Miracle-dz/UniFAR-FTUs huggingface dataset:
When Load Rebalancing Does Not Work for Distributed Hash Table

Distributed hash table (DHT) is the foundation of many widely used storage systems, for its prominent features of high scalability and load balancing. Recently, DHT-based systems have been deployed for the Internet-of-Things (IoT) application scenarios. Unfortunately, such systems can experience a breakdown in the scale-out and load rebalancing process. This phenomenon contradicts with the common conception of DHT systems, especially about its scalability and load balancing features. In this paper, we investigate the breakdown of DHT-based systems in the scale-out process. We formulate the load rebalancing problem of DHT by considering the impacts of write workloads and data movement. We show that, the average network bandwidth of each node and the intensity of the average write workload are the two key factors that determine the feasibility of DHT load rebalancing. We theoretically prove that load rebalancing is not feasible for a large DHT system under heavy write workloads in a node-by-node scale-out process.
Measuring an adaptive change in human decision-making from AI: Application to evaluate changes after AlphaGo

Across a growing number of domains, human experts are expected to learn from and adapt to AI with superior decision-making abilities. But how can we quantify such human adaptation to AI? We develop a simple measure of human adaptation to AI and test its usefulness in two case studies. In Case Study 1, we analyze 1.3 million move decisions made by professional Go players and find that a positive form of adaptation to AI (learning) occurred after the players could observe AI's reasoning processes rather than mere actions of AI. In Case Study 2, we test whether our measure is sensitive enough to capture a negative form of adaptation to AI (cheating aided by AI), which occurred in a match between professional Go players. We discuss our measure's applications in domains other than Go, especially in domains in which AI's decision-making ability will likely surpass that of human experts.
Decentralized Control with Graph Neural Networks

—Dynamical systems consisting of a set of autonomous agents face the challenge of having to accomplish a global task, relying only on local information. While centralized controllers are readily available, they face limitations in terms of scalability and implementation, as they do not respect the distributed information structure imposed by the network system of agents. Given the difﬁculties in ﬁnding optimal decentralized controllers, we propose a novel framework using graph neural networks (GNNs) to learn these controllers. GNNs are well-suited for the task since they are naturally distributed architectures and exhibit good scalability and transferability properties. The problems of ﬂocking and multi-agent path planning are explored to illustrate the potential of GNNs in learning decentralized controllers.
Adaptive Extreme Edge Computing for Wearable Devices

Wearable devices are a fast-growing technology with impact on personal healthcare for both society and economy. Due to the widespread of sensors in pervasive and distributed networks, power consumption, processing speed, and system adaptation are vital in future smart wearable devices. The visioning and forecasting of how to bring computation to the edge in smart sensors have already begun, with an aspiration to provide adaptive extreme edge computing. Here, we provide a holistic view of hardware and theoretical solutions toward smart wearable devices that can provide guidance to research in this pervasive computing era. We propose various solutions for biologically plausible models for continual learning in neuromorphic computing technologies for wearable sensors. To envision this concept, we provide a systematic outline in which prospective low power and low latency scenarios of wearable sensors in neuromorphic platforms are expected. We successively describe vital potential landscapes of neuromorphic processors exploiting complementary metal-oxide semiconductors (CMOS) and emerging memory technologies (e.g., memristive devices). Furthermore, we evaluate the requirements for edge computing within wearable devices in terms of footprint, power consumption, latency, and data size. We additionally investigate the challenges beyond neuromorphic computing hardware, algorithms and devices that could impede enhancement of adaptive edge computing in smart wearable devices.
Automatic Polyp Segmentation Using U-Net-ResNet50

Polyps are the predecessors to colorectal cancer which is considered as one of the leading causes of cancer-related deaths worldwide. Colonoscopy is the standard procedure for the identification, localization, and removal of colorectal polyps. Due to variability in shape, size, and surrounding tissue similarity, colorectal polyps are often missed by the clinicians during colonoscopy. With the use of an automatic, accurate, and fast polyp segmentation method during the colonoscopy, many colorectal polyps can be easily detected and removed. The ``Medico automatic polyp segmentation challenge'' provides an opportunity to study polyp segmentation and build an efficient and accurate segmentation algorithm. We use the U-Net with pre-trained ResNet50 as the encoder for the polyp segmentation. The model is trained on Kvasir-SEG dataset provided for the challenge and tested on the organizer's dataset and achieves a dice coefficient of 0.8154, Jaccard of 0.7396, recall of 0.8533, precision of 0.8532, accuracy of 0.9506, and F2 score of 0.8272, demonstrating the generalization ability of our model.
Statistics of nonpolarized points in the CMB polarization maps

The nonpolarized points (NPP) of the Q and U Stokes parameters of the CMB can be classified according to the geometry of the polarization field. We describe a procedure to identify these points in the pixelized sky and present the shape of the polarization angles in the vicinity of NPPs. We design a test of Gaussianity using the Kullback-Leibler divergence. We show that the total number density of nonpolarized points of the E and B families is closely related to the presence of lensing and the tensor-to-scalar ratio r . We further show that in the absence of lensing, the total number of NPPs of all types does not depend on r , while the lensing effect removes this degeneracy. This analysis is applied to the CMB maps from the 2018 Planck release. We show that there is a general consistency of SMICA and NILC maps compared to a reference set of Gaussian simulations. The strongest discrepancies are found in the Commander (with corresponding p value 0.07) and SEVEM ( p ¼ 0 . 01 ) maps.
A general perspective on the Metropolis-Hastings kernel

Since its inception the Metropolis–Hastings kernel has been applied in sophisticated ways to address ever more challenging and diverse sampling problems. Its success stems from the flexibility brought by the fact that its verification and sampling implementation rests on a local “detailed balance” condition, as opposed to a global condition in the form of a typically intractable integral equation. While checking the local condition is routine in the simplest scenarios, this proves much more difficult for complicated applications involving auxiliary structures and variables. Our aim is to develop a framework making establishing correctness of complex Markov chain Monte Carlo kernels a purely mechanical or algebraic exercise, while making communication of ideas simpler and unambiguous by allowing a stronger focus on essential features — a choice of embedding distribution, an involution and occasionally an acceptance function — rather than the induced, boilerplate structure of the kernels that often tends to obscure what is important. This framework can also be used to validate kernels that do not satisfy detailed balance, i.e. which are not reversible, but a modified version thereof.
On the Weyl problem for complete surfaces in the hyperbolic and anti-de Sitter spaces

The classical Weyl problem (solved by Lewy, Alexandrov, Pogorelov, and others) asks whether any metric of curvature K ≥ 0 on the sphere is induced on the boundary of a unique convex body in R3. The answer was extended to surfaces in hyperbolic space by Alexandrov in the 1950s, and a “dual” statement, describing convex bodies in terms of the third fundamental form of their boundary (e.g. their dihedral angles, for an ideal polyhedron) was later proved. We describe three conjectural generalizations of the Weyl problem in H3 and its dual to unbounded convex subsets and convex surfaces, in ways that are relevant to contemporary geometry since a number of recent results and well-known open problems can be considered as special cases. One focus is on convex domain having a “thin” asymptotic boundary, for instance a quasicircle – this part of the problem is strongly related to the theory of Kleinian groups. A second direction is towards convex subsets with a “thick” ideal boundary, for instance a disjoint union of disks – here one find connections to problems in complex analysis, such as the Koebe circle domain conjecture. A third direction is towards complete, convex disks of infinite area in H3 and surfaces in hyperbolic ends – with connections to questions on circle packings or grafting on the hyperbolic disk. Similar statements are proposed in anti-de Sitter geometry, a Lorentzian cousin of hyperbolic geometry where interesting new phenomena can occur, and in Minkowski and Half-pipe geometry. We also collect some partial new results mostly based on recent
Refined bounds for randomized experimental design

Experimental design is an approach for selecting samples among a given set so as to obtain the best estimator for a given criterion. In the context of linear regression, several optimal designs have been derived, each associated with a different criterion: mean square error, robustness, \emph{etc}. Computing such designs is generally an NP-hard problem and one can instead rely on a convex relaxation that considers probability distributions over the samples. Although greedy strategies and rounding procedures have received a lot of attention, straightforward sampling from the optimal distribution has hardly been investigated. In this paper, we propose theoretical guarantees for randomized strategies on E and G-optimal design. To this end, we develop a new concentration inequality for the eigenvalues of random matrices using a refined version of the intrinsic dimension that enables us to quantify the performance of such randomized strategies. Finally, we evidence the validity of our analysis through experiments, with particular attention on the G-optimal design applied to the best arm identification problem for linear bandits.
Effect of axion-like particles on the spectrum of the extragalactic gamma-ray background

Axion-like particles (ALPs) provide a feasible explanation for the observed lower TeV opacity of the Universe. If the anomaly TeV transparency is caused by ALPs, then the fluxes of distant extragalactic sources will be enhanced at photon energies beyond TeV, resulting in an enhancement of the observed extragalactic gamma-ray background (EGB) spectrum. In this work, we have investigated the ALP modulation on the EGB spectrum at TeV energies. Our results show that in the most optimistic case, the existence of ALPs can cause the EGB spectrum to greatly deviate from the prediction of a pure extragalactic-background-light (EBL) absorption scenario. The deviation occurs at approximately ≳1 TeV, and the current EGB measurements by Fermi-LAT cannot identify such an effect. We also find that most of the sensitive ALP parameters have been ruled out by existing constraints, leaving only a small region of unrestricted parameters that can be probed using the EGB effect investigated in this work. Observations from forthcoming very-high-energy instruments like LHAASO and CTA may be beneficial for the study of this effect.
Synthetic Source Language Augmentation for Colloquial Neural Machine Translation

Neural machine translation (NMT) is typically domain-dependent and style-dependent, and it requires lots of training data. State-of-the-art NMT models often fall short in handling colloquial variations of its source language and the lack of parallel data in this regard is a challenging hurdle in systematically improving the existing models. In this work, we develop a novel colloquial Indonesian-English test-set collected from YouTube transcript and Twitter. We perform synthetic style augmentation to the source of formal Indonesian language and show that it improves the baseline Id-En models (in BLEU) over the new test data.
Good practices for Bayesian Optimization of high dimensional structured spaces

The increasing availability of structured but high dimensional data has opened new opportunities for optimization. One emerging and promising avenue is the exploration of unsupervised methods for projecting structured high dimensional data into low dimensional continuous representations, simplifying the optimization problem and enabling the application of traditional optimization methods. However, this line of research has been purely methodological with little connection to the needs of practitioners so far. In this paper, we study the effect of different search space design choices for performing Bayesian Optimization in high dimensional structured datasets. In particular, we analyse the influence of the dimensionality of the latent space, the role of the acquisition function and evaluate new methods to automatically define the optimization bounds in the latent space. Finally, based on experimental results using synthetic and real datasets, we provide recommendations for the practitioners.
Data-mining of dislocation microstructures: concepts for coarse-graining of internal energies

Continuum models of dislocation plasticity require constitutive closure assumptions, e.g., by relating details of the dislocation microstructure to energy densities. Currently, there is no systematic way for deriving or extracting such information from reference simulations, such as discrete dislocation dynamics (DDD) or molecular dynamics. Here, a novel data-mining approach is proposed through which energy density data from systems of discrete dislocations can be extracted. Our approach relies on a systematic and controlled coarse-graining process and thereby is consistent with the length scale of interest. For data-mining, a range of different dislocation microstructures that were generated from 2D and 3D DDD simulations, are used. The analyses of the data sets result in energy density formulations as a function of various dislocation density fields. The proposed approach solves the long-standing problem of voxel-size dependent energy calculation during coarse graining of dislocation microstructures. Thus, it is crucial for any continuum dislocation dynamics simulation.
Inexact tensor methods and their application to stochastic convex optimization

We propose general non-accelerated and accelerated tensor methods under inexact information on the derivatives of the objective, analyze their convergence rate. Further, we provide conditions for the inexactness in each derivative that is sufficient for each algorithm to achieve the desired accuracy. As a corollary, we propose stochastic tensor methods for convex optimization and obtain sufficient mini-batch sizes for each derivative.
Multi-period travelling politician problem: A hybrid metaheuristic solution method

Abstract This paper studies the multi-period travelling politician problem whose objective is to maximise the net benefit accrued by a party leader during a fixed campaign period. The problem is also characterised by flexible depots since the daily tours realised by the party leader may not start and end at the same city. A hybrid multi-start Iterated Local Search method complemented with a Variable Neighbourhood Descent is developed to solve the problem heuristically. Two constructive procedures are devised to generate initial feasible solutions. The proposed method is tested on 45 problem instances involving 81 cities and 12 towns in Turkey. Computational results show that the hybrid metaheuristic approach outperforms a recently proposed two-phase matheuristic by producing 7 optimal solutions and 17 new best solutions. In addition, interesting practical insights are provided using scenario analysis that could assist campaign planners in their strategic decisions.
Nonreciprocal multimode and indirect couplings in cavity magnonics

We investigate the magnon-photon couplings by employing a small magnet within an irregular resonant cavity, which leads to a desirable nonreciprocity with a big isolation ratio. Moreover, the higher-order couplings between the spin wave modes with the polarized photon modes also exhibit the nonreciprocity. These couplings between polarized photon and spin waves could be regarded as a multi-mode hybridization mediated by cavity photons. We also derive a coupling matrix to predict the characteristics of this kind of indirect coupling. The existence of the indirect couplings broaden the ﬁeld range of the nonreciprocity of the system. The achieved nonreciprocal multi-mode magnon-photon couplings in a single system oﬀer a feasible method to improve the signal transmission quality.
Synthesis of calix[4]crowns containing soft and hard ion binding sites via click chemistry

A series of triazole-modified calix[4]crowns, 3, containing hard and soft ion binding sites were synthesized in good yields via click chemistry. The hard and soft ion binding sites may selectively complex alkali metal or transition metal cations. Compound 3a shows good complex selectivity towards Pb2+ and 3c shows the highest Li+/K+ extraction ratio, suggesting that the triazole groups play an important role in metal ion binding.
Consensus-Based Distributed Computation of Link-Based Network Metrics

Average consensus algorithms have wide applications in distributed computing systems where all the nodes agree on the average value of their initial states by only exchanging information with their local neighbors. In this letter, we look into link-based network metrics which are polynomial functions of pair-wise node attributes defined over the links in a network. Different from node-based average consensus, such link-based metrics depend on both the distribution of node attributes and the underlying network topology. We propose a general algorithm using the weighted average consensus protocol for the distributed computation of link-based network metrics and provide the convergence conditions and convergence rate analysis.
Simulations of photodegradation of toluene and formaldehyde in a monolith reactor using computational fluid dynamics

In this study, simulations were conducted on a monolith reactor for the photodegradation of toluene and formaldehyde. The monoliths in the reactor were treated as porous zones and the photocatalytic oxidation occurring on the monolith surfaces was modeled using Langmuir–Hinshelwood kinetics. A discrete ordinates model was used to simulate the light intensity with a novel approach, which involved an adjustable parameter—the absorption coefficient of the channel wall, for modeling the local light intensity across the porous media. The advantage of this approach was that despite its simplicity, it was able to capture and visualize the local light profile across the monolith channels and to integrate it into the reaction kinetics. Although it required a trial-and-error to determine the correct value of the channel wall absorption coefficient, the proposed model achieved a reasonable agreement between the simulation results and published experimental data. © 2010 American Institute of Chemical Engineers AIChE J, 2011
Preclinical pharmacokinetics of MK-0974, an orally active calcitonin-gene related peptide (CGRP)-receptor antagonist, mechanism of dose dependency and species differences

The underlying mechanism for low oral bioavailability of MK-0974, a potent calcitonin-gene related peptide (CGRP)-receptor antagonist, in monkeys and for species-dependent non-linear pharmacokinetics in monkeys and rats were investigated. In monkeys, MK-0974 displayed moderate clearance (14–20 ml min−1 kg−1), while oral bioavailability was 6%. The pharmacokinetics of MK-0974 remained linear across 0.5–10 mg kg−1 intravenous dose in monkeys, but the oral area under the plasma concentration–time curve (AUC) increase (5–30 mg kg−1) was 15-fold over dose-proportional. Based on a comparison of AUC following hepatic portal vein administration and cephalic vein infusion, MK-0974 exhibited a low-to-moderate hepatic extraction ratio (36%) in monkeys. Following oral dose of [14C]MK-0974 to monkeys, the hepatic portal AUC ratio of MK-0974 versus total radioactivity was 0.32, and the total radioactivity recovered in bile and urine was 45–83%. MK-0974 undergoes significant oxidative metabolism (cytochrome P450 (CYP) 3A) in monkey intestinal microsomes. In contrast, oral AUC of MK-0974 in rats was near dose-proportional (15–100 mg kg−1). Following oral administration of [14C]MK-0974 to rats, the hepatic portal AUC ratio of MK-0974 to total radioactivity (0.67) was higher than in monkeys. Additionally, the metabolic rate of MK-0974 was slower in rat than in monkey intestinal microsomes. Collectively, intestinal first-pass metabolism played a significant role in the low oral bioavailability in monkeys and contributed to the species-dependent non-linear oral pharmacokinetics in rats and monkeys of MK-0974.
Improvement of a CL-FIA System Using Maltose Phosphorylase for the Determination of Phosphate-Ion in Freshwater

Abstract In a previous study, a phosphate ion (Pi) biosensor consisting of a system of chemiluminescence flow-injection analysis (CL-FIA) combined with a reaction system of maltose phosphorylase, mutarotase, and glucose-oxidase (MP-MUT-GOD) was developed without a detailed investigation of the measurements of real samples. Therefore, in this study, after characterization of the measuring conditions in the CL-FIA system for freshwater samples, we investigated the influences of dissolved substances, such as metal ions, heavy metal ions, inorganic ions, and organic substances, in freshwater on the sensor response. The results of the study indicated that there were no substances giving irreversible effects to the enzyme activities of MP, MUT, or GOD used in this sensor; however, some cations or organic substances influenced the sensor response. We then compared the effects of the elimination of the dissolved substances by a chelate reagent, ion exchange resin, or combination of ion exchange resin and UV irradiation on the sensor response. Consequently, a cation-exchange resin (sulfonated), which improved most effectively the sensor response, was chosen as a pre-treatment method. By utilizing this pre-treatment method, the sensor system could determine the Pi concentrations in 31 samples of river or pond waters in Japan. The results obtained by this sensor system and the molybdenum-blue method were compared, and a regression curve (y = 2.78x) with good correlation (r = 0.958) was obtained between them. The reason of the slope at the regression curve was discussed. Finally, we introduce a trial type of an automatic CL-FIA biosensor system for future Pi determination. †This article is dedicated to Professor Dr. Pierre Coulet, Lyon I University, on the occasion of his retirement.