Miracle-dz/UniFAR-FTUs huggingface dataset:
Gossiped and Quantized Online Multi-Kernel Learning

In instances of online kernel learning where little prior information is available and centralized learning is unfeasible, past research has shown that distributed and online multi-kernel learning provides sub-linear regret as long as every pair of nodes in the network can communicate (i.e., the communications network is a complete graph). In addition, to manage the communication load, which is often a performance bottleneck, communications between nodes can be quantized. This letter expands on these results to non-fully connected graphs, which is often the case in wireless sensor networks. To address this challenge, we propose a gossip algorithm and provide a proof that it achieves sub-linear regret. Experiments with real datasets confirm our findings.
Adaptive Probabilistic Forecasting of Electricity (Net-)Load

Electricityload forecasting is a necessary capability for power system operators and electricity market participants. Both demand and supply characteristics evolve over time. On the demand side, unexpected events as well as longer-term changes in consumption habits affect demand patterns. On the production side, the increasing penetration of intermittent power generation significantly changes the forecasting needs. We address this challenge in two ways. First, our setting is adaptive; our models take into account the most recent observations available to automatically respond to changes in the underlying process. Second, we consider probabilistic rather than point forecasting; indeed, uncertainty quantification is required to operate electricity systems efficiently and reliably. Our methodology relies on the Kalman filter, previously used successfully for adaptive point load forecasting. The probabilistic forecasts are obtained by quantile regressions on the residuals of the point forecasting model. We achieve adaptive quantile regressions using the online gradient descent; we avoid the choice of the gradient step size considering multiple learning rates and aggregation of experts. We apply the method to two data sets: the regional net-load in Great Britain and the demand of seven large cities in the United States. Adaptive procedures improve forecast performance substantially in both use cases for both point and probabilistic forecasting.
Applications and Challenges of Sentiment Analysis in Real-life Scenarios

Sentiment analysis has benefited from the availability of lexicons and benchmark datasets created over decades of research. However, its applications to the real world are a driving force for research in SA. This chapter describes some of these applications and related challenges in real-life scenarios. In this chapter, we focus on five applications of SA: health, social policy, e-commerce, digital humanities and other areas of NLP. This chapter is intended to equip an NLP researcher with the `what', `why' and `how' of applications of SA: what is the application about, why it is important and challenging and how current research in SA deals with the application. We note that, while the use of deep learning techniques is a popular paradigm that spans these applications, challenges around privacy and selection bias of datasets is a recurring theme across several applications.
The Fine Structure of the Normal Incidence Synthetic Seismogram

Summary. The spectral function of a perfectly elastic, horizontally stratified medium has been demonstrated previously to provide an attractive formulation to describe the properties of the one-dimensional synthetic seismogram (Robinson & Treitel). Here we examine the mathematical framework of the Model in still greater detail. Knowledge of this fine structure of the synthetic seismogram leads to the solution of two particular seismic inverse problems. First, we consider a layered medium with an arbitrary surface reflection coefficient co, where |co|<1, and which contains an impulsive source immediately above the surface. Given the corresponding synthetic seismogram, we develop an inverse, or backward recursion formalism which recovers the entire series of original reflection coefficients. Second, we consider a similar problem for an impulsive source located just below the surface. Both inversion procedures constitute a continuation of the work of Goupillaud and of Sherwood & Trorey, and represent a generalization of the classical technique originally proposed by Kunetz which, however, only holds for the marine case, co=±1. The present approach is not so constrained and thereby becomes applicable to land seismograms as well. If products of third or higher order in the reflection coefficients can be neglected, significant simplifications arise in the theory. In that event the usual representation of the synthetic seismogram as a ratio of two polynomials in the complex variable z becomes particularly revealing. The numerator polynomial is then approximately equal to the z transform of the reflection coefficient series, while the denominator polynomial is approximately equal to the z transform of the autocorrelation of these reflection coefficients. The resulting simplified theory affords important computational savings in the appropriate backward recursion algorithms.
Relation between the noise correlations and the spin structure factor for Mott-insulating states in 
SU(N)
 Hubbard models

It is well established that the noise correlations measured by time-of-flight imaging in cold-atom experiments, which correspond to the density-density correlations in the momentum space of trapped atomic gases, can probe the spin structure factor deep in the Mott-insulating regime of SU(2) Hubbard models. We explicitly derive the mathematical relation between the noise correlations and the spin structure factor in the strong-interaction limit of SU$(N)$ Hubbard models at any integer filling $\rho$. By calculating the ground states of one-dimensional SU$(N)$ Fermi-Hubbard models for $2\leq N\leq 6$ with use of the density-matrix renormalization-group method, we confirm the relation numerically in the regime of strong interactions $U \gg t$, where $U$ and $t$ denote the onsite interaction and the hopping energy. We show that the deviation between the actual noise correlations and those obtained from the spin structure factor scales as approximately $(t/U)^2$ for $\rho=1$ at intermediate and large lattice sizes on the basis of numeric and semi-analytic arguments.
Case study Vanderbilt University Medical Center Data Chaos

The healthcare industry is growing rapidly in the United States because of the increased number of the aging population, shared consciousness of personal health problems, and medical technology improvements. As a result of the growing industry of healthcare, new emerging issues occur in the collection and storage of patient data, and new ways to process, analyze and distribute these data. This has exposed various security threats to personal health data (Lee, 2022). In this case study, we will discuss the issues Vanderbilt University Medical Center (VUMC) challenges while implementing EHR systems which are used to analyze and monitor health records by the users such as doctors, organizations staff, and pharmaceutical agencies(Kaul et al., 2020), and we will analyze these issues and provide solutions and recommendations to solve them.
A Comparison of Temporal Encoders for Neuromorphic Keyword Spotting with Few Neurons

With the expansion of AI-powered virtual assistants, there is a need for low-power keyword spotting systems providing a “wake-up” mechanism for subsequent computationally expensive speech recognition. One promising approach is the use of neuromorphic sensors and spiking neural networks (SNNs) implemented in neuromorphic processors for sparse event-driven sensing. However, this requires resource-efficient SNN mechanisms for temporal encoding, which need to consider that these systems process information in a streaming manner, with physical time being an intrinsic property of their operation. In this work, two candidate neurocomputational elements for temporal encoding and feature extraction in SNNs described in recent literature—the spiking time-difference encoder (TDE) and disynaptic excitatory-inhibitory (E-I) elements—are comparatively investigated in a keyword-spotting task on formants computed from spoken digits in the TIDIGITS dataset. While both encoders improve performance over direct classification of the formant features in the training data, enabling a complete binary classification with a logistic regression model, they show no clear improvements on the test set. Resource-efficient keyword spotting applications may benefit from the use of these encoders, but further work on methods for learning the time constants and weights is required to investigate their full potential.
GyroFlow+: Gyroscope-Guided Unsupervised Deep Homography and Optical Flow Learning

Existing homography and optical flow methods are erroneous in challenging scenes, such as fog, rain, night, and snow because the basic assumptions such as brightness and gradient constancy are broken. To address this issue, we present an unsupervised learning approach that fuses gyroscope into homography and optical flow learning. Specifically, we first convert gyroscope readings into motion fields named gyro field. Second, we design a self-guided fusion module (SGF) to fuse the background motion extracted from the gyro field with the optical flow and guide the network to focus on motion details. Meanwhile, we propose a homography decoder module (HD) to combine gyro field and intermediate results of SGF to produce the homography. To the best of our knowledge, this is the first deep learning framework that fuses gyroscope data and image content for both deep homography and optical flow learning. To validate our method, we propose a new dataset that covers regular and challenging scenes. Experiments show that our method outperforms the state-of-the-art methods in both regular and challenging scenes.
Smart tutor to provide feedback in programming courses

Artificial Intelligence (AI) is becoming more and more popular as time passes, allowing to perform tasks that were difficult to do in the past. From predictions to customization, AI is being used in many areas, not being educational environments outside this situation. AI is being used in educational settings to customize contents or to provide personalized feedback to the students, among others. In this scenario, AI in programming teaching is something that still has to be explored, since in this area we usually find assessment tools that allow grading the students work, but we can not find many tools aimed towards providing feedback to the students in the process of creating their program. In this work we present an AI based intelligent tutor that answers students programming questions. The tool has been tested by university students at the URJC along a whole course. Even if the tool is still in its preliminary phase, it helped the students with their questions, providing accurate answers and examples. The students were able to use the intelligent tutor easily and they thought that it could be a useful tool to use in other courses.
Forecasting the 2016–2017 Central Apennines Earthquake Sequence With a Neural Point Process

Point processes have been dominant in modeling the evolution of seismicity for decades, with the epidemic‐type aftershock sequence (ETAS) model being most popular. Recent advances in machine learning have constructed highly flexible point process models using neural networks to improve upon existing parametric models. We investigate whether these flexible point process models can be applied to short‐term seismicity forecasting by extending an existing temporal neural model to the magnitude domain and we show how this model can forecast earthquakes above a target magnitude threshold. We first demonstrate that the neural model can fit synthetic ETAS data, however, requiring less computational time because it is not dependent on the full history of the sequence. By artificially emulating short‐term aftershock incompleteness in the synthetic data set, we find that the neural model outperforms ETAS. Using a new enhanced catalog from the 2016–2017 Central Apennines earthquake sequence, we investigate the predictive skill of ETAS and the neural model with respect to the lowest input magnitude. Constructing multiple forecasting experiments using the Visso, Norcia and Campotosto earthquakes to partition training and testing data, we target M3+ events. We find both models perform similarly at previously explored thresholds (e.g., above M3), but lowering the threshold to M1.2 reduces the performance of ETAS unlike the neural model. We argue that some of these gains are due to the neural model's ability to handle incomplete data. The robustness to missing data and speed to train the neural model present it as an encouraging competitor in earthquake forecasting.
Perturbation-theory informed integrators for cosmological simulations

Large-scale cosmological simulations are an indispensable tool for modern cosmology. To enable model-space exploration, fast and accurate predictions are critical. In this paper, we show that the performance of such simulations can be further improved with time-stepping schemes that use input from cosmological perturbation theory. Specifically, we introduce a class of time-stepping schemes derived by matching the particle trajectories in a single leapfrog/Verlet drift-kick-drift step to those predicted by Lagrangian perturbation theory (LPT). As a corollary, these schemes exactly yield the analytic Zel'dovich solution in 1D in the pre-shell-crossing regime (i.e. before particle trajectories cross). One representative of this class is the popular FastPM scheme by Feng et al. 2016, which we take as our baseline. We then construct more powerful LPT-inspired integrators and show that they outperform FastPM and standard integrators in fast simulations in two and three dimensions with $\mathcal{O}(1 - 100)$ timesteps, requiring less steps to accurately reproduce the power spectrum and bispectrum of the density field. Furthermore, we demonstrate analytically and numerically that, for any integrator, convergence is limited in the post-shell-crossing regime (to order 3/2 for planar wave collapse), owing to the lacking regularity of the acceleration field, which makes the use of high-order integrators in this regime futile. Also, we study the impact of the timestep spacing and of a decaying mode present in the initial conditions. Importantly, we find that symplecticity of the integrator plays a minor role for fast approximate simulations with a small number of timesteps.
Predicting Socio-Economic Well-being Using Mobile Apps Data: A Case Study of France

Socio-economic indicators provide context for assessing a country's overall condition. These indicators contain information about education, gender, poverty, employment, and other factors. Therefore, reliable and accurate information is critical for social research and government policing. Most data sources available today, such as censuses, have sparse population coverage or are updated infrequently. Nonetheless, alternative data sources, such as call data records (CDR) and mobile app usage, can serve as cost-effective and up-to-date sources for identifying socio-economic indicators. This work investigates mobile app data to predict socio-economic features. We present a large-scale study using data that captures the traffic of thousands of mobile applications by approximately 30 million users distributed over 550,000 km square and served by over 25,000 base stations. The dataset covers the whole France territory and spans more than 2.5 months, starting from 16th March 2019 to 6th June 2019. Using the app usage patterns, our best model can estimate socio-economic indicators (attaining an R-squared score upto 0.66). Furthermore, using models' explainability, we discover that mobile app usage patterns have the potential to reveal socio-economic disparities in IRIS. Insights of this study provide several avenues for future interventions, including user temporal network analysis to understand evolving network patterns and exploration of alternative data sources.
Higher-Order Weakest Precondition Transformers via a CPS Transformation

Weakest precondition transformers are essential notions for program verification, and various extensions have been studied. However, only a few consider both higher-order languages and syntactic calculation of weakest precondition transformers. In this paper, we consider weakest precondition transformers for a higher-order functional language with computational effects and recursion and show that we can calculate them via a CPS transformation. We prove this in a general framework of categorical semantics. Because of this generality, two existing methods for program verification can be understood as instances of our result. Specifically, we show how to instantiate our result to (1) verification of trace properties by Kobayashi et al. and (2) expected cost analysis by Avanzini et al.
A DNN Optimizer that Improves over AdaBelief by Suppression of the Adaptive Stepsize Range

We make contributions towards improving adaptive-optimizer performance. Our improvements are based on suppression of the range of adaptive stepsizes in the AdaBelief optimizer. Firstly, we show that the particular placement of the parameter epsilon within the update expressions of AdaBelief reduces the range of the adaptive stepsizes, making AdaBelief closer to SGD with momentum. Secondly, we extend AdaBelief by further suppressing the range of the adaptive stepsizes. To achieve the above goal, we perform mutual layerwise vector projections between the gradient g_t and its first momentum m_t before using them to estimate the second momentum. The new optimization method is referred to as Aida. Thirdly, extensive experimental results show that Aida outperforms nine optimizers when training transformers and LSTMs for NLP, and VGG and ResNet for image classification over CIAF10 and CIFAR100 while matching the best performance of the nine methods when training WGAN-GP models for image generation tasks. Furthermore, Aida produces higher validation accuracies than AdaBelief for training ResNet18 over ImageNet. Code is availableat this URL
An allosteric switch ensures efficient unidirectional information transmission by the histidine kinase DesK from Bacillus subtilis

Phosphorylation carries chemical information in biological systems. In two-component systems (TCSs), the sensor histidine kinase and the response regulator are connected through phosphoryl transfer reactions that may be uni- or bidirectional. Directionality enables the construction of complex regulatory networks that optimize signal propagation and ensure the forward flow of information. We combined x-ray crystallography, hybrid quantum mechanics/molecular mechanics (QM/MM) simulations, and systems-integrative kinetic modeling approaches to study phosphoryl flow through the Bacillus subtilis thermosensing TCS DesK-DesR. The allosteric regulation of the histidine kinase DesK was critical to avoid back transfer of phosphoryl groups and futile phosphorylation-dephosphorylation cycles by isolating phosphatase, autokinase, and phosphotransferase activities. Interactions between the kinase’s ATP-binding domain and the regulator’s receiver domain placed the regulator in two distinct positions in the phosphotransferase and phosphatase complexes, thereby determining whether a key glutamine residue in DesK was properly situated to assist in the dephosphorylation reaction. Moreover, an energetically unfavorable phosphotransferase conformation when DesK was not phosphorylated minimized reverse phosphoryl transfer. DesR dimerization and a dissociative phosphoryl transfer reaction also enforced the direction of phosphoryl flow. Shorter or longer distances between the phosphoryl acceptor and donor residues shifted the phosphoryl transfer equilibrium by modulating the stabilizing effect of the Mg2+ cofactor. These mechanisms control the directionality of signal transmission and show how structure-encoded allostery stores and transmits information in signaling systems. Description Structural and kinetic factors ensure unidirectional information flow in a bacterial two-component system. Enforcing forward phosphoryl flow Two-component systems (TCSs) comprise a response regulator (RR) downstream of a sensor histidine kinase (HK) that often has both kinase and phosphatase activities. When inactive, the HK adopts a phosphatase state, thus keeping the system off. In the active kinase state, the HK undergoes autophosphorylation and shifts to a phosphotransfer state in which the HK transfers the phosphoryl group to the RR. Lima et al. used structural, computational, and biochemical approaches to investigate the determinants of phosphoryl transfer directionality in the Bacillus subtilis DesK-DesR TCS and how futile phosphotransfer cycles are minimized. An unfavorable phosphotransfer state when DesK is not phosphorylated, the distances between the residues participating in the phosphoryl transfer reaction, and the dimerization of the phosphorylated RR all contributed to ensuring the forward flow of information through this system. Similar principles may apply to other TCSs and offer a potential explanation for why some TCS phosphoryl transfer reactions are irreversible. – AMV
Rapid evolution of diet choice in an introduced population of Trinidadian guppies

Eco-evolutionary theory has brought an interest in the rapid evolution of functional traits. Among them, diet is an important determinant of ecosystem structure, affecting food web dynamics and nutrient cycling. However, it is largely unknown whether diet, or diet preference, has a hereditary basis and can evolve on contemporary timescales. Here, we study the diet preferences of Trinidadian guppies Poecilia reticulata collected from directly below an introduction site of fish transplanted from a high-predation environment into a low predation site where their densities and competition increased. Behavioural assays on F2 common garden descendants of the ancestral and derived populations showed that diet preference has rapidly evolved in the introduced population in only 12 years (approx. 36 generations). Specifically, we show that the preference for high-quality food generally found in high-predation guppies is lost in the newly derived low-predation population, who show an inertia toward the first encountered food. This result is predicted by theory stating that organisms should evolve less selective diets under higher competition. Demonstrating that diet preference can show rapid and adaptive evolution is important to our understanding of eco-evolutionary feedbacks and the role of evolution in ecosystem dynamics.
A three-dimensional field solution for permanent-magnet axial-field motors

A formula is presented for computing the field in axial-field permanent-magnet motors. The formula is based on a three-dimensional (3-D) analytical analysis and is expressed in terms of a finite sum of elementary functions. It is readily programmed and ideal for parametric studies of field strength. It is also well suited for parallel processing and could be developed into a motor model for real-time performance simulations. It is applied here to a practical motor geometry and verified by the use of 3-D finite element analysis (FEA).