Miracle-dz/UniFAR-FTUs huggingface dataset:
Decentralized Stochastic First-Order Methods for Large-scale Machine Learning

Decentralized consensus-based optimization is a general computational framework where a network of nodes cooperatively minimizes a sum of locally available cost functions via only local computation and communication. In this article, we survey recent advances on this topic, particularly focusing on decentralized, consensus-based, first-order gradient methods for largescale stochastic optimization. The class of consensus-based stochastic optimization algorithms is communication-efficient, able to exploit data parallelism, robust in random and adversarial environments, and simple to implement, thus providing scalable solutions to a wide range of largescale machine learning problems. We review different state-of-the-art decentralized stochastic optimization formulations, different variants of consensus-based procedures, and demonstrate how to obtain decentralized counterparts of centralized stochastic first-order methods. We provide several intuitive illustrations of the main technical ideas as well as applications of the algorithms in the context of decentralized training of machine learning models.
Non-Separating Planar Graphs

A graph $G$ is a non-separating planar graph if there is a drawing $D$ of $G$ on the plane such that (1) no two edges cross each other in $D$ and (2) for any cycle $C$ in $D$, any two vertices not in $C$ are on the same side of $C$ in $D$. Non-separating planar graphs are closed under taking minors and are a subclass of planar graphs and a superclass of outerplanar graphs. In this paper, we show that a graph is a non-separating planar graph if and only if it does not contain $K_1 \cup K_4$ or $K_1 \cup K_{2,3}$ or $K_{1,1,3}$ as a minor. Furthermore, we provide a structural characterisation of this class of graphs. More specifically, we show that any maximal non-separating planar graph is either an outerplanar graph or a wheel or it is a graph obtained from the disjoint union of two triangles by adding three vertex-disjoint paths between the two triangles. Lastly, to demonstrate an application of non-separating planar graphs, we use the characterisation of non-separating planar graphs to prove that there are maximal linkless graphs with $3n-3$ edges. Thus, maximal linkless graphs can have significantly fewer edges than maximum linkless graphs; Sachs exhibited linkless graphs with $n$ vertices and $4n-10$ edges (the maximum possible) in 1983.
ChromaGAN: An Adversarial Approach for Picture Colorization

The colorization of grayscale images is an ill-posed problem, with multiple correct solutions. In this paper, an adversarial learning approach is proposed. A generator network is used to infer the chromaticity of a given grayscale image. The same network also performs a semantic classiﬁcation of the image. This network is framed in an adversarial model that learns to colorize by incorporating perceptual and semantic understanding of color and class distributions. The model is trained via a fully self-supervised strategy. Qualitative and quantitative re-sults show the capacity of the proposed method to colorize images in a realistic way, achieving top-tier performances relative to the state-of-the-art.
Calibrating the Cosmic Distance Ladder Using Gravitational-wave Observations

Type Ia supernovae (SNe Ia) are among the pre-eminent distance ladders for precision cosmology due to their intrinsic brightness, which allows them to be observable at high redshifts. Their usefulness as unbiased estimators of absolute cosmological distances, however, depends on accurate understanding of their intrinsic brightness, or anchoring their distance scale. This knowledge is based on calibrating their distances with Cepheids. Gravitational waves from compact binary coalescences, being standard sirens, can be used to validate distances to SNe Ia when both occur in the same galaxy or galaxy cluster. The current measurement of distance by the advanced LIGO and Virgo detector network suffers from large statistical errors (∼50%). However, we find that, using a third-generation gravitational-wave detector network, standard sirens will allow us to measure distances with an accuracy of ∼0.1%–3% for sources within ≤300 Mpc. These are much smaller than the dominant systematic error of ∼5% due to the radial peculiar velocity of host galaxies. Therefore, gravitational-wave observations could soon add a new cosmic distance ladder for an independent calibration of distances to SNe Ia.
Enhancing Dynamic Symbolic Execution by Automatically Learning Search Heuristics

We present a technique to automatically generate search heuristics for dynamic symbolic execution. A key challenge in dynamic symbolic execution is how to effectively explore the program's execution paths to achieve high code coverage in a limited time budget. Dynamic symbolic execution employs a search heuristic to address this challenge, which favors exploring particular types of paths that are most likely to maximize the final coverage. However, manually designing a good search heuristic is nontrivial and typically ends up with suboptimal and unstable outcomes. The goal of this paper is to overcome this shortcoming of dynamic symbolic execution by automatically learning search heuristics. We define a class of search heuristics, namely a parametric search heuristic, and present an algorithm that efficiently finds an optimal heuristic for each subject program. Experimental results with industrial-strength symbolic execution tools (e.g., KLEE) show that our technique can successfully generate search heuristics that significantly outperform existing manually-crafted heuristics in terms of branch coverage and bug-finding.
Vertex-disjoint large induced forests in graphs

We prove that every $n$-vertex planar graph can be partitioned into three induced forests, two of which have at least $\lfloor(n+1)/3\rfloor$ vertices and can also be partitioned into three induced bipartite subgraphs, each having $\lfloor n/3\rfloor$ or $\lceil n/3\rceil$ vertices. These are proved by one unified tool, which also implies that for positive integers $\ell<k$, if an $n$-vertex graph has acyclic chromatic number at most $k$, then it has $\ell$ vertex-disjoint induced forests having at least $\lfloor (2n+k-\ell)/(k+\ell-1)\rfloor$ vertices.
Barycentric interpolation on Riemannian and semi-Riemannian spaces

Interpolation of data represented in curvilinear coordinates and possibly having some non-trivial, typically Riemannian or semi-Riemannian geometry is a ubiquitous task in all of physics. In this work, we present a covariant generalization of the barycentric coordinates and the barycentric interpolation method for Riemannian and semi-Riemannian spaces of arbitrary dimension. We show that our new method preserves the linear accuracy property of barycentric interpolation in a coordinate-invariant sense. In addition, we show how the method can be used to interpolate constrained quantities so that the given constraint is automatically respected. We showcase the method with two astrophysics related examples situated in the curved Kerr space–time. The first problem is interpolating a locally constant vector field, in which case curvature effects are expected to be maximally important. The second example is a general relativistic magnetohydrodynamics simulation of a turbulent accretion flow around a black hole, wherein high intrinsic variability is expected to be at least as important as curvature effects.
Artificial Intelligence and Law: What Do People Really Want?: Example of a French Multidisciplinary Working Group

This paper addresses issues related to the ethical consequences of using AI technologies in court decisions. With the prodigious technological leap made in the field of artificial intelligence in recent years, disruptive innovations have affected many business sectors, with economic, social and ethical consequences. But what do people really want about the application of artificial intelligence technologies in the law system? This article presents a general methodological approach to take into account the ethical aspect of the introduction of a new technology in a given domain. We apply this methodology in the specific case of the introduction of AI technologies in the law system. As a multidisciplinary working group interested in this application in the case of France, we have organized a series of workshops to discuss this topic and highlight the respective values and interests of each stakeholder. The result of this work in presented in the form of an ethical matrix that can be used as a tool by the public authorities to help decision-making on the subject with a prioritization of certain values in order to reflect the respect for fundamental rights.
The Application of ODCM for Building Well-Founded Legal Domain Ontologies: A Case Study in the Domain of Carriage of Goods by Sea

The ontology engineering community is facing several key challenges about the development of domain ontologies. One major challenge is the building of well-founded domain ontologies. This concept has raised recently and it refers to ontologies that are grounded in validated foundational ontologies. This paper addresses the effective contribution of ontology-driven conceptual modeling process (ODCM) for developing such ontologies in the legal domain. A case study in the domain of carriage of goods by sea is presented.
MPI Collectives for Multi-core Clusters: Optimized Performance of the Hybrid MPI+MPI Parallel Codes

The advent of multi-/many-core processors in clusters advocates hybrid parallel programming, which combines Message Passing Interface (MPI) for inter-node parallelism with a shared memory model for on-node parallelism. Compared to the traditional hybrid approach of MPI plus OpenMP, a new, but promising hybrid approach of MPI plus MPI-3 shared-memory extensions (MPI+MPI) is gaining attraction. We describe an algorithmic approach for collective operations (with allgather and broadcast as concrete examples) in the context of hybrid MPI+MPI, so as to minimize memory consumption and memory copies. With this approach, only one memory copy is maintained and shared by on-node processes. This allows the removal of unnecessary on-node copies of replicated data that are required between MPI processes when the collectives are invoked in the context of pure MPI. We compare our approach of collectives for hybrid MPI+MPI and the traditional one for pure MPI, and also have a discussion on the synchronization that is required to guarantee data integrity. The performance of our approach has been validated on a Cray XC40 system (Cray MPI) and NEC cluster (Open MPI), showing that it achieves comparable or better performance for allgather operations. We have further validated our approach with a standard computational kernel, namely distributed matrix multiplication, and a Bayesian Probabilistic Matrix Factorization code.
Research on Blended Learning of Equipment Application Courses in Military Academies

The traditional teaching mode of vocational and technical education for sergeancy in military academies is difficult to meet the requirements of the serve ability training under the new establishment. In order to optimize the teaching approach and improve the teaching quality, the blended learning mode equipment application courses is proposed in this paper. Combining the blended learning theory with the practical teaching experience, the designing of teaching goals, teaching environment construction, teaching organization and teaching effect evaluation of blended learning is studied respectively.
An Analysis of Contracts and Relationships between Supercomputing Centers and Electricity Service Providers

Increases in peak electricity demands and the growing use of renewable energy --- with associated intermittency and variable output --- present new challenges to electricity service providers (ESPs). ESPs employ demand charges, variable tariffs and demand response (DR) programs to influence the consumption behavior of consumers to partially mitigate these challenges. Due to their high load and potential flexibility, supercomputing centers (SCs) are increasingly gaining importance in the grid. This paper presents a qualitative study of service contracts between ESPs and SCs in the United States and Europe. From this we extract a contract typology used to understand how, and to what extent, variable tariffs, DR programs, and demand charges are imposed on SCs. Further, we highlight the actions taken by SCs in response to these contractual elements. Finally, we present perspectives on grid integration of SCs to enhance their collaboration with their ESPs to benefit supply stability and resilience.
LPMS: A Low-cost Topology-aware Process Mapping Method for Large-scale Parallel Applications on Shared HPC Systems

Topology-aware process mapping can reduce communication cost by embedding the application communication topology to the underlying networks. Being generally a NP-hard problem, process mapping methods strive to balance mapping cost and mapping performance. Moreover, many existing low-cost methods assume that the application owns the high-performance computer exclusively or the allocated resource forms a regular structure, which is no longer true under most supercomputers where the machine is often shared among users and applications, thus hinders their performance on such shard HPC systems. To address these issues, in this paper, we propose a label-propagation-based process mapping method, namely LPMS, that is both low-cost and fits well on shared HPC systems. Both analysis and experiments show that LPMS enjoys low algorithmic costs while maintaining performance even on a loaded shared HPC system. Real-world scientific application proxies gain a performance boost as large as 34.79% compared to default natural process placements on the TianHe-2 HPC system and a fat-tree-based cluster HPC system.
[ S 6P 03 ] hcp-Fe alloy as the candidate of the Earth ’ s inner core

Earth’s inner core exhibits elastic anisotropy, but the cause of the texturing is uncertain: solidification, deformation, or some combination. In experiments presented here we deformed in torsion directionally solidified Zn 3 wt pct Sn alloys at . 97 the melting temperature of pure Zn. We used a Zn-rich alloy because Zn is hexagonal close-packed at atmospheric pressure, as is likely Fe under inner core conditions. The directionally solidified alloys have the textured, columnar dendritic microstructure that has been proposed for the inner core. As a control we deformed in torsion the same alloy at the same temperature, but with a starting microstructure that is comparatively fine-grained, equiaxed, and untextured. We find evidence that the directionally solidified alloys deform by means of grain boundary sliding, presumably accompanied by di↵usion near the ends of the columnar crystals in order to preserve macroscopic shape. We also find that the directionally solidified alloys require approximately three times the torque to deform to the same strain than do the untextured alloys. Grain boundary sliding and greater hardness are due to insu cient slip systems to accommodate the strain, as a result of the texturing. This is supported by microcracking in the deformed directionally solidified alloys. Although the inner core is unlikely to exhibit semi-brittle deformation, the experiments suggest that a directionally solidified inner core could have a higher viscosity than one with a finer-grained, untextured microstructure, and that grain boundary sliding may be a dominant deformation mechanism, in spite of the large
Validation of Decision Models for an Autonomous Earth Surveillance Satellite

This paper presents a novel application of Validation and Verification to autonomous decisionmaking. We discuss the relevance of validating formal decision models for an autonomous Earth surveillance satellite, in order to prove all possible on-line decisions are constrained to a given set of validation properties. We distinguish safety and liveness properties depending on whether properties refer to the satellite's physical action dynamics or to the long-term consequences of decisions. We give some examples of both types of properties.
HUMZA WALI HUMAN ROBOT COLLABORATION IN ASSEMBLY PROCESSES

HUMZA WALI: Human Robot Collaboration in Assembly Processes Tampere University of technology Master of Science Thesis, 61 pages, 11 Appendix pages November 2018. Degree Programme in Automation Engineering, M.Sc. (Tech) Major: Fluid Power (Automation and Hydraulic Engineering). Minor: Life-Cycle Management and Engineering (Mechanical Engineering and Industrial Systems). Examiners: Prof. Minna Lanz, Ph.D. Asst. Prof. Roel Pieters, Ph.D.
Heritability estimates for an Accelerated Breeding Scheme ( ABS ) in clonally propagated crops-using sweetpotato as a model

Breeding of clonally propagated crops usually requires five or more years to produce outputs. An accelerated breeding scheme (ABS) can be designed in clonally propagated crops provided that the genotype by year interaction is low for the considered crop. The objectives of this study were to describe an ABS for clonally propagated crops and to investigate the efficiency of this breeding scheme on the basis of variance components, heritability estimations and model calculations for sweetpotato. In total 4243 clones from a seed nursery were planted nearly simultaneously at three locations in 1m row plots. The observed traits were storage root yield, upper biomass, dry matter, total carotenoids, iron and zinc contents of storage roots. Model calculations were carried out with a test capacity 12000 and 8000 field plots and a laboratory capacity of 8000 and 4000 samples. The ABS proposed reduces the time needed for one recurrent selection cycle in population improvement of sweetpotato form four to two years (the time needed for variety development is reduced from 7 to 4 years). The heritability estimates in the proposed ABS were within the range of 0.5 and 0.6 for storage root yield and upper biomass, 0.6 to 0.7 for iron and zinc and > 0.8 for dry matter and total carotenoids. For storage root yield the optimum resource allocation of 4000 and 12000 field plots was to plant 2000 or 4000 genotypes, at two and three locations, respectively. For low laboratory capacity the best allocation was to evaluate all genotypes at one location. For high laboratory capacity the best allocation was to use one or two locations. We conclude that our proposed ABS might be a very attractive breeding scheme for sweetpotato and other clonally propagated crops.
Principles Of Molecular Rheumatology

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NIKE-based Fast Privacy-preserving High-dimensional Data Aggregation for Mobile Devices

Mobile apps are widely used for personalization and improvement of user experiences. These apps have access to a large amount of sensitive data on which learning algorithms such as machine learning (ML) algorithms are applied locally. Moreover, apps share user data and locally-computed models. Aggregate sum is a building block for many ML algorithms such as deep learning, least-squares linear fit, and linear regression. In this paper, we present a system for secure aggregate sum computation in which the server wishes to periodically compute the aggregate sum of a set of mobile users’ private inputs without compromising the individual inputs. As the aggregate sum operation is performed often, we devise the aggregation scheme to be communication and computation efficient. First, we present a non-interactive pairwise key generation scheme for mobile users where the non-interactivity among users is achieved by outsourcing the keying material generation task to two non-colluding cryptographic secret providers. Second, we design an efficient aggregate sum scheme that has low communication and computation overheads and the failure-robust property, which improves over Bonawitz et al.’s scheme (CCS 2017). The security and performance of the scheme are analyzed, followed by comparing it with existing ones. We implement our scheme on a smartphone as well as on a desktop to measure its efficiency. We conduct experiments on high-dimensional inputs, and our experimental results demonstrate about 1.5x to 3x improvement over existing schemes. Energy consumption results of our scheme on smartphone are presented.
Low-Rank Tensor Subspace Tracking from Incomplete Data by CP Decomposition using Recursive Least Squares

We propose an online tensor subspace tracking algorithm based on the CP decomposition exploiting the recursive least squares (RLS), dubbed OnLine Low-rank Subspace tracking by TEnsor CP Decomposition (OLSTEC). Numerical evaluations show that the proposed OLSTEC algorithm gives faster convergence per iteration comparing with the state-of-the-art online algorithms.
A New Heterogeneous Packet Processing Architecture and Its Analytical Performance Model

Today’s IP routers have to simultaneously meet multiple requirements such as programmability, scalability, power, and price besides the traditional objective of high throughput. Software routers like Click offer the best flexibility but suffer from a lower level of processing throughput. A few recent works prove the potential of Graphic Processing Units (GPUs) for high-speed packet processing. However, current GPU architectures cannot guarantee quality-of-service (QoS) of IP routing due to the batched execution model. In this work, we propose a novel heterogeneous, integrated CPU/GPU microarchitecture, Hermes, which adaptively maintains a balance between packet latency and overall throughput. A complete set of router applications are implemented on this architecture. Experimental results show that Hermes achieves a 5X enhancement in throughput, a 81.2% reduction in average packet latency, and a 72.9% reduction in delay variance, when compared with a GPU accelerated software router. A byproduct of this research is an analytical model that catpures the throughput and processing latency of Hermes-alike heterogensous architecture. In this work, the model can be used to quickly estimate IP routing QoS metrics of different Hermes microarchitectural configurations under various traffic patterns. Simulation results reveal that the analytical model can accurate predict QoS metrics with an average error rate of less than 10%.