Transfer Learning for Human & AI

In the scheduling with non-uniform communication delay problem, the input is a set of jobs with precedence constraints . Associated with every precedenceconstraint between a pair of jobs is a communication delay . The objective is to assign the jobs to machines to minimizethe makespan of the schedule .…

The S-MP-SCOPF is formulated as a non-linear programming (NLP) problem . It computes optimal setpoints offlexibility resources and other conventional control means for congestion management and voltage control in day-ahead operation . The importance and performances of the proposed model areillustrated on two test systems of 5 nodes and 60 nodes, respectively, respectively.…

Detecting the presence of project management anti-patterns (AP) currently requires experts on the matter and is an expensive endeavor . Using the Fire Drill AP, we introduce a novel way to translate descriptions into detectable AP that are comprised of arbitrary metrics and events such as maintenance activities .…

This is the first open-source memory compilers that has been developed specifically to automate ResistiveRandom Access Memory (RRAM) generation . RRAM holds the promise of achieving high speed, high density and non-volatility . A novel RRAM architecture,additionally is proposed, and a number of generated RRAM arrays are evaluated to identify their worst case control line parasitics and worst case settlingtime across the memristors of their cells .…

Toxicokinetic-toxicodynamic (TKTD) modelling is essential to make sense of the time dependence of toxic effects . The relevant TKTD models for sub-lethal effects are based on Dynamic-Energy Budget (DEB) theory . In contrast, experimental tests score egg or offspring release by the mother .…

We propose here to make the connection between the definitions given by Turing and Wittgenstein about what it means to “follow a rule”. It will be here a presentation of the Turing test in order to observe that humans and machines have more in common than one might initially believe when it comes to interpreting signs .…

This paper introduces a model of a stylized organization that is comprised of departments that autonomously allocate tasks . The organization guides the departments’ behavior by either an individualistic, a balanced, or an altruistic linear incentive scheme . Short-sighted decisions appear favorable since they do not only increase performance in the short run but also result in significantly higher performances in the long run .…

We propose an Extended Hybrid High-Order scheme for the Poisson problem with weak singularities . The method is formulated by enriching the local polynomial spaces with appropriatesingular functions . Via a detailed error analysis, the method is shown toconverge optimally in both discrete and continuous energy norms .…

Lux features a high-level language for generating visualizations on-demand to encourage rapid visual experimentation with data . Lux has already been embraced by data sciencepractitioners, with over 1.9k stars on Github within its first 15 months . Luxadds no more than two seconds of overhead on top of pandas for over 98% of dataframes in the UCI repository .…

We employ kernel-based approaches that use samples from a probability distribution to approximate a Kolmogorov operator on a manifold . We also employ an efficient $k$-$d$ tree algorithm to compute the sparse kernel matrix, which is a computational bottleneck . These methods only require samples from the underlying distribution and, therefore, can be applied on high dimensions or on geometrically complex manifolds when spatialdiscretizations are not available .…

Random projections reduce the dimension of a set of vectors while preservingstructural information, such as distances between vectors in the set . This paper proposes a novel use of row-product random matrices in random projection . It requires substantially less memory than existing dimension reduction maps .…

Deep Reinforcement Learning (DRL) is being increasingly applied to the problem of resource allocation for emerging System-on-Chip applications . The majority of SoC resource management approaches have been targeting makespanminimization with fixed number of jobs in the system . In contrast, SoCRATESaims at minimizing average latency in a steady-state condition while assigning tasks in the ready queue to heterogeneous resources .…

We construct numerical approximations for Mean Field Games with fractional or nonlocal diffusions . The methods are monotone, stable, and consistent . We prove convergence along subsequences for (i) degenerate equations in onespace dimension and (ii) nondegenerate . equations in arbitrary dimensions .…

Participatory budgeting (PB) is a democratic process where citizens jointly decide on how to allocate public funds to indivisible projects . This paper focuses on PB processes where citizens may give additional money to projectsthey want to see funded . We introduce a formal framework for this kind of PB with donations .…

This article studies a novel distributed precoding design, coined teamminimum mean-square error (TMMSE) precoding . Building on the theory of teams, we derive a set of necessary and sufficientconditions for optimal TMMSE precoding, in the form of an infinite dimensionallinear system of equations .…

The authors studied one such US Department of Energy mission-critical condensed matter physics application, Dynamical ClusterApproximation (DCA++) This paper discusses how device memory-bound challenges were successfully reduced by proposing an effective “all-to-all” communication method — a ring communication algorithm . The ring algorithm was optimized with sub-ring communicatorsand multi-threaded support to further reduce communication overhead and exposemore concurrency, respectively .…

Shared autonomous vehicles (SAVs) can have significant impacts on the transport system and land use by replacing private vehicles . Sharing vehicleswithout drivers is expected to reduce parking demand, and as a side effect,increase congestion owing to the empty fleets made by SAVs picking up travelers and relocating .…

We are interested in the restoration of noisy and blurry images where the texture mainly follows a single direction . Problem sof this type arise, for example, in microscopy or computed tomography forcarbon or glass fibres . We solve the problem by an ADMM algorithm with provenconvergence and subproblems that can be solved exactly at a low computational cost .…

In discrete event simulation, event simultaneity occurs when any two events are scheduled to happen at the same point in simulated time . Simulation determinism is the expectation that the same semanticallyconfigured simulation will be guaranteed to repeatedly reproduce identical results .…

Deep learning has solved difficult problems in visual patternrecognition, it is mostly successful in tasks where there are lots of labeled training data available . The brain is able to perform most of these tasks in a general way from limited to no labeled data .…

In the last decade, deep neural networks have proven to be very powerful incomputer vision tasks . However, they are not robust toperturbations of the input data . Several methods able to generateadversarial samples make use of gradients, which usually are not available to an attacker in real-world scenarios .…

Modern traceability technologies promise to improve supply chain management by simplifying recalls, increasing demand visibility, or ascertainings sustainable supplier practices . We introduce a model of the dynamics of traceability technology adoption insupply chain networks to tackle the problem of selecting the smallest set of early adopters guaranteeing broad dissemination .…

Federated learning (FL) has recently emerged as an important and promising scheme in IoT, enabling devices to jointly learn a model without sharing their raw data sets . As the training data in FL is notcollected and stored centrally, FL training requires frequent model exchange, which is largely affected by the wireless communication network .…

A bounded container maintains a collection of elements that can be inserted and extracted as long as the number of stored elements does not exceed the capacity . We consider the concurrent implementations of a bounded container more or less memory-friendly depending on how much memory they use in addition to storing the elements .…

Crack propagation experiments evaluated by digitalimage correlation (DIC) for a carbon black filled ethylene propylene dienemonomer rubber (EPDM) and numerical modeling with the help of variational phase-field fracture . The main focus is the evolution of cracks in one-sidednotched EPDM strips containing a circular hole .…

Encoder-decoder-baseddialogue models tend to produce generic and dull responses during the decoding step . The proposed bidirectionalresponse generation method achieves state-of-the-art performance for response quality . The advantage of our method isthat the forward generation and backward reasoning steps are trainedsimultaneously through the use of a latent variable .…

We propose an extrinsic, continuous-Galerkin (CG), extended finite elementmethod (XFEM) that generalizes the work of Hansbo and Hansbo to allow multipleHeaviside enrichments within a single element in a hierarchical manner . Thisapproach enables complex, evolving XFEM surfaces in 3D that cannot be capturedusing existing CG-XFem approaches .…

Predictive coding (PC) is a general theory of cortical function . The local, gradient-based learning rules found in one kind of PC model have recently been shown to closely approximate backpropagation . The model may also be useful for developing local learning algorithms that are compatible with neuromorphic hardware .…

A basic model can be thought of as the ongoing trace of a multi-agent system . Every state records systemic confirmations and disconfirmations of whether an acting entity is able to bring about something . Qualitative inductive reasoning is then used in order to infer what actingentities are deemed able to do .…

Stochastic gradient descent (SGD) is one of the most popular algorithms in machine learning . The noise encountered in these applications is different from that in theoretical analyses of stochastic gradientalgorithms . The energy landscape resembles that of overparametrized deep learning problems .…

Distributed quantum systems and especially the Quantum Internet have the potential to fully demonstrate the power of quantumcomputing . Developing a general-purpose quantum computer is much more difficult than connecting many small quantum devices . One major challenge of implementing distributed quantum systems is programming them and verifying their correctness .…

An integration by parts formula for the transient heat equation in three spatial dimensions is available in the literature, but a proof of this formula seems to be missing . The available formula contains anintegral term including the time derivative of the fundamental solution of the heat equation, whose interpretation is difficult at second glance .…

This article presents the data used to evaluate the performance ofevolutionary clustering algorithm star (ECA*) compared to five traditional and modern clustering algorithms . Two experimental methods are employed to examinethe performance of ECA* against genetic algorithm for clustering++(GENCLUST++), learning vector quantisation (LVQ) , expectation maximisation(EM) and K-means (KM) The results of the experiments performeddemonstrate some limitations in the ECA*: (i) ECA*.…

Intelligent task placement and management of tasks in large-scale fog platforms is challenging due to the highly volatile nature of modern workload applications . Container orchestration platforms have emerged to alleviate this problem with prior art either using heuristics to quickly reach schedulingdecisions or AI driven methods like reinforcement learning and evolutionary approaches to adapt to dynamic scenarios .…

We propose and implement a Privacy-preserving Federated Learning (PPFL)framework for mobile systems to limit privacy leakages in federated learning . We leverage the widespread presence of Trusted Execution Environments (TEEs) in high-end and mobile devices, we utilize TEEs on clients for local training, and servers for secure aggregation .…

Cascade prediction estimates the size or the state of a cascade from eitherscope or macroscope . It is of paramount importance for understanding theinformation diffusion process such as the spread of rumors and the propagation of new technologies in social networks .…

This paper explores supply chain viability through empirical network-levelanalysis of supplier reachability under various scenarios . The global supply chain data was mined andanalyzed from about 45,000 firms with about 115,000 intertwined relationshipsspanning across 10 tiers of the backward supply chain of medical equipment .…

We introduce an automata model for describing interesting classes of differential privacy mechanisms/algorithms . These automata can model algorithms whose inputs can be anunbounded sequence of real-valued query answers . We consider the problem of checking whether there exists a constant $d$ such that the algorithm described by these automata are $d\epsilon$-differentially private .…

Gastin and Monmege (2018) gave abstract semantics for a version of weighted monadic second-order logic to give a more general and modular proof of the equivalence of the logic with weighted automata . The semantics is parameterized with respect to a semiring on which the valuesthat weighted formulas output are evaluated .…

A communicating system is $k$-synchronizable if all of the message sequencecharts representing the executions can be divided into slices of $k# sends . The reachability problem can be solved for $k$. However, the decision procedure assumes that thebound $K$ is fixed .…

In this letter, we propose a computationally efficient method for jointselection of cancellation carriers (CCs) and calculation of their values . The proposed new CCs selection method achieves higher OOB powerattenuation than algorithms known from literature as well as noticablereception performance improvement .…

The concept of multi-agent cloudrobotics enables robot-to-robot cooperation and creates a complementaryenvironment for the robots in executing large-scale applications . The optimal resource allocation for robotic tasks is challenging to achieve in such a complex environment . The data transmission delay between local robots, edge nodes, and cloud data centres adversely affects the real-timeinteractions and impedes service performance guarantee .…

#StopAsianHate and #StopAAPIHate are two of the most commonly used hashtagsthat represent the current movement to end hate crimes against the AsianAmerican and Pacific Islander community . 51.56% of the Twitter users show directsupport, 18.38% are news about anti-Asian hate crimes, while 5.43% show anegative attitude towards the movement .…

In recent years, data and computing resources are typically distributed inthe devices of end users, various regions or organizations . Because of laws or regulations, the distributed data and . computing resources cannot be directly shared among different regions or .…

This paper proposes a distributed Reinforcement Learning (RL) based framework that can be used for synthesizing MAC layer wireless protocols in IoT networks . The proposed framework does not rely on complex hardware capabilities such as carrier sensing and its associatedalgorithmic complexities that are often not supported in wireless transceiversof low-cost and low-energy IoT devices .…

At any position in the body of a SPARQL query, theautocompletion suggests matching subjects, predicates, or objects . The suggestions are context-sensitive in the sense that they lead to a non-empty result and are ranked by their relevance to the part of the query alreadytyped .…

We introduce Tuna, a static analysis approach to optimizing deep neuralnetwork programs . The optimization of tensor operations such as convolutionsand matrix multiplications is the key to improving the performance of deepneural networks . We use static analysis of the relative performance oftensor operations to optimize the deep learning program .…

Signed network embedding is an approach to learn low-dimensionalrepresentations of nodes in signed networks with both positive and negative links . MUSE is a MUlti-facetedattention-based Signed network Embedding framework to tackle this problem . The framework uses a joint intra- and interfacet attention mechanism to aggregate fine-grained information from neighbor nodes .…

A parallel implementation of a compatible discretization scheme forsteady-state Stokes problems is presented in this work . The scheme usesgeneralized moving least squares to generate differential operators and applyboundary conditions . This meshless scheme allows a high-order convergence forboth the velocity and pressure, while also incorporates finite-difference-likesparse discretizing .…