Miracle-dz/UniFAR-FTUs huggingface dataset:
Parallel solution of saddle point systems with nested iterative solvers based on the Golub‐Kahan Bidiagonalization

The Golub‐Kahan bidiagonalization is widely used in the singular value decomposition of rectangular matrices and has been generalized to an iterative solver for symmetric indefinite linear systems with a two‐by‐two block structure. In this work, we present a scalability study of this generalized solver as implemented in a recent release of the parallel numerical library PETSc (Portable, Extensible Toolkit for Scientific Computation). We present an improved solver performance for the two‐dimensional (2D) Stokes equations as compared to previous work. Furthermore, we investigate the performance of different parallel inner solvers in the outer Golub‐Kahan iteration for a three‐dimensional Stokes problem. The study includes parallel sparse direct solvers and multigrid methods. When increasing the number of cores for a fixed total problem size, the solver exhibits good speedups of up to 50% at the 1024 core count. For the tests in which the total problem size grows while the workload in each core stays constant, the parallel performance of the solver scales almost linearly with the increase in the core counts. In particular, the computation time increases only by about 15% when the number of cores increases from 80 to 1024 for a 2D test case.
Update Aware Device Scheduling for Federated Learning at the Wireless Edge

We study federated learning (FL) at the wireless edge, where power-limited devices with local datasets train a joint model with the help of a remote parameter server (PS). We assume that the devices are connected to the PS through a bandwidth-limited shared wireless channel. At each iteration of FL, a subset of the devices are scheduled to transmit their local model updates to the PS over orthogonal channel resources. We design novel scheduling policies, that decide on the subset of devices to transmit at each round not only based on their channel conditions, but also on the significance of their local model updates. Numerical results show that the proposed scheduling policy provides a better long-term performance than scheduling policies based only on either of the two metrics individually. We also observe that when the data is independent and identically distributed (i.i.d.) across devices, selecting a single device at each round provides the best performance, while when the data distribution is non-i.i. d. , more devices should be scheduled.
Predicting the Impact of Electric Field Stimulation in a Detailed Computational Model of Cortical Tissue

Neurostimulation using weak electric fields has generated excitement in recent years due to its potential as a medical intervention. However, study of this stimulation modality has been hampered by inconsistent results and large variability within and between studies. In order to begin addressing this variability we need to properly characterise the impact of the current on the underlying neuron populations. To develop and test a computational model capable of capturing the impact of electric field stimulation on networks of neurons. We construct a cortical tissue model with distinct layers and explicit neuron morphologies. We then apply a model of electrical stimulation and carry out multiple test case simulations. The cortical slice model is compared to experimental literature and shown to capture the main features of the electrophysiological response to stimulation. Namely, the model showed 1) a similar level of depolarisation in individual pyramidal neurons, 2) acceleration of intrinsic oscillations, and 3) retention of the spatial profile of oscillations in different layers. We then apply alternative electric fields to demonstrate how the model can capture differences in neuronal responses to the electric field. We demonstrate that the tissue response is dependent on layer depth, the angle of the apical dendrite relative to the field, and stimulation strength. We present publicly available computational modelling software that predicts the neuron network population response to electric field stimulation.
Rich-Item Recommendations for Rich-Users via GCNN: Exploiting Dynamic and Static Side Information

In this paper, we study the problem of recommendation system where the users and items to be recommended are rich data structures with multiple entity types and with multiple sources of side-information in the form of graphs. We provide a general formulation for the problem that captures the complexities of modern real-world recommendations and generalizes many existing formulations. In our formulation, each user/document that requires a recommendation and each item or tag that is to be recommended, both are modeled by a set of static entities and a dynamic component. The relationships between entities are captured by several weighted bipartite graphs. To effectively exploit these complex interactions and learn the recommendation model, we propose MEDRES- a multiple graph-CNN based novel deep-learning architecture. MEDRES uses AL-GCN, a novel graph convolution network block, that harnesses strong representative features from the underlying graphs. Moreover, in order to capture highly heterogeneous engagement of different users with the system and constraints on the number of items to be recommended, we propose a novel ranking metric pAp@k along with a method to optimize the metric directly. We demonstrate effectiveness of our method on two benchmarks: a) citation data, b) Flickr data. In addition, we present two real-world case studies of our formulation and the MEDRES architecture. We show how our technique can be used to naturally model the message recommendation problem and the teams recommendation problem in the Microsoft Teams (MSTeams) product and demonstrate that it is 5-6% points more accurate than the production-grade models.
Closed-loop frequency analyses of reset systems

Today, linear controllers cannot satisfy requirements of high-tech industry due to fundamental limitations like the waterbed effect. This is one of the reasons why nonlinear controllers, such as reset elements, are receiving increased attention. To analyze reset elements in the frequency domain, researchers use the Describing Function (DF) method. However, it cannot accurately predict the closed-loop frequency responses of the system because it neglects high order harmonics. To overcome these barriers, this paper proposes a mathematical framework to model the closed-loop frequency responses of reset systems including the closed-loop high order harmonics. Furthermore, pseudo-sensitivities for reset systems are defined to make their analyses more straight-forward. In addition, a user-friendly toolbox is developed based on the proposed approach to facilitate frequency analyses of reset systems. To show the effectiveness of the method, multiple illustrative examples on a high-tech precision positioning stage are used to compare the results of the closed-loop frequency responses obtained using our proposed method with DF method. The results demonstrated that the proposed method is significantly more precise than the DF method. Indeed, this developed toolbox can enable reset controllers to be widely-used in industry and academia.
An Internal Clock Based Space-time Neural Network for Motion Speed Recognition

In this work we present a novel internal clock based space-time neural network for motion speed recognition. The developed system has a spike train encoder, a Spiking Neural Network (SNN) with internal clocking behaviors, a pattern transformation block and a Network Dynamic Dependent Plasticity (NDDP) learning block. The core principle is that the developed SNN will automatically tune its network pattern frequency (internal clock frequency) to recognize human motions in a speed domain. We employed both cartoons and real-world videos as training benchmarks, results demonstrate that our system can not only recognize motions with considerable speed differences (e.g. run, walk, jump, wonder(think) and standstill), but also motions with subtle speed gaps such as run and fast walk. The inference accuracy can be up to 83.3% (cartoon videos) and 75% (real-world videos). Meanwhile, the system only requires six video datasets in the learning stage and with up to 42 training trials. Hardware performance estimation indicates that the training time is 0.84-4.35s and power consumption is 33.26-201mW (based on an ARM Cortex M4 processor). Therefore, our system takes unique learning advantages of the requirement of the small dataset, quick learning and low power performance, which shows great potentials for edge or scalable AI-based applications.
Evolutionary Dynamics of Higher-Order Interactions

We live and cooperate in networks. However, links in networks only allow for pairwise interactions, thus making the framework suitable for dyadic games, but not for games that are played in groups of more than two players. To remedy this, we introduce higher-order interactions, where a link can connect more than two individuals, and study their evolutionary dynamics. We first consider a public goods game on a uniform hypergraph, showing that it corresponds to the replicator dynamics in the well-mixed limit, and providing an exact theoretical foundation to study cooperation in networked groups. We also extend the analysis to heterogeneous hypergraphs that describe interactions of groups of different sizes and characterize the evolution of cooperation in such cases. Finally, we apply our new formulation to study the nature of group dynamics in real systems, showing how to extract the actual dependence of the synergy factor on the size of a group from data on large communities of international collaborations. Our work is a first step towards the implementation of new actions to boost cooperation in social groups. ∗Electronic address: unaialvarezr@gmail.com
The Impact of Audio Input Representations on Neural Network based Music Transcription

This paper thoroughly analyses the effect of different input representations on polyphonic multi-instrument music transcription. We use our own GPU based spectrogram extraction tool, nnAudio, to investigate the influence of using a linear-frequency spectrogram, log-frequency spectrogram, Mel spectrogram, and constant-Q transform (CQT). Our results show that a 8.33% increase in transcription accuracy and a 9.39% reduction in error can be obtained by choosing the appropriate input representation (log-frequency spectrogram with STFT window length 4,096 and 2,048 frequency bins in the spectrogram) without changing the neural network design (single layer fully connected). Our experiments also show that Mel spectrogram is a compact representation for which we can reduce the number of frequency bins to only 512 while still keeping a relatively high music transcription accuracy.
Multidimensional sound propagation in 3D high-order topological sonic insulator

High-order topological insulators (TIs) develop the conventional bulk-boundary correspondence theory and rise the interest in searching innovative topological materials. To realize a high-order TI with a wide passband of 1D and 2D transportation modes, we design non-trivial and trivial 3D sonic crystals whose combination mimics the Su-Schrieffer-Heeger model. The high-order topological boundary states can be found at the interfaces, including 0D corner state, 1D hinge state, and 2D surface state. The fabricated sample with the bent two-dimensional and one-dimensional acoustic channels exhibits the multidimensional sound propagation in space, and also verifies the transition between the complete band gap, hinge states, and surface states within the bulk band gap. Among them, the bandwidth of the single-mode hinge state achieves a large relative bandwidth 9.1%, in which sound transports one-dimensionally without significant leak into the surfaces or the bulk. The high-order topological states in the study pave the way for multidimensional sound manipulation in space.
Update of inclusive cross sections of single and pairs of identified light charged hadrons

We report new measurements of the production cross sections of pairs of charged pions and kaons as a function of their fractional energies using various fractional-energy definitions. Two different fractional-energy definitions were used and compared to the conventional fractional-energy definition reported previously. The new variables aim at either identifying dihadron cross sections in terms of single-hadron fragmentation functions, or to provide a means of characterizing the transverse momentum created in the fragmentation process. The results were obtained applying the updated initial-state radiation correction used in other recent Belle publications on light-hadron production cross sections. In addition, production cross sections of single charged pions, kaons, and protons were also updated using this initial-state radiation correction. The cross sections are obtained from a $558\,{\rm fb}^{-1}$ data sample collected at the $\Upsilon(4S)$ resonance with the Belle detector at the KEKB asymmetric-energy $e^+ e^-$ collider.
Unexpected parameter ranges of the 2009 A(H1N1) epidemic for Istanbul and the Netherlands

The data of the 2009 A(H1N1) epidemic in Istanbul, Turkey is unique in terms of the collected data, which include not only the hospitalization but also the fatality information recorded during the pandemic. The analysis of this data displayed an unexpected time shift between the hospital referrals and fatalities. This time shift, which does not conform to the SIR and SEIR models, was explained by multi-stage SIR and SEIR models [21]. In this study we prove that the delay for these models is half of the infectious period within a quadratic approximation, and we determine the epidemic parameters $R_0$, $T$ and $I_0$ of the 2009 A(H1N1) Istanbul and Netherlands epidemics.These epidemic parameters were estimated by comparing the normalized cumulative fatality data with the solutions of the SIR model. Two different error criteria, the $L_2$ norms of the error over the whole observation period and over the initial portion of the data, were used in order to obtain the best-fitting models. It was observed that, with respect to both criteria, the parameters of "good" models were agglomerated along a line in the $T$-$R_0$ plane, instead of being scattered uniformly around a "best" model. As this fact indicates the existence of a nearly invariant quantity, interval estimates for the parameters were given. As the initial phase of the epidemics were less influenced by the effects of medical interventions, the error norm based on the initial portion of the data was preferred. However, the presented parameter ranges are well out of the range for the usual influenza epidemic parameter values. To confirm our observations on the Istanbul data, the same error criteria were also used for the 2009 A(H1N1) epidemic for the Netherlands, which has a similar population density as in Istanbul. As in the Istanbul case, the parameter ranges do not match the usual influenza epidemic parameter values.
Segmentation and Recovery of Superquadric Models using Convolutional Neural Networks

In this paper we address the problem of representing 3D visual data with parameterized volumetric shape primitives. Specifically, we present a (two-stage) approach built around convolutional neural networks (CNNs) capable of segmenting complex depth scenes into the simpler geometric structures that can be represented with superquadric models. In the first stage, our approach uses a Mask RCNN model to identify superquadric-like structures in depth scenes and then fits superquadric models to the segmented structures using a specially designed CNN regressor. Using our approach we are able to describe complex structures with a small number of interpretable parameters. We evaluated the proposed approach on synthetic as well as real-world depth data and show that our solution does not only result in competitive performance in comparison to the state-of-the-art, but is able to decompose scenes into a number of superquadric models at a fraction of the time required by competing approaches. We make all data and models used in the paper available from this https URL.
A Partial Information Decomposition Based on Causal Tensors

We propose a partial information decomposition based on the newly introduced framework of causal tensors, i.e., multilinear stochastic maps that transform source data into destination data. The innovation that causal tensors introduce is that the framework allows for an exact expression of an indirect association in terms of the constituting, direct associations. This is not possible when expressing associations only in measures like mutual information or transfer entropy. Instead of a priori expressing associations in terms of mutual information or transfer entropy, the a posteriori expression of associations in these terms results in an intuitive definition of a nonnegative and left monotonic redundancy, which also meets the identity property. Our proposed redundancy satisfies the three axioms introduced by Williams and Beer. Symmetry and self-redundancy axioms follow directly from our definition. The data processing inequality ensures that the monotonicity axiom is satisfied. Because causal tensors can describe both mutual information as transfer entropy, the partial information decomposition applies to both measures. Results show that the decomposition closely resembles the decomposition of other another approach that expresses associations in terms of mutual information a posteriori. A negative synergistic term could indicate that there is an unobserved common cause.
Regret Bounds for Decentralized Learning in Cooperative Multi-Agent Dynamical Systems

Regret analysis is challenging in Multi-Agent Reinforcement Learning (MARL) primarily due to the dynamical environments and the decentralized information among agents. We attempt to solve this challenge in the context of decentralized learning in multi-agent linear-quadratic (LQ) dynamical systems. We begin with a simple setup consisting of two agents and two dynamically decoupled stochastic linear systems, each system controlled by an agent. The systems are coupled through a quadratic cost function. When both systems' dynamics are unknown and there is no communication among the agents, we show that no learning policy can generate sub-linear in $T$ regret, where $T$ is the time horizon. When only one system's dynamics are unknown and there is one-directional communication from the agent controlling the unknown system to the other agent, we propose a MARL algorithm based on the construction of an auxiliary single-agent LQ problem. The auxiliary single-agent problem in the proposed MARL algorithm serves as an implicit coordination mechanism among the two learning agents. This allows the agents to achieve a regret within $O(\sqrt{T})$ of the regret of the auxiliary single-agent problem. Consequently, using existing results for single-agent LQ regret, our algorithm provides a $\tilde{O}(\sqrt{T})$ regret bound. (Here $\tilde{O}(\cdot)$ hides constants and logarithmic factors). Our numerical experiments indicate that this bound is matched in practice. From the two-agent problem, we extend our results to multi-agent LQ systems with certain communication patterns.
Skills to not fall behind in school

Many recent studies emphasize how important the role of cognitive and social-emotional skills can be in determining people's quality of life. Although skills are of great importance in many aspects, in this paper we will focus our efforts to better understand the relationship between several types of skills with academic progress delay. Our dataset contains the same students in 2012 and 2017, and we consider that there was a academic progress delay for a specific student if he or she progressed less than expected in school grades. Our methodology primarily includes the use of a Bayesian logistic regression model and our results suggest that both cognitive and social-emotional skills may impact the conditional probability of falling behind in school, and the magnitude of the impact between the two types of skills can be comparable.
Strong constraints on the $K^0_{\mathrm{S}}\rightarrow\mu^{+}\mu^{-}$ branching fraction

A search for the decay $K^0_{\mathrm{S}}\rightarrow\mu^{+}\mu^{-}$ is performed using proton-proton collision data, corresponding to an integrated luminosity of $5.6\,\text{fb}^{-1}$ and collected with the LHCb experiment during 2016, 2017 and 2018 at a center-of-mass energy of $13\,\mathrm{TeV}$. The observed signal yield is consistent with zero, yielding an upper limit of ${\cal B}(K^0_{\mathrm{S}}\rightarrow\mu^{+}\mu^{-}) < 2.2 \times 10^{-10}$ at 90% CL. The limit reduces to ${\cal B}(K^0_{\mathrm{S}}\rightarrow\mu^{+}\mu^{-}) < 2.1 \times 10^{-10}$ at 90% CL once combined with the result from data taken in 2011 and 2012.
Fast quantum learning with statistical guarantees

Within the framework of statistical learning theory it is possible to bound the minimum number of samples required by a learner to reach a target accuracy. We show that if the bound on the accuracy is taken into account, quantum machine learning algorithms -- for which statistical guarantees are available -- cannot achieve polylogarithmic runtimes in the input dimension. This calls for a careful revaluation of quantum speedups for learning problems, even in cases where quantum access to the data is naturally available.
Mining social media data for biomedical signals and health-related behavior

Social media data have been increasingly used to study biomedical and health-related phenomena. From cohort-level discussions of a condition to population-level analyses of sentiment, social media have provided scientists with unprecedented amounts of data to study human behavior associated with a variety of health conditions and medical treatments. Here we review recent work in mining social media for biomedical, epidemiological, and social phenomena information relevant to the multilevel complexity of human health. We pay particular attention to topics where social media data analysis has shown the most progress, including pharmacovigilance and sentiment analysis, especially for mental health. We also discuss a variety of innovative uses of social media data for health-related applications as well as important limitations of social media data access and use.
FOS

With FPGAs now being deployed in the cloud and at the edge, there is a need for scalable design methods that can incorporate the heterogeneity present in the hardware and software components of FPGA systems. Moreover, these FPGA systems need to be maintainable and adaptable to changing workloads while improving accessibility for the application developers. However, current FPGA systems fail to achieve modularity and support for multi-tenancy due to dependencies between system components and the lack of standardised abstraction layers. To solve this, we introduce a modular FPGA operating system – FOS, which adopts a modular FPGA development flow to allow each system component to be changed and be agnostic to the heterogeneity of EDA tool versions, hardware and software layers. Further, to dynamically maximise the utilisation transparently from the users, FOS employs resource-elastic scheduling to arbitrate the FPGA resources in both time and spatial domain for any type of accelerators. Our evaluation on different FPGA boards shows that FOS can provide performance improvements in both single-tenant and multi-tenant environments while substantially reducing the development time and, at the same time, improving flexibility.
Coulomb-nuclear Interference in Elastic Scattering: Eikonal Calculation to All Orders of \(\alpha \)

The Coulomb-nuclear interference (CNI) has recently been used by the TOTEM Collaboration to analyse proton-proton elastic-scattering data from the LHC and to draw physics conclusions. This paper will present an eikonal calculation of the CNI effects performed to all orders of the fine structure constant, $\alpha$. This calculation will be used as a reference to benchmark several widely-used CNI formulae and to verify several recent claims by other authors.