the band. In addition, we find that the photons in this bath can also mediate complex emitter-emitter interactions tunable in range and phase when the emitters transition frequencies lie within a band-gap. Since these effects do not rely on polarization they can be observed in platforms beyond nanophotonics such as matter-waves or circuit QED ones, of which we discuss a possible implementation.
Constrained Monte Carlo Markov Chains on Graphs

This paper presents a novel theoretical Monte Carlo Markov chain procedure in the framework of graphs. It specifically deals with the construction of a Markov chain whose empirical distribution converges to a given reference one. The Markov chain is constrained over an underlying graph, so that states are viewed as vertices and the transition between two states can have positive probability only in presence of an edge connecting them. The analysis is carried out on the basis of the relationship between the support of the target distribution and the connectedness of the graph.
Exploiting Relevance for Online Decision-Making in High-Dimensions

Many sequential decision-making tasks require choosing at each decision step the right action out of the vast set of possibilities by extracting actionable intelligence from high-dimensional data streams. Most of the times, the high-dimensionality of actions and data makes learning of the optimal actions by traditional learning methods impracticable. In this work, we investigate how to discover and leverage sparsity in actions and data to enable fast learning. As our learning model, we consider a structured contextual multi-armed bandit (CMAB) with high-dimensional arm (action) and context (data) sets, where the rewards depend only on a few relevant dimensions of the joint context-arm set, possibly in a non-linear way. We depart from the prior work by assuming a high-dimensional, continuum set of arms, and allow relevant context dimensions to vary for each arm. We propose a new online learning algorithm called <italic>CMAB with Relevance Learning</italic> (CMAB-RL). CMAB-RL enjoys a substantially improved regret bound compared to classical CMAB algorithms whose regrets depend on the number of dimensions <inline-formula><tex-math notation="LaTeX">$d_x$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$d_a$</tex-math></inline-formula> of the context and arm sets. Importantly, we show that when the learner has prior knowledge on sparsity, given in terms of upper bounds <inline-formula><tex-math notation="LaTeX">$\overline{d}_x$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$\overline{d}_a$</tex-math></inline-formula> on the number of relevant context and arm dimensions, then CMAB-RL achieves <inline-formula><tex-math notation="LaTeX">$\tilde{O}(T^{1 - 1 /(2 + 2\overline{d}_x + \overline{d}_a)})$</tex-math></inline-formula> regret. Finally, we illustrate how CMAB algorithms can be used for optimal personalized blood glucose control in type 1 diabetes mellitus patients, and show that CMAB-RL outperforms other contextual MAB algorithms in this task.
DataPop: Knowledge Base Population using Distributed Voice Enabled Devices

Data scientists are constantly creating methods to efficiently and accurately populate big data sets for use in large-scale applications. Many recent efforts utilize crowd-sourcing and textual interfaces. In this paper, we propose a new method of curating data; namely, creating a multi-device Amazon Alexa Skill in the form of a research trivia game. Users experience a synchronized gaming experience with other Amazon Echo users, competing against one another while filling in gaps of a connected knowledge base. This allows for full exploitation of the speed improvement offered by voice interface technology in a game-based format.
Requisite Variety in Ethical Utility Functions for AI Value Alignment

Being a complex subject of major importance in AI Safety research, value alignment has been studied from various perspectives in the last years. However, no final consensus on the design of ethical utility functions facilitating AI value alignment has been achieved yet. Given the urgency to identify systematic solutions, we postulate that it might be useful to start with the simple fact that for the utility function of an AI not to violate human ethical intuitions, it trivially has to be a model of these intuitions and reflect their variety $ - $ whereby the most accurate models pertaining to human entities being biological organisms equipped with a brain constructing concepts like moral judgements, are scientific models. Thus, in order to better assess the variety of human morality, we perform a transdisciplinary analysis applying a security mindset to the issue and summarizing variety-relevant background knowledge from neuroscience and psychology. We complement this information by linking it to augmented utilitarianism as a suitable ethical framework. Based on that, we propose first practical guidelines for the design of approximate ethical goal functions that might better capture the variety of human moral judgements. Finally, we conclude and address future possible challenges.
Probing discontinuous precipitation in U-Nb

U-Nb's discontinuous precipitation, $\gamma^{bcc}_{matrix} \rightarrow \alpha^{orth}_{cellular} + \gamma'^{bcc}_{cellular}$, is intriguing in the sense that it allows formation and growth of the metastable $\gamma'$ phase during the course of its occurrence. Previous attempts to explain the thermodynamic origin of U-Nb's discontinuous precipitation hypothesized that the energy of $\alpha$ forms an intermediate common tangent with the first potential of the double-well energy of $\gamma$ at the $\gamma'$ composition. While this hypothesis is eligible and consistent with the experimental observation of gradual increase in $\gamma'$ composition at increasing temperature, it is in conflict with recent experiments whose results indicated a distribution of $\gamma'$ compositions in the vicinity of 50 at\%Nb. To shed some light onto this issue, the current work investigates the origin of U-Nb's discontinuous precipitation in view of fundamental thermodynamics and kinetics, taken from the perspective of phase-field theory. It has been showed that local misfit strain tends to play a crucial role in the formation and growth the discontinuous precipitation. Depending on the magnitude of strain developed at grain boundaries, either an increasing $\gamma'$ composition or a random distribution of $\gamma'$ composition around the equiatomic value with respect to increasing temperature could be expected.
Implementation of Bender Elements to Measure In-situ Stiffness of Soft Clays

Bender elements, composed of thin piezo-ceramics and elastic shims, have been used to measure shear wave velocities of specimens in laboratories. As a preliminary stage of their field applications, an in-house research of optimizing suitable bender elements and their geometrical arrangement has been carried out in a barrel of kaolinite slurry. Two types of measurement configuration, similar to cross-hole and in-hole seismic testing, have been implemented. Prototype instrumented rods were penetrated into a soft clay layer in the west coast of Korea and excellent shear waves were recorded. Development of penetration device (mandrel) and associated instrumented rods are in progress for deep investigation.
Treatment and Regime Effects of ALMP : How Do They Differ Between Migrants and Natives ?

Public Employment Service (PES) agencies and caseworkers often have substantial leeway in the design and implementation of active labor market policies (ALMP) for the unemployed, resulting in variation of usage. This paper presents a novel framework in which this variation is used for the joint assessment of different ways in which ALMP effects can operate. We examine an additional layer of impacts beyond the treatment effects on the treated job seekers called regime effects, which potentially affect all job seekers and which are defined by the extent to which programs are intended to be used in a market. We propose a novel method to jointly estimate regime effects for two types of programs, supportive (carrots) and restrictive (sticks) programs. We apply this to contrast regime and treatment effects on unemployment durations, employment, and post-unemployment earnings using register data that contain PES and case-worker identifiers for about 130,000 job seekers. The results show that “carrots” and “sticks” treatments prolong unemployment, but carrots increase earnings whereas sticks decrease them. We find regime effects of a similar order of magnitude. Higher intended usage of carrots and sticks reduces unemployment durations, but carrots raise earnings whereas sticks decrease them. These average effects could be, however, substantially affected by the cultural background of the job seeker and her past experience with economy and institutions. We are currently exploring the differences in treatment and regime effects of ALMP for different groups of migrants and natives. This is of increasing policy relevance given the current debates on migration and refugee integration. JEL Codes: J65, J68, J64
Proposal : Disk-based Large-Scale Graph Computation on a Single Machine

In [38] we proposed Parallel Sliding Windows (PSW), a novel method for efficiently processing large graphs from external memory (disk). Based on PSW, we designed and implemented a complete system, GraphChi, for vertex-centric graph computation. We demonstrated that GraphChi is capable of solving even the biggest graph computation problems on just a single PC, with performance often matching distributed computation frameworks. In the proposed thesis we extend the analysis of the performance of the Parallel Sliding Windows algorithm and GraphChi experimentally and theoretically. We compare algorithms based on PSW to previous work on I/O efficient graph algorithms and explore the limitations of our approach. Second, we describe extensions to GraphChi which allow it to overcome some of the limitation of the original system. Finally, we present a complete recommender system built around GraphChi and propose a new approach to scaling such systems from one machine to thousands of machines with nearly linear improvement in throughput. We claim that our approach has significant economical benefits due to savings in hardware resources and improvement in programmer productivity. GraphChi makes large-scale graph computation possible to anyone with a modern PC or laptop.
Planar Tri-Band Antenna Design

The paper briefly uncovers techniques used for a design of compact planar antennas in order to achieve the wideband and the multi-band capability. The main topic is aimed to the multi-objective optimization using genetic algorithms. A quarter-wavelength planar inverted-F antenna (PIFA) using a slot and shorted parasitic patches is chosen to cover GSM900, GSM1800 and ISM2400 bands. A global multi-objective optimization uses a binary genetic algorithm with a composite objective function to tune this antenna. The impedance match and the direction of maximum gain are desired parameters to improve.
JointNull : Combining Reconfigurable Analog Cancellation with Transmit Beamforming for Large-antenna Full-duplex Wireless

In this paper, we study the performance of a full-duplex architecture for large-antenna systems, where each receive antenna uses digital canceler but analog cancelers can be fewer in number than receive antennas. Analog cancelers are reconfigurable in the sense that they can be assigned to any antenna and convert it into a full-duplex antenna. We propose JointNull that jointly optimizes reconfigurable analog cancellation and transmit beamforming. JointNull assigns each antenna one of the three roles—fullduplex, half-duplex receive and half-duplex transmit antenna—and then designs a transmit precoder to suppress self-interference on receive antennas. We evaluate JointNull using channel measurements from an array with 72 antennas. We find that when combined with transmit beamforming, there is an optimal number of analog cancelers, less than total number of antennas, which achieve the maximum sum-rate. Further, JointNull achieves close to ideal full-duplex sum-rates using much fewer analog cancelers than the total number of antennas. For example, JointNull achieves 90% of ideal full-duplex sum-rate using only eight poor quality analog cancelers, each of which can cancel 20 dB of self-interference. Finally, rate region shows that when demand is highly biased towards downlink, JointNull achieves 90% of ideal sum-rate with even fewer or no analog cancelers.
Enhancing Multiobjective Evolutionary Algorithms by Local Dominance and Local Recombination: Performance Verification in Multiobjective 0/1 Knapsack Problems

This paper proposes a method to enhance single population multiobjective evolutionary algorithms (MOEAs) by searching based on local dominance and local recombination. In this method, first, all fitness vectors of individuals are transformed to polar coordinate vectors in objective function space. Then, the population is iteratively divided into several subpopulations by using declination angles. As a result, each sub-population covers a sub-region in the multiobjective space with its individuals located around the same search direction. Next, local dominance is calculated separately for each sub-population after alignment of its principle search direction by rotation. Selection, recombination, and mutation are applied to individuals within each sub-population. The proposed method can improve the performance of MOEAs that use dominance based selection, and can reduce the entire computational cost to calculate dominance among solutions as well. In this paper we verify the effectiveness of the proposed method obtaining Pareto optimal solutions in two representative MOEAs, i.e. NSGA-II and SPEA2, with Multiobjective 0/1 Knapsack Problems.
Exploiting Known Resonance Structure of Lipids for Improved Nuisance Removal from 1 H-MRSI Data

Purpose: To exploit the known resonance structures of lipid (and water) signals for improved nuisance removal from H-MRSI data. Methods: The proposed method uses the resonance structures of water and lipid molecules to construct a parametric model for the nuisance signals and combines it with the union-of-subspaces (UoSS) nuisance removal framework. Specifically, this parametric model is used to estimate the water and lipid signals, which are used to estimate the subspaces for the water and lipid signals, respectively. The estimated subspaces are then incorporated in the UoSS framework for improved nuisance removal performance. Results: The proposed method was validated using three-dimensional MRSI data collected from the brain using an FID sequence with short-TE and no suppression pulses from healthy subjects. Improvement in reducing the level of nuisance signals using the proposed method compared to the original UoSS method was observed without any noticeable distortion to the desired metabolite spectra. Moreover, a subspace-based spatiospectral reconstruction was also performed to further demonstrate the effectiveness of the proposed nuisance removal method. Conclusion: The known resonance structures of nuisance signals are useful prior knowledge that can be exploited for nuisance removal. Using this knowledge, the proposed method can effectively remove the overwhelming nuisance signals from H-MRSI data collected with neither water nor lipid suppression pulses, which is desirable for optimizing H-MRSI data acquisition.
An Application of the Knowledge Intensive Engineering Framework to Architectural Design

Because of increasing concerns about environmental problems and other issues, engineers are requested to evaluate their products from ever wide variety of aspects. To support this, we proposed the concept of knowledge intensive engineering, in which various kinds of knowledge are used in a exible and integrated manner aiming at generation of more added-value. In addition, we also proposed the Knowledge Intensive Engineering Framework (KIEF) system that forms a computational framework of knowledge intensive engineering. In this paper, we describe the concept of knowledge intensive engineering and KIEF. Then, we apply KIEF to architectural design to demonstrate the power of the system. To do so, we analyze knowledge that is used in architecture design and discuss how to implement the knowledge on KIEF. Finally, we show some results of this application and discuss the capability of KIEF.
Investigating The Effect of Soil Behavioral Model On The Performance of Micro Pile System Under Dynamic Loading

This article aims to perform finite element modeling for micro pile under dynamic loading, which investigate the effect of soil parameters around the micro pile in the bending moment provided along micro pile group. Today, micro piles are one of the most important and applied elements of reinforcement and optimization of foundations in seismic regions where are subjected to seismic damages. In this article, numerical modeling is performed by using 3D finite element software ABAQUS. Soil behavior has been considered as elastic and elastoplastic through Mohr coulomb model. Implicit method has been used in dynamic analysis. Investigations show that improvement of soil parameters reduce the bending moment applied on micro pile, and soil inelastic model increase the performance of micro pile in seismic loading. Key-Words: Micro Pile Group, Seismic, Finite Element, Elastoplastic
Distribution Malware Infection Vector

Software distribution and usage over the Internet has become an integral part of our daily life. On the one hand this is an efficient way to make software widely available to users, particularly for the open source and free software. On the other hand it bears the risk of infecting computer platforms by malicious software since today many software applications are downloaded and installed without appropriate security measures. This situation can obviously be exploited by cyber criminals, and also by Governments intending to deploy spyware against suspects. In this paper we present an efficient mechanism as well as the corresponding reference implementation for onthe-fly infecting of executable code with malicious software. Our algorithm deploys virus infection routines and network redirection attacks, without requiring to modify the application itself. This allows to even infect executables with a embedded signature when the signature is not automatically verified before execution. We briefly discuss also countermeasures such as secure channels, code authentication as well as trusted virtualization that enables the isolation of untrusted downloads from other application running in trusted domains or compartments.
Curcumin Attenuates Lead-Induced Cerebellar Toxicity in Rats via Inhibition of Oxidative Stress and Chelating Activity

Lead (Pb) is a toxic environmental heavy metal that induces serious clinical defect on all organs with the nervous system being the primary target. Curcumin is the main active constituent of turmeric rhizome (Curcuma longa) with strong antioxidant and anti-inflammatory properties. This study is aimed at evaluating the therapeutic potentials of curcumin on Pb-induced neurotoxicity. Thirty six male Sprague Dawley rats were randomly assigned into five (5) groups with 12 rats in the control (normal saline) and 6 rats for the lead treated group (LTG) (50 mg/kg lead acetate for 4 weeks), recovery group (RC) (50 mg/kg lead acetate for 4 weeks), treatment group 1 (Cur100) (50 mg/kg lead acetate for 4 weeks, followed by 100 mg/kg curcumin for 4 weeks) and treatment group 2 (Cur200) (50 mg/kg lead acetate for 4 weeks, followed by 200 mg/kg curcumin for 4 weeks) groups each. All experimental groups received the oral treatments through orogastric-tube on alternate days. Motor function was assessed using horizontal bar method while Pb concentration in the cerebellum of the rats were evaluated using ICP-MS techniques. Pb-administered rats showed significant decrease in motor scores, SOD activity with increase MDA levels and Pb concentration in their cerebellum with marked alterations in the histological architecture of the cerebellar cortex layers. However, treatment with curcumin improved the motor score, reduced Pb concentration in the cerebellum and ameliorates the markers of oxidative stress as well as restored the histological architecture of the cerebellum. The results this in study suggested that curcumin attenuates Pb-induced neurotoxicity via inhibition of oxidative stress and chelating activity.
An Error Analysis of Polynomial Form Dead Reckoning Model based on a Numerical Analysis

This paper aims to analyze an error of dead reckoned data gen- erated from received data in discrete temporal axis. That is, comparing with the data received temporally continuously, data acquired in every tempo- ral interval has a certain degradation or uncertainty of information. Our purpose is to introduce the mathematical model of this degradation with re- gard to the metrics of temporal interval. Polynomial models are introduced for dead reckoning of the data between frames using a parameter calculated from the data over the last several frames. By employing the method of error analysis of a numerical analysis to the above polynomial model, theoretical models, which approximate the degree of degradation in accuracy based on parameters such as update interval and change of the data, are found. This paper also confirms the adaptability of the theoretical model by conducting simulation experiments generated by pen motion of writing string of letters by human. Experimental results show that the proposed theoretical model approximates well the average error in the simulation. AMS Mathemarics Subject Classification: 68Q85, 68M07, 68Q25