Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep solar ALMA neural network estimator for image refinement and estimates of small-scale dynamics

The contrasts and magnitude of observable signatures of small-scale features degrade as angular resolution decreases. High-cadence time-series of synthetic observable maps at 1.25 mm were produced from 3D magnetohydrodynamic Bifrost simulations of the solar atmosphere and degraded to the angular resolution corresponding to observational data with the Atacama Large Millimeter/sub-millimeter Array (ALMA). The Deep Solar ALMA Neural Network Estimator (Deep-SANNE) is an artificial neural network trained to improve the resolution and contrast of solar observations. This is done by recognizing dynamic patterns in both the spatial and temporal domains of small-scale features at an angular resolution corresponding to observational data and correlated them to highly resolved nondegraded data from the magnetohydrodynamic simulations. A second simulation, was used to validate the performance. Deep-SANNE provides maps of the estimated degradation of the brightness temperature, which can be used to filter for locations that most probably show a high accuracy and as correction factors in order to construct refined images that show higher contrast and more accurate brightness temperatures than at the observational resolution. Deep-SANNE reveals more small-scale features and estimates the excess temperature of brightening events with an average accuracy of 94.0% relative to the highly resolved data, compared to 43.7% at the observational resolution. By using the additional information of the temporal domain, Deep-SANNE can restore high contrasts better than a standard two-dimensional deconvolver technique. Deep-SANNE is applied on observational solar ALMA data. The Deep-SANNE refined images are useful for analysing small-scale and dynamic features. They can identify locations in the data with high accuracy for an in-depth analysis and allow a more meaningful interpretation of solar observations.
Probabilistic Time Series Forecasting for Adaptive Monitoring in Edge Computing Environments

With increasingly more computation being shifted to the edge of the network, monitoring of critical infrastructures, such as intermediate processing nodes in autonomous driving, is further complicated due to the typically resource-constrained environments. In order to reduce the resource overhead on the network link imposed by monitoring, various methods have been discussed that either follow a filtering approach for data-emitting devices or conduct dynamic sampling based on employed prediction models. Still, existing methods are mainly requiring adaptive monitoring on edge devices, which demands device reconfigurations, utilizes additional resources, and limits the sophistication of employed models.In this paper, we propose a sampling-based and cloud-located approach that internally utilizes probabilistic forecasts and hence provides means of quantifying model uncertainties, which can be used for contextualized adaptations of sampling frequencies and consequently relieves constrained network resources. We evaluate our prototype implementation for the monitoring pipeline on a publicly available streaming dataset and demonstrate its positive impact on resource efficiency in a method comparison.
Towards Better Bounds for Finding Quasi-Identifiers

We revisit the problem of finding small ε-separation keys introduced by Motwani and Xu (2008). In this problem, the input is a data set consisting of m-dimensional tuples {x1,x2,. ,xn}. The goal is to find a small subset of coordinates that separates at least (1-ε)(n2) pairs of tuples. When n is large, they provided a fast algorithm that runs on Θ(m/ε) tuples sampled uniformly at random. We show that the sample size can be improved to Θ(m/√ε). Our algorithm also enjoys a faster running time. To obtain this result, we consider a decision problem that takes a subset of coordinates A ⊆ [m]. It rejects if A separates fewer than (1-ε)(n2) pairs of tuples, and accepts if A separates all (n2) pairs of tuples. The algorithm must be correct with probability at least 1-δ for all 2m choices of A. We show that for algorithms based on uniform sampling: - Θ (m/√ε) samples are sufficient and necessary so that δ ≤ e-m. - Ω(√log m/ε) samples are necessary so that δ is a constant. Closing the gap between the upper and lower bounds in this case is still an open question. The analysis is based on a constrained version of the balls-into-bins problem whose worst case can be determined using Karush Kuhn Tucker (KKT) conditions. We believe our analysis may be of independent interest. We also study a related problem that asks for the following sketching algorithm: with given parameters α,k and ε, the algorithm takes a subset of coordinates A of size at most k and returns an estimate of the number of unseparated pairs in A up to a (1±ε) factor if it is at least α (n2). We show that even for constant α and success probability, such a sketching algorithm must use Ømega(mk log ε-1) bits of space; on the other hand, uniform sampling yields a sketch of size Θ (mk/αε2) for this purpose.
The Gaia-ESO Survey: Probing the lithium abundances in old metal-rich dwarf stars in the solar vicinity

We test a scenario in which radial migration could affect the Li abundance pattern of dwarf stars in the solar neighbourhood. This may confirm that the Li abundance in these stars can not serve as a probe for the Li abundance in the interstellar medium. We use the high-quality data (including Li abundances) from the 6th internal Data Release of the Gaia-ESO survey. In this sample, we group stars by similarity in chemical abundances via hierarchical clustering. Our analysis treats both measured Li abundances and upper limits. The Li envelope of the previously identified radially migrated stars is well below the benchmark meteoritic value (<3.26 dex); the star with the highest detected abundance has A(Li) = 2.76 dex. This confirms the previous trends observed for old dwarf stars (median ages $\sim$ 8 Gyr), where Li decreases for [Fe/H]$\gtrsim$0. This result acts as supporting evidence that the abundance of Li measured in the upper envelope of old dwarf stars should not be considered a proxy for the interstellar medium Li. Our scenario also indicates that the stellar yields for [M/H]>0 should not be decreased, as recently proposed in the literature. Our study backs the recent studies that claimed that old dwarfs on the hot side of the dip are efficient probes of the ISM abundance of Li, provided atomic diffusion does not lower significantly the initial Li abundance in the atmospheres of metal-rich objects.
Emergence of cosmic space in Tsallis modified gravity from equilibrium and non-equilibrium thermodynamic perspective

In the context of Tsallis entropy, we explore the connection between the law of emergence and the thermodynamic laws from a more accurate non-equilibrium perspective. Here, the equilibrium Clausius relation does not conform to the standard energy-momentum conservation. Therefore, an effective gravitational coupling is introduced to rewrite the field equation similar to general relativity, and the corresponding generalized continuity equation is obtained. As a result, thermodynamic laws were modified with the non-equilibrium energy dissipation and entropy production terms, using which we derive the law of emergence. The investigation of the law of emergence and the entropy maximization principle with Tsallis entropy in the non-equilibrium perspective shows that both result in the same constraints as obtained in other gravity theories and the equilibrium context of Tsallis entropy, except for an additional constraint on the Tsallis parameter as a result of extra entropy production. Consequently, the thermodynamic interpretation of the expansion of the universe stays valid even with quantum corrections to the horizon entropy since the correction terms in Tsallis entropy can be treated as the quantum corrections to Bekenstein-Hawking entropy.
The Grenader Estimator: A Nonasymptotic Approach

On examine certaines proprietes non asymtotiques de l'estimateur de Grenander d'une densite decroissante f. Cet estimation est defini comme la pente du plus petit majorant concave de la fonction de repartition empirique. On montre que son risque, mesure avec perte L 1 , est borne par une certaine fonctionnelle qui depend de f et du nombre d'observations n. Le comportement asymptotique de la fonctionnelle quand n tend vers l'infini est en accord avec les performances asymptotiques connues de l'estimation de Grenander
MaskPlace: Fast Chip Placement via Reinforced Visual Representation Learning

Placement is an essential task in modern chip design, aiming at placing millions of circuit modules on a 2D chip canvas. Unlike the human-centric solution, which requires months of intense effort by hardware engineers to produce a layout to minimize delay and energy consumption, deep reinforcement learning has become an emerging autonomous tool. However, the learning-centric method is still in its early stage, impeded by a massive design space of size ten to the order of a few thousand. This work presents MaskPlace to automatically generate a valid chip layout design within a few hours, whose performance can be superior or comparable to recent advanced approaches. It has several appealing benefits that prior arts do not have. Firstly, MaskPlace recasts placement as a problem of learning pixel-level visual representation to comprehensively describe millions of modules on a chip, enabling placement in a high-resolution canvas and a large action space. It outperforms recent methods that represent a chip as a hypergraph. Secondly, it enables training the policy network by an intuitive reward function with dense reward, rather than a complicated reward function with sparse reward from previous methods. Thirdly, extensive experiments on many public benchmarks show that MaskPlace outperforms existing RL approaches in all key performance metrics, including wirelength, congestion, and density. For example, it achieves 60%-90% wirelength reduction and guarantees zero overlaps. We believe MaskPlace can improve AI-assisted chip layout design. The deliverables are released at https://laiyao1.github. io/maskplace.
Characterization of ultrathin metal-oxide-semiconductor structures using coupled current and capacitance-voltage models based on quantum calculation

We have developed a capacitance–voltage (C–V) and a current–voltage (I–V) quasistatic quantum model of ultrathin metal–oxide–semiconductor (MOS) structures based on the self-consistent solution of the Schrodinger and Poisson equations. The direct tunneling current takes into account the carrier distribution in energy subbands and uses the notions of corrected tunnel transparency and of impact frequency at the injecting electrode. These models are used to obtain the main physical parameters of n+-polysilicon/SiO2/〈100〉 p-Si MOS structures, with oxide thickness ranging from 1.2 to 3.5 nm. The extracted parameters are the oxide thickness (TOX), the substrate doping, both at the Si/SiO2 interface [NS(0)] and deep in the bulk [NS(∞)], and the polysilicon gate doping (NP) near the polysilicon/SiO2 interface. For this range of oxide thickness, the direct tunneling current strongly perturbs the C–V measurements, which must be corrected. Down to 1.5 nm oxide thickness, these parameters are obtained by C–V characte.
Azumaya loci of skein algebras

We compute the Azumaya loci of Kauffman-bracket skein algebras of closed surfaces at odd roots of unity and provide partial results for open surfaces as well. As applications, we give an alternative definition of the projective representations of the Torelli groups derived from non-semisimple TQFTs and we strengthen a result by Frohman-Kania Bartoszynska-L\^e about the dimensions of some quotients of the skein modules of closed 3-manifolds.
Enhancing Recommender Systems: A Strategy to Mitigate False Negative Impact

In implicit collaborative filtering (CF) task of recommender systems, recent works mainly focus on model structure design with promising techniques like graph neural networks (GNNs). Effective and efficient negative sampling methods that suit these models, however, remain underdeveloped. One challenge is that existing hard negative samplers tend to suffer from severer over-fitting in model training. In this work, we first study the reason behind the over-fitting, and illustrate it with the incorrect selection of false negative instances with the support of experiments. In addition, we empirically observe a counter-intuitive phenomenon, that is, polluting hard negative samples' embeddings with a quite large proportional of positive samples' embeddings will lead to remarkable performance gains for prediction accuracy. On top of this finding, we present a novel negative sampling strategy, i.e., positive-dominated negative synthesizing (PDNS). Moreover, we provide theoretical analysis and derive a simple equivalent algorithm of PDNS, where only a soft factor is added in the loss function. Comprehensive experiments on three real-world datasets demonstrate the superiority of our proposed method in terms of both effectiveness and robustness.
Detection of Einstein Telescope gravitational wave signals from binary black holes using deep learning

The expected volume of data from the third-generation gravitational waves (GWs) Einstein Telescope (ET) detector would make traditional GWs search methods such as match ﬁltering impractical. This is due to the large template bank required and the diﬃculties in waveforms modelling. In contrast, machine learning (ML) algorithms have shown a promising alternative for GWs data analysis, where ML can be used in developing semi-automatic and automatic tools for the detection, denoising and parameter estimation of GWs sources. Compared to second generation detectors, ET will have a wider accessible frequency band but also a lower noise. The ET will have a detection rate for Binary Black Holes (BBHs) and Binary Neutron Stars (BNSs) of order 10 5 − 10 6 year − 1 and 7 × 10 4 year − 1 respectively. In this work, we explore the possibility and eﬃciency of using convolutional neural networks (CNNs) for the detection of BBHs’ mergers in synthetic GWs signals buried in gaussian noise. The data was generated according to the ET’s parameters using open-source tools. Without performing data whitening or applying bandpass ﬁltering, we trained four CNN networks with the state-of-the-art performance in computer vision, namely VGG, ResNet and DenseNet. ResNet has signiﬁcantly better performance, detecting BBHs sources with SNR of 8 or higher with 98.5% accuracy, and with 92.5%, 85%, 60% and 62% accuracy for sources with SNR range of 7-8, 6-7, 5-6 and 4-5 respectively. ResNet, in qualitative evaluation, was able to detect a BBH’s merger at 60 Gpc with 4.3 SNR. It was also shown that, using CNN for BBH’s merger on long time series data is computationally eﬃcient, and can be used for near-real-time detection.
Certified data-driven physics-informed greedy auto-encoder simulator

A parametric adaptive greedy Latent Space Dynamics Identification (gLaSDI) framework is developed for accurate, efficient, and certified data-driven physics-informed greedy auto-encoder simulators of high-dimensional nonlinear dynamical systems. In the proposed framework, an auto-encoder and dynamics identification models are trained interactively to discover intrinsic and simple latent-space dynamics. To effectively explore the parameter space for optimal model performance, an adaptive greedy sampling algorithm integrated with a physics-informed error indicator is introduced to search for optimal training samples on the fly, outperforming the conventional predefined uniform sampling. Further, an efficient k-nearest neighbor convex interpolation scheme is employed to exploit local latent-space dynamics for improved predictability. Numerical results demonstrate that the proposed method achieves 121 to 2,658x speed-up with 1 to 5% relative errors for radial advection and 2D Burgers dynamical problems.
A fast and simple $O (z \log n)$-space index for finding approximately longest common substrings

We describe how, given a text $T [1.n]$ and a positive constant $\epsilon$, we can build a simple $O (z \log n)$-space index, where $z$ is the number of phrases in the LZ77 parse of $T$, such that later, given a pattern $P [1.m]$, in $O (m \log \log z + \mathrm{polylog} (m + z))$ time and with high probability we can find a substring of $P$ that occurs in $T$ and whose length is at least a $(1 - \epsilon)$-fraction of the length of a longest common substring of $P$ and $T$.
Cutting Medusa's Path - Tackling Kill-Chains with Quantum Computing

This paper embarks upon exploration of quantum vulnerability analysis. By introducing vulnerability graphs, related to attack graphs, this paper provides background theory and a subsequent method for solving significant cybersecurity problems with quantum computing. The example given is to prioritize patches by expressing the connectivity of various vulnerabilities on a network with a QUBO and then solving this with quantum annealing. Such a solution is then proved to remove all kill-chains (paths to security compromise) on a network. The results demonstrate that the quantum computer's solve time is almost constant compared to the exponential increase in classical solve time for vulnerability graphs of expected real world density. As such, this paper presents a novel example of advantageous quantum vulnerability analysis.
On Microscopic, Macroscopic, and Kinetic Modeling of Vehicular Traffic

A system of vehicular traffic can be modeled in various ways, such as microscopic, macroscopic, and kinetic models. Microscopic models focus on the modeling of individual cars with their deterministic or stochastic interactions. On the other hand, with macroscopic models we can determine the relation between some observable values of a vehicular system, such as density and flux, which usually have the form of partial differential equations of conservation type. In kinetic models, the traffic is resembled as a system of interacting gas particles described by a distribution function with a time evolution. This mesoscopic model is formulated in a Boltzmann-like equation, based on corresponding gain and loss terms of microscopic interactions. These above mentioned modeling approaches have actually interrelations with one another, such that microsopic behaviour of vehicles will imply certain macroscopic state of the traffic system and, by using kinetic model, the detailed transition from microscopic to macroscopic state can be explained. This paper is intended to present a study on this interrelations between microscopic, macroscopic, and kinetic models based on the optimal velocity model. Some numerical simulation results are also presented. Keywords: Vehicular Traffic; Microscopic Interaction; Macroscopic Model; Kinetic Model 2010 Mathematics Subject Classification: 90B20
Space-efficient RLZ-to-LZ77 conversion

Consider a text $T [1.n]$ prefixed by a reference sequence $R = T [1. \ell]$. We show how, given $R$ and the $z'$-phrase relative Lempel-Ziv parse of $T [\ell + 1.n]$ with respect to $R$, we can build the LZ77 parse of $T$ in $n\,\mathrm{polylog} (n)$ time and $O (\ell + z')$ total space.
Task-Oriented Communication for Edge Video Analytics

With the development of artificial intelligence (AI) techniques and the increasing popularity of camera-equipped devices, many edge video analytics applications are emerging, calling for the deployment of computation-intensive AI models at the network edge. Edge inference is a promising solution to move the computation-intensive workloads from low-end devices to a powerful edge server for video analytics, but the device-server communications will remain a bottleneck due to the limited bandwidth. This paper proposes a task-oriented communication framework for edge video analytics, where multiple devices collect the visual sensory data and transmit the informative features to an edge server for processing. To enable low-latency inference, this framework removes video redundancy in spatial and temporal domains and transmits minimal information that is essential for the downstream task, rather than reconstructing the videos at the edge server. Specifically, it extracts compact task-relevant features based on the deterministic information bottleneck (IB) principle, which characterizes a tradeoff between the informativeness of the features and the communication cost. As the features of consecutive frames are temporally correlated, we propose a temporal entropy model (TEM) to reduce the bitrate by taking the previous features as side information in feature encoding. To further improve the inference performance, we build a spatial-temporal fusion module at the server to integrate features of the current and previous frames for joint inference. Extensive experiments on video analytics tasks evidence that the proposed framework effectively encodes task-relevant information of video data and achieves a better rate-performance tradeoff than existing methods.
On the local connectivity of attractors of Markov IFS

We prove an extension of M. Hata's theorem [4] for planar Markov Iterated Function Systems satisfying a strong version of the Open Set Condition. More precisely, if the attractor of such a system is connected, then it is locally connected. We construct counterexamples to show that all additional hypothesis are necessary.
TrustGAN: Training safe and trustworthy deep learning models through generative adversarial networks

Deep learning models have been developed for a variety of tasks and are deployed every day to work in real conditions. Some of these tasks are critical and models need to be trusted and safe, e.g. military communications or cancer diagnosis. These models are given real data, simulated data or combination of both and are trained to be highly predictive on them. However, gathering enough real data or simulating them to be representative of all the real conditions is: costly, sometimes impossible due to confidentiality and most of the time impossible. Indeed, real conditions are constantly changing and sometimes are intractable. A solution is to deploy machine learning models that are able to give predictions when they are confident enough otherwise raise a flag or abstain. One issue is that standard models easily fail at detecting out-of-distribution samples where their predictions are unreliable. We present here TrustGAN, a generative adversarial network pipeline targeting trustness. It is a deep learning pipeline which improves a target model estimation of the confidence without impacting its predictive power. The pipeline can accept any given deep learning model which outputs a prediction and a confidence on this prediction. Moreover, the pipeline does not need to modify this target model. It can thus be easily deployed in a MLOps (Machine Learning Operations) setting. The pipeline is applied here to a target classification model trained on MNIST data to recognise numbers based on images. We compare such a model when trained in the standard way and with TrustGAN. We show that on out-of-distribution samples, here FashionMNIST and CIFAR10, the estimated confidence is largely reduced. We observe similar conclusions for a classification model trained on 1D radio signals from AugMod, tested on RML2016.04C. We also publicly release the code.